Search results for: software-based validation

Authors: R. Lhissou , A. El Harti , K. Chokmani, E. Bachaoui, A. El Ghmari

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Soil salinity is a serious environmental hazard in many countries around the world especially the arid and semi-arid countries like Morocco. Salinization causes negative effects on the ground; it affects agricultural production, infrastructure, water resources and biodiversity. Remote sensing can provide soil salinity information for large areas, and in a relatively short time. In addition, remote sensing is not limited by extremes in terrain or hazardous condition. Contrariwise, experimental methods for monitoring soil salinity by direct measurements in situ are very demanding of time and resources, and also very limited in spatial coverage. In the irrigated perimeter of Tadla plain in central Morocco, the increased use of saline groundwater and surface water, coupled with agricultural intensification leads to the deterioration of soil quality especially by salinization. In this study, we assessed several spectral indices of soil salinity cited in the literature using Landsat TM satellite images and field measurements of electrical conductivity (EC). Three Landsat TM satellite images were taken during 3 months in the dry season (September, October and November 2011). Based on field measurement data of EC collected in three field campaigns over the three dates simultaneously with acquisition dates of Landsat TM satellite images, a two assessment techniques are used to validate a soil salinity spectral indices. Firstly, the spectral indices are validated locally by pixel. The second validation technique is made using a window of size 3x3 pixels. The results of the study indicated that the second technique provides getting a more accurate validation and the assessment has shown its limits when it comes to assess across the pixel. In addition, the EC values measured from field have a good correlation with some spectral indices derived from Landsat TM data and the best results show an r² of 0.88, 0.79 and 0.65 for Salinity Index (SI) in the three dates respectively. The results have shown the usefulness of spectral indices as an auxiliary variable in the spatial estimation and mapping salinity in irrigated land.

Keywords: remote sensing, spectral indices, soil salinity, irrigated land

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Authors: Salman Obaidoon, Mohamed Hassan, Omer Bakather

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As environmental regulations increase, the need for a clean and inexpensive energy is becoming necessary these days using an available raw material with high efficiency and low emissions of toxic gases. This paper presents a study on modifying a gas turbine power plant fired by diesel, which is located in Saudi Arabia in order to increase the efficiency and capacity of the station as well as decrease the rate of emissions. The studied power plant consists of 30 units with different capacities and total net power is 1470 MW. The study was conducted on unit number 25 (GT-25) which produces 72.3 MW with 29.5% efficiency. In the beginning, the unit was modeled and simulated by using WinSim Design II software. In this step, actual unit data were used in order to test the validity of the model. The net power and efficiency obtained from software were 76.4 MW and 32.2% respectively. A difference of about 6% was found in the simulated power plant compared to the actual station which means that the model is valid. After the validation of the model, the simple gas turbine power plant was converted to a combined cycle power plant (CCPP). In this case, the exhausted gas released from the gas turbine was introduced to a heat recovery steam generator (HRSG), which consists of three heat exchangers: an economizer, an evaporator and a superheater. In this proposed model, many scenarios were conducted in order to get the optimal operating conditions. The net power of CCPP was increased to 116.4 MW while the overall efficiency of the unit was reached to 49.02%, consuming the same amount of fuel for the gas turbine power plant. For the purpose of comparing the rate of emissions of carbon dioxide on each model. It was found that the rate of CO₂ emissions was decreased from 15.94 kg/s to 9.22 kg/s by using the combined cycle power model as a result of reducing of the amount of diesel from 5.08 kg/s to 2.94 kg/s needed to produce 76.5 MW. The results indicate that the rate of emissions of carbon dioxide was decreased by 42.133% in CCPP compared to the simple gas turbine power plant.

