Search results for: plant disease classification

Authors: Taner Ersoz, Filiz Ersoz, Seyma Ozbilge

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In this study, the clients who applied to a bank branch for loan were analyzed through data mining. The study was composed of the information such as amounts of loans received by personal and SME clients working with the bank branch, installment numbers, number of delays in loan installments, payments available in other banks and number of banks to which they are in debt between 2010 and 2013. The client risk profile was examined through Classification and Regression Tree (CART) analysis, one of the decision tree classification methods. At the end of the study, 5 different types of customers have been determined on the decision tree. The classification of these types of customers has been created with the rating of those posing a risk for the bank branch and the customers have been classified according to the risk ratings.

Keywords: client classification, loan suitability, risk rating, CART analysis

Procedia PDF Downloads 338

Authors: F. Jiménez, R. Jódar, M. Martín, G. Sánchez, G. Sciavicco

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Abstract—Attribute or feature selection is one of the basic strategies to improve the performances of data classification tasks, and, at the same time, to reduce the complexity of classifiers, and it is a particularly fundamental one when the number of attributes is relatively high. Its application to unsupervised classification is restricted to a limited number of experiments in the literature. Evolutionary computation has already proven itself to be a very effective choice to consistently reduce the number of attributes towards a better classification rate and a simpler semantic interpretation of the inferred classifiers. We present a feature selection wrapper model composed by a multi-objective evolutionary algorithm, the clustering method Expectation-Maximization (EM), and the classifier C4.5 for the unsupervised classification of data extracted from a psychological test named BASC-II (Behavior Assessment System for Children - II ed.) with two objectives: Maximizing the likelihood of the clustering model and maximizing the accuracy of the obtained classifier. We present a methodology to integrate feature selection for unsupervised classification, model evaluation, decision making (to choose the most satisfactory model according to a a posteriori process in a multi-objective context), and testing. We compare the performance of the classifier obtained by the multi-objective evolutionary algorithms ENORA and NSGA-II, and the best solution is then validated by the psychologists that collected the data.

Keywords: evolutionary computation, feature selection, classification, clustering

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Authors: Weiliang Wen, Xinyu Guo, Ying Zhang, Jianjun Du, Boxiang Xiao

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Reconstructing and analyzing three-dimensional (3D) models from situ measured data is important for a number of researches and applications in plant science, including plant phenotyping, functional-structural plant modeling (FSPM), plant germplasm resources protection, agricultural technology popularization. It has many scales like cell, tissue, organ, plant and canopy from micro to macroscopic. The techniques currently used for data capture, feature analysis, and 3D reconstruction are quite different of different scales. In this context, morphological data acquisition, 3D analysis and modeling of plants on different scales are introduced systematically. The commonly used data capture equipment for these multiscale is introduced. Then hot issues and difficulties of different scales are described respectively. Some examples are also given, such as Micron-scale phenotyping quantification and 3D microstructure reconstruction of vascular bundles within maize stalks based on micro-CT scanning, 3D reconstruction of leaf surfaces and feature extraction from point cloud acquired by using 3D handheld scanner, plant modeling by combining parameter driven 3D organ templates. Several application examples by using the 3D models and analysis results of plants are also introduced. A 3D maize canopy was constructed, and light distribution was simulated within the canopy, which was used for the designation of ideal plant type. A grape tree model was constructed from 3D digital and point cloud data, which was used for the production of science content of 11th international conference on grapevine breeding and genetics. By using the tissue models of plants, a Google glass was used to look around visually inside the plant to understand the internal structure of plants. With the development of information technology, 3D data acquisition, and data processing techniques will play a greater role in plant science.

Keywords: plant, three dimensional modeling, multi-scale, plant phenotyping, three dimensional data acquisition

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Authors: Vishwa Joshi

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The study of mood recognition in the field of music has gained a lot of momentum in the recent years with machine learning and data mining techniques and many audio features contributing considerably to analyze and identify the relation of mood plus music. In this paper we consider the same idea forward and come up with making an effort to build a system for automatic recognition of mood underlying the audio song’s clips by mining their audio features and have evaluated several data classification algorithms in order to learn, train and test the model describing the moods of these audio songs and developed an open source framework. Before classification, Preprocessing and Feature Extraction phase is necessary for removing noise and gathering features respectively.

