Search results for: tree identification

Authors: W. E. Igwegbe, B. C. Okoro, J. C. Osuagwu

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The study was aimed at assessing the effectiveness of reducing the concentrations of heavy metals in waste water using Pawpaw (Carica papaya) wood as a bio-sorbent. The heavy metals considered include; zinc, cadmium, lead, copper, iron, selenium, nickel, and manganese. The physiochemical properties of carica papaya stem were studied. The experimental sample was obtained from a felled trunk of matured pawpaw tree. Waste water for experimental use was prepared by dissolving soil samples collected from a dump site at Owerri, Imo state in water. The concentration of each metal remaining in solution as residual metal after bio-sorption was determined using Atomic absorption Spectrometer. The effects of ph, contact time and initial heavy metal concentration were studied in a batch reactor. The results of Spectrometer test showed that there were different functional groups detected in the carica papaya stem biomass. Optimum bio-sorption occurred at pH 5.9 with 5g/100ml solution of bio-sorbent. The results of the study showed that the treated wastewater is fit for irrigation purpose based on Canada wastewater quality guideline for the protection of Agricultural standard. This approach thus provides a cost effective and environmentally friendly option for treating waste water.

Keywords: biomass, bio-sorption, Carica papaya, heavy metal, wastewater

Procedia PDF Downloads 370

Authors: Jae Young Jang, Jong Hoon Ahn, Jae-Woong Lim, So Young Kang, Mi Kyeong Lee

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Rhus verniciflua is commonly known as a lacquer tree in Korea. Stem barks of R. verniciflua have been used as an immunostimulator in traditional medicine. It contains phenolic compounds and is known for diverse biological activities such as antioxidant and antimicrobial activity. However, it also causes allergic dermatitis due to urushiols derivatives. For the development of active natural resources with less toxicity, the content of phenolic compounds and urushiols of different parts of R. verniciflua such as stem barks, lignum and leaves were quantitated by colorimetric assay and HPLC analysis. The urushiols content were the highest in stem barks, and followed by leaves. The lignum contained trace amount of urushiols. The phenolic contents, however, were the most abundant in lignum, and followed by leaves and stem barks. These results clear showed that the content of urushiols and phenolic differs depending on the parts of R. verniciflua. Antimicrobial activity of different parts of R. verniciflua against fish pathogenic bacteria was also investigated using Edwardsiella tarda. Lignum of R. verniciflua was the most effective in antimicrobial activity against E. tarda and phenolic constituents are suggested to be active constituents for activity. Taken together, phenolic compounds are responsible for antimicrobial activity of R. verniciflua. The lignum of R. verniciflua contains high content of phenolic compounds with less urushiols, which suggests efficient antimicrobial activity with less toxicity. Therefore, lignum of R. verniciflua are suggested as good sources for antimicrobial activity against fish bacterial diseases.

Keywords: different parts, phenolic compounds, Rhus verniciflua, urushiols

Procedia PDF Downloads 318

Authors: Joseph M. Rich, Vinay A. Duddalwar, Assad A. Oberai

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Prostate adenocarcinoma is the most common cancer in males, with osseous metastases as the commonest site of metastatic prostate carcinoma (mPC). Treatment monitoring is based on the evaluation and characterization of lesions on multiple imaging studies, including Computed Tomography (CT). Monitoring of the osseous disease burden, including follow-up of lesions and identification and characterization of new lesions, is a laborious task for radiologists. Deep learning algorithms are increasingly used to perform tasks such as identification and segmentation for osseous metastatic disease and provide accurate information regarding metastatic burden. Here, nnUNet was used to produce a model which can segment CT scan images of prostate adenocarcinoma vertebral bone metastatic lesions. nnUNet is an open-source Python package that adds optimizations to deep learning-based UNet architecture but has not been extensively combined with transfer learning techniques due to the absence of a readily available functionality of this method. The IRB-approved study data set includes imaging studies from patients with mPC who were enrolled in clinical trials at the University of Southern California (USC) Health Science Campus and Los Angeles County (LAC)/USC medical center. Manual segmentation of metastatic lesions was completed by an expert radiologist Dr. Vinay Duddalwar (20+ years in radiology and oncologic imaging), to serve as ground truths for the automated segmentation. Despite nnUNet’s success on some medical segmentation tasks, it only produced an average Dice Similarity Coefficient (DSC) of 0.31 on the USC dataset. DSC results fell in a bimodal distribution, with most scores falling either over 0.66 (reasonably accurate) or at 0 (no lesion detected). Applying more aggressive data augmentation techniques dropped the DSC to 0.15, and reducing the number of epochs reduced the DSC to below 0.1. Datasets have been identified for transfer learning, which involve balancing between size and similarity of the dataset. Identified datasets include the Pancreas data from the Medical Segmentation Decathlon, Pelvic Reference Data, and CT volumes with multiple organ segmentations (CT-ORG). Some of the challenges of producing an accurate model from the USC dataset include small dataset size (115 images), 2D data (as nnUNet generally performs better on 3D data), and the limited amount of public data capturing annotated CT images of bone lesions. Optimizations and improvements will be made by applying transfer learning and generative methods, including incorporating generative adversarial networks and diffusion models in order to augment the dataset. Performance with different libraries, including MONAI and custom architectures with Pytorch, will be compared. In the future, molecular correlations will be tracked with radiologic features for the purpose of multimodal composite biomarker identification. Once validated, these models will be incorporated into evaluation workflows to optimize radiologist evaluation. Our work demonstrates the challenges of applying automated image segmentation to small medical datasets and lays a foundation for techniques to improve performance. As machine learning models become increasingly incorporated into the workflow of radiologists, these findings will help improve the speed and accuracy of vertebral metastatic lesions detection.