Keywords: combined cycle power plant, efficiency, heat recovery steam generator, simulation, validation, WinSim design II software

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Authors: Ian Campbell, Gillian Mitchell, Paul James, Na Li, Ella Thompson

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The genetic cause of the majority of multiple-case breast cancer families remains unresolved. Next generation sequencing has emerged as an efficient strategy for identifying predisposing mutations in individuals with inherited cancer. We are conducting whole exome sequence analysis of germ line DNA from multiple affected relatives from breast cancer families, with the aim of identifying rare protein truncating and non-synonymous variants that are likely to include novel cancer predisposing mutations. Data from more than 200 exomes show that on average each individual carries 30-50 protein truncating mutations and 300-400 rare non-synonymous variants. Heterogeneity among our exome data strongly suggest that numerous moderate penetrance genes remain to be discovered, with each gene individually accounting for only a small fraction of families (~0.5%). This scenario marks validation of candidate breast cancer predisposing genes in large case-control studies as the rate-limiting step in resolving the missing heritability of breast cancer. The aim of this study is to screen genes that are recurrently mutated among our exome data in a larger cohort of cases and controls to assess the prevalence of inactivating mutations that may be associated with breast cancer risk. We are using the Agilent HaloPlex Target Enrichment System to screen the coding regions of 168 genes in 1,000 BRCA1/2 mutation-negative familial breast cancer cases and 1,000 cancer-naive controls. To date, our interim analysis has identified 21 genes which carry an excess of truncating mutations in multiple breast cancer families versus controls. Established breast cancer susceptibility gene PALB2 is the most frequently mutated gene (13/998 cases versus 0/1009 controls), but other interesting candidates include NPSR1, GSN, POLD2, and TOX3. These and other genes are being validated in a second cohort of 1,000 cases and controls. Our experience demonstrates that beyond PALB2, the prevalence of mutations in the remaining breast cancer predisposition genes is likely to be very low making definitive validation exceptionally challenging.

Keywords: predisposition, familial, exome sequencing, breast cancer

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Authors: Salman Akrokayan

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Synthetic cDNA (ScDNA) of granulocyte colony-stimulating factor (G-CSF) was constructed using a DNA synthesizer with the aim to increase its expression level. 5' end of the ScDNA of G-CSF coding region was modified by decreasing the GC content without altering the predicted amino acids sequence. The identity of the resulting protein from ScDNA was confirmed by the highly specific enzyme-linked immunosorbent assay. In conclusion, a synthetic G-CSF cDNA in combination with the recombinant DNA protocol offers a rapid and reliable strategy for synthesizing the target protein. However, the commercial utilization of this methodology requires rigorous validation and quality control.

Keywords: synthetic cDNA, recombinant G-CSF, cloning, gene expression

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Authors: Edward A. Osifodunrin, Jose Manuel Dias Lopes

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In Nigeria, enormous efforts/resources had, over the years, been expended on promoting financial inclusion (FI); however, it is seemingly discouraging that many of its self-declared targets on FI remained unachieved, especially amongst the Rural Dwellers and Actors in the Informal Sectors (RDAIS). Expectedly, many reasons had been earmarked for these failures: low literacy level, huge informal/rural sectors, etc. This study posits that in spite of these truly-debilitating factors, these FI policy failures could have been avoided or mitigated if the principles of active and better-managed citizens’ participation had been strictly followed in the (re)design/implementation of its FI policies. In other words, in a bid to mitigate the prevalent FE in Nigeria, this study hypothesizes the positive impact of increased/active citizens’ participation on FI outcome(s), backed by a preliminary empirical validation. Also, the study introduces the RDAIS-focused participatory financial inclusion policy (PFIP) as a major FI policy regeneration/improvement tool. The three categories of respondents that served as research subjects are FI experts in Nigeria (n = 72), RDAIS from the very rural/remote village of Unguwar Dogo in Northern Nigeria (n = 43), and RDAIS from another rural village of Sekere (n = 56) in the Southern region of Nigeria. Using survey design (5-point Likert scale questionnaires), random/stratified sampling, and descriptive/inferential statistics, the study often recorded independent consensus (amongst these three categories of respondents) that RDAIS’s active participation in iterative FI policy initiation, (re)design, implementation, (re)evaluation could indeed give improved FI outcomes. However, some questionnaire items also recorded divergent opinions and various statistically significant differences in the mean scores of these three categories. The PFIP (or any customized version of it) should then be carefully integrated into the NFIS of Nigeria (and possibly in the NFIS of other developing countries) to truly/fully provide FI policy integration for these excluded RDAIS and arrest the prevalence of FE.