Keywords: music, mood, features, classification

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Authors: Anum Akhter, Sumaira Altaf

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Purpose: To study the effect of low vision devices i.e. telescope and magnifying glasses on distance visual acuity and near visual acuity of children with Stargardt’s disease. Setting: Low vision department, Alshifa Trust Eye Hospital, Rawalpindi, Pakistan. Methods: 52 children having Stargardt’s disease were included in the study. All children were diagnosed by pediatrics ophthalmologists. Comprehensive low vision assessment was done by me in Low vision clinic. Visual acuity was measured using ETDRS chart. Refraction and other supplementary tests were performed. Children with Stargardt’s disease were provided with different telescopes and magnifying glasses for improving far vision and near vision. Results: Out of 52 children, 17 children were males and 35 children were females. Distance visual acuity and near visual acuity improved significantly with low vision aid trial. All children showed visual acuity better than 6/19 with a telescope of higher magnification. Improvement in near visual acuity was also significant with magnifying glasses trial. Conclusions: Low vision aids are useful for improvement in visual acuity in children. Children with Stargardt’s disease who are having a problem in education and daily life activities can get help from low vision aids.

Keywords: Stargardt, s disease, low vision aids, telescope, magnifiers

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Authors: Jorge A. Ruiz-Vanoye, Ocotlán Díaz-Parra, Alejandro Fuentes-Penna, Daniel Vélez-Díaz, Edith Olaco García

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In this paper, we present the use of the discriminant analysis to select evolutionary algorithms that better solve instances of the vehicle routing problem with time windows. We use indicators as independent variables to obtain the classification criteria, and the best algorithm from the generic genetic algorithm (GA), random search (RS), steady-state genetic algorithm (SSGA), and sexual genetic algorithm (SXGA) as the dependent variable for the classification. The discriminant classification was trained with classic instances of the vehicle routing problem with time windows obtained from the Solomon benchmark. We obtained a classification of the discriminant analysis of 66.7%.

Keywords: Intelligent Transportation Systems, data-mining techniques, evolutionary algorithms, discriminant analysis, machine learning

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Authors: C. Lanzerstorfer

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The off-gas from the basic oxygen furnace (BOF), where pig iron is converted into steel, is treated in the primary ventilation system. This system is in full operation only during oxygen-blowing when the BOF converter vessel is in a vertical position. When pig iron and scrap are charged into the BOF and when slag or steel are tapped, the vessel is tilted. The generated emissions during charging and tapping cannot be captured by the primary off-gas system. To capture these emissions, a secondary ventilation system is usually installed. The emissions are captured by a canopy hood installed just above the converter mouth in tilted position. The aim of this study was to investigate the dependence of Zn and other components on the particle size of BOF secondary ventilation dust. Because of the high temperature of the BOF process it can be expected that Zn will be enriched in the fine dust fractions. If Zn is enriched in the fine fractions, classification could be applied to split the dust into two size fractions with a different content of Zn. For this air classification experiments with dust from the secondary ventilation system of a BOF were performed. The results show that Zn and Pb are highly enriched in the finest dust fraction. For Cd, Cu and Sb the enrichment is less. In contrast, the non-volatile metals Al, Fe, Mn and Ti were depleted in the fine fractions. Thus, air classification could be considered for the treatment of dust from secondary BOF off-gas cleaning.

Keywords: air classification, converter dust, recycling, zinc

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Authors: K. Assis, K. P. Chong, A. S. Idris, C. M. Ho

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Oil palm or Elaeis guineensis is considered as the golden crop in Malaysia. But oil palm industry in this country is now facing with the most devastating disease called as Ganoderma Basal Stem Rot disease. The objective of this paper is to analyze the economic loss due to this disease. There were three commercial oil palm sites selected for collecting the required data for economic analysis. Yield parameter used to measure the loss was the total weight of fresh fruit bunch in six months. The predictors include disease severity, change in disease severity, number of infected neighbor palms, age of palm, planting generation, topography, and first order interaction variables. The estimation model of yield loss was identified by using backward elimination based regression method. Diagnostic checking was conducted on the residual of the best yield loss model. The value of mean absolute percentage error (MAPE) was used to measure the forecast performance of the model. The best yield loss model was then used to estimate the economic loss by using the current monthly price of fresh fruit bunch at mill gate.

