Search results for: prediction interval

Authors: Ronak Gravand

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Eocene deposits in Zagros basin in tow zones of interior Fars and Izeh include limestone and marly limestone succession along with abundant fossils. The significance of this area is due to its hydro carbonic resources. In Dashte Kuh section, limestone and marly limestone deposits with medium to thick creamy layers containing benthic foraminifera could be seen. Bio-zones identified in such deposits include Opertorbitolites Subzone, Somalina Subzone, Alveolina Nummulites Assemblage Subzone and Nummulites fabianii Silvestriella tetraedra Assembelage Zone. In Nil Kuh section, marly limestone of the succession contain abundant plagic foraminifera. The zones identified in this succession include Morozovella aragonesis Range Zone, Hantkenina nuttalli Range Zone, Hantkenina nuttalli Turborotalia cerro-azulensis Interval Zone, Turborotalia cerro-azulensis Range Zone and Morozovella aragonesis Range Zone.

Keywords: zagros basin, foraminifera , biozone, Iran

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Authors: John Kabuba

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The study investigated the implementation of the Neural Network (NN) techniques for prediction of the loading of Cu ions onto clinoptilolite. The experimental design using analysis of variance (ANOVA) was chosen for testing the adequacy of the Neural Network and for optimizing of the effective input parameters (pH, temperature and initial concentration). Feed forward, multi-layer perceptron (MLP) NN successfully tracked the non-linear behavior of the adsorption process versus the input parameters with mean squared error (MSE), correlation coefficient (R) and minimum squared error (MSRE) of 0.102, 0.998 and 0.004 respectively. The results showed that NN modeling techniques could effectively predict and simulate the highly complex system and non-linear process such as ion-exchange.

Keywords: clinoptilolite, loading, modeling, neural network

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Authors: Yagya Murti Aryal, Bipin Adhikari, Pradeep Gyawali

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The Lesser Himalaya zone of Nepal consists of thrusting and folding belts, which play an important role in the sustainable management of groundwater in the Himalayan regions. The study area is located in the Dolakha and Ramechhap Districts of Bagmati Province, Nepal. Geologically, these districts are situated in the Lesser Himalayas and partly encompass the Higher Himalayan rock sequence, which includes low-grade to high-grade metamorphic rocks. Following the Gorkha Earthquake in 2015, numerous springs dried up, and many others are currently experiencing depletion due to the distortion of the natural groundwater flow. The primary objective of this study is to identify potential groundwater areas and determine suitable sites for artificial groundwater recharge. Two distinct statistical approaches were used to develop models: The Frequency Ratio (FR) and Shannon Entropy (SE) methods. The study utilized both primary and secondary datasets and incorporated significant role and controlling factors derived from field works and literature reviews. Field data collection involved spring inventory, soil analysis, lithology assessment, and hydro-geomorphology study. Additionally, slope, aspect, drainage density, and lineament density were extracted from a Digital Elevation Model (DEM) using GIS and transformed into thematic layers. For training and validation, 114 springs were divided into a 70/30 ratio, with an equal number of non-spring pixels. After assigning weights to each class based on the two proposed models, a groundwater potential map was generated using GIS, classifying the area into five levels: very low, low, moderate, high, and very high. The model's outcome reveals that over 41% of the area falls into the low and very low potential categories, while only 30% of the area demonstrates a high probability of groundwater potential. To evaluate model performance, accuracy was assessed using the Area under the Curve (AUC). The success rate AUC values for the FR and SE methods were determined to be 78.73% and 77.09%, respectively. Additionally, the prediction rate AUC values for the FR and SE methods were calculated as 76.31% and 74.08%. The results indicate that the FR model exhibits greater prediction capability compared to the SE model in this case study.

