Search results for: principal component analysis

Authors: Nivine Abbas, Sergio Lagomarsino, Serena Cattari

Abstract:

The resistance domain of a generally loaded rigid shallow foundation is normally represented as an interaction diagram limited by a failure surface in the three dimensional (3D) load space (N, V, M), where N is the vertical centric load component, V is the horizontal load component and M is the bending moment component. Usually, this resistance domain is constructed neglecting the foundation sliding mechanism that take place at the level of soil-foundation interface once the applied horizontal load exceeds the interface frictional resistance of the foundation. This issue is translated in the literature by the fact that the failure limit in the (2D) load space (N, V) is constructed as a parabola having an initial slope, at the center of the coordinate system, that depends, in some works, only of the soil friction angle, and in other works, has an empirical value. However, considering a given geometry of the foundation lying on a given soil type, the initial slope of the failure limit must change, for instance, when varying the roughness of the foundation surface at its interface with the soil. The present study discusses the effect of the soil-foundation interface condition on the construction of the resistance domain, and proposes a correction to be applied to the failure limit in order to overcome this effect.

Keywords: soil-foundation interface, sliding mechanism, soil shearing, resistance domain, rigid shallow foundation

Procedia PDF Downloads 460

Authors: Aron Miranda Burgos, Ergon Cugler de Moraes Silva

Abstract:

In the present article, it is observed that the constant advancement of issues related to misinformation impacts the guarantee of the public policy cycle. Thus, it is found that the dissemination of false information has a direct influence on each of the component stages of this cycle. Therefore, in order to maintain scientific and theoretical credibility in the qualitative analysis process, it was necessary to logically interpose the concepts of firehosing of falsehood, fake news, public policy cycle, as well as using the epistemological and pragmatic mechanism at the intersection of such academic concepts, such as the scientific method. It was found, through the analysis of official documents and public notes, how the multiple theoretical perspectives evidence the commitment of the provision and elaboration of public policies, verifying the way in which the fake news impact each part of the process in this atmosphere.

Keywords: firehosing of falsehood, governance, misinformation, post-truth

Procedia PDF Downloads 139

Authors: Sara Tokhi Arab, Rozo Noguchi, Tofeal Ahamed

Abstract:

Drought is a natural disaster that occurs in a region due to a lack of precipitation and high temperatures over a continuous period or in a single season as a consequence of climate change. Precipitation deficits and prolonged high temperatures mostly affect the agricultural sector, water resources, socioeconomics, and the environment. Consequently, it causes agricultural product loss, food shortage, famines, migration, and natural resources degradation in a region. Agriculture is the first sector affected by drought. Therefore, it is important to develop an agricultural drought risk and loss assessment to mitigate the drought impact in the agriculture sector. In this context, the main purpose of this study was to assess yield loss using composite drought indices in the drought-affected vineyards. In this study, the CDI was developed for the years 2016 to 2020 by comprising five indices: the vegetation condition index (VCI), temperature condition index (TCI), deviation of NDVI from the long-term mean (NDVI DEV), normalized difference moisture index (NDMI) and precipitation condition index (PCI). Moreover, the quantitative principal component analysis (PCA) approach was used to assign a weight for each input parameter, and then the weights of all the indices were combined into one composite drought index. Finally, Bayesian regularized artificial neural networks (BRANNs) were used to evaluate the yield variation in each affected vineyard. The composite drought index result indicated the moderate to severe droughts were observed across the Kabul Province during 2016 and 2018. Moreover, the results showed that there was no vineyard in extreme drought conditions. Therefore, we only considered the severe and moderated condition. According to the BRANNs results R=0.87 and R=0.94 in severe drought conditions for the years of 2016 and 2018 and the R= 0.85 and R=0.91 in moderate drought conditions for the years of 2016 and 2018, respectively. In the Kabul Province within the two years drought periods, there was a significate deficit in the vineyards. According to the findings, 2018 had the highest rate of loss almost -7 ton/ha. However, in 2016 the loss rates were about – 1.2 ton/ha. This research will support stakeholders to identify drought affect vineyards and support farmers during severe drought.

Keywords: grapes, composite drought index, yield loss, satellite remote sensing

Procedia PDF Downloads 157

Authors: Shinji Kawakura, Ryosuke Shibasaki

Abstract:

We have been grouping and developing various kinds of practical, promising sensing applied systems concerning agricultural advancement and technical tradition (guidance). These include advanced devices to secure real-time data related to worker motion, and we analyze by methods of various advanced statistics and human dynamics (e.g. primary component analysis, Ward system based cluster analysis, and mapping). What is more, we have been considering worker daily health and safety issues. Targeted fields are mainly common farms, meadows, and gardens. After then, we observed and discussed time-line style, changing data. And, we made some suggestions. The entire plan makes it possible to improve both the aforementioned applied systems and farms.