Keywords: deep learning, image segmentation, medicine, nnUNet, prostate carcinoma, radiomics

Procedia PDF Downloads 95

Authors: Ulrich Wake, Eniman Syamsuddin

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The model for camera model identificaiton is trained using pretrained model ResNet43 and ResNet50. The dataset consists of 500 photos of each phone. Dataset is divided into 1280 photos for training, 320 photos for validation and 400 photos for testing. The model is trained using One Cycle Policy Method and tested using Test-Time Augmentation. Furthermore, the model is trained for 50 epoch using regularization such as drop out and early stopping. The result is 90% accuracy for validation set and above 85% for Test-Time Augmentation using ResNet50. Every model is also trained by slightly updating the pretrained model’s weights

Keywords: ​ One Cycle Policy, ResNet34, ResNet50, Test-Time Agumentation

Procedia PDF Downloads 207

Authors: Enkelejda Shkurti, Diamant Shtiza

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Background: Health-related quality of life (HRQoL) is a significant consequence in health care. The objective of our study was to recognize features related to lower HRQoL scores in children with anorectal malformation (ARM) and Hirschsprung disease (HD). Methods: Children younger than 18 years, with HD or ARM, that were assessed at our private clinic in Tirana, Albania, from December 2018 to October 2019, were acknowledged. The outcomes of broad questionnaires concerning diagnosis, symptoms, and preceding health/surgical history and authenticated tools to measure urinary status, stooling grade, and HRQoL were appraised. Results: In patients aged 0-6 years, vomiting and abdominal enlargement were allied with a substantial decrease in total HRQoL scores. In children > 6 years of age, vomiting, abdominal swelling, and abdominal discomfort were also linked to a considerably lower HRQoL. The main indicator of lower HRQoL scores on regression tree analysis in all age clusters was the occurrence of psychosomatic, behavioral, or progressive comorbidity. Conclusion: Children with both HD or ARM that have a psychosomatic, behavioral, or growing problem experience considerably lower HRQoL than patients deprived of such problems, proposing that establishment of behavioral/growing sustenance as part of the care of these patients may have a considerable influence on their HRQoL.

Keywords: anorectal malformation, Hirsch Sprung disease, quality of life, Albania

Procedia PDF Downloads 172

Authors: Songul Cinaroglu

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Background: Random Forest is an efficient, multi-class machine learning method using for classification, regression and other tasks. This method is operating by constructing each tree using different bootstrap sample of the data. Determining the number of trees in random forests is an open question in the literature for studies about improving classification performance of random forests. Aim: The aim of this study is to analyze whether there is an optimal number of trees in Random Forests and how performance of Random Forests differ according to increase in number of trees using sample health data sets in R programme. Method: In this study we analyzed the performance of Random Forests as the number of trees grows and doubling the number of trees at every iteration using “random forest” package in R programme. For determining minimum and optimal number of trees we performed Mc Nemar test and Area Under ROC Curve respectively. Results: At the end of the analysis it was found that as the number of trees grows, it does not always means that the performance of the forest is better than forests which have fever trees. In other words larger number of trees only increases computational costs but not increases performance results. Conclusion: Despite general practice in using random forests is to generate large number of trees for having high performance results, this study shows that increasing number of trees doesn’t always improves performance. Future studies can compare different kinds of data sets and different performance measures to test whether Random Forest performance results change as number of trees increase or not.

Keywords: classification methods, decision trees, number of trees, random forest

Procedia PDF Downloads 394

Authors: Asim Mehmood, Abdul Karim, Muhammad J. Jaskani, Faisal S. Awan, Muhammad W. Sajid

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Guava (Psidium guajava L.) is an important commercial fruit crop of Pakistan. It is an allogamous crop having 25-40% cross pollination which on the one hand leads to clonal degradation and on the other hand can add variations to generated new cultivars. Morpho-genetic characterization of 37 guava accessions was carried out for study of the genetic diversity among guava accessions located in province Punjab, Pakistan. For morphological analysis, 17 morphological traits were studied, and strong positive correlation was found among the 7 morphological traits which included thickness of outer flesh in relation to core diameter, fruit length, fruit width, fruit juiciness, fruit size, fruit sweetness and number of seeds. For genetic characterization, 18 microsatellites were used, and the sizes of reproducible and scorable bands ranged from 150 to 320 bp. These 18 primer pairs amplified a total of 85 alleles in P. guajava, with an average total number of 4.7 alleles per locus and no more than two displayed bands (nuclear SSR loci). The phylogenetic tree based on the morphological and genetic traits showed the diversity of these 37 guava genotypes into two major groups. These results indicated that Pakistani guava is quite diverse and a more detail study is needed to define the level of genetic variability.