Keywords: citizens’ participation, development, financial inclusion, formal financial services, national financial inclusion strategy, participatory financial inclusion policy, rural dwellers and actors in the informal sectors

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Authors: Sepulveda Juan

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This article explains the elements and considerations taken into account when implementing and applying patent evaluation and scientometric study in the identifications of technology trends, and the tools that led to the implementation of a software application for patent revision. Univariate analysis helped recognize the technological leaders in the field of energy, and steered the way for a multivariate analysis of this sample, which allowed for a graphical description of the techniques of mature technologies, as well as the detection of emerging technologies. This article ends with a validation of the methodology as applied to the case of fuel cells.

Keywords: patents, scientometric, renewable energy, technology maps

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Authors: V. Markogianni, D. Kalivas, G. Petropoulos, E. Dimitriou

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Lake water quality monitoring in combination with the use of earth observation products constitutes a major component in many water quality monitoring programs. Landsat 8 images of Trichonis Lake (Greece) acquired on 30/10/2013 and 30/08/2014 were used in order to explore the possibility of Landsat 8 to estimate water quality parameters and particularly CDOM absorption at specific wavelengths, chlorophyll-a and nutrient concentrations in this oligotrophic freshwater body, characterized by inexistent quantitative, temporal and spatial variability. Water samples have been collected at 22 different stations, on late August of 2014 and the satellite image of the same date was used to statistically correlate the in-situ measurements with various combinations of Landsat 8 bands in order to develop algorithms that best describe those relationships and calculate accurately the aforementioned water quality components. Optimal models were applied to the image of late October of 2013 and the validation of the results was conducted through their comparison with the respective available in-situ data of 2013. Initial results indicated the limited ability of the Landsat 8 sensor to accurately estimate water quality components in an oligotrophic waterbody. As resulted by the validation process, ammonium concentrations were proved to be the most accurately estimated component (R = 0.7), followed by chl-a concentration (R = 0.5) and the CDOM absorption at 420 nm (R = 0.3). In-situ nitrate, nitrite, phosphate and total nitrogen concentrations of 2014 were measured as lower than the detection limit of the instrument used, hence no statistical elaboration was conducted. On the other hand, multiple linear regression among reflectance measures and total phosphorus concentrations resulted in low and statistical insignificant correlations. Our results were concurrent with other studies in international literature, indicating that estimations for eutrophic and mesotrophic lakes are more accurate than oligotrophic, owing to the lack of suspended particles that are detectable by satellite sensors. Nevertheless, although those predictive models, developed and applied to Trichonis oligotrophic lake are less accurate, may still be useful indicators of its water quality deterioration.

Keywords: landsat 8, oligotrophic lake, remote sensing, water quality

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Authors: Arij Yehya, Suhaila Ghuloum, Abdlmoneim Abdulhakam, Azza Al-Mujalli, Mark Opler, Samer Hammoudeh, Yahya Hani, Sundus Mari, Reem Elsherbiny, Ziyad Mahfoud, Hassen Al-Amin

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Introduction: The Positive and Negative Syndrome Scale (PANSS) is a valid instrument developed by Kay and colleagues6 to assess symptoms of patients with schizophrenia. It consists of 30 items that factor the symptoms into three subscales: positive, negative and general psychopathology. This scale has been translated and validated in several languages. Objective: This study aims to determine the validity and psychometric properties of the Arabic version of the PANSS. Methods: A standardized translation and cultural adaptation method was adopted. Patients diagnosed with schizophrenia (n=98), according to psychiatrist’s diagnosis based on DSM-IV criteria, were recruited from the Psychiatry Department at Rumailah Hospital, Qatar. A first rater confirmed the diagnosis using the Arabic version of Mini International Neuropsychiatric Interview (MINI 6). A second and independent rater-administered the Arabic version of PANSS. Also, a control group (n=101), with no history of psychiatric disorder was recruited from the family and friends of the patients and from primary health care centers in Qatar. Results: There were more males than females in our sample of patients with schizophrenia (68.9% and 31.6%, respectively). On the other hand, in the control group the number of females outweighed that of males (58.4% and 41.6% respectively). The scale had a good internal consistency with Cronbach’s alpha 0.91. There was a significant difference between the scores on the three subscales of the PANSS. Patients with schizophrenia scored significantly higher (p<.0001) than the control subjects on subscales for positive symptoms 20.01(SD=7.21) and 7.30(SD=1.38), negative symptoms 18.89(SD=8.88) and 7.37(SD=2.38) and general psychopathology 34.41 (SD=11.56) and 16.93 (SD=3.93), respectively. Factor analysis and ROC curve were carried out to further test the psychometrics of the scale. Conclusions: The Arabic version of PANSS is a reliable and valid tool to assess both positive and negative symptoms of patients with schizophrenia in a balanced manner. In addition to providing the Arab population with a standardized tool to monitor symptoms of schizophrenia, this version provides a gateway to compare the prevalence of positive and negative symptoms in the Arab world which can be compared to others done elsewhere.