Keywords: advanced statistical analysis, wearable sensing system, tradition of skill, supporting for workers, detecting crisis

Procedia PDF Downloads 394

Authors: Yutae Lee

Abstract:

In this paper, we consider the performance and availability of a redundancy model. The redundancy model is a form of resilience that ensures service availability in the event of component failure. This paper considers a 2N redundancy model. In the model there are at most one active service unit and at most one standby service unit. The active one is providing the service while the standby is prepared to take over the active role when the active fails. We design our analysis model using Stochastic Reward Nets, and then evaluate the performance and availability of 2N redundancy model using Stochastic Petri Net Package (SPNP).

Keywords: availability, performance, stochastic reward net, 2N redundancy

Procedia PDF Downloads 420

Authors: P. M. Sreekanth

Abstract:

Teak (Tectona grandis L. f.) is a tree species indigenous to India and other Southeastern countries. It produces high-value timber and is easily established in plantations. Reforestation requires a constant supply of high quality seeds. Seed Production Areas (SPA) of teak are improved stands used for collection of open-pollinated quality seeds in large quantities. Information on the genetic diversity of major teak SPAs in India is scanty. The genetic structure of four important seed production areas of Kerala State in Southern India was analyzed employing amplified fragment length polymorphism markers using ten selective primer combinations on 80 samples (4 populations X 20 trees). The study revealed that the gene diversity of the SPAs varied from 0.169 (Konni SPA) to 0.203 (Wayanad SPA). The percentage of polymorphic loci ranged from 74.42 (Parambikulam SPA) to 84.06 (Konni SPA). The mean total gene diversity index (HT) of all the four SPAs was 0.2296 ±0.02. A high proportion of genetic diversity was observed within the populations (83%) while diversity between populations was lower (17%) (GST = 0.17). Principal coordinate analysis and STRUCTURE analysis of the genotypes indicated that the pattern of clustering was in accordance with the origin and geographic location of SPAs, indicating specific identity of each population. A UPGMA dendrogram was prepared and showed that all the twenty samples from each of Konni and Parambikulam SPAs clustered into two separate groups, respectively. However, five Nilambur genotypes and one Wayanad genotype intruded into the Konni cluster. The higher gene flow estimated (Nm = 2.4) reflected the inclusion of Konni origin planting stock in the Nilambur and Wayanad plantations. Evidence for population structure investigated using 3D Principal Coordinate Analysis of FAMD software 1.30 indicated that the pattern of clustering was in accordance with the origin of SPAs. The present study showed that assessment of genetic diversity in seed production plantations can be achieved using AFLP markers. The AFLP fingerprinting was also capable of identifying the geographical origin of planting stock and there by revealing the occurrence of the errors in genotype labeling. Molecular marker-based selective culling of genetically similar trees from a stand so as to increase the genetic base of seed production areas could be a new proposition to improve quality of seeds required for raising commercial plantations of teak. The technique can also be used to assess the genetic diversity status of plus trees within provenances during their selection for raising clonal seed orchards for assuring the quality of seeds available for raising future plantations.

Keywords: AFLP, genetic structure, spa, teak

Procedia PDF Downloads 308

Authors: Davoud Maleki, Neda Zamani

Abstract:

The present study aims at exploring the effective accountability model of Islamic Azad University using a qualitative approach of grounded theory. The data of this study were obtained from semi-structured interviews with 25 professors and scholars in Islamic Azad University of Tehran who were selected by theoretical sampling method. In the data analysis, the stepwise method and Strauss and Corbin analytical methods (1992) were used. After identification of the main component (balanced response to stakeholders’ needs) and using it to bring the categories together, expressions and ideas representing the relationships between the main and subcomponents, and finally, the revealed components were categorized into six dimensions of the paradigm model, with the relationships among them, including causal conditions (7 components), main component (balanced response to stakeholders’ needs), strategies (5 components), environmental conditions (5 components), intervention features (4 components), and consequences (3 components). Research findings show an exploratory model for describing the relationships between causal conditions, main components, accountability strategies, environmental conditions, university environmental features, and that consequences.

Keywords: accountability, effectiveness, Islamic Azad University, grounded theory

Procedia PDF Downloads 86

Authors: Zain Ul Hassan, Muhammad Zain Ul Abadin, Muhammad Zubair Khan

Abstract:

Unmanned aerial vehicles (UAVs) have now become of predominant importance for various operations, and an immense amount of work is going on in this specific category. The structural stability and life of these UAVs is key factor that should be considered while deploying them to different intelligent operations as their failure leads to loss of sensitive real-time data and cost. This paper presents an applied research on the development of a structural health monitoring system for a UAV designed and fabricated by deploying modular approach. Firstly, a modular UAV has been designed which allows to dismantle and to reassemble the components of the UAV without effecting the whole assembly of UAV. This novel approach makes the vehicle very sustainable and decreases its maintenance cost to a significant value by making possible to replace only the part leading to failure. Then the SHM for the designed architecture of the UAV had been specified as a combination of wings integrated with strain gauges, on-board data logger, bridge circuitry and the ground station. For the research purpose sensors have only been attached to the wings being the most load bearing part and as per analysis was done on ANSYS. On the basis of analysis of the load time spectrum obtained by the data logger during flight, fatigue life of the respective component has been predicted using fracture mechanics techniques of Rain Flow Method and Miner’s Rule. Thus allowing us to monitor the health of a specified component time to time aiding to avoid any failure.