Keywords: Psidium guajava L, genetic diversity, SSR markers, polymorphism, dendrogram

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Authors: Y. Tiamaz, N. Souissi

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The purpose of this paper is to present a classification of Lean models which aims to capture all the concepts related to this approach and thus facilitate its implementation. This classification allows the identification of the most relevant models according to several dimensions. From this perspective, we present a review and an analysis of Lean models literature and we propose dimensions for the classification of the current proposals while respecting among others the axes of the Lean approach, the maturity of the models as well as their application domains. This classification allowed us to conclude that researchers essentially consider the Lean approach as a toolbox also they design their models to solve problems related to a specific environment. Since Lean approach is no longer intended only for the automotive sector where it was invented, but to all fields (IT, Hospital, ...), we consider that this approach requires a generic model that is capable of being implemented in all areas.

Keywords: lean approach, lean models, classification, dimensions, holistic view

Procedia PDF Downloads 431

Authors: Hamid Mustafa, Adeela Ajmal, Kim EuiSoo, Noor-ul-Ain

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World wild, buffalo production is considered as most important component of food industry. Efficient buffalo production is related with reproductive performance of this species. Lack of knowledge of reproductive efficiency and its related genes in buffalo species is a major constraint for sustainable buffalo production. In this study, we performed some bioinformatics analysis on Follicle Stimulating Hormone Receptor (FSHR) gene and explored the possible relationship of this gene among different buffalo breeds and with other farm animals. We also found the evolution pattern for this gene among these species. We investigate CDS lengths, Stop codon variation, homology search, signal peptide, isoelectic point, tertiary structure, motifs and phylogenetic tree. The results of this study indicate 4 different motif in this gene, which are Activin-recp, GS motif, STYKc Protein kinase and transmembrane. The results also indicate that this gene has very close relationship with cattle, bison, sheep and goat. Multiple alignment (MA) showed high conservation of motif which indicates constancy of this gene during evolution. The results of this study can be used and applied for better understanding of this gene for better characterization of Follicle Stimulating Hormone Receptor (FSHR) gene structure in different farm animals, which would be helpful for efficient breeding plans for animal’s production.

Keywords: buffalo, FSHR gene, bioinformatics, production

Procedia PDF Downloads 529

Authors: Kalyan S. Ghosh, B. L. Dhananjaya

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Snake bite causes fatal injuries in multi-organs and even many deaths due to several adverse physiological effects of various phospholipases (PLA2s) present in snake venom. Though these PLA2s bear highly homologues sequences and also structure but exhibit a different extent of those pharmacological effects. In this study, we have explored the difference in the neurotoxicity of two PLA2 namely PLA2-V, PLA2-VIIIa present in the venom from Vipera russellii. Bioinformatics studies on sequences of these two proteins along with detailed structural comparison enable us to explore the differences unambiguously. The identification of the residues involved in neurotoxicity will further lead towards proper designing of inhibitors against such killing effects of the venom.

Keywords: electrostatic potential, homology modeling, hydrophobicity, neurotoxicity, Phospholipase A2