Keywords: Arabic version, assessment, diagnosis, schizophrenia, validation

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Authors: Marcelo Hayashi, João Lima, Bruno Chieregatti, Ernani Volpe

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The adjoint method has been used as a successful tool to obtain sensitivity gradients in aerodynamic design and optimisation for many years. This work presents an alternative approach to the continuous adjoint formulation that enables one to compute gradients of a given measure of merit with respect to control parameters other than those pertaining to geometry. The procedure is then applied to the steady 2–D compressible Euler and incompressible Navier–Stokes flow equations. Finally, the results are compared with sensitivities obtained by finite differences and theoretical values for validation.

Keywords: adjoint method, aerodynamics, sensitivity theory, non-geometric sensitivities

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Authors: Joshua Kim Schimpf, Kyun Doo Kim, Jaseok Heo

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In this study, the droplet behavior of under horizontal stratified flow regime for air and water flow in horizontal pipe experiments from a 0.24 m, 0.095 m, and 0.0486 m size diameter pipe are examined. The effects of gravity, pipe diameter, and turbulent diffusion on droplet deposition are considered. Models for droplet entrainment and deposition are proposed that considers developing length. Validation for experimental data dedicated from the REGARD, CEA and Williams, University of Illinois, experiment were performed using SPACE (Safety and Performance Analysis Code for Nuclear Power Plants).

Keywords: droplet, entrainment, deposition, horizontal

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Authors: Dale Dzemydiene, Aurelija Burinskiene, Arunas Miliauskas, Kristina Ciziuniene

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Data flow and the purpose of reporting the data are different and dependent on business needs. Different parameters are reported and transferred regularly during freight delivery. This business practices form the dataset constructed for each time point and contain all required information for freight moving decisions. As a significant amount of these data is used for various purposes, an integrating methodological approach must be developed to respond to the indicated problem. The proposed methodology contains several steps: (1) collecting context data sets and data validation; (2) multi-objective analysis for optimizing freight transfer services. For data validation, the study involves Grubbs outliers analysis, particularly for data cleaning and the identification of statistical significance of data reporting event cases. The Grubbs test is often used as it measures one external value at a time exceeding the boundaries of standard normal distribution. In the study area, the test was not widely applied by authors, except when the Grubbs test for outlier detection was used to identify outsiders in fuel consumption data. In the study, the authors applied the method with a confidence level of 99%. For the multi-objective analysis, the authors would like to select the forms of construction of the genetic algorithms, which have more possibilities to extract the best solution. For freight delivery management, the schemas of genetic algorithms' structure are used as a more effective technique. Due to that, the adaptable genetic algorithm is applied for the description of choosing process of the effective transportation corridor. In this study, the multi-objective genetic algorithm methods are used to optimize the data evaluation and select the appropriate transport corridor. The authors suggest a methodology for the multi-objective analysis, which evaluates collected context data sets and uses this evaluation to determine a delivery corridor for freight transfer service in the multi-modal transportation network. In the multi-objective analysis, authors include safety components, the number of accidents a year, and freight delivery time in the multi-modal transportation network. The proposed methodology has practical value in the management of multi-modal transportation processes.

Keywords: multi-objective, analysis, data flow, freight delivery, methodology

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Authors: Parth Prajapati, A. R. Srinivas

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The purpose of this paper is to validate the concept of the Lotus Mechanism using Computer Aided Engineering (CAE) tools considering the statics and dynamics through actual time dependence involving inertial forces acting on the mechanism joints. For a 1.2 m mirror made of hexagonal segments, with simple harnesses and three-point supports, the maximum diameter is 400 mm, minimum segment base thickness is 1.5 mm, and maximum rib height is considered as 12 mm. Manufacturing challenges are explored for the segments using manufacturing research and development approaches to enable use of large lightweight mirrors required for the future space system.

