Search results for: correlated random damping

Authors: Aliasghar Baziar, Hassan Masoumi, Alireza Ale Saadi

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The control of the response of electric motors plays a significant role in the damping of transient responses. In this regard, this paper presents a static VAR compensator (SVC) based on a fuzzy logic which is applied to an industrial power network consisting of three phase synchronous, asynchronous and DC motor loads. The speed and acceleration variations of a specific machine are the inputs of the proposed fuzzy logic controller (FLC). In order to verify the effectiveness and proficiency of the proposed Fuzzy Logic based SVC (FLSVC), several non-linear time-domain digital simulation tests are performed. The proposed fuzzy model can properly control the response of electric motors. The results show that the FLSVC is successful to improve the voltage profile significantly over a wide range of operating conditions and disturbances thus improving the overall dynamic performance of the network.

Keywords: fuzzy logic controller, VAR compensator, single cage asynchronous motor, DC motor

Procedia PDF Downloads 616

Authors: Cristobal García

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The visual inspection of bolted joints in wind turbines is dangerous, expensive, and impractical due to the non-possibility to access the platform by workboat in certain sea state conditions, as well as the high costs derived from the transportation of maintenance technicians to offshore platforms located far away from the coast, especially if helicopters are involved. Consequently, the wind turbine operators have the need for simpler and less demanding techniques for the analysis of the bolts tightening. Vibration-based structural health monitoring is one of the oldest and most widely-used means for monitoring the health of onshore and offshore wind turbines. The core of this work is to find out if the modal parameters can be efficiently used as a key performance indicator (KPIs) for the assessment of joint bolts in a 1:50 scale tower of a floating offshore wind turbine (12 MW). A non-destructive vibration test is used to extract the vibration signals of the towers with different damage statuses. The procedure can be summarized in three consecutive steps. First, an artificial excitation is introduced by means of a commercial shaker mounted on the top of the tower. Second, the vibration signals of the towers are recorded for 8 s at a sampling rate of 20 kHz using an array of commercial accelerometers (Endevco, 44A16-1032). Third, the natural frequencies, damping, and overall vibration mode shapes are calculated using the software Siemens LMS 16A. Experiments show that the natural frequencies, damping, and mode shapes of the tower are directly dependent on the fixing conditions of the towers, and therefore, the variations of both parameters are a good indicator for the estimation of the static axial force acting in the bolt. Thus, this vibration-based structural method proposed can be potentially used as a diagnostic tool to evaluate the tightening torques of the bolted joints with the advantages of being an economical, straightforward, and multidisciplinary approach that can be applied for different typologies of connections by operation and maintenance technicians. In conclusion, TSI, in collaboration with the consortium of the FIBREGY project, is conducting innovative research where vibrations are utilized for the estimation of the tightening torque of a 1:50 scale steel-based tower prototype. The findings of this research carried out in the context of FIBREGY possess multiple implications for the assessment of the bolted joint integrity in multiple types of connections such as tower-to-nacelle, modular, tower-to-column, tube-to-tube, etc. This research is contextualized in the framework of the FIBREGY project. The EU-funded FIBREGY project (H2020, grant number 952966) will evaluate the feasibility of the design and construction of a new generation of marine renewable energy platforms using lightweight FRP materials in certain structural elements (e.g., tower, floating platform). The FIBREGY consortium is composed of 11 partners specialized in the offshore renewable energy sector and funded partially by the H2020 program of the European Commission with an overall budget of 8 million Euros.

Keywords: SHM, vibrations, connections, floating offshore platform

Procedia PDF Downloads 105

Authors: Manal Suleiman, George Abu-Aqil, Uraib Sharaha, Klaris Riesenberg, Itshak Lapidot, Ahmad Salman, Mahmoud Huleihel

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Klebsiella pneumoniae is one of the most aggressive multidrug-resistant bacteria associated with human infections resulting in high mortality and morbidity. Thus, for an effective treatment, it is important to diagnose both the species of infecting bacteria and their susceptibility to antibiotics. Current used methods for diagnosing the bacterial susceptibility to antibiotics are time-consuming (about 24h following the first culture). Thus, there is a clear need for rapid methods to determine the bacterial susceptibility to antibiotics. Infrared spectroscopy is a well-known method that is known as sensitive and simple which is able to detect minor biomolecular changes in biological samples associated with developing abnormalities. The main goal of this study is to evaluate the potential of infrared spectroscopy in tandem with Random Forest and XGBoost machine learning algorithms to diagnose the susceptibility of Klebsiella pneumoniae to antibiotics within approximately 20 minutes following the first culture. In this study, 1190 Klebsiella pneumoniae isolates were obtained from different patients with urinary tract infections. The isolates were measured by the infrared spectrometer, and the spectra were analyzed by machine learning algorithms Random Forest and XGBoost to determine their susceptibility regarding nine specific antibiotics. Our results confirm that it was possible to classify the isolates into sensitive and resistant to specific antibiotics with a success rate range of 80%-85% for the different tested antibiotics. These results prove the promising potential of infrared spectroscopy as a powerful diagnostic method for determining the Klebsiella pneumoniae susceptibility to antibiotics.