Keywords: ganoderma, oil palm, regression model, yield loss, economic loss

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Authors: Jiahe Liu, Hongyang Yu, Feng Qian, Miao Luo

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SMPL-X was a powerful parametric human body model that included male, neutral, and female models, with significant gender differences between these three models. During the process of 3D human body reconstruction, the correct selection of standard templates was crucial for obtaining accurate results. To address this issue, we developed an efficient gender classification algorithm to automatically select the appropriate template for 3D human body reconstruction. The key to this gender classification algorithm was the precise analysis of human body features. By using the SMPL-X model, the algorithm could detect and identify gender features of the human body, thereby determining which standard template should be used. The accuracy of this algorithm made the 3D reconstruction process more accurate and reliable, as it could adjust model parameters based on individual gender differences. SMPL-X and the related gender classification algorithm have brought important advancements to the field of 3D human body reconstruction. By accurately selecting standard templates, they have improved the accuracy of reconstruction and have broad potential in various application fields. These technologies continue to drive the development of the 3D reconstruction field, providing us with more realistic and accurate human body models.

Keywords: gender classification, joint detection, SMPL-X, 3D reconstruction

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Authors: Zhong Ma, Zhuping Wang, Congxin Liu, Xiangzeng Liu

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Satellite imagery classification is a challenging problem with many practical applications. In this paper, we designed a deep convolution neural network (DCNN) to classify the satellite imagery. The contributions of this paper are twofold — First, to cope with the large-scale variance in the satellite image, we introduced the inception module, which has multiple filters with different size at the same level, as the building block to build our DCNN model. Second, we proposed a genetic algorithm based method to efficiently search the best hyper-parameters of the DCNN in a large search space. The proposed method is evaluated on the benchmark database. The results of the proposed hyper-parameters search method show it will guide the search towards better regions of the parameter space. Based on the found hyper-parameters, we built our DCNN models, and evaluated its performance on satellite imagery classification, the results show the classification accuracy of proposed models outperform the state of the art method.

Keywords: satellite imagery classification, deep convolution network, genetic algorithm, hyper-parameter optimization

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Authors: Temosho Promise Chuene, T. Chitura

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Background: Foot and mouth disease is a real challenge in South Africa. The disease is a serious threat to the viability of livestock farming initiatives and affects local and international livestock trade. In Limpopo Province, the Kruger National Park and other game reserves are home to the African buffalo (Syncerus caffer), a notorious reservoir of the picornavirus, which causes foot and mouth disease. Out of the virus’s seven (7) distinct serotypes, Southern African Territories (SAT) 1, 2, and 3 are commonly endemic in South Africa. The broad objective of the study was to establish the trend of foot and mouth disease in Limpopo Province over a seven-year period (2014-2020), as well as the adoption and comprehensive reporting of the measures that are taken to contain disease outbreaks in the study area. Methods: The study used secondary data from the World Organization for Animal Health (WOAH) on reported cases of foot and mouth disease in South Africa. Descriptive analysis (frequencies and percentages) and Analysis of variance (ANOVA) were used to present and analyse the data. Result: The year 2020 had the highest prevalence of foot and mouth disease (3.72%), while 2016 had the lowest prevalence (0.05%). Serotype SAT 2 was the most endemic, followed by SAT 1. Findings from the study demonstrated the seasonal nature of foot and mouth disease in the study area, as most disease cases were reported in the summer seasons. Slaughter of diseased and at-risk animals was the only documented disease control strategy, and information was missing for some of the years. Conclusion: The study identified serious underreporting of the adopted control strategies following disease outbreaks. Adoption of comprehensive disease control strategies coupled with thorough reporting can help to reduce outbreaks of foot and mouth disease and prevent losses to the livestock farming sector of South Africa and Limpopo Province in particular.

