Search results for: root uptake models

Authors: Abdunaser Abduelmula, Maria Luisa M. Bastos, José A. Gonçalves

Abstract:

The modeling of the earth's surface and evaluation of urban environment, with 3D models, is an important research topic. New stereo capabilities of high-resolution optical satellites images, such as the tri-stereo mode of Pleiades, combined with new image matching algorithms, are now available and can be applied in urban area analysis. In addition, photogrammetry software packages gained new, more efficient matching algorithms, such as SGM, as well as improved filters to deal with shadow areas, can achieve denser and more precise results. This paper describes a comparison between 3D data extracted from tri-stereo and dual stereo satellite images, combined with pixel based matching and Wallis filter. The aim was to improve the accuracy of 3D models especially in urban areas, in order to assess if satellite images are appropriate for a rapid evaluation of urban environments. The results showed that 3D models achieved by Pleiades tri-stereo outperformed, both in terms of accuracy and detail, the result obtained from a Geo-eye pair. The assessment was made with reference digital surface models derived from high-resolution aerial photography. This could mean that tri-stereo images can be successfully used for the proposed urban change analyses.

Keywords: 3D models, environment, matching, pleiades

Procedia PDF Downloads 330

Authors: Gonzalo García-Reyes

Abstract:

Conformastat spherically symmetric exact solutions of Einstein's field equations representing matter distributions made of fluid both perfect and anisotropic from given solutions of Poisson's equation of Newtonian gravity are investigated. The approach is used in the construction of new relativistic models of thick spherical shells and three-component models of galaxies (bulge, disk, and dark matter halo), writing, in this case, the metric in cylindrical coordinates. In addition, the circular motion of test particles (rotation curves) along geodesics on the equatorial plane of matter configurations and the stability of the orbits against radial perturbations are studied. The models constructed satisfy all the energy conditions.

Keywords: general relativity, exact solutions, spherical symmetry, galaxy, kinematics and dynamics, dark matter

Procedia PDF Downloads 87

Authors: Subhan Ahmad, Pradeep Bhargava, Ajay Chourasia

Abstract:

Shear failure in reinforced concrete beams without shear reinforcement leads to loss of property and life since a very little or no warning occurs before failure as in case of flexural failure. Shear strength of reinforced concrete beams decreases as its depth increases. This phenomenon is generally called as the size effect. In this paper, a comparative analysis is performed to estimate the performance of shear strength models in capturing the size effect of reinforced concrete beams made with conventional concrete, self-compacting concrete, and recycled aggregate concrete. Four shear strength models that account for the size effect in shear are selected from the literature and applied on the datasets of slender reinforced concrete beams. Beams prepared with conventional concrete, self-compacting concrete, and recycled aggregate concrete are considered for the analysis. Results showed that all the four models captured the size effect in shear effectively and produced conservative estimates of the shear strength for beams made with normal strength conventional concrete. These models yielded unconservative estimates for high strength conventional concrete beams with larger effective depths ( > 450 mm). Model of Bazant and Kim (1984) captured the size effect precisely and produced conservative estimates of shear strength of self-compacting concrete beams at all the effective depths. Also, shear strength models considered in this study produced unconservative estimates of shear strength for recycled aggregate concrete beams at all effective depths.

Keywords: reinforced concrete beams; shear strength; prediction models; size effect

Procedia PDF Downloads 161

Authors: Aliro Villacorta, Laura Rubio, Mohamed Alaraby, Montserrat López Mesas, Victor Fuentes-Cebrian, Oscar H. Moriones, Ricard Marcos, Alba Hernández.

Abstract:

Micro and nano plastics (MNPLs) are emergent environmental pollutants requiring urgent information on their potential risks to human health. One of the problems associated with the evaluation of their undesirable effects is the lack of real samples matching those resulting from the environmental degradation of plastic wastes. To such end, we propose an easy method to obtain polyethylene terephthalate nano plastics from water plastic bottles (PET-NPLs) but, in principle, applicable to any other plastic goods sources. An extensive characterization indicates that the proposed process produces uniform samples of PET-NPLs of around 100 nm, as determined by using a multi-angle and dynamic light scattering methodology. An important point to be highlighted is that to avoid the metal contamination resulting from methods using metal blades/burrs for milling, trituration, or sanding, we propose to use diamond burrs to produce metal-free samples. To visualize the toxicological profile of the produced PET-NPLs, we have evaluated their ability to be internalized by cells, their cytotoxicity, and their ability to induce oxidative stress and induce DNA damage. In this preliminary approach, we have detected their cellular uptake, but without the induction of significant biological effects. Thus, no relevant increases in toxicity, reactive oxygen species (ROS) induction, or DNA damage -as detected with the comet assay- have been observed. The use of real samples, as produced in this study, will generate relevant data in the discussion about the potential health risks associated with MNPLs exposures.