Keywords: urinary tract infection (UTI), Klebsiella pneumoniae, bacterial susceptibility, infrared spectroscopy, machine learning

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Authors: Yasuyo Makido, Vivek Shandas, David J. Sailor, M. Salim Ferwati

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Mitigating extreme urban heat is challenging in a desert climate such as Doha, Qatar, since outdoor daytime temperature area often too high for the human body to tolerate. Recent studies demonstrate that cities in arid and semiarid areas can exhibit ‘urban cool islands’ - urban areas that are cooler than the surrounding desert. However, the variation of temperatures as a result of the time of day and factors leading to temperature change remain at the question. To address these questions, we examined the spatial and temporal variation of air temperature in Doha, Qatar by conducting multiple vehicle-base local temperature observations. We also employed three statistical approaches to model surface temperatures using relevant predictors: (1) Ordinary Least Squares, (2) Regression Tree Analysis and (3) Random Forest for three time periods. Although the most important determinant factors varied by day and time, distance to the coast was the significant determinant at midday. A 70%/30% holdout method was used to create a testing dataset to validate the results through Pearson’s correlation coefficient. The Pearson’s analysis suggests that the Random Forest model more accurately predicts the surface temperatures than the other methods. We conclude with recommendations about the types of development patterns that show the greatest potential for reducing extreme heat in air climates.

Keywords: desert cities, tree-structure regression model, urban cool Island, vehicle temperature traverse

Procedia PDF Downloads 381

Authors: Hu Ding, Kai Liu, Guoan Tang

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The Chinese Loess Plateau suffers from serious gully erosion induced by natural and human causes. Gully features detection including gully-affected area and its two dimension parameters (length, width, area et al.), is a significant task not only for researchers but also for policy-makers. This study aims at gully-affected area detection in three catchments of Chinese Loess Plateau, which were selected in Changwu, Ansai, and Suide by using unmanned aerial vehicle (UAV). The methodology includes a sequence of UAV data generation, image segmentation, feature calculation and selection, and random forest classification. Two experiments were conducted to investigate the influences of segmentation strategy and feature selection. Results showed that vertical and horizontal root-mean-square errors were below 0.5 and 0.2 m, respectively, which were ideal for the Loess Plateau region. The segmentation strategy adopted in this paper, which considers the topographic information, and optimal parameter combination can improve the segmentation results. Besides, the overall extraction accuracy in Changwu, Ansai, and Suide achieved was 84.62%, 86.46%, and 93.06%, respectively, which indicated that the proposed method for detecting gully-affected area is more objective and effective than traditional methods. This study demonstrated that UAV can bridge the gap between field measurement and satellite-based remote sensing, obtaining a balance in resolution and efficiency for catchment-scale gully erosion research.

Keywords: unmanned aerial vehicle (UAV), object-analysis image analysis, gully erosion, gully-affected area, Loess Plateau, random forest

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Authors: Maciej Major, Izabela Major

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The aim of this paper is a numerical analysis of three-layered block design to walls construction subjected to the dynamic load. The block consists of the layers: concrete with rubber pads in shape of crosses, space filled with air and concrete with I-shape rubber pads. The main purpose of rubber inserts embedded during the production process is additional protection against the transversal dynamic load. For the analysis, as rubber, the Zahorski hyperelastic incompressible material model was assumed. A concentrated force as dynamic load applied to the external block surface was investigated. The results for the considered block observed as the stress distribution plot were compared to the results obtained for the solid concrete block. In order to estimate the percentage damping of proposed composite, rubber-concrete block in relation to the solid block the numerical analysis with the use of finite element method based on ADINA software was performed.

Keywords: dynamics, composite, rubber, Zahorski

Procedia PDF Downloads 229

Authors: Pau Rodriguez, Jordi Gonzalez, Josep M. Gonfaus, Xavier Roca

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Image sharing in social networks has increased exponentially in the past years. Officially, there are 600 million Instagrammers uploading around 100 million photos and videos per day. Consequently, there is a need for developing new tools to understand the content expressed in shared images, which will greatly benefit social media communication and will enable broad and promising applications in education, advertisement, entertainment, and also psychology. Following these trends, our work aims to take advantage of the existing relationship between text and personality, already demonstrated by multiple researchers, so that we can prove that there exists a relationship between images and personality as well. To achieve this goal, we consider that images posted on social networks are typically conditioned on specific words, or hashtags, therefore any relationship between text and personality can also be observed with those posted images. Our proposal makes use of the most recent image understanding models based on neural networks to process the vast amount of data generated by social users to determine those images most correlated with personality traits. The final aim is to train a weakly-supervised image-based model for personality assessment that can be used even when textual data is not available, which is an increasing trend. The procedure is described next: we explore the images directly publicly shared by users based on those accompanying texts or hashtags most strongly related to personality traits as described by the OCEAN model. These images will be used for personality prediction since they have the potential to convey more complex ideas, concepts, and emotions. As a result, the use of images in personality questionnaires will provide a deeper understanding of respondents than through words alone. In other words, from the images posted with specific tags, we train a deep learning model based on neural networks, that learns to extract a personality representation from a picture and use it to automatically find the personality that best explains such a picture. Subsequently, a deep neural network model is learned from thousands of images associated with hashtags correlated to OCEAN traits. We then analyze the network activations to identify those pictures that maximally activate the neurons: the most characteristic visual features per personality trait will thus emerge since the filters of the convolutional layers of the neural model are learned to be optimally activated depending on each personality trait. For example, among the pictures that maximally activate the high Openness trait, we can see pictures of books, the moon, and the sky. For high Conscientiousness, most of the images are photographs of food, especially healthy food. The high Extraversion output is mostly activated by pictures of a lot of people. In high Agreeableness images, we mostly see flower pictures. Lastly, in the Neuroticism trait, we observe that the high score is maximally activated by animal pets like cats or dogs. In summary, despite the huge intra-class and inter-class variabilities of the images associated to each OCEAN traits, we found that there are consistencies between visual patterns of those images whose hashtags are most correlated to each trait.