Keywords: livestock farming, African buffalo, prevalence, serotype, slaughter

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Authors: Qamar Iqbal

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Companies strive to improve their forecasting methods to predict the fluctuations in customer demand. These fluctuation and variation in demand affect the manufacturing operations and can limit a company’s ability to fulfill customer demand on time. Companies keep the inventory buffer and maintain the stocking levels to reduce the impact of demand variation. A mid-size company deals with thousands of stock keeping units (skus). It is neither easy and nor efficient to control and manage each sku. Inventory classification provides a tool to the management to increase their ability to support customer demand. The paper presents a framework that shows how inventory classification can play a role to increase supply chain responsiveness. A case study will be presented to further elaborate the method both for build-to-order and build-to-forecast manufacturing environments. Results will be compared that will show which manufacturing setting has advantage over another under different circumstances. The outcome of this study is very useful to the management because this will give them an insight on how inventory classification can be used to increase their ability to respond to changing customer needs.

Keywords: inventory classification, supply chain responsiveness, forecast, manufacturing environment

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Authors: Ying Yi Wu

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The study of finite groups is a central topic in algebraic structures, and one of the most fundamental questions in this field is the classification of finite groups up to isomorphism. In this paper, we investigate the cyclic property of groups of prime order, which is a crucial result in the classification of finite abelian groups. We prove the following statement: If p is a prime, then every group G of order p is cyclic. Our proof utilizes the properties of group actions and the class equation, which provide a powerful tool for studying the structure of finite groups. In particular, we first show that any non-identity element of G generates a cyclic subgroup of G. Then, we establish the existence of an element of order p, which implies that G is generated by a single element. Finally, we demonstrate that any two generators of G are conjugate, which shows that G is a cyclic group. Our result has significant implications in the classification of finite groups, as it implies that any group of prime order is isomorphic to the cyclic group of the same order. Moreover, it provides a useful tool for understanding the structure of more complicated finite groups, as any finite abelian group can be decomposed into a direct product of cyclic groups. Our proof technique can also be extended to other areas of group theory, such as the classification of finite p-groups, where p is a prime. Therefore, our work has implications beyond the specific result we prove and can contribute to further research in algebraic structures.

Keywords: group theory, finite groups, cyclic groups, prime order, classification.

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Authors: Marcelino Caetano Noronha, Vosco Pereira, Jose Soares Pinto, Ferdinando Da C. Saores

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One particularly popular social media platform is Youtube. It’s a video-sharing platform where users can submit videos, and other users can like, dislike or comment on the videos. In this study, we conduct a binary classification task on YouTube’s video comments and review from the users regarding the accession process of Timor Leste to become the eleventh member of the Association of South East Asian Nations (ASEAN). We scrape the data directly from the public YouTube video and apply several pre-processing and weighting techniques. Before conducting the classification, we categorized the data into two classes, namely positive and negative. In the classification part, we apply Support Vector Machine (SVM) algorithm. By comparing with Naïve Bayes Algorithm, the experiment showed SVM achieved 84.1% of Accuracy, 94.5% of Precision, and Recall 73.8% simultaneously.

Keywords: classification, YouTube, sentiment analysis, support sector machine

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Authors: Gamze Uslu, Sebnem Baydere, Alper K. Demir

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In this study, data loss tolerance of Support Vector Machines (SVM) based activity recognition model and multi activity classification performance when data are received over a lossy wireless sensor network is examined. Initially, the classification algorithm we use is evaluated in terms of resilience to random data loss with 3D acceleration sensor data for sitting, lying, walking and standing actions. The results show that the proposed classification method can recognize these activities successfully despite high data loss. Secondly, the effect of differentiated quality of service performance on activity recognition success is measured with activity data acquired from a multi hop wireless sensor network, which introduces high data loss. The effect of number of nodes on the reliability and multi activity classification success is demonstrated in simulation environment. To the best of our knowledge, the effect of data loss in a wireless sensor network on activity detection success rate of an SVM based classification algorithm has not been studied before.