Keywords: dynamics, manufacturing, reflectors, segmentation, statics

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Authors: Arcady Ponosov

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A novel mathematical approach is suggested, which facilitates a compressed representation and efficient validation of parameter-rich ordinary differential equation models describing the dynamics of complex, especially biology-related, systems and which is based on identification of the system's latent variables. In particular, an efficient parameter estimation method for the compressed non-linear dynamical systems is developed. The method is applied to the so-called 'power-law systems' being non-linear differential equations typically used in Biochemical System Theory.

Keywords: generalized law of mass action, metamodels, principal components, synergetic systems

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Authors: Lewis J. Woollin, Robert I. Davidson, Paul Watson, Philip J. Hyde

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Introduction: In-vitro testing of arthroplasty materials is of paramount importance when ensuring that they can withstand the performance requirements encountered in-vivo. One common machine used for in-vitro testing is a pin-on-plate tribometer, an early stage screening device that generates data on the wear characteristics of arthroplasty bearing materials. These devices test vertically loaded rotating cylindrical pins acting against reciprocating plates, representing the bearing surfaces. In this study, a pin-on-plate machine has been developed that provides several improvements over current technology, thereby progressing arthroplasty bearing research. Historically, pin-on-plate tribometers have been used to investigate the performance of arthroplasty bearing materials under conditions commonly encountered during a standard gait cycle; nominal operating pressures of 2-6 MPa and an operating frequency of 1 Hz are typical. There has been increased interest in using pin-on-plate machines to test more representative in-vivo conditions, due to the drive to test 'beyond compliance', as well as their testing speed and economic advantages over hip simulators. Current pin-on-plate machines do not accommodate the increased performance requirements associated with more extreme kinematic conditions, therefore a next-generation pin-on-plate tribometer has been developed to bridge the gap between current technology and future research requirements. Methodology: The design was driven by several physiologically relevant requirements. Firstly, an increased loading capacity was essential to replicate the peak pressures that occur in the natural hip joint during running and chair-rising, as well as increasing the understanding of wear rates in obese patients. Secondly, the introduction of mid-cycle load variation was of paramount importance, as this allows for an approximation of the loads present in a gait cycle to be applied and to test the fatigue properties of materials. Finally, the rig must be validated against previous-generation pin-on-plate and arthroplasty wear data. Results: The resulting machine is a twelve station device that is split into three sets of four stations, providing an increased testing capacity compared to most current pin-on-plate tribometers. The loading of the pins is generated using a pneumatic system, which can produce contact pressures of up to 201 MPa on a 3.2 mm² round pin face. This greatly exceeds currently achievable contact pressures in literature and opens new research avenues such as testing rim wear of mal-positioned hip implants. Additionally, the contact pressure of each set can be changed independently of the others, allowing multiple loading conditions to be tested simultaneously. Using pneumatics also allows the applied pressure to be switched ON/OFF mid-cycle, another feature not currently reported elsewhere, which allows for investigation into intermittent loading and material fatigue. The device is currently undergoing a series of validation tests using Ultra-High-Molecular-Weight-Polyethylene pins and 316L Stainless Steel Plates (polished to a Ra < 0.05 µm). The operating pressures will be between 2-6 MPa, operating at 1 Hz, allowing for validation of the machine against results reported previously in the literature. The successful production of this next-generation pin-on-plate tribometer will, following its validation, unlock multiple previously unavailable research avenues.

Keywords: arthroplasty, mechanical design, pin-on-plate, total joint replacement, wear testing

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Authors: Jacques Cuenca, Claudio Colangeli, Agnieszka Mroz, Karl Janssens, Gunther Riexinger, Antonio D'Antuono, Giuseppe Pandarese, Milena Martarelli, Gian Marco Revel, Carlos Barcena Martin