Keywords: emotions and effects of mood, social impact theory in social psychology, social influence, social structure and social networks

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Authors: Faiza Babur Khan, Sohail Asghar

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With an emergence of distributed environment, cloud computing is proving to be the most stimulating computing paradigm shift in computer technology, resulting in spectacular expansion in IT industry. Many companies have augmented their technical infrastructure by adopting cloud resource sharing architecture. Cloud computing has opened doors to unlimited opportunities from application to platform availability, expandable storage and provision of computing environment. However, from a security viewpoint, an added risk level is introduced from clouds, weakening the protection mechanisms, and hardening the availability of privacy, data security and on demand service. Issues of trust, confidentiality, and integrity are elevated due to multitenant resource sharing architecture of cloud. Trust or reliability of cloud refers to its capability of providing the needed services precisely and unfailingly. Confidentiality is the ability of the architecture to ensure authorization of the relevant party to access its private data. It also guarantees integrity to protect the data from being fabricated by an unauthorized user. So in order to assure provision of secured cloud, a roadmap or model is obligatory to analyze a security problem, design mitigation strategies, and evaluate solutions. The aim of the paper is twofold; first to enlighten the factors which make cloud security critical along with alleviation strategies and secondly to propose an intrusion detection model that identifies the attackers in a preventive way using machine learning Random Forest classifier with an accuracy of 99.8%. This model uses less number of features. A comparison with other classifiers is also presented.

Keywords: cloud security, threats, machine learning, random forest, classification

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Authors: Yue Hu, Xilu Zhao, Takao Yamaguchi, Manabu Sasajima, Yoshio Koike, Akira Hara

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This study tried optimization of design parameter of a cone loudspeaker unit as an example of the high flexibility of the products design. We developed an acoustic analysis software program that considers the impact of damping caused by air viscosity. In sound reproduction, it is difficult to each design the parameter of the loudspeaker. To overcome the limitation of the design problem in practice, this paper proposes a new an acoustic analysis algorithm to optimize design the parameter of the loudspeaker. The material character of cone paper and the loudspeaker edge was the design parameter, and the vibration displacement of the cone paper was the objective function. The results of the analysis were compared with the predicted value. They had high accuracy to the predicted value. These results suggest that, though the parameter design is difficult by experience and intuition, it can be performed comparatively easily using the optimization design by the developed acoustic analysis software.

Keywords: air viscosity, loudspeaker, cone paper, edge, optimization

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Authors: Mohanad Talal Alfach

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This paper concerns the seismic behaviour of soil-piles-bridge reinforced by additional micropiles. The analysis carried out by three-dimensional finite element modelling using the FE software ABAQUS. The soil behaviour is assumed to be elastic with Rayleigh damping, while the micropiles are modeled as 3D elastic beam elements. The bridge deck slab was represented by a concentrated mass at the top of the pier column. The interaction between the added micropiles and the existing piles as well as the performance of the retrofitted soil-pile-superstructure system were investigated for different configurations of additional micropiles (number, position, inclination). Numerical simulation results show that additional micropiles constitute an efficient retrofitting solution. Analysis of results also shows that spacing between existing piles and retrofitting micropiles has little effect; while it is observed a substantial improvement (in case of weak piles/micropiles - soil interface) with reducing the inclination angle of retrofitting micropiles.

Keywords: retrofitting, seismic, finite element, micropiles, elastic

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

Procedia PDF Downloads 122

Authors: Sana Ridene, Soumaya Sayeb, Houda Helali, Mohammed Ben Hassen

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Nonwoven structures have a range of applications which include Medical, filtration, geotextile and recently this unconventional fabric is finding a niche in fashion apparel. In this paper, a modified form of Vangheluwe rheological model is used to describe the mechanical behavior of nonwovens fabrics in uniaxial tension. This model is an association in parallel of three Maxwell elements characterized by damping coefficients η1, η2 and η3 and E1, E2, E3 elastic modulus and a nonlinear spring C. The model is verified experimentally with two types of nonwovens (50% viscose /50% Polyester) and (40% viscose/60% Polyester) and a range of three square weights values. Comparative analysis of the theoretical model and the experimental results of tensile test proofs a high correlation between them. The proposed model can fairly well replicate the behavior of nonwoven fabrics during relaxation and sample traction. This allowed us to predict the mechanical behavior in tension and relaxation of fabrics starting only from their technical parameters (composition and weight).

Keywords: mechanical behavior, tensile strength, relaxation, rheological model

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Authors: Xiangtuo Chen, Paul-Henry Cournéde

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Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches.