Keywords: activity recognition, support vector machines, acceleration sensor, wireless sensor networks, packet loss

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Authors: Yul Y. Nazaruddin, Anas Y. Widiaribowo, Satriyo Nugroho

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Boiler is one of the critical unit in a petrochemical plant. Steam produced by the boiler is used for various processes in the plant such as urea and ammonia plant. An alternative method to optimize the boiler combustion system is presented in this paper. Adaptive Neuro-Fuzzy Inference System (ANFIS) approach is applied to model the boiler using real-time operational data collected from a boiler unit of the petrochemical plant. Nonlinear equation obtained is then used to optimize the air to fuel ratio using Genetic Algorithm, resulting an optimal ratio of 15.85. This optimal ratio is then maintained constant by ratio controller designed using inverse dynamics based on ANFIS. As a result, constant value of oxygen content in the flue gas is obtained which indicates more efficient combustion process.

Keywords: ANFIS, boiler, combustion process, genetic algorithm, optimization.

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Authors: Raana Babadi Fathipour

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Over the past decades, the climatic CO2 concentration and worldwide normal temperature have been expanding, and this drift is anticipated to before long gotten to be more extreme. This situation of climate alter escalate abiotic stretch components (such as dry spell, flooding, saltiness, and bright radiation) that debilitate timberland and related environments as well as trim generation. These variables can contrarily influence plant development and advancement with a ensuing lessening in plant biomass aggregation and surrender, in expansion to expanding plant defenselessness to biotic stresses. As of late, biostimulants have ended up a hotspot as an viable and economical elective to reduce the negative impacts of stresses on plants. In any case, the larger part of biostimulants has destitute solidness beneath natural conditions, which leads to untimely debasement, shortening their organic movement. To unravel these bottlenecks, small scale- and nano-based definitions containing biostimulant atoms and/or microorganisms are picking up consideration as they illustrate a few points of interest over their routine details. In this survey, we center on the embodiment of plant development controllers and plant acquainted microorganisms as a technique to boost their application for plant assurance against abiotic stresses. We moreover address the potential restrictions and challenges confronted for the execution of this innovation, as well as conceivable outcomes with respect to future inquire about.

Keywords: bio stimulants, Seed priming, nano biotechnology, plant growth-promoting, rhizobacteria, plant growth regulators, microencapsulation

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Authors: Imen Elleuch, Wael Ouarda, Gargouri Bilel

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We proposes in this paper a Deep Learning based approach to classify text in order to enrich an Arabic ontology based on the objects classes of Gaston Gross. Those object classes are defined by taking into account the syntactic and semantic features of the treated language. Thus, our proposed approach is a hybrid one. In fact, it is based on the one hand on the object classes that represents a knowledge based-approach on classification of text and in the other hand it uses the deep learning approach that use the word embedding-based-approach to classify text. We have applied our proposed approach on a corpus constructed from an Arabic dictionary. The obtained semantic classification of text will enrich the Arabic objects classes ontology. In fact, new classes can be added to the ontology or an expansion of the features that characterizes each object class can be updated. The obtained results are compared to a similar work that treats the same object with a classical linguistic approach for the semantic classification of text. This comparison highlight our hybrid proposed approach that can be ameliorated by broaden the dataset used in the deep learning process.

Keywords: deep-learning approach, object-classes, semantic classification, Arabic

Procedia PDF Downloads 88

Authors: Lormaine Anne Branzuela, Elysa Largo, Julie Marisol Pagalilauan, Neil Concibido, Monet Concepcion Detras