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This study presents an on-site acoustic inspection methodology for quality and performance evaluation of building components. The work focuses on global and detailed sound source localisation, by successively performing acoustic beamforming and sound intensity measurements. A portable experimental setup is developed, consisting of an omnidirectional broadband acoustic source and a microphone array and sound intensity probe. Three main acoustic indicators are of interest, namely the sound pressure distribution on the surface of components such as walls, windows and junctions, the three-dimensional sound intensity field in the vicinity of junctions, and the sound transmission loss of partitions. The measurement data is post-processed and converted into a three-dimensional numerical model of the acoustic indicators with the help of the simultaneously acquired geolocation information. The three-dimensional acoustic indicators are then integrated into an augmented reality platform superimposing them onto a real-time visualisation of the spatial environment. The methodology thus enables a measurement-supported inspection process of buildings and the correction of errors during construction and refurbishment. Two experimental validation cases are shown. The first consists of a laboratory measurement on a full-scale mockup of a room, featuring a prefabricated panel. The latter is installed with controlled defects such as lack of insulation and joint sealing material. It is demonstrated that the combined acoustic and augmented reality tool is capable of identifying acoustic leakages from the building defects and assist in correcting them. The second validation case is performed on a prefabricated room at a near-completion stage in the factory. With the help of the measurements and visualisation tools, the homogeneity of the partition installation is evaluated and leakages from junctions and doors are identified. Furthermore, the integration of acoustic indicators together with thermal and geometrical indicators via the augmented reality platform is shown.

Keywords: acoustic inspection, prefabricated building components, augmented reality, sound source localization

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Authors: Bello Abdullahi, Yahaya M. Ibrahim, Ahmed D. Ibrahim, Kabir Bala

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Public sector tendering has traditionally been conducted using manual paper-based processes which are known to be inefficient, less transparent and more prone to manipulations and errors. The advent of the Internet and the World Wide Web has led to the development of numerous e-Tendering systems that addressed some of the problems associated with the manual paper-based tendering system. However, most of these systems rarely support the evaluation of tenders and where they do it is mostly based on the single decision maker which is not suitable in public sector tendering, where for the sake of objectivity, transparency, and fairness, it is required that the evaluation is conducted through a tender evaluation committee. Currently, in Nigeria, the public tendering process in general and the evaluation of tenders, in particular, are largely conducted using manual paper-based processes. Automating these manual-based processes to digital-based processes can help in enhancing the proficiency of public sector tendering in Nigeria. This paper is part of a larger study to develop an electronic tendering system that supports the whole tendering lifecycle based on Nigerian procurement law. Specifically, this paper presents the design and implementation of part of the system that supports group evaluation of tenders based on a technique called fuzzy multi-attributes group decision making. The system was developed using Object-Oriented methodologies and Unified Modelling Language and hypothetically applied in the evaluation of technical and financial proposals submitted by bidders. The system was validated by professionals with extensive experiences in public sector procurement. The results of the validation showed that the system called NPS-eTender has an average rating of 74% with respect to correct and accurate modelling of the existing manual tendering domain and an average rating of 67.6% with respect to its potential to enhance the proficiency of public sector tendering in Nigeria. Thus, based on the results of the validation, the automation of the evaluation process to support tender evaluation committee is achievable and can lead to a more proficient public sector tendering system.

Keywords: e-Tendering, e-Procurement, group decision making, tender evaluation, tender evaluation committee, UML, object-oriented methodologies, system development

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Authors: Abdelhadi Lotfi, Abdelkader Benyettou

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In this work, a training algorithm for probabilistic neural networks (PNN) is presented. The algorithm addresses one of the major drawbacks of PNN, which is the size of the hidden layer in the network. By using a cross-validation training algorithm, the number of hidden neurons is shrunk to a smaller number consisting of the most representative samples of the training set. This is done without affecting the overall architecture of the network. Performance of the network is compared against performance of standard PNN for different databases from the UCI database repository. Results show an important gain in network size and performance.