Keywords: crop yield prediction, crop model, sensitivity analysis, paramater estimation, particle swarm optimization, random forest

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Authors: Sudhir Kumar Singh, Debashish Chakravarty

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Slope stability analysis is an important aspect in the field of geotechnical engineering. It is also important from safety, and economic point of view as any slope failure leads to loss of valuable lives and damage to property worth millions. This paper aims at mitigating the risk of slope failure by studying the effect of different joint sets on the stability of mine pit slopes under the influence of various external factors, namely degree of saturation, rainfall intensity, and seismic coefficients. Supervised machine learning approach has been utilized for making accurate and reliable predictions regarding the stability of slopes based on the value of Factor of Safety. Numerous cases have been studied for analyzing the stability of slopes using the popular Finite Element Method, and the data thus obtained has been used as training data for the supervised machine learning models. The input data has been trained on different supervised machine learning models, namely Random Forest, Decision Tree, Support vector Machine, and XGBoost. Distinct test data that is not present in training data has been used for measuring the performance and accuracy of different models. Although all models have performed well on the test dataset but Random Forest stands out from others due to its high accuracy of greater than 95%, thus helping us by providing a valuable tool at our disposition which is neither computationally expensive nor time consuming and in good accordance with the numerical analysis result.

Keywords: finite element method, geotechnical engineering, machine learning, slope stability

Procedia PDF Downloads 90

Authors: Mohsen Bahrami, Banafsheh Nikmehr, Yueqiang Song, Anuradha Koduru, Ayse K. Vuruskan, Hongkun Lu, Tamer M. Yalcinkaya

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In recent years, we have witnessed an explosion of studies aimed at using a combination of artificial intelligence (AI) and time-lapse imaging data on embryos to improve IVF outcomes. However, despite promising results, no study has used a matched cohort of transferred embryos which only differ in pregnancy outcome, i.e., embryos from a single clinic which are similar in parameters, such as: morphokinetic condition, patient age, and overall clinic and lab performance. Here, we used time-lapse data on embryos with known pregnancy outcomes to see if the rich spatiotemporal information embedded in this data would allow the prediction of the pregnancy outcome regardless of such critical parameters. Methodology—We did a retrospective analysis of time-lapse data from our IVF clinic utilizing Embryoscope 100% of the time for embryo culture to blastocyst stage with known clinical outcomes, including live birth vs nonpregnant (embryos with spontaneous abortion outcomes were excluded). We used time-lapse data from 200 elective single transfer embryos randomly selected from January 2019 to June 2021. Our sample included 100 embryos in each group with no significant difference in patient age (P=0.9550) and morphokinetic scores (P=0.4032). Data from all patients were combined to make a 4th order tensor, and feature extraction were subsequently carried out by a tensor decomposition methodology. The features were then used in a machine learning classifier to classify the two groups. Major Findings—The performance of the model was evaluated using 100 random subsampling cross validation (train (80%) - test (20%)). The prediction accuracy, averaged across 100 permutations, exceeded 80%. We also did a random grouping analysis, in which labels (live birth, nonpregnant) were randomly assigned to embryos, which yielded 50% accuracy. Conclusion—The high accuracy in the main analysis and the low accuracy in random grouping analysis suggest a consistent spatiotemporal pattern which is associated with pregnancy outcomes, regardless of patient age and embryo morphokinetic condition, and beyond already known parameters, such as: early cleavage or early blastulation. Despite small samples size, this ongoing analysis is the first to show the potential of AI methods in capturing the complex morphokinetic changes embedded in embryo time-lapse data, which contribute to successful pregnancy outcomes, regardless of already known parameters. The results on a larger sample size with complementary analysis on prediction of other key outcomes, such as: euploidy and aneuploidy of embryos will be presented at the meeting.

Keywords: IVF, embryo, machine learning, time-lapse imaging data

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Authors: F. Polito, M. Cucinotta, A. Conti, C. Lo Giudice, C. Tomasello, F. Angileri, D. La Torre, M. Aguennouz

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Glioblastoma multiforme (GBM) is the most aggressive form of brain tumors, with an extremely poor prognosis. Telomeres lenght is associated with tumor progression in several type of human cancers and telomere elongation is a common molecular feature of advanced malignancies. Among the telomeric shelterin proteins PTOP is required for telomeric protein complex assembly, telomerase recruitment and activity, and telomere length regulation through a PTOP-telomerase interaction. Previous studies suggest that PTOP upregulation is involved in radioresistance and telomere lengthening in colorectal cancer cells. Moreover, in human osteosarcoma cells PTOP deletion led to telomere shortening, increased apoptosis and radiation sensitivity enhancement. However, to date, little is known about the role of PTOP in progression of glioma cancers. In light of this background aim of the study is to investigate the expression of PTOP in different grades of human glioma and its correlation with the pathological grade of gliomas, grades of malignancy, proliferative activity and apoptosis. Fifteen Low Grade Astrocytomas (LGA), 18 Anaplastic Astrocytomas (AA) and 26 Glioblastoma Multiforme (GBM) samples were analyzed. Three samples of normal brain tissue (NBT) were used as controls. The expression levels of PTOP, h-TERT, BIRC1 and cyclin D1 were determined by real time PCR and/or western blot. Results obtained shows that PTOP expression in glioma tissues is tightly correlated with clinical grade ( p < 0.01 ). No correlation was found between PTOP expression and other clinicopathologic parameters. The expression of PTOP was positively correlated with the expression of hTERT and TERF1. Furthermore PTOP positively correlates with cyclin D1 and negatively correlates with the expression of BIRC1. Our findings indicate that PTOP might play key role in the progression of glioma regulating telomerase activity and likely through regulation of cell cycle and apoptosis. In conclusion results obtained prompted us to speculate that PTOP might represents a potential molecular bio marker and a therapeutic target for the treatment of glioblastoma tumors.