Keywords: nanoplastics, polyethylene terephthalate, physicochemical characterization, cell uptake, cytotoxicity

Procedia PDF Downloads 97

Authors: Ramzi Farra, Detre Teschner, Marc Willinger, Robert Schlögl

Abstract:

Introduction: The conventional approach of characterizing solid catalysts under static conditions, i.e., before and after reaction, does not provide sufficient knowledge on the physicochemical processes occurring under dynamic conditions at the molecular level. Hence, the necessity of improving new in situ characterizing techniques with the potential of being used under real catalytic reaction conditions is highly desirable. In situ Prompt Gamma Activation Analysis (PGAA) is a rapidly developing chemical analytical technique that enables us experimentally to assess the coverage of surface species under catalytic turnover and correlate these with the reactivity. The catalytic HCl oxidation (Deacon reaction) over bulk ceria will serve as our example. Furthermore, the in situ Transmission Electron Microscopy is a powerful technique that can contribute to the study of atmosphere and temperature induced morphological or compositional changes of a catalyst at atomic resolution. The application of such techniques (PGAA and TEM) will pave the way to a greater and deeper understanding of the dynamic nature of active catalysts. Experimental/Methodology: In situ Prompt Gamma Activation Analysis (PGAA) experiments were carried out to determine the Cl uptake and the degree of surface chlorination under reaction conditions by varying p(O2), p(HCl), p(Cl2), and the reaction temperature. The abundance and dynamic evolution of OH groups on working catalyst under various steady-state conditions were studied by means of in situ FTIR with a specially designed homemade transmission cell. For real in situ TEM we use a commercial in situ holder with a home built gas feeding system and gas analytics. Conclusions: Two complimentary in situ techniques, namely in situ PGAA and in situ FTIR were utilities to investigate the surface coverage of the two most abundant species (Cl and OH). The OH density and Cl uptake were followed under multiple steady-state conditions as a function of p(O2), p(HCl), p(Cl2), and temperature. These experiments have shown that, the OH density positively correlates with the reactivity whereas Cl negatively. The p(HCl) experiments give rise to increased activity accompanied by Cl-coverage increase (opposite trend to p(O2) and T). Cl2 strongly inhibits the reaction, but no measurable increase of the Cl uptake was found. After considering all previous observations we conclude that only a minority of the available adsorption sites contribute to the reactivity. In addition, the mechanism of the catalysed reaction was proposed. The chlorine-oxygen competition for the available active sites renders re-oxidation as the rate-determining step of the catalysed reaction. Further investigations using in situ TEM are planned and will be conducted in the near future. Such experiments allow us to monitor active catalysts at the atomic scale under the most realistic conditions of temperature and pressure. The talk will shed a light on the potential and limitations of in situ PGAA and in situ TEM in the study of catalyst dynamics.

Keywords: CeO2, deacon process, in situ PGAA, in situ TEM, in situ FTIR

Procedia PDF Downloads 291

Authors: C. Amer, M. Sahin

Abstract:

The design of an optimised horizontal axis 5-meter-long wind turbine rotor blade in according with IEC 61400-2 standard is a research and development project in order to fulfil the requirements of high efficiency of torque from wind production and to optimise the structural components to the lightest and strongest way possible. For this purpose, a research study is presented here by focusing on the structural characteristics of a composite wind turbine blade via finite element modelling and analysis tools. In this work, first, the required data regarding the general geometrical parts are gathered. Then, the airfoil geometries are created at various sections along the span of the blade by using CATIA software to obtain the two surfaces, namely; the suction and the pressure side of the blade in which there is a hat shaped fibre reinforced plastic spar beam, so-called chassis starting at 0.5m from the root of the blade and extends up to 4 m and filled with a foam core. The root part connecting the blade to the main rotor differential metallic hub having twelve hollow threaded studs is then modelled. The materials are assigned as two different types of glass fabrics, polymeric foam core material and the steel-balsa wood combination for the root connection parts. The glass fabrics are applied using hand wet lay-up lamination with epoxy resin as METYX L600E10C-0, is the unidirectional continuous fibres and METYX XL800E10F having a tri-axial architecture with fibres in the 0,+45,-45 degree orientations in a ratio of 2:1:1. Divinycell H45 is used as the polymeric foam. The finite element modelling of the blade is performed via MSC PATRAN software with various meshes created on each structural part considering shell type for all surface geometries, and lumped mass were added to simulate extra adhesive locations. For the static analysis, the boundary conditions are assigned as fixed at the root through aforementioned bolts, where for dynamic analysis both fixed-free and free-free boundary conditions are made. By also taking the mesh independency into account, MSC NASTRAN is used as a solver for both analyses. The static analysis aims the tip deflection of the blade under its own weight and the dynamic analysis comprises normal mode dynamic analysis performed in order to obtain the natural frequencies and corresponding mode shapes focusing the first five in and out-of-plane bending and the torsional modes of the blade. The analyses results of this study are then used as a benchmark prior to modal testing, where the experiments over the produced wind turbine rotor blade has approved the analytical calculations.