Keywords: groundwater potential mapping, frequency ratio, Shannon’s Entropy, Lesser Himalaya Zone, sustainable groundwater management

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Authors: Nur Allisha Othman, Rosnah Shamsudin, Zaulia Othman, Siti Hajar Othman

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Mitigating short storage life of fruit like banana uses minimally process or known as fresh cut can contribute to the growing demand especially in South East Asian countries. The effect of different types of packaging material on fresh-cut Nipah (Musa acuminata balbisiana) were studied. Fresh cut banana cultivar (cv) Nipah are packed in polypropylene plastic (PP), low density polypropylene plastic (LDPE), polymer plastic film (shrink wrap) and polypropylene container as control for 12 days at low temperature (4ᵒC). Quality of physical and chemical evaluation such as colour, texture, pH, TA, TSS, and vitamin C were examined every 2 days interval for 12 days at 4ᵒC. Result shows that the PP is the most suitable packaging for banana cv Nipah because it can reduce respiration and physicochemical quality changes of banana cv Nipah. Different types of packaging significantly affected quality of fresh-cut banana cv Nipah. PP bag was the most suitable packaging to maintain quality and prolong storage life of fresh-cut banana cv Nipah for 12 days at 4ᵒC.

Keywords: physicochemical, PP, LDPE, shrink wrap, browning, respiration

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Authors: Zan Azma Nasruddin, Irwan Mazlin, Nor Aziah Daud, Fauziah Redzuan, Fariza Hanis Abdul Razak

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This paper focuses on a machine learning that learn the correct pronunciation of Makhraj Huroofs. Usually, people need to find an expert to pronounce the Huroof accurately. In this study, the researchers have developed a system that is able to learn the selected Huroofs which are ha, tsa, zho, and dza using the Convolutional Neural Network. The researchers present the chosen type of the CNN architecture to make the system that is able to learn the data (Huroofs) as quick as possible and produces high accuracy during the prediction. The researchers have experimented the system to measure the accuracy and the cross entropy in the training process.

Keywords: convolutional neural network, Makhraj recognition, speech recognition, signal processing, tensorflow

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Authors: Autcha Araveeporn

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This paper presents a study about a nonparametric regression model consisting of a smoothing spline method and a penalized spline regression method. We also compare the techniques used for estimation and prediction of nonparametric regression model. We tried both methods with crude oil prices in dollars per barrel and the Stock Exchange of Thailand (SET) index. According to the results, it is concluded that smoothing spline method performs better than that of penalized spline regression method.

Keywords: nonparametric regression model, penalized spline regression method, smoothing spline method, Stock Exchange of Thailand (SET)

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Authors: H. Hosseini, B. V. Berdyshev, I. Iskopintsev

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This paper presents a rheological model for producing shape-memory thermoplastic polymers. Shape-memory occurs as a result of internal rearrangement of the structural elements of a polymer. A non-linear viscoelastic model was developed that allows qualitative and quantitative prediction of the stress-strain behavior of shape-memory polymers during heating. This research was done to develop a technique to determine the maximum possible change in size of heat-shrinkable products during heating. The rheological model used in this work was particularly suitable for defining process parameters and constructive parameters of the processing equipment.

Keywords: elastic deformation, heating, shape-memory polymers, stress-strain behavior, viscoelastic model

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Authors: Lukas Reznak, Maria Reznakova