Keywords: glioblastoma, PTOP, telomere, brain tumors

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Authors: Abdur Rosyid, Mohamed El-Madany, Mohanad Alata

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Due to simplicity and low cost, rotordynamic system is often modeled by using lumped parameters. Recently, finite elements have been used to model rotordynamic system as it offers higher accuracy. However, it involves high degrees of freedom. In some applications such as control design, this requires higher cost. For this reason, various model reduction methods have been proposed. This work demonstrates the quality of model reduction of rotor-bearing-support system through substructuring. The quality of the model reduction is evaluated by comparing some first natural frequencies, modal damping ratio, critical speeds and response of both the full system and the reduced system. The simulation shows that the substructuring is proven adequate to reduce finite element rotor model in the frequency range of interest as long as the numbers and the locations of master nodes are determined appropriately. However, the reduction is less accurate in an unstable or nearly-unstable system.

Keywords: rotordynamic, finite element model, timoshenko beam, 3D solid elements, Guyan reduction method

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Authors: H. Donya

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Monte Carlo (MC) techniques play a fundamental role in radiotherapy. A two non-water-equivalent of different media were used to evaluate the dose in water. For such purpose, Lu2SiO5 (LSO) and Y2SiO5 (YSO) orthosilicates scintillators are chosen for MC simulation using Penelope code. To get higher efficiency in dose calculation, variance reduction techniques are discussed. Overall results of this investigation ensured that the LSO/YSO bi-media a good combination to tackle over-response issue in dynamic photon radiotherapy.

Keywords: Lu2SiO5 (LSO) and Y2SiO5 (YSO) orthosilicates, Monte Carlo, correlated sampling, radiotherapy

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Authors: Sheryl Avendaño, Miguel Ospina, Hebert Montegranario

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Full Wave Inversion (FWI) is a variant of seismic tomography for obtaining velocity profiles by an optimization process that combine forward modelling (or solution of wave equation) with the misfit between synthetic and observed data. In this research we are modelling wave propagation in a visco-acoustic medium in the frequency domain. We apply finite differences for the numerical solution of the wave equation with a mix between usual and rotated grids, where density depends on velocity and there exists a damping function associated to a linear dissipative medium. The velocity profiles are obtained from an initial one and the data have been modeled for a frequency range 0-120 Hz. By an iterative procedure we obtain an estimated velocity profile in which are detailed the remarkable features of the velocity profile from which synthetic data were generated showing promising results for our method.

Keywords: seismic inversion, full wave inversion, visco acoustic wave equation, finite diffrence methods

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Authors: Esther O. Adebitan, Florence Oyelade

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The Millennium Development Goals (MDGs) was initiated by the UN member nations’ aspiration for the betterment of human life. It is expressed in a set of numerical ‎and time-bound targets. In more recent time, the aspiration is shifting away from just the achievement to the sustainability of achieved MDGs beyond the 2015 target. The main objective of this study was assessing how much the hotel industry within the Nigerian Federal Capital Territory (FCT) as a member of the global community is involved in the achievement of sustainable MDGs within the FCT. The study had two population groups consisting of 160 hotels and the communities where these are located. Stratified random sampling technique was adopted in selecting 60 hotels based on large, medium ‎and small hotels categorisation, while simple random sampling technique was used to elicit information from 30 residents of three of the hotels host communities. The study was guided by tree research questions and two hypotheses aimed to ascertain if hotels see the need to be involved in, and have policies in pursuit of achieving sustained MDGs, and to determine public opinion regarding hotels contribution towards the achievement of the MDGs in their communities. A 22 item questionnaire was designed ‎and administered to hotel managers while 11 item questionnaire was designed ‎and administered to hotels’ host communities. Frequency distribution and percentage as well as Chi-square were used to analyse data. Results showed no significant involvement of the hotel industry in achieving sustained MDGs in the FCT and that there was disconnect between the hotels and their immediate communities. The study recommended that hotels should, as part of their Corporate Social Responsibility pick at least one of the goals to work on in order to be involved in the attainment of enduring Millennium Development Goals.

Keywords: MDGs, hotels, FCT, host communities, corporate social responsibility

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Authors: Baleeqs, O. Adegoke, Adeola, O. Aburime

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Juvenile delinquency is fast becoming one of the largest problems facing many societies due to many different factors ranging from parental factors to bullying at schools all which had led to different theoretical notions by different scholars. Delinquency is an illegal or immoral behaviour, especially by the young person who behaves in a way that is illegal or that society does not approve of. The purpose of the study was to investigate causes and effects of delinquent behaviours among adolescent in juvenile home in Osun State. A descriptive survey research type was employed. The random sampling technique was used to select 100 adolescents in Juvenile home in Osun State. Questionnaires were developed and given to them. The data collected from this study were analyzed using frequency counts and percentage for the demographic data in section A, while the two research hypotheses postulated for this study were tested using t-test statistics at the significance level of 0.05. Findings revealed that the greatest school effects of delinquent behaviours among adolescent in juvenile home in Osun by respondents were their aggressive behaviours. Findings revealed that there was a significant difference in the causes and effects of delinquent behaviours among adolescent in juvenile home in Osun State. It was also revealed that there was no significant difference in the causes and effects of delinquent behaviours among secondary school students in Osun based on gender. These recommendations were made in order to address the findings of this study: More number of teachers should be appointed in the observation home so that it will be possible to provide teaching to the different age group of delinquents. Developing the infrastructure facilities of short stay homes and observation home is a top priority. Proper counseling session’s interval is highly essential for these juveniles.