Keywords: fracture mechanics, rain flow method, structural health monitoring system, unmanned aerial vehicle

Procedia PDF Downloads 294

Authors: Zhenyu Du, Ed Collings, James Meredith

Abstract:

The high cost of composite materials versus conventional materials remains a major barrier to uptake in the transport sector. This is exacerbated by a shortage of skilled labour which makes the labour content of a hand laid composite component (~40 % of total cost) an obvious target for reduction. Automation is a method to remove labour cost and improve quality. This work focuses on the challenges and benefits to automating the manufacturing process from raw fibre to trimmed component. It will detail the experimental work required to complete an automation cell, the control strategy used to integrate all machines and the final benefits in terms of throughput and cost.

Keywords: automation, low cost technologies, processing and manufacturing technologies, resin transfer moulding

Procedia PDF Downloads 292

Authors: Bitewulign Mekonnen

Abstract:

Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

Procedia PDF Downloads 94

Authors: Mohammad Taghi Karimi, Maryam Hasan Zahraee

Abstract:

Background: Low back pain (LBP) is a common health and socioeconomic problem, especially the chronic one. The joint contact force is an important parameter during walking which increases the incidence of injury and degenerative joint disease. To our best knowledge, there are not enough evidences in literature on the muscular forces and joint contact forces in subjects with low back pain. Purpose: The main hypothesis associated with this research was that joint contact force of L4/L5 of non-specific chronic low back pain subjects was the same as that of normal. Therefore, the aim of this study was to determine the joint contact force difference between non-specific chronic low back pain and normal subjects. Method: This was an experimental-comparative study. 20 normal subjects and 20 non-specific chronic low back pain patients were recruited in this study. Qualysis motion analysis system and a Kistler force plate were used to collect the motions and the force applied on the leg, respectively. OpenSimm software used to determine joint contact force and muscle forces in this study. Some parameters such as force applied on the legs (pelvis), kinematic of hip and pelvic, peaks of muscles, force of trunk musculature and joint contact force of L5/S1 were used for further analysis. Differences between mean values of all data were measured using two-sample t-test among the subjects. Results: The force produced by Semitendinosus, Biceps Femoris, and Adductor muscles were significantly different between low back pain and normal subjects. Moreover, the mean value of breaking component of the force of the knee joint increased significantly in low back pain subjects, besides a significant decrease in mean value of the vertical component of joint reaction force compared to the normal ones. Conclusions: The forces produced by the trunk and pelvic muscles, and joint contact forces differ significantly between low back pain and normal subjects. It seems that those with non-specific chronic low back pain use trunk muscles more than normal subjects to stabilize the pelvic during walking.

Keywords: low back pain, joint contact force, kinetic, muscle force

Procedia PDF Downloads 238

Authors: Amol M. Jadhav, Sharad S. Chudhari, S. S. Khedkar

Abstract:

This review presents a through survey of research paper work on the experimental analysis, FEM Analysis & simulation of the metal spinning process. In this literature survey all the papers being taken from Elsevier publication and most of the from journal of material processing technology. In a last two decade or so, metal spinning process gradually used as chip less formation for the production of engineering component in a small to medium batch quantities. The review aims to provide include into the experimentation, FEM analysis of various components, simulation of metal spinning process and act as guide for research working on metal spinning processes. The review of existing work has several gaps in current knowledge of metal spinning processes. The evaluation of experiment is thickness strain, the spinning force, the twisting angle, the surface roughness of the conventional & shear metal spinning process; the evaluation of FEM of metal spinning to path definition with sufficient fine mesh to capture behavior of work piece; The evaluation of feed rate of roller, direction of roller,& type of roller stimulated. The metal spinning process has the more flexible to produce a wider range of product shape & to form more challenge material.

Keywords: metal spinning, FEM analysis, simulation of metal spinning, mechanical engineering

Procedia PDF Downloads 387

Authors: A. Brahma

Abstract:

Analysis of deferred deformations of concrete under sustained load shows that the creep has a leading role on deferred deformations of concrete structures. Knowledge of the creep characteristics of concrete is a Necessary starting point in the design of structures for crack control. Such knowledge will enable the designer to estimate the probable deformation in pre-stressed concrete or reinforced and the appropriate steps can be taken in design to accommodate this movement. In this study, we propose a prediction model that involves the acting principal parameters on the deferred behaviour of concrete structures. For the estimation of the model parameters Levenberg-Marquardt method has proven very satisfactory. A confrontation between the experimental results and the predictions of models designed shows that it is well suited to describe the evolution of the creep of concrete structures.