Procedia PDF Downloads 434

Authors: M. Doudi, M. H. Pazandeh, L. Rahimzadeh Torabi

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Bedsores are pressure ulcers that occur on the skin or tissue due to being immobile and lying in bed for extended periods. Bedsores have the potential to progress into open ulcers, increasing the possibility of variety of bacterial infection. Acinetobacter baumannii, a pathogen of considerable clinical importance, exhibited a significant correlation with Bedsores (pressure ulcers) infections, thereby manifesting a wide spectrum of antibiotic resistance. The emergence of drug resistance has led researchers to focus on alternative methods, particularly phage therapy, for tackling bacterial infections. Phage therapy has emerged as a novel therapeutic approach to regulate the activity of these agents. The management of bacterial infections greatly benefits from the clinical utilization of bacteriophages as a valuable antimicrobial intervention. The primary objective of this investigation consisted of isolating and discerning potent bacteriophage capable of targeting multi drug-resistant (MDR) and extensively drug-resistant (XDR) bacteria obtained from pressure ulcers. In present study, analyzed and isolated A. baumannii strains obtained from a cohort of patients suffering from pressure ulcers at Taleghani Hospital in Ahvaz, Iran. An approach that included biochemical and molecular identification techniques was used to determine the taxonomic classification of bacterial isolates at the genus and species levels. The molecular identification process was facilitated by using the 16S rRNA gene in combination with universal primers 27 F, and 1492 R. Bacteriophage was obtained through the isolation process conducted on treatment plant sewage located in Isfahan, Iran. The main goal of this study was to evaluate different characteristics of phage, such as their appearance, range of hosts they can infect, how quickly they can enter a host, their stability at varying temperatures and pH levels, their effectiveness in killing bacteria, the growth pattern of a single phage stage, mapping of enzymatic digestion, and identification of proteomics patterns. The findings demonstrated that an examination was conducted on a sample of 50 specimens, wherein 15 instances of A. baumannii were identified. These microorganisms are the predominant Gram-negative agents known to cause wound infections in individuals suffering from bedsores. The study's findings indicated a high prevalence of antibiotic resistance in the strains isolated from pressure ulcers, excluding the clinical strains that exhibited responsiveness to colistin.According to the findings obtained from assessments of host range and morphological characteristics of bacteriophage VbɸAB-1, it can be concluded that this phage possesses specificity towards A. Baumannii BAH_Glau1001 was classified as a member of the Plasmaviridae family. The bacteriophage mentioned earlier showed the strongest antibacterial effect at a temperature of 18 °C and a pH of 6.5. Through the utilization of sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) analysis on protein fragments, it was established that the bacteriophage VbɸAB-1 exhibited a size range between 50 and 75 kilodaltons (KDa). The numerous research findings on the effectiveness of phages and the safety studies conducted suggest that the phages studied in this research can be considered as a practical solution and recommended approach for controlling and treating stubborn pathogens in burn wounds among hospitalized patients.

Keywords: acinetobacter baumannii, extremely drug- resistant, phage therapy, surgery wound

Procedia PDF Downloads 92

Authors: Dawei Cai

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In this paper, we present a new wearable device that provide an automatic guidance servie for visitors. By combining the position information from NFC and the orientation information from a 6 axis acceleration and terrestrial magnetism sensor, the head's direction can be calculated. We developed an algorithm to calculate the device orientation based on the data from acceleration and terrestrial magnetism sensor. If visitors want to know some explanation about an exhibit in front of him, what he has to do is just lift up his mobile device. The identification program will automatically identify the status based on the information from NFC and MEMS, and start playing explanation content for him. This service may be convenient for old people or disables or children.

Keywords: wearable device, ubiquitous computing, guide sysem, MEMS sensor, NFC

Procedia PDF Downloads 423

Authors: Asem M. Othman

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The expansion of the hospitality industry is unmistakable. Services, by nature, are intangible. Hence, service quality, in general, is a complicated process to be measured and evaluated. Hotels, as a service sector and part of the hospitality industry, are growing rapidly. This research paper was carried out to identify the quality determinants that may affect hotel guests’ service quality perception. In this research paper, each quality determinant will be discussed, illustrated, and justified thoroughly via a systematic literature review. The purpose of this paper is to set the stage to measure the significant influence of the service quality determinants on guest satisfaction. The knowledge produced from this study will assist practitioners and/or hotel service providers to imply into their policies.

Keywords: service quality, hotel service, quality management, quality determinants

Procedia PDF Downloads 269

Authors: Marc Diesse, Hochschule Heilbronn

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We address the question of identifying the configuration space singularities of linkages, i.e., points where the configuration space is not locally a submanifold of Euclidean space. Because the configuration space cannot be smoothly parameterized at such points, these singularity types have a significantly negative impact on the kinematics of the linkage. It is known that Jacobian methods do not provide sufficient conditions for the existence of CS-singularities. Herein, we present several additional algebraic criteria that provide the sufficient conditions. Further, we use those criteria to analyze certain classes of planar linkages. These examples will also show how the presented criteria can be checked using algorithmic methods.

Keywords: linkages, configuration space-singularities, real algebraic geometry, analytic geometry

Procedia PDF Downloads 145

Authors: Likewin Thomas, M. V. Manoj Kumar, B. Annappa

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Traditional execution process follows the path of execution drawn by the process analyst without observing the behaviour of resource and other real-time constraints. Identifying process model, predicting the behaviour of resource and recommending the optimal path of execution for a real time process is challenging. The proposed AlfyMiner: αyM iner gives a new dimension in process execution with the novel techniques Process Model Analyser: PMAMiner and Resource behaviour Analyser: RBAMiner for recommending the probable path of execution. PMAMiner discovers next probable activity for currently executing activity in an online process using variant matching technique to identify the set of next probable activity, among which the next probable activity is discovered using decision tree model. RBAMiner identifies the resource suitable for performing the discovered next probable activity and observe the behaviour based on; load and performance using polynomial regression model, and waiting time using queueing theory. Based on the observed behaviour αyM iner recommend the probable path of execution with; next probable activity and the best suitable resource for performing it. Experiments were conducted on process logs of CoSeLoG Project1 and 72% of accuracy is obtained in identifying and recommending next probable activity and the efficiency of resource performance was optimised by 59% by decreasing their load.