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Recession of an economy has a profound negative effect on all involved stakeholders. It follows that timely prediction of recessions has been of utmost interest both in the theoretical research and in practical macroeconomic modelling. Current mainstream of recession prediction is based on standard OLS models of continuous GDP using macroeconomic data. This approach is not suitable for two reasons: the standard continuous models are proving to be obsolete and the macroeconomic data are unreliable, often revised many years retroactively. The aim of the paper is to explore a different branch of recession forecasting research theory and verify the findings on real data of the Czech Republic and Germany. In the paper, the authors present a family of discrete choice probit models with parameters estimated by the method of maximum likelihood. In the basic form, the probits model a univariate series of recessions and expansions in the economic cycle for a given country. The majority of the paper deals with more complex model structures, namely dynamic and bivariate extensions. The dynamic structure models the autoregressive nature of recessions, taking into consideration previous economic activity to predict the development in subsequent periods. Bivariate extensions utilize information from a foreign economy by incorporating correlation of error terms and thus modelling the dependencies of the two countries. Bivariate models predict a bivariate time series of economic states in both economies and thus enhance the predictive performance. A vital enabler of timely and successful recession forecasting are reliable and readily available data. Leading indicators, namely the yield curve and the stock market indices, represent an ideal data base, as the pieces of information is available in advance and do not undergo any retroactive revisions. As importantly, the combination of yield curve and stock market indices reflect a range of macroeconomic and financial market investors’ trends which influence the economic cycle. These theoretical approaches are applied on real data of Czech Republic and Germany. Two models for each country were identified – each for in-sample and out-of-sample predictive purposes. All four followed a bivariate structure, while three contained a dynamic component.

Keywords: bivariate probit, leading indicators, recession forecasting, Czech Republic, Germany

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Authors: U. Chattaraj, K. Dhusiya, M. Raviteja

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Perception reaction time of drivers is an outcome of human thought process, which is vague and approximate in nature and also varies from driver to driver. So, in this study a fuzzy logic based model for prediction of the same has been presented, which seems suitable. The control factors, like, age, experience, intensity of driving of the driver, speed of the vehicle and distance of stimulus have been considered as premise variables in the model, in which the perception reaction time is the consequence variable. Results show that the model is able to explain the impacts of the control factors on perception reaction time properly.

Keywords: driver, fuzzy logic, perception reaction time, premise variable

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Authors: Amir Hedjripour

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The required dewatering configuration for a contaminated sediment dam is discussed to meet no-spill criteria for a defined Average Recurrence Interval (ARI). There is an option for the sediment dam to pump the contaminated water to another storage facility before its capacity is exceeded. The system is subjected to a range of storm durations belonging to the design ARI with concurrent dewatering to the other storage facility. The model is set up in 1-minute time intervals and temporal patterns of storm events are used to de-segregate the total storm depth into partial durations. By running the model for selected storm durations, the maximum water volume in the dam is recorded as the critical volume, which indicates the required storage capacity for that storm duration. Runoff from upstream catchment and the direct rainfall over the dam open area are calculated by taking into account the time of concentration for the catchment. Total 99 different storm durations from 5 minutes to 72 hours were modelled together with five dewatering scenarios from 50 l/s to 500 l/s. The optimised dam/pump configuration is selected by plotting critical points for all cases and storage-dewatering envelopes. A simple economic analysis is also presented in the paper using Present-Value (PV) analysis to assist with the financial evaluation of each configuration and selection of the best alternative.

Keywords: contaminated water, optimisation, pump, sediment dam

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Authors: H. Hotait, X. Chiementin, L. Rasolofondraibe

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This paper presents a continuous work to detect the abnormal state in the rolling bearing by studying the vibration signature analysis and calculation of the remaining useful life. To achieve these aims, two methods; the first method is the classification to detect the degradation state by the AOM-OPTICS (Acousto-Optic Modulator) method. The second one is the prediction of the degradation state using least-squares support vector regression and then compared with the linear degradation model. An experimental investigation on ball-bearing was conducted to see the effectiveness of the used method by applying the acquired vibration signals. The proposed model for predicting the state of bearing gives us accurate results with the experimental and numerical data.

Keywords: bearings, automatization, optimization, prognosis, classification, defect detection

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Authors: Joaquim de Almeida Dultra, Daiana Cristina Pereira Santana, Fátima Karoline Alves Araújo Dultra, Liliane Akemi Kawano Shibasaki, Mariana Machado Mendes de Carvalho, Ieda Margarida Crusoé Rocha Rebello