Keywords: classification, probabilistic neural networks, network optimization, pattern recognition

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Authors: Yusuf Yilmaz, Gamze Gediz Ilis

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Gas Nitrogen where has a boiling point of -189.52oC at atmospheric pressure widely used in the industry. Nitrogen that used in the industry should be transported in liquid form to the plant area. Ambient air vaporizer (AAV) generally used for vaporization of cryogenic gases such as liquid nitrogen (LN2), liquid oxygen (LOX), liquid natural gas (LNG), and liquid argon (LAR) etc. AAV is a group of star-shaped fin vaporizer. The design and the effect of the shape of fins of the vaporizer is one of the most important criteria for the performance of the vaporizer. In this study, the performance of AAV working with liquid nitrogen was analyzed numerically in a star-shaped aluminum finned pipe. The numerical analysis is performed in order to investigate the heat capacity of the vaporizer per meter pipe length. By this way, the vaporizer capacity can be predicted for the industrial applications. In order to achieve the validation of the numerical solution, the experimental setup is constructed. The setup includes a liquid nitrogen tank with a pressure of 9 bar. The star-shaped aluminum finned tube vaporizer is connected to the LN2 tank. The inlet and the outlet pressure and temperatures of the LN2 of the vaporizer are measured. The mass flow rate of the LN2 is also measured and collected. The comparison of the numerical solution is performed by these measured data. The ambient conditions of the experiment are given as boundary conditions to the numerical model. The surface tension and contact angle have a significant effect on the boiling of liquid nitrogen. Average heat transfer coefficient including convective and nucleated boiling components should be obtained for liquid nitrogen saturated flow boiling in the finned tube. Fluent CFD module is used to simulate the numerical solution. The turbulent k-ε model is taken to simulate the liquid nitrogen flow. The phase change is simulated by using the evaporation-condensation approach used with user-defined functions (UDF). The comparison of the numerical and experimental results will be shared in this study. Besides, the performance capacity of the star-shaped finned pipe vaporizer will be calculated in this study. Based on this numerical analysis, the performance of the vaporizer per unit length can be predicted for the industrial applications and the suitable pipe length of the vaporizer can be found for the special cases.

Keywords: liquid nitrogen, numerical modeling, two-phase flow, cryogenics

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Authors: Rafika Bibi, Khaled Khaladi, Hind Mokran, Mohamed Salah Boukhechem

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A high performance liquid chromatographic method with ultraviolet detection at 264nm was developed and validate for quantitative determination and separation of cefazolin and cefotaxime in urine, the mobile phase consisted of acetonitrile and phosphate buffer pH4,2(15 :85) (v/v) pumped through ODB 250× 4,6 mm, 5um column at a flow rate of 1ml/min, loop of 20ul. In this condition, the validation of this technique showed that it is linear in a range of 0,01 to 10ug/ml with a good correlation coefficient ( R>0,9997), retention time of cefotaxime, cefazolin was 9.0, 10.1 respectively, the statistical evaluation of the method was examined by means of within day (n=6) and day to day (n=5) and was found to be satisfactory with high accuracy and precision.

Keywords: cefazolin, cefotaxime, HPLC, bioscience, biochemistry, pharmaceutical

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Authors: Swati Tomar, Sunil Kumar Gupta

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Anammox is a promising and cost effective alternative to conventional treatment systems that facilitates direct oxidation of ammonium nitrogen under anaerobic conditions with nitrite as an electron acceptor without addition of any external carbon sources. The present study investigates the process kinetics of laboratory scale anammox hybrid reactor (AHR) which combines the dual advantages of attached and suspended growth. The performance & behaviour of AHR was studied under varying hydraulic retention time (HRTs) and nitrogen loading rate (NLRs). The experimental unit consisted of 4 numbers of 5L capacity anammox hybrid reactor inoculated with mixed seed culture containing anoxic and activated sludge. Pseudo steady state (PSS) ammonium and nitrite removal efficiencies of 90.6% and 95.6%, respectively, were achieved during acclimation phase. After establishment of PSS, the performance of AHR was monitored at seven different HRTs of 3.0, 2.5, 2.0, 1.5, 1.0, 0.5 and 0.25 d with increasing NLR from 0.4 to 4.8 kg N/m3d. The results showed that with increase in NLR and decrease in HRT (3.0 to 0.25 d), AHR registered appreciable decline in nitrogen removal efficiency from 92.9% to 67.4 %, respectively. The HRT of 2.0 d was considered optimal to achieve substantial nitrogen removal of 89%, because on further decrease in HRT below 1.5 days, remarkable decline in the values of nitrogen removal efficiency were observed. Analysis of data indicated that attached growth system contributes an additional 15.4 % ammonium removal and reduced the sludge washout rate (additional 29% reduction). This enhanced performance may be attributed to 25% increase in sludge retention time due to the attached growth media. Three kinetic models, namely, first order, Monod and Modified Stover-Kincannon model were applied to assess the substrate removal kinetics of nitrogen removal in AHR. Validation of the models were carried out by comparing experimental set of data with the predicted values obtained from the respective models. For substrate removal kinetics, model validation revealed that Modified Stover-Kincannon is most precise (R2=0.943) and can be suitably applied to predict the kinetics of nitrogen removal in AHR. Lawrence and McCarty model described the kinetics of bacterial growth. The predicted value of yield coefficient and decay constant were in line with the experimentally observed values.