Keywords: cross-organization process mining, process behaviour, path of execution, polynomial regression model

Procedia PDF Downloads 333

Authors: James Taylor

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This paper considers the political, geo-philosophical musings of Deleuze and Guattari on spatialisation, place and movement in relation to the religious nomad (wandering ascetics and reclusive forest monks) inhabiting the borderlands of Thailand. A nomadic science involves improvised ascetic practices between the molar lines striated by modern state apparatuses. The wandering ascetics, inhabiting a frontier political ecology, stand in contrast to the appropriating, sedentary metaphysics and sanctifying arborescence of statism and its corollary place-making, embedded in rootedness and territorialisation. It is argued that the religious nomads, residing on the endo-exteriorities of the state, came to represent a rhizomatic and politico-ontological threat to centre-nation and its apparatus of capture. The paper also theorises transitions and movement at the borderlands in the context of the state’s monastic reforms. These reforms, and its pervasive royal science, problematised the interstitial zones of the early ascetic wanderers in their radical cross-cutting networks and lines, moving within and across demarcated frontiers. Indeed, the ascetic wanderers and their allegorical war machine were seen as a source of wild, free-floating charisma and mystical power, eventually appropriated by the centre-nation in it’s becoming unitary and fixed.

Keywords: Deleuze and Guattari, religious nomad, centre-nation, borderlands, Buddhism

Procedia PDF Downloads 83

Authors: Djouhou Fowe Michelle Carole

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In the last few decades, there have been considerable efforts by the international community to develop strategies that reduce global poverty and hunger. Despite the modest success in reducing food insecurity, there are still around 795 million people worldwide who remain undernourished, the majority of whom are in sub-Saharan Africa. In many of these impoverished communities, agriculture still remains one of the most important sectors in driving economic growth and reducing poverty. For the growing population, with higher food demand and fixed agricultural land, sustainable intensification is proposed as an important strategy to respond to the challenges of low yields, environmental degradation, and adaptation to climate change. Adoption of agroforestry technologies is increasingly being promoted as a promising solution. This study was conducted to determine the perceptions of the Cameroonian population and farmers on agroforestry. The methodology used was based on a survey to determine their knowledge level of agroforestry, their representation of its advantages and disadvantages, and the reasons that might motivate them whether or not to adopt agroforestry. Participants were randomly selected and received a questionnaire. Data were subjected to a descriptive analysis using SPSS software. The obtained results showed that less than 50% of the general population had already heard about agroforestry at least once; they have basic knowledge about this concept and its advantages. Farmers had been particularly sensitive to tree's food production function and seemed to value their environmental assets. However, various constraints could affect the possible adoption of agroforestry techniques.

Keywords: agroforestry, quality and sustainable agriculture, perceptions, advantages, limits

Procedia PDF Downloads 175

Authors: Mustafa Alhamdi

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Industrial application to classify gamma rays and neutron events is investigated in this study using deep machine learning. The identification using a convolutional neural network and recursive neural network showed a significant improvement in predication accuracy in a variety of applications. The ability to identify the isotope type and activity from spectral information depends on feature extraction methods, followed by classification. The features extracted from the spectrum profiles try to find patterns and relationships to present the actual spectrum energy in low dimensional space. Increasing the level of separation between classes in feature space improves the possibility to enhance classification accuracy. The nonlinear nature to extract features by neural network contains a variety of transformation and mathematical optimization, while principal component analysis depends on linear transformations to extract features and subsequently improve the classification accuracy. In this paper, the isotope spectrum information has been preprocessed by finding the frequencies components relative to time and using them as a training dataset. Fourier transform implementation to extract frequencies component has been optimized by a suitable windowing function. Training and validation samples of different isotope profiles interacted with CdTe crystal have been simulated using Geant4. The readout electronic noise has been simulated by optimizing the mean and variance of normal distribution. Ensemble learning by combing voting of many models managed to improve the classification accuracy of neural networks. The ability to discriminate gamma and neutron events in a single predication approach using deep machine learning has shown high accuracy using deep learning. The paper findings show the ability to improve the classification accuracy by applying the spectrogram preprocessing stage to the gamma and neutron spectrums of different isotopes. Tuning deep machine learning models by hyperparameter optimization of neural network models enhanced the separation in the latent space and provided the ability to extend the number of detected isotopes in the training database. Ensemble learning contributed significantly to improve the final prediction.