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Defects originating from mandibular resections can cause significant functional impairment and facial disharmony, and they have complex rehabilitation. The aim of this report is to demonstrate the authors' experience facing challenging rehabilitation after mandibular resection in a patient with ameloblastoma. Clinical and surgical steps are described simultaneously, highlighting the adaptation of the final fixed prosthesis, reported in an unprecedented way in the literature. A 37-year-old male patient was seen after a sports accident, where a pathological fracture in the symphysis and left mandibular body was identified, where a large radiolucent lesion was found. The patient underwent resection, bone graft, distraction osteogenesis, rehabilitation with dental implants, prosthesis, and finally, orofacial harmonization, in an interval of six years. Rehabilitation should consider the patient's needs individually and should have as the main objective to provide similar aesthetics and function to that present before the disease. We also emphasize the importance of interdisciplinary work during the course of rehabilitation.

Keywords: ameloblastoma, mandibular reconstruction, distraction osteogenesis, dental implants. dental prosthesis, implant-supported, treatment outcome

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Authors: Zhang Tianci, Ding Meng, Zuo Hongfu, Zeng Lina, Sun Zejun

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Automation of airport operations can greatly improve ground movement efficiency. In this paper, we study the speed profile design problem for advanced airport ground movement control and guidance. The problem is constrained by the surface four-dimensional trajectory generated in taxi planning. A decomposed approach of two stages is presented to solve this problem efficiently. In the first stage, speeds are allocated at control points which ensure smooth speed profiles can be found later. In the second stage, detailed speed profiles of each taxi interval are generated according to the allocated control point speeds with the objective of minimizing the overall fuel consumption. We present a swarm intelligence based algorithm for the first-stage problem and a discrete variable driven enumeration method for the second-stage problem since it only has a small set of discrete variables. Experimental results demonstrate the presented methodology performs well on real world speed profile design problems.

Keywords: airport ground movement, fuel consumption, particle swarm optimization, smoothness, speed profile design

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Authors: S. A. Khan, B. Mishra, O. Sherchan

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Background and Aims: Thyroid disorder is the most common endocrine disorder after diabetes mellitus. Females are more vulnerable to this disease, and old age is an important risk factor. This study was undertaken to investigate the burden of thyroid disorder in Nepalese postmenopausal women. Methods: In the present cross-sectional study, we included 271 post-menopausal women. Three ml of blood was collected following standard protocol after taking the written consent. Serum was separated and analyzed for free T3, free T4, and Thyroid Stimulating Hormone (TSH) by Chemiluminescence Immunoassay (CLIA) method in Snibe Maglumi 1000 analyzer. Data obtained was analyzed in SPSS Version 21. P < 0.05 was set for statistical significant at 95% Confidence Interval (CI). Results: Majority of the participants belong to Janjati (46.5%) ethnicity, followed by Brahmin/Chhetri (41.7%), residing either in urban or suburban locality. Most of them were non-vegetarian, non-smoker, and non-alcoholic. Subjects were divided into hyperthyroid (TSH < 0.3 uIU/ml), hypothyroid (TSH > 4.5 uIU/ml), and euthyroid (TSH=0.3-4.5 uIU/ml) based on TSH value. We reported 10.3% hyperthyroid and 29.2% hypothyroid cases. TSH was significantly correlated with T3 (r=-0.244; p < 0.001) T4 (r=-0.398; p < 0.001); age (r=-0.138; p=0.023) and BMI (r=0.123; p=0.043). Multiple linear regression model for TSH reveals only T3 and T4 were significantly associated with TSH (p < 0.001; p=0.001). Conclusion: To conclude, nearly 39.5% of the postmenopausal women had thyroid disorder. Postmenopausal women are vulnerable to thyroid disorder; therefore, requires regular thyroid monitoring.

Keywords: thyroid stimulating hormone, TSH, T3, T4, thyroid disorder

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Authors: Seung Cho Yang, Jae Sung Lee, Se Hee Park

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This study suggests the analysis method to predict the damages of tunnel concrete caused by fires. The result obtained from the analyses of concrete temperatures at a fire in a tunnel using ABAQUS was compared with the test result. After the reliability of the analysis method was verified, the temperatures of a tunnel at a real fire and those of concrete during the fire were estimated to predict fire damages. The temperatures inside the tunnel were estimated by FDS, a CFD model. It was deduced that the fire performance of tunnel lining and the fire damages of the structure at an actual fire could be estimated by the analysis method.