Keywords: dynamic analysis, fiber reinforced composites, horizontal axis wind turbine blade, hand-wet layup, modal testing

Procedia PDF Downloads 426

Authors: Faruk Ozdemir, Roy Kalawsky, Peter Hubbard

Abstract:

Accurate estimation of RUL provides a basis for effective predictive maintenance, reducing unexpected downtime for industrial equipment. However, while models such as the Random Forest have effective predictive capabilities, they are the so-called ‘black box’ models, where interpretability is at a threshold to make critical diagnostic decisions involved in industries related to aviation. The purpose of this work is to present a prognostic framework that embeds Explainable Artificial Intelligence (XAI) techniques in order to provide essential transparency in Machine Learning methods' decision-making mechanisms based on sensor data, with the objective of procuring actionable insights for the aviation industry. Sensor readings have been gathered from critical equipment such as turbofan jet engine and landing gear, and the prediction of the RUL is done by a Random Forest model. It involves steps such as data gathering, feature engineering, model training, and evaluation. These critical components’ datasets are independently trained and evaluated by the models. While suitable predictions are served, their performance metrics are reasonably good; such complex models, however obscure reasoning for the predictions made by them and may even undermine the confidence of the decision-maker or the maintenance teams. This is followed by global explanations using SHAP and local explanations using LIME in the second phase to bridge the gap in reliability within industrial contexts. These tools analyze model decisions, highlighting feature importance and explaining how each input variable affects the output. This dual approach offers a general comprehension of the overall model behavior and detailed insight into specific predictions. The proposed framework, in its third component, incorporates the techniques of causal analysis in the form of Granger causality tests in order to move beyond correlation toward causation. This will not only allow the model to predict failures but also present reasons, from the key sensor features linked to possible failure mechanisms to relevant personnel. The causality between sensor behaviors and equipment failures creates much value for maintenance teams due to better root cause identification and effective preventive measures. This step contributes to the system being more explainable. Surrogate Several simple models, including Decision Trees and Linear Models, can be used in yet another stage to approximately represent the complex Random Forest model. These simpler models act as backups, replicating important jobs of the original model's behavior. If the feature explanations obtained from the surrogate model are cross-validated with the primary model, the insights derived would be more reliable and provide an intuitive sense of how the input variables affect the predictions. We then create an iterative explainable feedback loop, where the knowledge learned from the explainability methods feeds back into the training of the models. This feeds into a cycle of continuous improvement both in model accuracy and interpretability over time. By systematically integrating new findings, the model is expected to adapt to changed conditions and further develop its prognosis capability. These components are then presented to the decision-makers through the development of a fully transparent condition monitoring and alert system. The system provides a holistic tool for maintenance operations by leveraging RUL predictions, feature importance scores, persistent sensor threshold values, and autonomous alert mechanisms. Since the system will provide explanations for the predictions given, along with active alerts, the maintenance personnel can make informed decisions on their end regarding correct interventions to extend the life of the critical machinery.

Keywords: predictive maintenance, explainable artificial intelligence, prognostic, RUL, machine learning, turbofan engines, C-MAPSS dataset

Procedia PDF Downloads 7

Authors: Awol Seid Ebrie

Abstract:

HIV results as an incurable disease, AIDS. After a person is infected with virus, the virus gradually destroys all the infection fighting cells called CD4 cells and makes the individual susceptible to opportunistic infections which cause severe or fatal health problems. Several studies show that the CD4 cells count is the most determinant indicator of the effectiveness of the treatment or progression of the disease. The objective of this paper is to investigate the progression of the disease over time among patient under HAART treatment. Two main approaches of the generalized multilevel ordinal models; namely the proportional odds model and the nonproportional odds model have been applied to the HAART data. Also, the multilevel part of both models includes random intercepts and random coefficients. In general, four models are explored in the analysis and then the models are compared using the deviance information criteria. Of these models, the random coefficients nonproportional odds model is selected as the best model for the HAART data used as it has the smallest DIC value. The selected model shows that the progression of the disease increases as the time under the treatment increases. In addition, it reveals that gender, baseline clinical stage and functional status of the patient have a significant association with the progression of the disease.

Keywords: nonproportional odds model, proportional odds model, random coefficients model, random intercepts model

Procedia PDF Downloads 421

Authors: Rayhaneh Amooaghaie, Maryam Norouzi

Abstract:

In this study, the effects of nTiO₂ on seed germination and growth of six plant species (wheat, soybean, tomato, canola, cucumber, and lettuce) were evaluated in petri dish (direct exposure) and in soil in a greenhouse experiment (soil exposure). Data demonstrate that under both culture conditions, low or mild concentrations of nTiO₂ either stimulated or had no effect on seed germination, root growth and vegetative biomass while high concentrations had an inhibitory effect. However, results showed that the impacts of nTiO₂ on plant growth in soil were partially consistent with those observed in pure culture. Based on both experiment sets, among above six species, lettuce and canola were the most susceptible and the most tolerant species to nTiO₂ toxicity. However, results revealed the impacts of nTiO₂ on plant growth in soil were less than petri dish exposure probability due to dilution in soil and complexation/aggregation of nTiO₂ that would lead to lower exposure of plants. The high concentrations of nTiO₂ caused significant reductions in fresh and dry weight of aerial parts and root and chlorophyll and carotenoids contents of all species which also coincided with further accumulation of malondialdehyde (MDA). These findings suggest that decreasing growth might be the result of an nTiO₂-induced oxidative stress and disturbance of photosynthesis systems.