Keywords: machine learning, nuclear physics, Monte Carlo simulation, noise estimation, feature extraction, classification

Procedia PDF Downloads 150

Authors: Regina Chua

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To reduce the costs of re-fielding a large survey, a Random Forest classifier was applied to measure the accuracy of classifying individuals into their assigned segments with the fewest possible questions. Given a long survey, one needed to determine the most predictive ten or fewer questions that would accurately assign new individuals to custom segments. Furthermore, the solution needed to be quick in its classification and usable in non-Python environments. In this paper, a supervised Random Forest classifier was modeled on a dataset with 7,000 individuals, 60 questions, and 254 features. The Random Forest consisted of an iterative collection of individual decision trees that result in a predicted segment with robust precision and recall scores compared to a single tree. A random 70-30 stratified sampling for training the algorithm was used, and accuracy trade-offs at different depths for each segment were identified. Ultimately, the Random Forest classifier performed at 87% accuracy at a depth of 10 with 20 instead of 254 features and 10 instead of 60 questions. With an acceptable accuracy in prioritizing feature selection, new tools were developed for non-Python environments: a worksheet with a formulaic version of the algorithm and an embedded function to predict the segment of an individual in real-time. Random Forest was determined to be an optimal classification model by its feature selection, performance, processing speed, and flexible application in other environments.

Keywords: machine learning, supervised learning, data science, random forest, classification, prediction, predictive modeling

Procedia PDF Downloads 91

Authors: H. Agbaria, A. Salman, M. Huleihel, G. Beck, D. H. Rich, S. Mordechai, J. Kapelushnik

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Viral and bacterial infections are responsible for variety of diseases. These infections have similar symptoms like fever, sneezing, inflammation, vomiting, diarrhea and fatigue. Thus, physicians may encounter difficulties in distinguishing between viral and bacterial infections based on these symptoms. Bacterial infections differ from viral infections in many other important respects regarding the response to various medications and the structure of the organisms. In many cases, it is difficult to know the origin of the infection. The physician orders a blood, urine test, or 'culture test' of tissue to diagnose the infection type when it is necessary. Using these methods, the time that elapses between the receipt of patient material and the presentation of the test results to the clinician is typically too long ( > 24 hours). This time is crucial in many cases for saving the life of the patient and for planning the right medical treatment. Thus, rapid identification of bacterial and viral infections in the lab is of great importance for effective treatment especially in cases of emergency. Blood was collected from 50 patients with confirmed viral infection and 50 with confirmed bacterial infection. White blood cells (WBCs) and plasma were isolated and deposited on a zinc selenide slide, dried and measured under a Fourier transform infrared (FTIR) microscope to obtain their infrared absorption spectra. The acquired spectra of WBCs and plasma were analyzed in order to differentiate between the two types of infections. In this study, the potential of FTIR microscopy in tandem with multivariate analysis was evaluated for the identification of the agent that causes the human infection. The method was used to identify the infectious agent type as either bacterial or viral, based on an analysis of the blood components [i.e., white blood cells (WBC) and plasma] using their infrared vibrational spectra. The time required for the analysis and evaluation after obtaining the blood sample was less than one hour. In the analysis, minute spectral differences in several bands of the FTIR spectra of WBCs were observed between groups of samples with viral and bacterial infections. By employing the techniques of feature extraction with linear discriminant analysis (LDA), a sensitivity of ~92 % and a specificity of ~86 % for an infection type diagnosis was achieved. The present preliminary study suggests that FTIR spectroscopy of WBCs is a potentially feasible and efficient tool for the diagnosis of the infection type.

Keywords: viral infection, bacterial infection, linear discriminant analysis, plasma, white blood cells, infrared spectroscopy

Procedia PDF Downloads 223

Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

Procedia PDF Downloads 42

Authors: Mohammed Falalu Hamza

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A phytochemical investigation conducted on the leaves extract of Cryptocarya latifolia (Lauraceae) revealed the presence of two major essential oils; Nerolidol (1) and Copaene (2) with the aid of gas chromatography-mass spectrometry (GC-MS). The compounds exhibited good anti-oxidant capacity using 2,2-diphenyl-1-picryl-hydrazyl (DPPH) radical scavenging assay. The result shows that the anti-oxidant capacity of the compounds is dependent on concentration similar to the standard (ascorbic acid). This study shows that the leaves extract of C. latifolia is a good source of important natural antioxidants.

Keywords: broad-leaved quince, phytochemical, anti-oxidant, essential oils

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Authors: Chia-Ling Li

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The scope of this article includes the production of 10,000 metric tons of non-phthalate per annum. The production process will include hydrogenation, separation, purification, and recycling of unprocessed feedstock. Based on experimental data, conversion and selectivity were chosen as reaction model parameters. The synthesis and separation processes of non-phthalate and phthalate were established by using Aspen Plus software. The article will be divided into six parts: estimation of physical properties, integration of production processes, purification case study, utility consumption, economic feasibility study and identification of bottlenecks. The purities of products was higher than 99.9 wt. %. Process parameters have important guiding significance to the commercialization of hydrogenation of phthalate.