Keywords: fire resistance, heat transfer, numerical analysis, tunnel fire

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Authors: Kharina Budiman, Adinda Syifa Azhari, Eleonora Agustine

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Industrial activities get increase in this globalization era, one of the major impacts of industrial activities is a problem to the environment. This can happen because at the industrial production term will bring out pollutant in the shape of solid, liquid or gas. Normally this pollutant came from some dangerous materials for environment. However not every industry produces the same amount of pollutant, every industry produces different kind of pollution. To compare the pollution impact of industrial activities, soil sample has been taken around the animal feed industry and the fertilizer industry. This study applied the rock magnetic method and used Bartington MS2B to measured magnetic susceptibility (χ) as the physical parameter. This study tested soil samples using the value of susceptibility low frequency (χ lf) and Frequency Dependent (χ FD). Samples only taken in the soil surface with 0-5 cm depth and sampling interval was 20 cm. The animal feed factory has susceptibility low frequency (χ lf) = 111,9 – 325,7 and Frequency Dependent (χ FD) = 0,8 – 3,57 %. And the fertilizer factory has susceptibility low frequency (χ lf) = 187,1 – 494,8 and Frequency Dependent (χ FD) = 1,37 – 2,46 %. Based on the results, the highest value of susceptibility low frequency (χ lf) is the fertilizer factory, but the highest value of Frequency Dependent (FD) is the animal feed factory.

Keywords: industrial, pollution, magnetic susceptibility, χlf, χfd, animal feed industry and fertilizer industry

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Authors: Khashayar Kazemzadeh, Mohammad Hanif Dasoomi

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According to the increasing volume of traffic, lane changing plays a crucial role in traffic flow. Lane changing in traffic depends on several factors including road geometrical design, speed, drivers’ behavioral characteristics, etc. A great deal of research has been carried out regarding these fields. Despite of the other significant factors, the drivers’ behavioral characteristics of lane changing has been emphasized in this paper. This paper has predicted the effective equation based on personal characteristics of lane changing by regression models.

Keywords: effective equation, lane changing, drivers’ behavioral characteristics, regression models

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Authors: Toby Li, Julian Zhu

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In this paper, a model based on a simplified geometry is introduced to give a very conservative collision probability prediction for the Starlink satellite in its most densely clustered region. Under the model in this paper, the probability of collision for Starlink satellite where it clustered most densely is found to be 8.484 ∗ 10^−4. It is found that the predicted collision probability increased nonlinearly with the increased safety distance set. This simple model provides evidence that the continuous development of maneuver avoidance systems is necessary for the future of the orbital safety of satellites under the harsher Lower Earth Orbit environment.

Keywords: Starlink, collision probability, debris, geometry model

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Authors: F. Sabri, J. Jamali

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In this article, the wrinkling failure of orthotropic composite membranes due to in-plane shear deformation is investigated using nonlinear finite element analyses. A nonlinear post-buckling analysis is performed to show the evolution of shear-induced wrinkles. The method of investigation is based on the post-buckling finite element analysis adopted from commercial FEM code; ANSYS. The resulting wrinkling patterns, their amplitude and their wavelengths under the prescribed loads and boundary conditions were confirmed by experimental results. Our study reveals that wrinkles develop when both the magnitudes and coverage of the minimum principal stresses in the laminated composite laminates are sufficiently large to trigger wrinkling.

Keywords: composite, FEM, membrane, wrinkling

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Authors: V. Jagadish, Mathiyalagan V.