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Energy is a necessity both for the people and the country. The demand for energy is continually increasing, but the supply is not doing the same. The reopening of the Bataan Nuclear Power Plant (BNPP) in the Philippines has been circulating in the media for the current time. The general public has been hesitant in accepting the inclusion of nuclear energy in the Philippine energy mix due to perceived unsafe conditions of the plant. This study evaluated the possible operations of a nuclear power plant, which is of the same type as the BNPP, considering the safety of the workers, the public, and the environment using a Process Hazard Analysis (PHA) method. What-If Technique was utilized to identify the hazards and consequences on the operations of the plant, together with the level of risk it entails. Through the brainstorming sessions of the PHA team, it was found that the most critical system on the plant is the primary system. Possible leakages on pipes and equipment due to weakened seals and welds and blockages on coolant path due to fouling were the most common scenarios identified, which further caused the most critical scenario – radioactive leak through sump contamination, nuclear meltdown, and equipment damage and explosion which could result to multiple injuries and fatalities, and environmental impacts.

Keywords: process safety management, process hazard analysis, what-If technique, nuclear power plant

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Authors: Mehmet Savsar

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Reliability and availability of power stations are extremely important in order to achieve a required level of power generation. In particular, in the hot desert climate of Kuwait, reliable power generation is extremely important because of cooling requirements at temperatures exceeding 50-centigrade degrees. In this paper, a particular power plant, named Sabiya Power Plant, which has 8 steam turbines and 13 gas turbine stations, has been studied in detail; extensive data are collected; and availability of station units are determined. Furthermore, a simulation model is developed and used to analyze the effects of different maintenance policies on availability of these stations. The results show that significant improvements can be achieved in power plant availabilities if appropriate maintenance policies are implemented.

Keywords: power plants, steam turbines, gas turbines, maintenance, availability, simulation

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Authors: Colin Smith, Linsey S Passarella

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Due to the proliferation and decentralized nature of academic publication, no widely accepted scheme exists for organizing papers by their scientific field of study (FoS) to the author’s best knowledge. While many academic journals require author provided keywords for papers, these keywords range wildly in scope and are not consistent across papers, journals, or field domains, necessitating alternative approaches to paper classification. Past attempts to perform field-of-study (FoS) classification on scientific texts have largely used a-hierarchical FoS schemas or ignored the schema’s inherently hierarchical structure, e.g. by compressing the structure into a single layer for multi-label classification. In this paper, we introduce an application of a Layered Neural Network (LNN) to the problem of performing supervised hierarchical classification of scientific fields of study (FoS) on research papers. In this approach, paper embeddings from a pretrained language model are fed into a top-down LNN. Beginning with a single neural network (NN) for the highest layer of the class hierarchy, each node uses a separate local NN to classify the subsequent subfield child node(s) for an input embedding of concatenated paper titles and abstracts. We compare our LNN-FOS method to other recent machine learning methods using the Microsoft Academic Graph (MAG) FoS hierarchy and find that the LNN-FOS offers increased classification accuracy at each FoS hierarchical level.

Keywords: hierarchical classification, layer neural network, scientific field of study, scientific taxonomy

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Authors: Sannikumar Patel, Brian Nolan, Markus Hofmann, Philip Owende, Kunjan Patel

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Sentiment analysis and opinion mining have become emerging topics of research in recent years but most of the work is focused on data in the English language. A comprehensive research and analysis are essential which considers multiple languages, machine translation techniques, and different classifiers. This paper presents, a comparative analysis of different approaches for multilingual sentiment analysis. These approaches are divided into two parts: one using classification of text without language translation and second using the translation of testing data to a target language, such as English, before classification. The presented research and results are useful for understanding whether machine translation should be used for multilingual sentiment analysis or building language specific sentiment classification systems is a better approach. The effects of language translation techniques, features, and accuracy of various classifiers for multilingual sentiment analysis is also discussed in this study.