Keywords: concrete structure, creep, modelling, prediction

Procedia PDF Downloads 291

Authors: Giselle Tran, Ralitza Parina, Phuong T. Nguyen

Abstract:

Background/Aims: Pancreatic cysts (PC) have recently become an increasingly common entity, often diagnosed as incidental findings on cross-sectional imaging. Clinically, management of the lesions is difficult because of uncertainties in their potential for malignant degeneration. Prior series have reported that carcinoembryonic antigen (CEA), a biomarker collected from cyst fluid aspiration, has a high diagnostic accuracy for discriminating between mucinous and non-mucinous lesions, at the patient’s initial presentation. To the author’s best knowledge, no prior studies have reported PC CEA levels obtained from endoscopic ultrasound fine-needle aspiration (EUS-FNA) over years of serial EUS surveillance imaging. Methods: We report a consecutive retrospective series of 624 patients who underwent EUS evaluation for a PC between 11/20/2009 and 11/13/2018. Of these patients, 401 patients had CEA values obtained at the point of entry. Of these, 157 patients had two or more CEA values obtained over the course of their EUS surveillance. Of the 157 patients (96 F, 61 M; mean age 68 [range, 62-76]), the mean interval of EUS follow-up was 29.7 months [3.5-128]. The mean number of EUS procedures was 3 [2-7]. To assess CEA value fluctuations, we defined an appreciable increase in CEA as "spikes" – two-times increase in CEA on a subsequent EUS-FNA of the same cyst, with the second CEA value being greater than 1000 ng/mL. Using this definition, cysts with a spike in CEA were compared to those without a spike in a bivariate analysis to determine if a CEA spike is associated with poorer outcomes and the presence of high-risk features. Results: Of the 157 patients analyzed, 29 had a spike in CEA. Of these 29 patients, 5 had a cyst with size increase >0.5cm (p=0.93); 2 had a large cyst, >3cm (p=0.77); 1 had a cyst that developed a new solid component (p=0.03); 7 had a cyst with a solid component at any time during surveillance (p=0.08); 21 had a complex cyst (p=0.34); 4 had a cyst categorized as "Statistically Higher Risk" based on molecular analysis (p=0.11); and 0 underwent surgical resection (p=0.28). Conclusion: With serial EUS imaging in the surveillance of PC, an increase in CEA level defined as a spike did not predict poorer outcomes. Most notably, a spike in CEA did not correlate with the number of patients sent to surgery or patients with an appreciable increase in cyst size. A spike in CEA did not correlate with the development of a solid nodule within the PC nor progression on molecular analysis. Future studies should focus on the selected use of CEA analysis when patients undergo EUS surveillance evaluation for PCs.

Keywords: carcinoembryonic antigen (CEA), endoscopic ultrasound (EUS), fine-needle aspiration (FNA), pancreatic cyst, spike

Procedia PDF Downloads 142

Authors: Thanatip Lerttanaporn, Tuanjai Somboonwiwat, Charoenchai Khompatraporn

Abstract:

The automotive parts industry is one of the vital sectors in Thai economy and now is facing a greater competition from ASEAN Economic Community (AEC). This article identifies important factors that impact the competitiveness of Thai automotive parts industry. There are eight groups of factors with a total of 58 factors. Due to a variety of factors, the Exploratory Factor Analysis and Principle Component Analysis have been applied to classify factors into groups or principle components. The results show that there are 15 groups and four of them are critical, covering 80% of important value. These four critical groups are then used to formulate strategies to improve the competitiveness of the Thai automotive parts industry.

Keywords: factor analysis, Thai automotive parts, principle components, exploratory factor, ASEAN economic community

Procedia PDF Downloads 254

Authors: Lauren Freed

Abstract:

This research proposal outlines an examination of homeschool oversight in the Massachusetts educational system amid the backdrop of ideological differences between various parties with contributing interests. This mixed methodology study will follow an interpretive policy research approach, involving the use of existing data, surveys, and focus groups. The aim is to capture distinct sets of meanings, values, feelings, and beliefs by principal stakeholders, while exploring the ways in which they/each interact with, interpret, and implement homeschool guidelines set forth by the Massachusetts Supreme Judicial Court Decision Care and Protection of Charles (1987). This analysis will identify and contextualize the attitudes, administrative choices, financial implications, and educational impacts that result from the process and practice of enacting current homeschool oversight policy in Massachusetts. The following question will guide this study: How do districts, homeschooling parents, and Massachusetts Department of Elementary and Secondary Education (DESE) regulate, fund, collect, interpret, implement and report Massachusetts homeschool oversight policy? The resulting analysis will produce a unique and original baseline snapshot of qualitative and quantifiable point-in-time data based on the registered homeschool population in the state of Massachusetts.