Keywords: chlorophyll, lipid peroxidation, nano TiO₂, seed germination

Procedia PDF Downloads 165

Authors: Abhishek Saha, Serene Tay, Gerard Pijcke

Abstract:

The availability of high-resolution topography and rainfall information in urban areas has made it necessary to revise modeling approaches used for simulating flood risk assessments. Lidar derived elevation models that have 1m or lower resolutions are becoming widely accessible. The classical approaches of 1D-2D flow models where channel flow is simulated and coupled with a coarse resolution 2D overland flow models may not fully utilize the information provided by high-resolution data. In this context, a study was undertaken to compare three different modeling approaches to simulate flooding in an urban area. The first model used is the base model used is Sobek, which uses 1D model formulation together with hydrologic boundary conditions and couples with an overland flow model in 2D. The second model uses a full 2D model for the entire area with shallow water equations at the resolution of the digital elevation model (DEM). These models are compared against another shallow water equation solver in 2D, which uses a subgrid method for grid refinement. These models are simulated for different horizontal resolutions of DEM varying between 1m to 5m. The results show a significant difference in inundation extents and water levels for different DEMs. They are also sensitive to the different numerical models with the same physical parameters, such as friction. The study shows the importance of having reliable field observations of inundation extents and levels before a choice of model and data can be made for spatial flood risk assessments.

Keywords: flooding, DEM, shallow water equations, subgrid

Procedia PDF Downloads 141

Authors: Sandeep Kaur, Jenny Vuong, Marcel Julliard, Sean O'Donoghue

Abstract:

Time-series data are useful for modelling as they can enable model-evaluation. However, when reconstructing models from phosphoproteomic data, often non-exact methods are utilised, as the knowledge regarding the network structure, such as, which kinases and phosphatases lead to the observed phosphorylation state, is incomplete. Thus, such reactions are often hypothesised, which gives rise to uncertainty. Here, we propose a framework, implemented via a web-based tool (as an extension to Minardo), which given time-series phosphoproteomic datasets, can generate κ models. The incompleteness and uncertainty in the generated model and reactions are clearly presented to the user via the visual method. Furthermore, we demonstrate, via a toy EGF signalling model, the use of algorithmic verification to verify κ models. Manually formulated requirements were evaluated with regards to the model, leading to the highlighting of the nodes causing unsatisfiability (i.e. error causing nodes). We aim to integrate such methods into our web-based tool and demonstrate how the identified erroneous nodes can be presented to the user via the visual method. Thus, in this research we present a framework, to enable a user to explore phosphorylation proteomic time-series data in the context of models. The observer can visualise which reactions in the model are highly uncertain, and which nodes cause incorrect simulation outputs. A tool such as this enables an end-user to determine the empirical analysis to perform, to reduce uncertainty in the presented model - thus enabling a better understanding of the underlying system.

Keywords: κ-models, model verification, time-series phosphoproteomic datasets, uncertainty and error visualisation

Procedia PDF Downloads 255

Authors: Ashok E. Kalange

Abstract:

Diagnosis according to Ayurveda is to find the root cause of a disease. Out of the eight different kinds of examinations, Nadi-Pariksha (pulse examination) is important. Nadi-Pariksha is done at the root of the thumb by examining the radial artery using three fingers. Ancient Ayurveda identifies the health status by observing the wrist pulses in terms of 'Vata', 'Pitta' and 'Kapha', collectively called as tridosha, as the basic elements of human body and in their combinations. Diagnosis by traditional pulse analysis – NadiPariksha - requires a long experience in pulse examination and a high level of skill. The interpretation tends to be subjective, depending on the expertise of the practitioner. Present work is part of the efforts carried out in making Nadi-Parikshan objective. Nadi Parikshan Yantra (three point pulse examination system) is developed in our laboratory by using three pressure sensors (one each for the Vata, Pitta and Kapha points on radial artery). The radial pulse data was collected of a large number of subjects. The radial pulse data collected is analyzed on the basis of relative amplitudes of the three point pulses as well as in frequency and time domains. The same subjects were examined by Ayurvedic physician (Nadi Vaidya) and the dominant Dosha - Vata, Pitta or Kapha - was identified. The results are discussed in details in the paper.

Keywords: Nadi Parikshan Yantra, Tridosha, Nadi Pariksha, human pulse data analysis

Procedia PDF Downloads 189

Authors: Oluwayemisi Femi-Pius, Kazeem Arogundade, Eberechukwu Eke, Jimmy Eko

Abstract:

Introduction: Globally, community involvement in improving their own health has been widely adopted as a strategy in Sub-Saharan Africa principally to ensure equitable access to essential health care as well as improve the uptake of maternal and newborn health services especially in poor-resource settings. Method: The Saving Mother Giving Life (SMGL) Initiative implemented by Pathfinder International with funding support from USAID conducted a Health Facility Assessment (HFA) and found out that maternal mortality ratio in Cross River State was 812 per 100,000 live birth and perinatal mortality was 160 per 1000 live birth. To reduce maternal and perinatal mortality, Pathfinder International mobilized, selected and trained community members as community volunteers, traditional birth attendants, and emergency transport service volunteer drivers mainly to address the delay in decision making and reaching the health facility among pregnant women. Results: The results showed that maternal mortality ratio in Cross River State decrease by 25% from 812 per 100,000 live birth at baseline to 206 per 100,000 live birth at June 2018 and perinatal mortality reduced by 35% from 160 per 100,000 at baseline to 58 per 1000 live birth at June 2018. Data also show that ANC visit increased from 7,451 to 11,344; institutional delivery increased from 8,931 at baseline to 10,784 in June 2018. There was also a remarkable uptake of post-partum family planning from 0 at baseline to 233 in June 2018. Conclusion: There is clear evidence that community involvement yields positive maternal outcomes and is pivotal for sustaining most health interventions.

Keywords: maternal mortality, Nigeria, pathfinder international, perinatal mortality, saving mother giving life

Procedia PDF Downloads 192

Authors: Abd Elrahman Elgamala, N. Darwish, I. Bondouk, Sh. Hamada

Abstract:

Available experimental angular distributions for ⁶Li elastically scattered from ¹⁶O nucleus in the energy range 13.0–50.0 MeV are investigated and reanalyzed using optical model of the conventional phenomenological potential and also using double folding optical model of different interaction models: DDM3Y1, CDM3Y1, CDM3Y2, and CDM3Y3. All the involved models of interaction are of M3Y Paris except DDM3Y1 which is of M3Y Reid and the main difference between them lies in the different values for the parameters of the incorporated density distribution function F(ρ). We have extracted the renormalization factor N_R for ⁶Li+¹⁶O nuclear system in the energy range 13.0–50.0 MeV using the aforementioned interaction models.

Keywords: elastic scattering, optical model, folding potential, density distribution

Procedia PDF Downloads 141

Authors: D.Mendes, J. Henriques, P. Carvalho, T. Rocha, S. Paredes, R. Cabiddu, R. Trimer, R. Mendes, A. Borghi-Silva, L. Kaminsky, E. Ashley, R. Arena, J. Myers

Abstract:

The continued exploration of clinically relevant predictive models continues to be an important pursuit. Cardiorespiratory fitness (CRF) portends clinical vital information and as such its accurate prediction is of high importance. Therefore, the aim of the current study was to develop a data-driven model, based on computational intelligence techniques and, in particular, clustering approaches, to predict CRF. Two prediction models were implemented and compared: 1) the traditional Wasserman/Hansen Equations; and 2) an interpretable clustering approach. Data used for this analysis were from the 'FRIEND - Fitness Registry and the Importance of Exercise: The National Data Base'; in the present study a subset of 10690 apparently healthy individuals were utilized. The accuracy of the models was performed through the computation of sensitivity, specificity, and geometric mean values. The results show the superiority of the clustering approach in the accurate estimation of CRF (i.e., maximal oxygen consumption).

Keywords: cardiorespiratory fitness, data-driven models, knowledge extraction, machine learning

Procedia PDF Downloads 286

Authors: Muhammad Aown Sammar Raza

Abstract:

Environmental stresses are one of the major reasons for poor crop yield across the globe. Among the various environmental stresses, drought stress is the most damaging one, especially in arid and semi-arid regions. Wheat is the major staple food of many countries of the world, which is badly affected by drought stress. In order to fulfill the dietary needs of increasing population with depleting water resources there is a need to adopt technologies which result in sufficient crop yield with less water consumption. One of them is partial root zone drying. Keeping in view these conditions, a wire house experiment was conducted at agronomic research area of University College of Agriculture and Environmental Sciences, The Islamia University Bahawalpur during 2015, to screen out the different wheat varieties for partial root zone drying (PRD). Five approved local wheat varieties (V1= Galaxy-2013, V2= Punjab-2011, V3 = Faisalabad-2008, V4 = Lasani-2008 and V5 = V.8200) and two irrigation levels (I1= control irrigation and I2 = PRD irrigation) with completely randomized design having four replications were used in the experiment. Among the varieties, Galaxy-2013 performed the best and attained maximum plant height, leaf area, stomatal conductance, photosynthesis, total sugars, proline contents and antioxidant enzymes activities and minimum values of growth and physiological parameters were recorded in variety V.8200. For irrigation levels, higher values of growth, physiological and water related parameters were recorded in control treatment (I1) except leaf water potential, osmotic potential, total sugars and proline contents. However, enzyme activities were higher under PRD treatment for all varieties. It was concluded that Galaxy-2013 is the most compatible and V.8200 is the most susceptible variety for PRD, respectively and more quality traits and enzymatic activities were recorded under PRD irrigation as compared to control treatment.