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Prediction of Door Touch temperature of a Cooking Oven using CFD Simulation. Self-Clean cycle is carried out in Cooking ovens to convert food spilling into ashes which makes cleaning easy. During this cycle cavity of oven is exposed to high temperature around 460 C. At this operating point the user may prone to touch the Door surfaces, Side Shield, Control Panel. To prevent heat experienced by user, cooling system is built in oven. The most effective cooling system is developed with existing design constraints through CFD Simulations. Cross Flow fan is used for Cooling system due to its cost effectiveness and it can give more air flow with low pressure drop.

Keywords: CFD, MRF, RBM, RANS, new product development, simulation, thermal analysis

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Authors: Saba Ebrahimi, Hedieh Ashrafi

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Predicting the cover type of forests is a challenge for natural resource managers. In this project, we aim to perform a comprehensive comparative study of two well-known classification methods, support vector machine (SVM) and decision tree (DT). The comparison is first performed among different types of each classifier, and then the best of each classifier will be compared by considering different evaluation metrics. The effect of boosting and bagging for decision trees is also explored. Furthermore, the effect of principal component analysis (PCA) and feature selection is also investigated. During the project, the forest cover-type dataset from the remote sensing and GIS program is used in all computations.

Keywords: classification methods, support vector machine, decision tree, forest cover-type dataset

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Authors: Reza Hedayati, Meysam Jahanbakhshi

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Fracture Mechanics is used to predict debonding propagation in adhesive joint between aluminum and composite plates. Three types of loadings and two types of glass-epoxy composite sequences: [0/90]2s and [0/45/-45/90]s are considered for the composite plate and their results are compared. It was seen that generally the cases with stacking sequence of [0/45/-45/90]s have much shorter lives than cases with [0/90]2s. It was also seen that in cases with λ=0 the ends of the debonding front propagates forward more than its middle, while in cases with λ=0.5 or λ=1 it is vice versa. Moreover, regardless of value of λ, the difference between the debonding propagations of the ends and the middle of the debonding front is very close in cases λ=0.5 and λ=1. Another main conclusion was the non-dimensionalized debonding front profile is almost independent of sequence type or the applied load value.

Keywords: fatigue, debonding, Paris law, APDL, adhesive

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Authors: Sarah Fatima, Ahmad A. Malik, Saima Sadaf

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This study was conducted in order to assess the dietary record and vitamin A status of young females of Lahore. A total sample of 376 consisted of 16 – 20 years of unmarried college going females. Three main tools were adopted: questionnaire, 3-day food diary and serum retinol test. The anthropometric measurements showed that a total of 32.6% of the sample was underweight (BMI < 18.5) and 54.5% had a healthy weight (BMI 18.5 – 22.9). The average Vitamin A intake of the sample was 257.95 µg/day while the RDA for the selected age group was 700 µg/day. The mean energy intake of the adolescents was 1153.64 kcal/ day, whereas the Estimated Energy Requirement (EER) for this age group was 2368 kcal/day. The mean serum Vitamin A level was 24.81µg/dL. 69.6% of the sample was deficient in serum Vitamin A i.e. serum retinol < 24 µg/dL. 30.4% had serum retinol in normal limit (24 – 84 µg/dL) from which 25.3% lied in lower limit (24 – 44 µg/dL) and only 5.1% had serum retinol in 44 – 64 µg/dL range. A slightly negative correlation (r = - 0.21, 95% confidence interval) was found between dietary intake of Vitamin A and serum Vitamin A It was concluded that the dietary intake of major nutrients and vitamin A is not adequate in the selected group. This is also confirmed by the lower serum retinol levels. Hence, vitamin An intake and status are generally inadequate, and vitamin deficiency is prevalent in the unmarried young females of Lahore.