Keywords: alternative education, homeschooling, home education, home schooling policy

Procedia PDF Downloads 187

Authors: Bliss Singhal

Abstract:

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

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

Procedia PDF Downloads 84

Authors: Weiam Daear, Patrick Lai, Elmar Prenner

Abstract:

The human body offers many avenues that could be used for drug delivery. The pulmonary route, which is delivered through the lungs, presents many advantages that have sparked interested in the field. These advantages include; 1) direct access to the lungs and the large surface area it provides, and 2) close proximity to the blood circulation. The air-blood barrier of the alveoli is about 500 nm thick. The air-blood barrier consist of a monolayer of lipids and few proteins called the lung surfactant and cells. This monolayer consists of ~90% lipids and ~10% proteins that are produced by the alveolar epithelial cells. The two major lipid classes constitutes of various saturation and chain length of phosphatidylcholine (PC) and phosphatidylglycerol (PG) representing 80% of total lipid component. The major role of the lung surfactant monolayer is to reduce surface tension experienced during breathing cycles in order to prevent lung collapse. In terms of the pulmonary drug delivery route, drugs pass through various parts of the respiratory system before reaching the alveoli. It is at this location that the lung surfactant functions as the air-blood barrier for drugs. As the field of nanomedicine advances, the use of nanoparticles (NPs) as drug delivery vehicles is becoming very important. This is due to the advantages NPs provide with their large surface area and potential specific targeting. Therefore, studying the interaction of NPs with lung surfactant and whether they affect its stability becomes very essential. The aim of this research is to develop a biomimetic model of the human lung surfactant followed by a biophysical analysis of the interaction of polymeric NPs. This biomimetic model will function as a fast initial mode of testing for whether NPs affect the stability of the human lung surfactant. The model developed thus far is an 8-component lipid system that contains major PC and PG lipids. Recently, a custom made 16:0/16:1 PC and PG lipids were added to the model system. In the human lung surfactant, these lipids constitute 16% of the total lipid component. According to the author’s knowledge, there is not much monolayer data on the biophysical analysis of the 16:0/16:1 lipids, therefore more analysis will be discussed here. Biophysical techniques such as the Langmuir Trough is used for stability measurements which monitors changes to a monolayer's surface pressure upon NP interaction. Furthermore, Brewster Angle Microscopy (BAM) employed to visualize changes to the lateral domain organization. Results show preferential interactions of NPs with different lipid groups that is also dependent on the monolayer fluidity. Furthermore, results show that the film stability upon compression is unaffected, but there are significant changes in the lateral domain organization of the lung surfactant upon NP addition. This research is significant in the field of pulmonary drug delivery. It is shown that NPs within a certain size range are safe for the pulmonary route, but little is known about the mode of interaction of those polymeric NPs. Moreover, this work will provide additional information about the nanotoxicology of NPs tested.

Keywords: Brewster angle microscopy, lipids, lung surfactant, nanoparticles

Procedia PDF Downloads 178

Authors: Fei Gao, Xinyang Dong, Yisu Ge, Shufang Lu, Libo Weng

Abstract:

Vehicle color recognition plays an important role in traffic accident investigation. However, due to the influence of illumination, weather, and noise, vehicle color recognition still faces challenges. In this paper, a vehicle color classification network based on spatial cluster loss and channel attention mechanism (SCNet) is proposed for vehicle color recognition. A channel attention module is applied to extract the features of vehicle color representative regions and reduce the weight of nonrepresentative color regions in the channel. The proposed loss function, called spatial clustering loss (SC-loss), consists of two channel-specific components, such as a concentration component and a diversity component. The concentration component forces all feature channels belonging to the same class to be concentrated through the channel cluster. The diversity components impose additional constraints on the channels through the mean distance coefficient, making them mutually exclusive in spatial dimensions. In the comparison experiments, the proposed method can achieve state-of-the-art performance on the public datasets, VCD, and VeRi, which are 96.1% and 96.2%, respectively. In addition, the ablation experiment further proves that SC-loss can effectively improve the accuracy of vehicle color recognition.

Keywords: feature extraction, convolutional neural networks, intelligent transportation, vehicle color recognition

Procedia PDF Downloads 183

Authors: Xiaoyun Hui, Fiona Gentle, Clemens Engelke, Margaret C. Graham

Abstract:

Although past work has shown that phosphorus (P), an important nutrient, may form complexes with aqueous humic substances (HS), the principal component of natural organic matter, the nature of such interactions is poorly understood. Humic complexation may not only enhance P concentrations but it may change its bioavailability within such waters and, in addition, influence its transport within catchment settings. This project is examining the relationships and associations of P, HS, and iron (Fe) in Loch Meadie, Sutherland, North Scotland, a mesohumic freshwater loch which has been assessed as reference condition with respect to P. The aim is to identify characteristic spectroscopic parameters which can enhance the performance of the model currently used to predict reference condition TP levels for highly-coloured Scottish lochs under the Water Framework Directive. In addition to Loch Meadie, samples from other reference condition lochs in north Scotland and Shetland were analysed. By including different types of reference condition lochs (clear water, mesohumic and polyhumic water) this allowed the relationship between total phosphorus (TP) and HS to be more fully explored. The pH, [TP], [Fe], UV/Vis absorbance/spectra, [TOC] and [DOC] for loch water samples have been obtained using accredited methods. Loch waters were neutral to slightly acidic/alkaline (pH 6-8). [TP] in loch waters were lower than 50 µg L-1, and in Loch Meadie waters were typically <10 µg L-1. [Fe] in loch waters were mainly <0.6 mg L-1, but for some loch water samples, [Fe] were in the range 1.0-1.8 mg L-1and there was a positive correlation with [TOC] (r2=0.61). Lochs were classified as clear water, mesohumic or polyhumic based on water colour. The range of colour values of sampled lochs in each category were 0.2–0.3, 0.2–0.5 and 0.5–0.8 a.u. (10 mm pathlength), respectively. There was also a strong positive correlation between [DOC] and water colour (R2=0.84). The UV/Vis spectra (200-700 nm) for water samples were featureless with only a slight “shoulder” observed in the 270–290 nm region. Ultrafiltration was then used to separate colloidal and truly dissolved components from the loch waters and, since it contained the majority of aqueous P and Fe, the colloidal component was fractionated by gel filtration chromatography method. Gel filtration chromatographic fractionation of the colloids revealed two brown-coloured bands which had distinctive UV/Vis spectral features. The first eluting band had larger and more aromatic HS molecules than the second band, and in addition both P and Fe were primarily associated with the larger, more aromatic HS. This result demonstrated that P was able to form complexes with Fe-rich components of HS, and thus provided a scientific basis for the significant correlation between [Fe] and [TP] that the previous monitoring data of reference condition lochs from Scottish Environment Protection Agency (SEPA) showed. The distinctive features of the HS will be used as the basis for an improved spectroscopic tool.