Keywords: behaviour, delinquency, juvenile, random sampling, statistical techniques, survey

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Authors: Khanh To Duc, Monica Chiogna, Gianfranco Adimari

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With three diagnostic categories, assessment of the performance of diagnostic tests is achieved by the analysis of the receiver operating characteristic (ROC) surface, which generalizes the ROC curve for binary diagnostic outcomes. The volume under the ROC surface (VUS) is a summary index usually employed for measuring the overall diagnostic accuracy. When the true disease status can be exactly assessed by means of a gold standard (GS) test, unbiased nonparametric estimators of the ROC surface and VUS are easily obtained. In practice, unfortunately, disease status verification via the GS test could be unavailable for all study subjects, due to the expensiveness or invasiveness of the GS test. Thus, often only a subset of patients undergoes disease verification. Statistical evaluations of diagnostic accuracy based only on data from subjects with verified disease status are typically biased. This bias is known as verification bias. Here, we consider the problem of correcting for verification bias when continuous diagnostic tests for three-class disease status are considered. We assume that selection for disease verification does not depend on disease status, given test results and other observed covariates, i.e., we assume that the true disease status, when missing, is missing at random. Under this assumption, we discuss several solutions for ROC surface analysis based on imputation and re-weighting methods. In particular, verification bias-corrected estimators of the ROC surface and of VUS are proposed, namely, full imputation, mean score imputation, inverse probability weighting and semiparametric efficient estimators. Consistency and asymptotic normality of the proposed estimators are established, and their finite sample behavior is investigated by means of Monte Carlo simulation studies. Two illustrations using real datasets are also given.

Keywords: imputation, missing at random, inverse probability weighting, ROC surface analysis

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Authors: Davie G. Dave, Prisca H. Mugabe, Tonderai Mutibvu

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Globally, HWCs are on the rise. Such is the case with urban areas in Zimbabwe, yet little has been documented about it. This study was done to provide insights into the occurrence of human-wildlife conflicts in urban areas. The study was carried out in Harare, Bindura, Masvingo, Beitbridge, and Chiredzi to determine the cause, nature, extent, and frequency of occurrence of HWC, to determine the key wildlife species involved in conflicts and management practices done to combat wildlife conflicts in these areas. Several sampling techniques encompassing multi-stage sampling, stratified random, purposive, and simple random sampling were employed for placing residential areas into three strata according to population density, selecting residential areas, and selecting actual participants. Data were collected through a semi-structured questionnaire and key informant interviews. The results revealed that property destruction and crop damage were the most prevalent conflicts. Of the 15 animals that were cited, snakes, baboons, and monkeys were associated with the most conflicts. The occurrence of HWCs was mainly attributed to the increase in both animal and human populations. To curtail these HWCs, the local people mainly used non-lethal methods, whilst lethal methods were used by authorities for some of the reported cases. The majority of the conflicts were seasonal and less severe. There were growing concerns by respondents on the issues of wildlife conflicts, especially in those areas that had primates, such as Warren Park in Harare and Limpopo View in Beitbridge. There are HWCs hotspots in urban areas, and to ameliorate this, suggestions are that there is a need for a multi-action approach that includes general awareness campaigns on HWCs and land use planning that involves the creation of green spaces to ease wildlife management.

Keywords: human-wildlife conflicts, mitigation measures, residential areas, types of conflicts, urban areas

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Authors: Sogol Zarafshan

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The present study aimed to identify important factors affecting the effectiveness of quality control circles in a hospital, as well as rank them using a combination of fuzzy VIKOR and Grey Relational Analysis (GRA). The study population consisted of five academic members and five experts in the field of nursing working in a hospital, who were selected using a purposive sampling method. Also, a sample of 107 nurses was selected through a simple random sampling method using their employee codes and the random-number table. The required data were collected using a researcher-made questionnaire which consisted of 12 factors. The validity of this questionnaire was confirmed through giving the opinions of experts and academic members who participated in the present study, as well as performing confirmatory factor analysis. Its reliability also was verified (α=0.796). The collected data were analyzed using SPSS 22.0 and LISREL 8.8, as well as VIKOR–GRA and IPA methods. The results of ranking the factors affecting the effectiveness of quality control circles showed that the highest and lowest ranks were related to ‘Managers’ and supervisors’ support’ and ‘Group leadership’. Also, the highest hospital performance was for factors such as ‘Clear goals and objectives’ and ‘Group cohesiveness and homogeneity’, and the lowest for ‘Reward system’ and ‘Feedback system’, respectively. The results showed that although ‘Training the members’, ‘Using the right tools’ and ‘Reward system’ were factors that were of great importance, the organization’s performance for these factors was poor. Therefore, these factors should be paid more attention by the studied hospital managers and should be improved as soon as possible.