Keywords: antioxidant enzymes activities, osmolytes concentration, partial root zone drying, photosynthetic rate, water relations, wheat

Procedia PDF Downloads 245

Authors: M. Bodner, M. Scampicchio

Abstract:

Fourier Transform Infrared (FT-IR) spectroscopy coupled with chemometrics was used to distinguish between butter samples and non-butter samples. Further, quantification of the content of margarine in adulterated butter samples was investigated. Fingerprinting region (1400-800 cm^–1) was used to develop unsupervised pattern recognition (Principal Component Analysis, PCA), supervised modeling (Soft Independent Modelling by Class Analogy, SIMCA), classification (Partial Least Squares Discriminant Analysis, PLS-DA) and regression (Partial Least Squares Regression, PLS-R) models. PCA of the fingerprinting region shows a clustering of the two sample types. All samples were classified in their rightful class by SIMCA approach; however, nine adulterated samples (between 1% and 30% w/w of margarine) were classified as belonging both at the butter class and at the non-butter one. In the two-class PLS-DA model’s (R² = 0.73, RMSEP, Root Mean Square Error of Prediction = 0.26% w/w) sensitivity was 71.4% and Positive Predictive Value (PPV) 100%. Its threshold was calculated at 7% w/w of margarine in adulterated butter samples. Finally, PLS-R model (R² = 0.84, RMSEP = 16.54%) was developed. PLS-DA was a suitable classification tool and PLS-R a proper quantification approach. Results demonstrate that FT-IR spectroscopy combined with PLS-R can be used as a rapid, simple and safe method to identify pure butter samples from adulterated ones and to determine the grade of adulteration of margarine in butter samples.

Keywords: adulterated butter, margarine, PCA, PLS-DA, PLS-R, SIMCA

Procedia PDF Downloads 143

Authors: Elias K. Maragos, Petros E. Maravelakis

Abstract:

In Dynamic Data Envelopment Analysis (DDEA), which is a subfield of Data Envelopment Analysis (DEA), the productivity of Decision Making Units (DMUs) is considered in relation to time. In this case, as it is accepted by the most of the researchers, there are outputs, which are produced by a DMU to be used as inputs in a future time. Those outputs are known as intermediates. The common models, in DDEA, do not take into account the shape of the distribution of those inputs, outputs or intermediates data, assuming that the distribution of the virtual value of them does not deviate from linearity. This weakness causes the limitation of the accuracy of the analytical power of the traditional DDEA models. In this paper, the authors, using the concept of piecewise linear inputs and outputs, propose an extended DDEA model. The proposed model increases the flexibility of the traditional DDEA models and improves the measurement of the dynamic performance of DMUs.

Keywords: Dynamic Data Envelopment Analysis, DDEA, piecewise linear inputs, piecewise linear outputs

Procedia PDF Downloads 161

Authors: A. G. Matveev

Abstract:

The copyrights system is a combination of different elements. The number, content and the correlation of these elements are different for different legal orders. The models of copyrights systems display this system in terms of the interaction of economic and author's moral rights. Monistic and dualistic models are the most popular ones. The article deals with different points of view on the monism and dualism in copyright system. A specific model of the copyright in Switzerland in the XXth century is analyzed. The evolution of a French dualistic model of copyright is shown. The author believes that one should talk not about one, but rather about a number of dualism forms of copyright system.

Keywords: copyright, exclusive copyright, economic rights, author's moral rights, rights of personality, monistic model, dualistic model

Procedia PDF Downloads 420

Authors: Yogendra Sisodia

Abstract:

Researchers are becoming more interested in extracting useful information from legal documents thanks to the development of large-scale language models in natural language processing (NLP), and deep learning has accelerated the creation of powerful text mining models. Legal fields like contracts benefit greatly from semantic text search since it makes it quick and easy to find related clauses. After collecting sentence embeddings, it is relatively simple to locate sentences with a comparable meaning throughout the entire legal corpus. The author of this research investigated two pre-trained language models for this task: MiniLM and Roberta, and further fine-tuned them on Legal Contracts. The author used Multiple Negative Ranking Loss for the creation of sentence transformers. The fine-tuned language models and sentence transformers showed promising results.

Keywords: legal contracts, multiple negative ranking loss, natural language inference, sentence transformers, semantic textual similarity

Procedia PDF Downloads 108

Authors: Herlandson C. Moura, Jorge H. Bidinotto, Eduardo M. Belo

Abstract:

The present work proposes the development of an adaptive control system which enables the suppression of Pilot Induced Oscillations (PIO) in Digital Fly-By-Wire (DFBW) aircrafts. The proposed system consists of a Modified Model Reference Adaptive Control (M-MRAC) integrated with the Gain Scheduling technique. The PIO oscillations are detected using a Real Time Oscillation Verifier (ROVER) algorithm, which then enables the system to switch between two reference models; one in PIO condition, with low proneness to the phenomenon and another one in normal condition, with high (or medium) proneness. The reference models are defined in a closed loop condition using the Linear Quadratic Regulator (LQR) control methodology for Multiple-Input-Multiple-Output (MIMO) systems. The implemented algorithms are simulated in software implementations with state space models and commercial flight simulators as the controlled elements and with pilot dynamics models. A sequence of pitch angles is considered as the reference signal, named as Synthetic Task (Syntask), which must be tracked by the pilot models. The initial outcomes show that the proposed system can detect and suppress (or mitigate) the PIO oscillations in real time before it reaches high amplitudes.