Keywords: total phosphorus, humic substances, Scottish loch water, WFD model

Procedia PDF Downloads 546

Authors: Adi Gielgun-Katz, Alina S. Rusu

Abstract:

In the last two decades, education systems worldwide have integrated new pedagogical methods and strategies in lesson plans, such as innovative technologies, social-emotional learning (SEL), gamification, mixed learning, multiple literacies, and many others. Visual language, such as photographs, is known to transcend cultures and languages, and it is commonly used by youth to express positions and affective states in social networks. Therefore, visual language needs more educational attention as a linguistic and communicative component that can create connectedness among the students and their teachers. Nowadays, when SEL is gaining more and more space and meaning in the area of academic improvement in relation to social well-being, and taking and sharing pictures is part of the everyday life of the majority of people, it becomes natural to add the visual language to SEL approach as a reinforcement strategy for connecting education to the contemporary culture and language of the youth. This article presents a program conducted in a high school class in Israel, which combines the five SEL with photography techniques, i.e., Social-Emotional Learning Visual Empowerments (SELVEs) program (experimental group). Another class of students from the same institution represents the control group, which is participating in the SEL program without the photography component. The SEL component of the programs addresses skills such as: troubleshooting, uncertainty, personal strengths and collaboration, accepting others, control of impulses, communication, self-perception, and conflict resolution. The aim of the study is to examine the effects of programs on the level of the five SEL aspects in the two groups of high school students: Self-Awareness, Social Awareness, Self-Management, Responsible Decision Making, and Relationship Skills. The study presents a quantitative assessment of the SEL programs’ impact on the students. The main hypothesis is that the students’ questionnaires' analysis will reveal a better understanding and improvement of the five aspects of the SEL in the group of students involved in the photography-enhanced SEL program.

Keywords: social-emotional learning, photography, education program, adolescents

Procedia PDF Downloads 84

Authors: Fumihiko Isada, Yuriko Isada

Abstract:

The aim of this research is to clarify the difference by industry segment or product characteristics as regards organisation management for an open innovation to raise R&D performance. In particular, the trait of the pharmaceutical industry is defined in comparison with IT component industry segment. In considering open innovation, both inter-organisational relation and the management in an organisation are important issues. As methodology, a questionnaire was conducted. In conclusion, suitable organisation management according to the difference in industry segment or product characteristics became clear.

Keywords: empirical study, industry segment, open innovation, product-development organisation pattern

Procedia PDF Downloads 426

Authors: Dmitriy V. Chulkov

Abstract:

Escalation of commitment is defined as continuation of a project after receiving negative information about it. While literature in management and psychology identified various factors contributing to escalation behavior, this phenomenon has received little analysis in economics, potentially due to the apparent irrationality of escalation. In this study, we present an economic model of escalation with asymmetric information in a principal-agent setup where the agents are responsible for a project selection decision and discover the outcome of the project before the principal. Our theoretical model complements the existing literature on several accounts. First, we link the incentive to escalate commitment to a project with the turnover decision by the manager. When a manager learns the outcome of the project and stops it that reveals that a mistake was made. There is an incentive to continue failing projects and avoid admitting the mistake. This incentive is enhanced when the agent may voluntarily resign from the firm before the outcome of the failing project is revealed, and thus not bear the full extent of reputation damage due to project failure. As long as some successful managers leave the firm for extraneous reasons, outside firms find it difficult to link failing projects with certainty to managers that left a firm. Second, we demonstrate that non-CEO managers have reputation concerns separate from those of the CEO, and thus may escalate commitment to projects they oversee, when such escalation can attenuate damage to reputation from impending project failure. Such incentive for escalation will be present for non-CEO managers if the CEO delegates responsibility for a project to a non-CEO executive. If reputation matters for promotion to the CEO, the incentive for a rising executive to escalate in order to protect reputation is distinct from that of a CEO. Third, our theoretical model is supported by empirical analysis of changes in the firm’s operations measured by the presence of discontinued operations at the time of turnover among the top four members of the top management team. Discontinued operations are indicative of termination of failing projects at a firm. The empirical results demonstrate that in a large dataset of over three thousand publicly traded U.S. firms for a period from 1993 to 2014 turnover by top executives significantly increases the likelihood that the firm discontinues operations. Furthermore, the type of turnover matters as this effect is strongest when at least one non-CEO member of the top management team leaves the firm and when the CEO departure is due to a voluntary resignation and not to a retirement or illness. Empirical results are consistent with the predictions of the theoretical model and suggest that escalation of commitment is primarily observed in decisions by non-CEO members of the top management team.