Keywords: economic analysis, hydrogenation, non-phthalate, process simulation

Procedia PDF Downloads 276

Authors: Dalia. G. Aseel

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Begomoviruses are economically important plant viruses that infect dicotyledonous plants and exclusively transmitted by the whitefly Bemisia tabaci. Here, replicative form was isolated from Okra, Cotton, Tomato plants and whitefly infected with Begomoviruses. Using coat protein specific primers (AV1), the viral infection was verified with amplicon at 450 bp. The sequence of OLCuV-AV1 gene was recorded and received an accession number (FJ441605) from Genebank. The phylogenetic tree of OLCuV was closely related to Okra leaf curl virus previously isolated from Cameroon and USA with nucleotide sequence identity of 92%. The protein purification was carried out using His-Tag methodology by using Affinity Chromatography. The purified protein was separated on SDS-PAGE analysis and an enriched expected size of band at 30 kDa was observed. Furthermore, RAPD and SDS-PAGE were used to detect genetic variability between different hosts of okra leaf curl virus (OLCuV), cotton leaf curl virus (CLCuV), tomato yellow leaf curl virus (TYLCuV) and the whitefly vector. Finally, the present study would help to understand the relationship between the whitefly and different economical crops in Egypt.

Keywords: okra leaf curl virus, AV1 gene, sequencing, phylogenetic, cloning, purified protein, genetic diversity and viral proteins

Procedia PDF Downloads 148

Authors: C. C. Igwe

Abstract:

Several parts of Neem tree (Azadirachta indica) are used in traditional medicine in many West African countries for the treatment of various human diseases. The leaf, stem - bark and seed were air dried for 8, 5 and 7 days, respectively. The shells were carfully separated from the seeds, each powdered sample obtained with mechanical miller and 250 mm sieve. The neem samples were individually subjected to extraction with acetone, n-hexane for 48hr and 72 hr, respectively. Physico-chemical and antibacterial evaluation were carried out using standard methods. Results of physico - chemical analyses of the extracted oil from the seed shows that it has a brownish colour, with a smell similar to garlic while the moisture content, refractive index are 0.76% and 1.47 respectively. Other vital chemical results obtained from the neem oil such as saponification value (234.62), acid value (10.84 %), free fatty acid (5.84 %) and peroxide value (10.52%) indicated the oil extracted satisfied standard oils parameters for quality soap and cosmetics production. The antibacterial screening by disc diffusion revealed the oil demonstrated high activity against Staphylococcus aureus. Both the physio-chemical and antibacterial of samples have been certified by National Agency for Food and Drugs Administration and Control. The preliminary results of this study may validate the medicinal value of the plant. Further studies are in progress to clarify the in vivo potentials of neem extracts in the management of human communicable diseases and this is a subject of investigation in our group.

Keywords: anti-bacterial, neem extract, physico-chemical analyses, staphylococcus aureus

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Authors: M. Ziółkowska, J. T. Duda, J. Milewska-Duda

Abstract:

This paper describes an approach to the adsorption phenomena modeling aimed at specifying the adsorption mechanisms on localized or nonlocalized adsorbent sites, when applied to the nanocarbons. The concept comes from the fundamental thermodynamic description of adsorption equilibrium and is based on numerical calculations of the hydrogen adsorbed particles volume on the surface of selected nanocarbons: single-walled nanotube and nanocone. This approach enables to obtain information on adsorption mechanism and then as a consequence to take appropriate mathematical adsorption model, thus allowing for a more reliable identification of the material porous structure. Theoretical basis of the approach is discussed and newly derived results of the numerical calculations are presented for the selected nanocarbons.

Keywords: adsorption, mathematical modeling, nanocarbons, numerical analysis

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Authors: L. Madouni, M. Ould Ouali, N. E. Hannachi

Abstract:

Two constitutive models for concrete are available in ABAQUS/Explicit, the Brittle Cracking Model and the Concrete Damaged Plasticity Model, and their suitability and limitations are well known. The aim of the present paper is to implement a damage-friction concrete constitutive model and to evaluate the performance of this model by comparing the predicted response with experimental data. The constitutive formulation of this material model is reviewed. In order to have consistent results, the parameter identification and calibration for the model have been performed. Several numerical simulations are presented in this paper, whose results allow for validating the capability of the proposed model for reproducing the typical nonlinear performances of concrete structures under different monotonic and cyclic load conditions. The results of the evaluation will be used for recommendations concerning the application and further improvements of the investigated model.

Keywords: Abaqus, concrete, constitutive model, numerical simulation

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Authors: Thanasit Suksutdhi

Abstract:

This survey research aims to study the standard of service quality of food and beverage service staffs in hotel business by studying the service standard of three sample hotels, Siam Kempinski Hotel Bangkok, Four Seasons Resort Chiang Mai, and Banyan Tree Phuket. In order to find the international service standard of food and beverage service, triangular research, i.e. quantitative, qualitative, and survey were employed. In this research, questionnaires and in-depth interview were used for getting the information on the sequences and method of services. There were three parts of modified questionnaires to measure service quality and guest’s satisfaction including service facilities, attentiveness, responsibility, reliability, and circumspection. This study used sample random sampling to derive subjects with the return rate of the questionnaires was 70% or 280. Data were analyzed by SPSS to find arithmetic mean, SD, percentage, and comparison by t-test and One-way ANOVA. The results revealed that the service quality of the three hotels were in the international level which could create high satisfaction to the international customers. Recommendations for research implementations were to maintain the area of good service quality, and to improve some dimensions of service quality such as reliability. Training in service standard, product knowledge, and new technology for employees should be provided. Furthermore, in order to develop the service quality of the industry, training collaboration between hotel organization and educational institutions in food and beverage service should be considered.