Keywords: cross-language analysis, machine learning, machine translation, sentiment analysis

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Authors: Mthokozisi Simelane

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Background: Malaria remains a life-threatening disease in tropical regions despite the advances in the treatment of this disease, it still remains a significant burden as some parasites have become resistant to the currently available drugs. This has created a necessity for the development of alternative, more efficient antimalarial drugs. Warburgia salutaris is a traditional medicinal plant used in malaria treatment by Zulu traditional healers. Materials and methods: The W. salutaris stem-bark was extracted with dichloromethane and the compound was isolated through column chromatography. The compound was identified and characterized by spectroscopic analysis (1H NMR, 13C NMR, IR and MS) and the structure was also confirmed by x-ray crystallography. The anti-plasmodial activity (in vitro) was studied on NF54 Plasmodium falciparum strain (CQS). Cytotoxicity was measured using the MTT assay on HEK239 and HEPG2 cell lines. Docking of Mukaadial acetate was conducted in AutoDock Vina. Structural modifications were conducted in UCSF Chimera and molecular interactions examined in LigPlot. Results: The compound, Mukaadial Acetate showed appreciable inhibition (IC50 0.44±0.10 µg/ml) of the parasite growth and cytotoxicity activity of 0.124±0.109 and 0.199±0.083 (µg/ml) on HEK293 and HEPG2 cells respectively. Molecular docking revealed that Mukaadial Acetate binds to the purine, pyrophosphate and ribose binding sites of the PfHGXPRT with an optimum binding conformation and forms hydrogen bond, steric and hydrophobic interactions with the residues inhabiting the respective binding sites. Conclusion: It is apparent that W. salutaris contains components (including Mukaadial Acetate) that exhibit antimalarial activity. This study scientifically validates the use of this plant in folk medicine.

Keywords: plasmodium falciparum, molecular docking, antimalarial activity, PfHGXPRT, Warburgia salutaris, mukaadial acetate

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Authors: Javad Khaligh, Aghileh Heydari, Ali Akbar Heydari

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Mathematical epidemiological models for the spread of disease through a population are used to predict the prevalence of a disease or to study the impacts of treatment or prevention measures. Initial conditions for these models are measured from statistical data collected from a population since these initial conditions can never be exact, the presence of chaos in mathematical models has serious implications for the accuracy of the models as well as how epidemiologists interpret their findings. This paper confirms the chaotic behavior of a model for dengue fever and SI by investigating sensitive dependence, bifurcation, and 0-1 test under a variety of initial conditions.

Keywords: epidemiological models, SEIR disease model, bifurcation, chaotic behavior, 0-1 test

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Authors: Abdolreza Esmaeilzadeh, Mehri Mirzaei

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Objective: Rheumatic disease is a chronic disease that causes pain, stiffness, swelling and limited motion and function of many joints. RA is the most common form of autoimmune arthritis, affecting more than 1.3 million Americans. Of these, about 75% are women. Materials and Methods: This study was formed due to the misconception about ANA test, which is frequently performed with methods based upon solid phase as ELISA. This experiment was conducted on 430 patients, with clinical symptoms that are likely affected with rheumatic diseases, simultaneously by means of ANA and FANA. Results: 36 cases (8.37%) of patients, despite positive ANA, have demonstrated negative results via Indirect Immunofluorescence Assay (IIFA), (false positive). 116 cases (27%) have demonstrated negative ANA results, by means of the ELISA technique, although they had positive IIFA results. Conclusion: Other advantages of IIFA are antibody titration and specific pattern detection that have the capability of distinguishing positive dsDNA results. According to the restrictions and false negative cases, in patients, IIFA test is highly recommended for these disease's diagnosis.

Keywords: autoimmune disease, IIFA, EIA, rheumatic disease

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Authors: Bharti Chogtu Magazine, Dhanya Soodana Mohan, Shruti Nair, Tanwi Trushna

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The study was undertaken to analyse the cardiovascular drugs being prescribed in patients with concomitant reactive airway disease and hypertension or ischemic heart diseases (IHD). Also, the effect of beta-blockers on respiratory symptoms in these patients was recorded. Data was collected from medical records of patients with reactive airway disease and concomitant hypertension and IHD. It included demographic details of the patients, diagnosis, drugs prescribed and the patient outcome regarding the exacerbation of asthma symptoms with intake of beta blockers. Medical records of 250 patients were analysed.13% of patients were prescribed beta-blockers. 12% of hypertensive patients, 16.6% of IHD patients and 20% of patients with concomitant hypertension and IHD were prescribed beta blockers. Of the 33 (13%) patients who were on beta-blockers, only 3 patients had an exacerbation of bronchial asthma symptoms. Cardioselective beta-blockers under supervision appear to be safe in patients with reactive airway disease and concomitant hypertension and IHD.