Keywords: discontinued operations, escalation of commitment, executive turnover, top management teams

Procedia PDF Downloads 365

Authors: Grace Omowunmi Soyebi

Abstract:

Data Mining is the extraction of information from a large database which helps in predicting a trend or behavior, thereby helping management make knowledge-driven decisions. One principal problem of most hospitals in rural areas is making use of the file management system for keeping records. A lot of time is wasted when a patient visits the hospital, probably in an emergency, and the nurse or attendant has to search through voluminous files before the patient's file can be retrieved; this may cause an unexpected to happen to the patient. This Data Mining application is to be designed using a Structured System Analysis and design method, which will help in a well-articulated analysis of the existing file management system, feasibility study, and proper documentation of the Design and Implementation of a Computerized medical record system. This Computerized system will replace the file management system and help to easily retrieve a patient's record with increased data security, access clinical records for decision-making, and reduce the time range at which a patient gets attended to.

Keywords: data mining, medical record system, systems programming, computing

Procedia PDF Downloads 209

Authors: Rajendra Kanojia

Abstract:

Femoral components with inbuilt rotation require thicker flexion resection of the lateral femoral condyle and could potential risk to damage the popliteus tendon especially in the smaller Asian knees. We prospectively evaluated 10 patients with bilateral varus osteoarthritis knee to size the cuts and their location in relation to the popliteus tendon. Two different types of implant were used on either side, one side requires resection in 3° external rotation (group A) and other side femoral component with inbuilt external roation (group B). We had popliteus tendon injury in 3 knees all from group B. Risk of damaging the popliteus tendon was found higher in group B.

Keywords: popliteaus tendon injury, TKA, orthopaedic surgery, biomechanics and clinical applications

Procedia PDF Downloads 334

Authors: George Oliveira, Luana D. Fronza, Luiza Medeiros, Patricia D. M. Plentz

Abstract:

The research presented in this article is part of a wide project that proposes a scheduling system for multi-robots in intelligent warehouses employing multi-robot path-planning (MPP) and multi-robot task allocation (MRTA) to reconcile multiple restrictions (task delivery time, task priorities, charging capacity, and robots battery capacity). We present the software component capable of interconnecting an open streaming processing architecture and robot operating system (ROS), ensuring communication and message exchange between robots and the environment in which they are inserted. Simulation results show the good performance of our proposed technique for connecting ROS and streaming platforms.

Keywords: complex distributed systems, mobile robots, smart warehouses, streaming platforms

Procedia PDF Downloads 193

Authors: Naser B. Almari, Salem S. Alghamdi, Muhammad Afzal, Mohamed Helmy El Shal

Abstract:

Wheat landraces are a rich genetic resource for boosting agronomic qualities in breeding programs while also providing diversity and unique adaptation to local environmental conditions. These genotypes have grown increasingly important in the face of recent climate change challenges. This research aimed to look at the genetic diversity of Saudi Durum wheat landraces using morpho-phenological and molecular data. The principal components analysis (PCA) analysis recorded 78.47 % variance and 1.064 eigenvalues for the first six PCs of the total, respectively. The significant characters contributed more to the diversity are the length of owns at the tip relative to the length of the ear, culm: glaucosity of the neck, flag leaf: glaucosity of the sheath, flag leaf: anthocyanin coloration of auricles, plant: frequency of plants with recurved flag leaves, ear: length, and ear: shape in profile in the PC1. The significant wheat genotypes contributed more in the PC1 (8, 14, 497, 650, 569, 590, 594, 598, 600, 601, and 604). The cluster analysis recorded an 85.42 cophenetic correlation among the 22 wheat genotypes and grouped the genotypes into two main groups. Group, I contain 8 genotypes, however, the 2nd group contains 12 wheat genotypes, while two genotypes (13 and 497) are standing alone in the dendrogram and unable to make a group with any one of the genotypes. The second group was subdivided into two subgroups. The genotypes (14, 602, and 600) were present in the second sub-group. The genotypes were grouped into two main groups. The first group contains 17 genotypes, while the second group contains 3 (8, 977, and 594) wheat genotypes. The genotype (602) was standing alone and unable to make a group with any wheat genotype. The genotypes 650 and 13 also stand alone in the first group. Using the Mantel test, the data recorded a significant (R2 = 0.0006) correlation (phenotypic and genetic) among 22 wheat durum genotypes.