Keywords: vitamin A, young Females, vitamin deficiency, Lahore

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Authors: Srushti Patil, Preethi Baligar, Gopalkrishna Joshi, Gururaj N. Bhadri

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In designing learning environments, the instructional strategies can be tailored to suit the learning style of an individual to ensure effective learning. In this study, the information shared on social media like Facebook is being used to predict learning style of a learner. Previous research studies have shown that Facebook data can be used to predict user personality. Users with a particular personality exhibit an inherent pattern in their digital footprint on Facebook. The proposed work aims to correlate the user's’ personality, predicted from Facebook data to the learning styles, predicted through questionnaires. For Millennial learners, Facebook has become a primary means for information sharing and interaction with peers. Thus, it can serve as a rich bed for research and direct the design of learning environments. The authors have conducted this study in an undergraduate freshman engineering course. Data from 320 freshmen Facebook users was collected. The same users also participated in the learning style and personality prediction survey. The Kolb’s Learning style questionnaires and Big 5 personality Inventory were adopted for the survey. The users have agreed to participate in this research and have signed individual consent forms. A specific page was created on Facebook to collect user data like personal details, status updates, comments, demographic characteristics and egocentric network parameters. This data was captured by an application created using Python program. The data captured from Facebook was subjected to text analysis process using the Linguistic Inquiry and Word Count dictionary. An analysis of the data collected from the questionnaires performed reveals individual student personality and learning style. The results obtained from analysis of Facebook, learning style and personality data were then fed into an automatic classifier that was trained by using the data mining techniques like Rule-based classifiers and Decision trees. This helps to predict the user personality and learning styles by analysing the common patterns. Rule-based classifiers applied for text analysis helps to categorize Facebook data into positive, negative and neutral. There were totally two models trained, one to predict the personality from Facebook data; another one to predict the learning styles from the personalities. The results show that the classifier model has high accuracy which makes the proposed method to be a reliable one for predicting the user personality and learning styles.

Keywords: educational data mining, Facebook, learning styles, personality traits

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Authors: Adel Hanna, Tahar Ayadat, Mohammad Etezad, Cyrille Cros

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A number of theoretical methods have been developed over the years to calculate the amount settlement of the soil reinforced with group of stone columns. The results deduced from these methods sometimes show large disagreement with the experimental observations. The reason of this divergence might be due to the fact that many of the previous methods assumed the deform shape of the columns which is different with the actual case. A new method to calculate settlement of the ground reinforced with group of stone columns is presented in this paper which overcomes the restrictions made by previous theories. This method is based on results deduced from numerical modeling. Results obtained from the model are validated.

Keywords: stone columns, group, soft soil, settlement, prediction

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Authors: Etienne K. Ngoy

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This analysis suggests that the comprehensive degradation caused by any environmental factor on fiber reinforced plastics under mechanical stress can be measured as a change in viscoelastic properties of the material. The change in viscoelastic characteristics is experimentally determined as a time-dependent function expressing the amplification of the stress relaxation. The variation of this experimental function provides a measure of the environmental degradation rate. Where real service environment conditions can be reliably simulated in the laboratory, it is possible to generate master curves that include environmental degradation effect and hence predict the durability of the fiber reinforced plastics under environmental degradation.

Keywords: environmental effects, fiber reinforced plastics durability, prediction, stress effect

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Authors: Mehran Dizbadi, Daniyal Safarzadeh, Behrooz Arastoo, Ansgar Brunn

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Rapid and uncontrolled expansion of cities has led to unplanned aerial development. In this way, modeling and predicting the urban growth of a city helps decision-makers. In this study, the aspect of sustainable urban development has been studied for the city of Mashhad. In general, the prediction of urban aerial development is one of the most important topics of modern town management. In this research, using the Cellular Automaton (CA) model developed for geo data of Geographic Information Systems (GIS) and presenting a simple and powerful model, a simulation of complex urban processes has been done.

Keywords: urban modeling, sustainable development, fuzzy cellular automaton, geo-information system

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Authors: Suzan Wedyan

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Data mining is one of the main phases in the Knowledge Discovery Database (KDD) which is responsible of finding hidden and useful knowledge from databases. There are many different tasks for data mining including regression, pattern recognition, clustering, classification, and association rule. In recent years a promising data mining approach called associative classification (AC) has been proposed, AC integrates classification and association rule discovery to build classification models (classifiers). This paper surveys and critically compares several AC algorithms with reference of the different procedures are used in each algorithm, such as rule learning, rule sorting, rule pruning, classifier building, and class allocation for test cases.