Keywords: Quality control circles, Fuzzy VIKOR, Grey Relational Analysis, Importance–Performance Analysis

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Authors: Swati Bhasin, Ali Ahmed, Valence Xavier, Ben Liu

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Aims: Diarrhea is a problem that is a frequent clinic referral to the gastroenterology and surgical team from the General practitioner. To establish a diagnosis, these patients undergo colonoscopy. The current practice at our district general hospital is to perform random left and right colonic biopsies. National guidelines issued by the British Society of Gastroenterology advise all patients presenting with chronic diarrhea should have an Ileoscopy as an indicator for colonoscopy completion. Our primary aim was to check if Terminal ileum (TI) biopsy is required to establish a diagnosis of inflammatory bowel disease (IBD). Methods: Data was collected retrospectively from November 2018 to November 2019. The target population were patients who underwent colonoscopies for diarrhea. Demographic data, endoscopic and histology findings of TI were assessed and analyzed. Results: 140 patients with a mean age of 57 years (19-84) underwent a colonoscopy (M: F; 1:2.3). 92 patients had random colonic biopsies taken and based on the histological results of these, 15 patients (16%) were diagnosed with IBD. The TI was successfully intubated in 40 patients, of which 32 patients had colonic biopsies taken as well. 8 patients did not have a colonic biopsy taken. Macroscopic abnormality in the TI was detected in 5 patients, all of whom were biopsied. Based on histological results of the biopsy, 3 patients (12%) were diagnosed with IBD. These 3 patients (100%) also had colonic biopsies taken simultaneously and showed inflammation. None of the patients had a diagnosis of IBD confirmed on TI intubation alone (where colonic biopsies were not done). None of the patients has a diagnosis of IBD confirmed on TI intubation alone (where colonic biopsies were negative). Conclusion: TI intubation is a highly-skilled, time-consuming procedure with a higher risk of perforation, which as per our study, has little additional diagnostic value in finding IBD for symptoms of diarrhea if colonic biopsies are taken. We propose that diarrhea is a colonic symptom; therefore, colonic biopsies are positive for inflammation if the diarrhea is secondary to IBD. We conclude that all of the IBDs can be diagnosed simply with colonic biopsies.

Keywords: biopsy, colon, IBD, terminal ileum

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Authors: Barenten Suciu

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In this paper, a theoretical study on the forced vibration of one degree of freedom system equipped with inerter, working under load-type or displacement-type excitation, is presented. Differential equations of movement are solved under cosinusoidal excitation, and explicit relations for the magnitude, resonant magnitude, phase angle, resonant frequency, and critical frequency are obtained. Influence of the inertance and damping on these dynamic characteristics is clarified. From the obtained results, one concludes that the inerter increases the magnitude of vibration and the phase angle of the damped mechanical system. Moreover, the magnitude ratio and difference of phase angles are not depending on the actual type of excitation. Consequently, such kind of similitude allows for the comparison of various theoretical and experimental results, which can be broadly found in the literature.

Keywords: displacement-type excitation, inerter, load-type excitation, one degree of freedom vibration, parallel connection

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Authors: Habte Tadesse Likassa, Bacha Edosa

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Customer’s satisfaction was very important thing that is required for the existence of banks to be more productive and success in any organization and business area. The main goal of the study is assessing and identifying factors that influence customer’s satisfaction in West Shoa Zone of Commercial Bank of Ethiopia (Holeta, Ginchi, Ambo, Gedo and Bako). Stratified random sampling procedure was used in the study and by using simple random sampling (lottery method) 520 customers were drawn from the target population. By using Probability Proportional Size Techniques sample size for each branch of banks were allocated. Both descriptive and inferential statistics methods were used in the study. A binary logistic regression model was fitted to see the significance of factors affecting customer’s satisfaction in this study. SPSS statistical package was used for data analysis. The result of the study reveals that the overall level of customer’s satisfaction in the study area is low (38.85%) as compared those who were not satisfied (61.15%). The result of study showed that all most all factors included in the study were significantly associated with customer’s satisfaction. Therefore, it can be concluded that based on the comparison of branches on their customers satisfaction by using odd ratio customers who were using Ambo and Bako are less satisfied as compared to customers who were in Holeta branch. Additionally, customers who were in Ginchi and Gedo were more satisfied than that of customers who were in Holeta. Since the level of customers satisfaction was low in the study area, it is more advisable and recommended for concerned body works cooperatively more in maximizing satisfaction of their customers.

Keywords: customers, satisfaction, binary logistic, complain handling process, waiting time

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Authors: Behzad Mohammadzadeh, Huyk Chun Noh

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The plate is one of the popular structural elements used in a wide range of industries and structures. They may be subjected to blast loads during explosion events, missile attacks or aircraft attacks. This study is to investigate dynamic responses of the rectangular plate subjected to explosive loads. The effects of material properties and plate thickness on responses of the plate are to be investigated. The compressive pressure is applied to the surface of the plate. Different amounts of thickness in the range from 10mm to 30mm are considered for the plate to evaluate the changes in responses of the plate with respect to the plate thickness. Two different properties are considered for the steel. First, the analysis is performed by considering only the elastic-plastic properties for the steel plate. Later on damping is considered to investigate its effects on the responses of the plate. To do analysis, the numerical method using a finite element based package ABAQUS is applied. Finally, dynamic responses and graphs showing the relation between maximum displacement of the plate and aim parameters are provided.