Keywords: beta blockers, hypertension, ischemic heart disease, asthma

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Authors: Meli̇sa Aşci, Berk Kiliç, Emine Ulusoy

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Ingestion of microplastics in humans has been increasing rapidly, as such hazardous materials are abundant in multiple food products, specifically milks. With increasing consumption rates, humans have been ingesting microplastics on a daily basis, making them prone to be intoxicated and even cause the disruption of intracellular pathways and liver cell disruption, and eventually tissue and organ damage. In this experiment, different milk types(animal-based and plant-based) were tested for microplastics. Results showed that animal-based milks contained a higher concentration of microplastics compared to plant-based milks. Research has shown that in addition to causing health issues in humans, microplastics can also affect livestock animals and plants.

Keywords: microplastics, plant-based milks, animal-based milks, preventive nutrition

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Authors: Mehmet Bulent Ozdemir, Sultan Çagirici, Sahika Pinar Akyer, Fikri Turk

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Neuroanatomical appearance can be correlated with clinical or other characteristics of illness. With the introduction of diagnostic imaging machines, producing 3D images of anatomic structures, calculating the correlation between subjects and pattern of the structures have become possible. The aim of this study is to examine the 3D structure of hippocampus in cases with Alzheimer disease in different dementia severity. For this purpose, 62 female and 38 male- 68 patients’s (age range between 52 and 88) MR scanning were imported to the computer. 3D model of each right and left hippocampus were developed by a computer aided propramme-Surf Driver 3.5. Every reconstruction was taken by the same investigator. There were different apperance of hippocampus from normal to abnormal. In conclusion, These results might improve the understanding of the correlation between the morphological changes in hippocampus and clinical staging in Alzheimer disease.

Keywords: Alzheimer disease, hippocampus, computer-assisted anatomy, 3D

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Authors: Babak Moradi, Hassan Zare-Maivan

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A greenhouse investigation has been conducted to study the effect of crude oil on morphology and protein content of Avicennia marina plant. Avicennia marina seeds were sown in different concentrations of the crude oil mixed soil (i.e., 2.5, 5, 7.5, and 10 w/w). Controls and replicates were also set up. Morphological traits were recorded 4 months after plantation. Avicennia marina seedlings could tolerate up to 10% (w/w). Results demonstrated that there was a reduction in plant shoot and root biomass with the increase of crude oil concentration. Plant height, total leaf number and length reduced significantly with increase of crude oil contamination. Investigation revealed that there is a great impact of crude oil contamination on protein content of the roots of the experimental plant. Protein content of roots grown in different concentrations of crude oil were more than those of the control plant. Further, results also showed that protein content was increased with increased concentration of crude oil.

Keywords: Avicennia marina, morphology, oil contamination, protein content

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Authors: Sangbok Lee

Abstract:

There has been much effort on process improvement for outpatient clinics to provide quality and acute care to patients. One of the efforts is no-show analysis or prediction. This work analyzes patient no-shows along with patient health conditions. The health conditions refer to clinical symptoms that each patient has, out of the followings; hyperlipidemia, diabetes, metastatic solid tumor, dementia, chronic obstructive pulmonary disease, hypertension, coronary artery disease, myocardial infraction, congestive heart failure, atrial fibrillation, stroke, drug dependence abuse, schizophrenia, major depression, and pain. A dataset from a regional hospital is used to find the relationship between the number of the symptoms and no-show probabilities. Additional analysis reveals how each symptom or combination of symptoms affects no-shows. In the above analyses, cross-classification of patients by age and gender is carried out. The findings from the analysis will be used to take extra care to patients with particular health conditions. They will be forced to visit clinics by being informed about their health conditions and possible consequences more clearly. Moreover, this work will be used in the preparation of making institutional guidelines for patient reminder systems.

Keywords: healthcare system, no show analysis, process improvment, statistical data analysis

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