Keywords: associative classification, classification, data mining, learning, rule ranking, rule pruning, prediction

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Authors: Ali Bayram, Burak Uz, Ilhan Dolasik, Remzi Yiğiter

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The GABARAP (GABAA-receptor-associated protein) family consists of GABARAP, GABARAPL1 (GABARAP-like 1) and GABARAPL2 (GABARAP-like 2). GABARAPL1, like GABARAP, was described to interact with both GABAA receptor and tubulin, and to be involved in intracellular GABAA receptor trafficking and promoting tubulin polymerization. In addition, GABARAPL1 is thought to be involved in various physiological (autophagosome closure, regulation of circadian rhythms) and/or pathological mechanisms (cancer, neurodegeneration). Alzheimer’s disease (AD) is a progressive neuro degenerative disorder characterized with impaired cognitive functions. Disruption of the GABAergic neuro transmission as well as cholinergic and glutamatergic interactions, may also be involved in the pathogenesis of AD. GABARAPL1 presents a regulated tissue expression and is the most expressed gene among the GABARAP family members in the central nervous system. We, herein, conducted a study to investigate the GABARAPL1 mRNA expression levels in patients with AD. 50 patients with AD and 49 control patients were enrolled to the present study. Messenger RNA expression levels of GABARAPL1 were detected by real-time polymerase chain reaction. GABARAPL1 mRNA expression in AD / control patients was 0,495 (95% confidence interval: 0,404-0,607), p= 0,00000002646. Reduced activity of GABARAPL1 gene might play a role, at least partly, in the pathophysiology of AD.

Keywords: Alzheimer’s disease, GABARAPL1, mRNA expression, RT-PCR

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Authors: Azimollah Aleshzadeh, Enver Vural Yavuz

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The main goal of this study is to produce landslide susceptibility maps using different data-driven bivariate statistical approaches; namely, entropy weight method (EWM), evidence belief function (EBF), and information content model (ICM), at Uzundere county, Erzurum province, in the north-eastern part of Turkey. Past landslide occurrences were identified and mapped from an interpretation of high-resolution satellite images, and earlier reports as well as by carrying out field surveys. In total, 42 landslide incidence polygons were mapped using ArcGIS 10.4.1 software and randomly split into a construction dataset 70 % (30 landslide incidences) for building the EWM, EBF, and ICM models and the remaining 30 % (12 landslides incidences) were used for verification purposes. Twelve layers of landslide-predisposing parameters were prepared, including total surface radiation, maximum relief, soil groups, standard curvature, distance to stream/river sites, distance to the road network, surface roughness, land use pattern, engineering geological rock group, topographical elevation, the orientation of slope, and terrain slope gradient. The relationships between the landslide-predisposing parameters and the landslide inventory map were determined using different statistical models (EWM, EBF, and ICM). The model results were validated with landslide incidences, which were not used during the model construction. In addition, receiver operating characteristic curves were applied, and the area under the curve (AUC) was determined for the different susceptibility maps using the success (construction data) and prediction (verification data) rate curves. The results revealed that the AUC for success rates are 0.7055, 0.7221, and 0.7368, while the prediction rates are 0.6811, 0.6997, and 0.7105 for EWM, EBF, and ICM models, respectively. Consequently, landslide susceptibility maps were classified into five susceptibility classes, including very low, low, moderate, high, and very high. Additionally, the portion of construction and verification landslides incidences in high and very high landslide susceptibility classes in each map was determined. The results showed that the EWM, EBF, and ICM models produced satisfactory accuracy. The obtained landslide susceptibility maps may be useful for future natural hazard mitigation studies and planning purposes for environmental protection.

Keywords: entropy weight method, evidence belief function, information content model, landslide susceptibility mapping

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