Keywords: service standard, food and beverage department, sequence of service, service method

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Authors: Martin Cai, Khadijah B. Hashim, Leng Leo, Edmund F. Tian

Abstract:

Analysis of traditional Chinese medicinal (TCM) supplements has always been a laborious task, particularly in the case of multi‐ingredient formulations. Traditionally, herbal extracts are analysed using one or few markers compounds. In the recent years, however, pharmaceutical companies are introducing health supplements of TCM active ingredients to cater to the needs of consumers in the fast-paced society in this age. As such, new problems arise in the aspects of composition identification as well as quality analysis. In most cases of products or supplements formulated with multiple TCM herbs, the chemical composition, and nature of each raw material differs greatly from the others in the formulation. This results in a requirement for individual analytical processes in order to identify the marker compounds in the various botanicals. Thin-layer Chromatography (TLC) is a simple, cost effective, yet well-regarded method for the analysis of natural products, both as a Pharmacopeia-approved method for identification and authentication of herbs, and a great analytical tool for the discovery of chemical compositions in herbal extracts. Recent technical advances introduced High-Performance TLC (HPTLC) where, with the help of automated equipment and improvements on the chromatographic materials, both the quality and reproducibility are greatly improved, allowing for highly standardised analysis with greater details. Here we report an industrial consultancy project with ONI Global Pte Ltd for the analysis of LAC Liver Protector, a TCM formulation aimed at improving liver health. The aim of this study was to identify 4 key components of the supplement using HPTLC, following protocols derived from Chinese Pharmacopeia standards. By comparing the TLC profiles of the supplement to the extracts of the herbs reported in the label, this project proposes a simple and cost-effective analysis of the presence of the 4 marker compounds in the multi‐ingredient formulation by using 4 different HPTLC methods. With the increasing trend of small and medium-sized enterprises (SMEs) bringing natural products and health supplements into the market, it is crucial that the qualities of both raw materials and end products be well-assured for the protection of consumers. With the technology of HPTLC, science can be incorporated to help SMEs with their quality control, thereby ensuring product quality.

Keywords: traditional Chinese medicine supplement, high performance thin layer chromatography, active ingredients, product quality

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Authors: Vikas S. Minchekar

Abstract:

The eyewitness is a crucial source of evidence in the criminal judicial system. However, rely on the reminiscence of an eyewitness especially intoxicated eyewitness is not always judicious. It might lead to some serious consequences. Day by day, alcohol-related crimes or the criminal incidences in bars, nightclubs, and restaurants are increasing rapidly. Tackling such cases is very complicated to any investigation officers. The people in that incidents are violated due to the alcohol consumption hence, their ability to identify the suspects or recall these phenomena is affected. The studies on the effects of alcohol consumption on motor activities such as driving and surgeries have received much attention. However, the effect of alcohol intoxication on memory has received little attention from the psychology, law, forensic and criminology scholars across the world. In the Indian context, the published articles on this issue are equal to none up to present day. This field experiment investigation aimed at to finding out the effect of alcohol consumption on identification accuracy in lineups. Forty adult, social drinkers, and twenty sober adults were randomly recruited for the study. The sober adults were assigned into 'placebo' beverage group while social drinkers were divided into two group e. g. 'low dose' of alcohol (0.2 g/kg) and 'high dose' of alcohol (0.8 g/kg). The social drinkers were divided in such a way that their level of blood-alcohol concentration (BAC) will become different. After administering the beverages for the placebo group and liquor to the social drinkers for 40 to 50 minutes of the period, the five-minute video clip of mock crime is shown to all in a group of four to five members. After the exposure of video, clip subjects were given 10 portraits and asked them to recognize whether they are involved in mock crime or not. Moreover, they were also asked to describe the incident. The subjects were given two opportunities to recognize the portraits and to describe the events; the first opportunity is given immediately after the video clip and the second was 24 hours later. The obtained data were analyzed by one-way ANOVA and Scheffe’s posthoc multiple comparison tests. The results indicated that the 'high dose' group is remarkably different from the 'placebo' and 'low dose' groups. But, the 'placebo' and 'low dose' groups are equally performed. The subjects in a 'high dose' group recognized only 20% faces correctly while the subjects in a 'placebo' and 'low dose' groups are recognized 90 %. This study implied that the intoxicated witnesses are less accurate to recognize the suspects and also less capable of describing the incidents where crime has taken place. Moreover, this study does not assert that intoxicated eyewitness is generally less trustworthy than their sober counterparts.

Keywords: intoxicated eyewitness, memory, social drinkers, lineups

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