Keywords: adaptive control, digital Fly-By-Wire, oscillations suppression, PIO

Procedia PDF Downloads 134

Authors: Riona Dighe

Abstract:

This research attempts to identify an alternative approach to the measurement of Gross National Happiness (GNH). It uses artificial intelligence (AI), incorporating natural language processing (NLP) and sentiment analysis to measure GNH. We use ‘off the shelf’ NLP models responsible for the sentiment analysis of a sentence as a building block for this research. We constructed an algorithm using NLP models to derive a sentiment analysis score against sentences. This was then tested against a sample of 20 respondents to derive a sentiment analysis score. The scores generated resembled human responses. By utilising the MLP classifier, decision tree, linear model, and K-nearest neighbors, we were able to obtain a test accuracy of 89.97%, 54.63%, 52.13%, and 47.9%, respectively. This gave us the confidence to use the NLP models against sentences in websites to measure the GNH of a country.

Keywords: artificial intelligence, NLP, sentiment analysis, gross national happiness

Procedia PDF Downloads 119

Authors: Fazıl Gökgöz, Fahrettin Filiz

Abstract:

Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: deep learning, long short term memory, energy, renewable energy load forecasting

Procedia PDF Downloads 266

Authors: Adel Bougamouza, Boualam Remini, Abd El Hadi Ammari, Feteh Sakhraoui

Abstract:

The assessment of sediments being carried by a river is importance for planning and designing of various water resources projects. In this study, Artificial Neural Network Techniques are used to estimate the daily suspended sediment concentration for the corresponding daily discharge flow in the upstream of Foum El Gherza dam, Biskra, Algeria. The FFNN, GRNN, and RBNN models are established for estimating current suspended sediment values. Some statistics involving RMSE and R2 were used to evaluate the performance of applied models. The comparison of three AI models showed that the RBNN model performed better than the FFNN and GRNN models with R2 = 0.967 and RMSE= 5.313 mg/l. Therefore, the ANN model had capability to improve nonlinear relationships between discharge flow and suspended sediment with reasonable precision.

Keywords: artificial neural network, Oued Abiod watershed, feedforward network, generalized regression network, radial basis network, sediment concentration

Procedia PDF Downloads 418

Authors: Do-Jin Jang, Sung-Ah Kim

Abstract:

In designing a kinetic façade, it is hard for the designer to make digital models due to its complex geometry with motion. This paper aims to present a methodology of converting a point cloud of a physical model into a single digital model with a certain topology and motion. The method uses a Microsoft Kinect sensor, and color markers were defined and applied to three paper folding-inspired designs. Although the resulted digital model cannot represent the whole folding range of the physical model, the method supports the designer to conduct a performance-oriented design process with the rough physical model in the reduced folding range.

Keywords: design media, kinetic facades, tangible user interface, 3D scanning

Procedia PDF Downloads 413

Authors: Ojoniyi Oluwafeyekikunmi Okiki

Abstract:

Notwithstanding advances in disturbing wound care and control, infections remain a main motive of mortality, morbidity, and financial disruption in tens of millions of wound sufferers around the sector. Animal models have become popular gear for analyzing a big selection of outside worrying wound infections and trying out new antimicrobial techniques. This evaluation covers experimental infections in animal models of surgical wounds, pores and skin abrasions, burns, lacerations, excisional wounds, and open fractures. Animal modes of external stressful wound infections stated via extraordinary investigators vary in animal species used, microorganism traces, the quantity of microorganisms carried out, the dimensions of the wounds, and, for burn infections, the period of time the heated object or liquid is in contact with the skin. As antibiotic resistance continues to grow, new antimicrobial procedures are urgently needed. Those have to be examined using popular protocols for infections in external stressful wounds in animal models.

Keywords: surgical wounds, animals, wound infections, burns, wound models, colony-forming gadgets, lacerated wounds

Procedia PDF Downloads 8

Authors: E. Karacabey, Ş. G. Özçelik, M. S. Turan, C. Baltacıoğlu, E. Küçüköner

Abstract:

Main concern about deep-fat-fried food materials is their high final oil contents absorbed during frying process and/or after cooling period, since diet including high content of oil is accepted unhealthy by consumers. Different methods have been evaluated to decrease oil content of fried food stuffs. One promising method is partially drying of food material before frying. In the present study it was aimed to control and decrease the final oil content of zucchini slices by means of partial drying and to optimize process conditions. Conventional oven drying was used to decrease moisture content of zucchini slices at a certain extent. Process performance in terms of oil uptake was evaluated by comparing oil content of predried and then fried zucchini slices with those determined for directly fried ones. For predrying and frying processes, oven temperature and weight loss and frying oil temperature and time pairs were controlled variables, respectively. Zucchini slices were also directly fried for sensory evaluations revealing preferred properties of final product in terms of surface color, moisture content, texture and taste. These properties of directly fried zucchini slices taking the highest score at the end of sensory evaluation were determined and used as targets in optimization procedure. Response surface methodology was used for process optimization. The properties, determined after sensory evaluation, were selected as targets; meanwhile oil content was aimed to be minimized. Results indicated that final oil content of zucchini slices could be reduced from 58% to 46% by controlling conditions of predrying and frying processes. As a result, it was suggested that predrying could be one choose to reduce oil content of fried zucchini slices for health diet. This project (113R015) has been supported by TUBITAK.