Keywords: impulsive loaded plates, dynamic analysis, ABAQUS, material nonlinearity

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Authors: John Faucett, Hao Wu, Bruce Moore, Sean Nadji

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The demands of the ministry often conflict with responsibilities at home, and clergy often experience domain ambiguity between the domains of work and family. However, the unique level of family involvement in the pastor’s profession might enrich the pastor’s ministry as well as the functioning of the family unit. Life in the church family might offer clergy family members a sense of meaning and purpose, social support, and a feeling of belonging. Church activities can offer enhanced opportunities for family interaction. The purpose of this study was to investigate the relationships of work/family enrichment to clergy job satisfaction, burnout, engagement, and withdrawal. Method: Participants were clergy serving within a state conference of the United Methodist Church. A survey was administered electronically, with e-mails and the United Methodist Church (UMC) Facebook page used as access points to the survey. Usable responses for this portion of the survey were obtained from 132 clergy. Participants completed The Work-Family Enrichment Scales, The Utrecht Work Engagement Scale, The Scale of Emotional Exhaustion in Ministry, The Satisfaction in Ministry Scale, and a scale of withdrawal developed for the present study. They also answered questions relating to how involved their spouses are in their ministry and the degree to which spouse involvement in church ministry strengthens church ministry. Findings: Higher scores for work to family enrichment correlated positively with job satisfaction (r = - .69, p < .01) and engagement (r = .50, p < .01), and negatively with burnout (r = -.48, p < .01) and withdrawal (r = -.46, p < .01). Higher scores for family to work enrichment correlated positively with job satisfaction (r = .29, p = .01) and engagement (.24, p < .05), and negatively with burnout (r = -.48, p < .01), and withdrawal (r = -.46, p < .01). Hierarchical regression analysis suggested that clergy perceptions concerning the degree to which spouse involvement in church ministry strengthens church ministry moderates the relationship between degree of spouse involvement in church activities and clergy withdrawal. To the degree that spouse involvement is believed to strengthen ministry, high spouse involvement is related to less clergy withdrawal (Multiple R-Squared = .068, Adj. R-Squared = .043, F = 2.69 on 3 & 110 DF, p = .05). Concluding Statement: Clergy job attitudes are related to work/family enrichment. Spouse involvement in parish ministry is associated with less clergy withdrawal, as long as clergy believe spouse involvement strengthens their ministry.

Keywords: clergy, emotional exhaustion, job engagement, job satisfaction, work/family enrichment

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Authors: Carolina Schencke, Cristian Sandoval, Belgica Vasquez, Mariano Del Sol

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Introduction: In a burn injury, the successful repair requires not only the participation of various cells, such as granulocytes and fibroblasts, but also of collagen, which plays a crucial role as a structural and regulatory molecule of scar tissue. Since honey and ascorbic acid have presented a great therapeutic potential to cellular and structural level, experimental studies have proposed its combination in the treatment of wounds. Aim: To evaluate stereological and morphometric parameters of healing wounds, caused by burns, treated with honey Ulmo (Eucryphia cordifolia) unsupplemented, comparing its effect with Ulmo honey supplemented with ascorbic acid. Materials and Methods: Fifteen healthy adult guinea pigs (Cavia porcellus) were used, of both sexes, average weight 450 g from the Centro de Excelencia en Estudios Morfológicos y Quirúrgicos (CEMyQ) at the Universidad de La Frontera, Chile. The animals were divided at random into three groups: positive control (C+), honey only (H) and supplemented honey (SH) and were fed on pellets supplemented with ascorbic acid and water ad libitum, under ambient conditions controlled for temperature, ambient noise and a cycle of 12h light–darkness. The protocol for the experiment was approved by the Scientific Ethics Committee of the Universidad de La Frontera, Chile. The parameters measured were number density per area (NA), volume density (VV), and surface density (SV) of fibroblast; NA and VV of polymorphonuclear cells (PMN) and, evaluation of the content of collagen fibers in the scar dermis. One-way ANOVA was used for statistics analysis and its respective Post hoc tests. Results: The ANOVA analysis for NA, VV and SV of fibroblasts, NA and VV of PMN, and evaluation of collagen content, type I and III, showed that at least one group differs from other (P≤ 0.001). There were differences (P= 0.000) in NA of fibroblast between the groups [C+= 3599.560 mm-2 (SD= 764.461), H= 3355.336 mm-2 (SD= 699.443) and SH= 4253.025 mm-2 (SD= 1041.751)]. The VV and SV of fibroblast increased (P= 0.000) in the SH group [20.400% (SD= 5.897) and 100.876 mm2/mm3 (SD= 29.431), respectively], compared to the C+ [16.324% (SD= 7.719) and 81.676 mm2/mm3 (SD= 28.884), respectively). The mean values of NA and VV of PMN were higher (P= 0.000) in the H [756.875 mm-2 (SD= 516.489) and 2.686% (SD= 2.380), respectively) group. Regarding to the evaluation of the content of collagen fibers, type I and III, the one-way analysis of ANOVA showed a statistically significant difference (P< 0.05). The content of collagen fibers type I was higher in C+ (1988.292 μm2; SD= 1312.379), while the content of collagen fibers type III was higher in SH (1967.163 μm2; SD= 1047.944 μm2) group. Conclusions: The stereological results were correlated with the stage of healing observed for each group. These results suggest that the combination of honey with ascorbic acid potentiate the healing effect, where both participated synergistically.

Keywords: ascorbic acid, morphometry, stereology, Ulmo honey

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