Keywords: durum wheat, PCA, cluster analysis, SRAP, genetic diversity

Procedia PDF Downloads 115

Authors: Jasmina Pluncevic Gligoroska, Sanja Manchevska, Vaska Antevska, Lidija Todorovska, Beti Dejanova, Sunchica Petrovska, Ivanka Karagjozova, Elizabeta Sivevska

Abstract:

Introduction: Anthropometric measurements are integral part of regular medical examinations of athletes. In addition to the quantification of the size of the body, these measurements indicate the quality of the physical status, because of its association with sports performance. The purpose of this study was to examine whether there are differences in anthropometric parameters and body mass components in female athletes who participate in two different types of sports. Methods: A total of 27 athletes, 15 handball players and 12 basketball players, at the average age of 22.7 years (age span from 17 to 30 years) entered the study. Anthropometric method by Matiegka was used for determination of body components. Sixteen anthropometric measures were taken: height, weight, four diameters of joints, four circumferences of limbs and six skin folds. Results: Handball players were 169.6±6.7 cm tall and 63,75±7.5 kg heavy. Their average relative muscle mass (absolute mass in kg) was 51% (32.5kg), while bone component was 16.8% (10.7kg) and fat component was 14.3% (7.74kg). The basketball players were 177.4±8.2cm tall and 70.37±12.1kg heavy. Their average relative muscle mass (absolute mass in kg) was 51.9 % (36.6kg), bone component was 16.37% (11.5kg) and fat component was 15.36% (9.4kg). The comparison of anthropometric values showed that basketball players were statistically significantly higher and heavier than handball players (p<0.05). Statistically significant difference (p<0.05) was observed in the range of upper leg circumference (higher in basketball players) and the forearm skin fold (higher in the basketball players). Conclusion: Handball players and basketball players significantly differed in basic anthropometric measures (height and weight), but the body components had almost identical values. The anthropometric measurements that have been taken did not show significant difference between handball and basketball female players despite the different physical demands of the games.

Keywords: anthropometry, body components, basketball, handball female players

Procedia PDF Downloads 463

Authors: Oktira Roka Aji, Maelita R. Moeis, Ihsanawati, Ernawati A. Giri-Rachman

Abstract:

Globally, tuberculosis (TB) remains a leading cause of death. The emergence of multidrug-resistant strains and extensively drug-resistant strains have become a major public concern. One of the potential candidates for drug target is the cytoplasmic domain of PhoR Histidine Kinase, a part of the Two Component System (TCS) PhoR-PhoP in Mycobacterium tuberculosis (Mtb). TCS PhoR-PhoP relay extracellular signal to control the expression of 114 virulent associated genes in Mtb. The 3D structure of PhoR cytoplasmic domain is needed to screen novel drugs using structure based drug discovery. The PhoR cytoplasmic domain from Mtb H37Rv was overexpressed in E. coli BL21(DE3), then purified using IMAC Ni-NTA Agarose his-tag affinity column and DEAE-ion exchange column chromatography. The molecular weight of the purified protein was estimated to be 37 kDa after SDS-PAGE analysis. This sample was used for pre-crystallization screening by applying sitting drop vapor diffusion method using Natrix (HR2-116) 48 solutions crystal screen kit at 25ºC. Needle-like crystals were observed after the seventh day of incubation in test solution No.47 (0.1 M KCl, 0.01 M MgCl2.6H2O, 0.05 M Tris-Cl pH 8.5, 30% v/v PEG 4000). Further testing is required for confirming the crystal.

Keywords: tuberculosis, two component system, histidine kinase, needle-like crystals

Procedia PDF Downloads 432

Authors: Zoran Šverko, Saša Vlahinić, Nino Stojković, Ivan Markovinović

Abstract:

In this article, the classification of target letters is performed using data from the EEG P300 Speller paradigm. Neural networks trained with the results of dynamic connectivity analysis between different brain regions are used for classification. Dynamic connectivity analysis is based on the adaptive window size and the imaginary part of the complex Pearson correlation coefficient. Brain dynamics are analysed using the relative intersection of confidence intervals for the imaginary component of the complex Pearson correlation coefficient method (RICI-imCPCC). The RICI-imCPCC method overcomes the shortcomings of currently used dynamical connectivity analysis methods, such as the low reliability and low temporal precision for short connectivity intervals encountered in constant sliding window analysis with wide window size and the high susceptibility to noise encountered in constant sliding window analysis with narrow window size. This method overcomes these shortcomings by dynamically adjusting the window size using the RICI rule. This method extracts information about brain connections for each time sample. Seventy percent of the extracted brain connectivity information is used for training and thirty percent for validation. Classification of the target word is also done and based on the same analysis method. As far as we know, through this research, we have shown for the first time that dynamic connectivity can be used as a parameter for classifying EEG signals.

Keywords: dynamic connectivity analysis, EEG, neural networks, Pearson correlation coefficients

Procedia PDF Downloads 214