Keywords: health process, optimization, response surface methodology, oil uptake, conventional oven

Procedia PDF Downloads 366

Authors: Brigitta Bodnár, Lajos Kovács, Zoltán Kupihár

Abstract:

The cancer cells require higher amount of nucleoside building blocks for their proliferation, therefore they have significantly higher uptake of nucleosides by the different nucleoside transporters. Therefore, the conjugation with nucleosides may significantly increase the efficiency and selectivity of potential active pharmaceutical ingredients. On the other hand, the advantage of using a nucleoside could be either the higher activity on targeted enzymes overrepresented in cancer cells or an enhanced cellular uptake of the bioconjugates in these cells compared to the healthy ones. This fact can be used to make the nucleosides, as targeting moieties covalently bound to anti-cancer drug molecules which can selectively accumulate in cancer cells. However, in order to form the nucleoside-drug conjugates, such nucleoside building blocks are needed, which can selectively be coupled to the drug molecules containing even a high number of diverse functional groups. One of the most selective conjugation techniques is the copper-catalyzed azide-alkyne click reaction that requires the presence of an alkyl group on one of the conjugated molecules and an azide group on the other. In case of nucleosides, the development of azide group is simpler for which the replacement of the 5'-hydroxy group is the most suitable. This transformation generally involves many side reactions and result in very low yields. In addition, during our experiments, the transformation of the 2'-deoxyguanosine to the corresponding 5'-deoxy-5’-azido-2’-deoxyguanosine could not be performed with any of the methods described in the literature. Therefore, we have tried to overcome these difficulties with not only using the traditional process based on the 2 step exchange of tosyl to azide, but also using the Mitsunobu reaction which requires only one step. However, this path proved to be unsuccessful in spite of the optimizing the reaction conditions. Finally, a method has been developed whereby the azide groups were incorporated into the 5’-position resulting in significantly better yields compared to all other previous methods, and we were able to produce all the four nucleoside derivatives.

Keywords: 5'-azidonucleosides, bioconjugate, click reaction, proliferation

Procedia PDF Downloads 246

Authors: Debarati Bhaumik, Diptish Dey

Abstract:

Multilevel models, increasingly deployed in industries such as insurance, food production, and entertainment within functions such as marketing and supply chain management, need to be transparent and ethical. Applications usually result in binary classification within groups or hierarchies based on a set of input features. Using open-source datasets, we demonstrate that popular explainability methods, such as SHAP and LIME, consistently underperform inaccuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution (negative versus positive contribution to the outcome). Besides accuracy, the computational intractability of SHAP for binomial classification is a cause of concern. For transparent and ethical applications of these hierarchical statistical models, sound audit frameworks need to be developed. In this paper, we propose an audit framework for technical assessment of multilevel regression models focusing on three aspects: (i) model assumptions & statistical properties, (ii) model transparency using different explainability methods, and (iii) discrimination assessment. To this end, we undertake a quantitative approach and compare intrinsic model methods with SHAP and LIME. The framework comprises a shortlist of KPIs, such as PoCE (Percentage of Correct Explanations) and MDG (Mean Discriminatory Gap) per feature, for each of these three aspects. A traffic light risk assessment method is furthermore coupled to these KPIs. The audit framework will assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit businesses deploying multilevel models to be future-proof and aligned with the European Commission’s proposed Regulation on Artificial Intelligence.

Keywords: audit, multilevel model, model transparency, model explainability, discrimination, ethics

Procedia PDF Downloads 95

Authors: Moriom Akter Mousumi, Spyridon Paparrizos, Fulco Ludwig

Abstract:

Rice yield reduction due to climate change-induced disease occurrence is becoming a great concern for coastal farmers of Bangladesh. The development of participatory climate information services (CIS) based on farmers’ needs could implicitly facilitate farmers to get disease forecasts and make better decisions to manage diseases. Therefore, this study aimed to investigate how participatory climate information services assist coastal rice farmers to take up rice disease forecasts and better manage rice diseases by improving their informed decision-making. Through participatory approaches, we developed a tailor-made agrometeorological service through the DROP app to forecast rice diseases and manage them in advance. During farmers field schools (FFS) we communicated 7-day disease forecasts during face-to-face weekly meetings using printed paper and, messenger app derived from DROP app. Results show that the majority of the farmers understand disease forecasts through visualization, symbols, and text. The majority of them use disease forecast information directly from the DROP app followed by face-to-face meetings, messenger app, and printed paper. Farmers participation and engagement during capacity building training at FFS also assist them in making more informed decisions and improved management of diseases using both preventive measures and chemical measures throughout the rice cultivation period. We conclude that the development of participatory CIS and the associated capacity-building and training of farmers has increased farmers' understanding and uptake of disease forecasts to better manage of rice diseases. Participatory services such as the DROP app offer great potential as an adaptation option for climate-smart rice production under changing climatic conditions.

Keywords: participatory climate service, disease forecast, disease management, informed decision making, coastal Bangladesg

Procedia PDF Downloads 46