Keywords: anammox, kinetics, modelling, nitrogen removal, sludge wash out rate, AHR

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Authors: Sultan Mahmud, Qamar Zaman, Travis Esau, Young Chang

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Strawberry powdery mildew (PM) is a serious disease that has a significant impact on strawberry production. Field scouting is still a major way to find PM disease, which is not only labor intensive but also almost impossible to monitor disease severity. To reduce the loss caused by PM disease and achieve faster automatic detection of the disease, this paper proposes an approach for detection of the disease, based on image texture and classified with support vector machines (SVMs) and k-nearest neighbors (kNNs). The methodology of the proposed study is based on image processing which is composed of five main steps including image acquisition, pre-processing, segmentation, features extraction and classification. Two strawberry fields were used in this study. Images of healthy leaves and leaves infected with PM (Sphaerotheca macularis) disease under artificial cloud lighting condition. Colour thresholding was utilized to segment all images before textural analysis. Colour co-occurrence matrix (CCM) was introduced for extraction of textural features. Forty textural features, related to a physiological parameter of leaves were extracted from CCM of National television system committee (NTSC) luminance, hue, saturation and intensity (HSI) images. The normalized feature data were utilized for training and validation, respectively, using developed classifiers. The classifiers have experimented with internal, external and cross-validations. The best classifier was selected based on their performance and accuracy. Experimental results suggested that SVMs classifier showed 98.33%, 85.33%, 87.33%, 93.33% and 95.0% of accuracy on internal, external-I, external-II, 4-fold cross and 5-fold cross-validation, respectively. Whereas, kNNs results represented 90.0%, 72.00%, 74.66%, 89.33% and 90.3% of classification accuracy, respectively. The outcome of this study demonstrated that SVMs classified PM disease with a highest overall accuracy of 91.86% and 1.1211 seconds of processing time. Therefore, overall results concluded that the proposed study can significantly support an accurate and automatic identification and recognition of strawberry PM disease with SVMs classifier.

Keywords: powdery mildew, image processing, textural analysis, color co-occurrence matrix, support vector machines, k-nearest neighbors

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Authors: Satya P. Dubey, Hrushikesh A Abhyankar, Veronica Marchante, James L. Brighton, Björn Bergmann

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Aims: To develop a mathematical model that simulates the ROP of PLA taking into account the effect of alternative energy to be implemented in a continuous reactive extrusion production process of PLA. Introduction: The production of large amount of waste is one of the major challenges at the present time, and polymers represent 70% of global waste. PLA has emerged as a promising polymer as it is compostable, biodegradable thermoplastic polymer made from renewable sources. However, the main limitation for the application of PLA is the traces of toxic metal catalyst in the final product. Thus, a safe and efficient production process needs to be developed to avoid the potential hazards and toxicity. It has been found that alternative energy sources (LASER, ultrasounds, microwaves) could be a prominent option to facilitate the ROP of PLA via continuous reactive extrusion. This process may result in complete extraction of the metal catalysts and facilitate less active organic catalysts. Methodology: Initial investigation were performed using the data available in literature for the reaction mechanism of ROP of PLA based on conventional metal catalyst stannous octoate. A mathematical model has been developed by considering significant parameters such as different initial concentration ratio of catalyst, co-catalyst and impurity. Effects of temperature variation and alternative energies have been implemented in the model. Results: The validation of the mathematical model has been made by using data from literature as well as actual experiments. Validation of the model including alternative energies is in progress based on experimental data for partners of the InnoREX project consortium. Conclusion: The model developed reproduces accurately the polymerisation reaction when applying alternative energy. Alternative energies have a great positive effect to increase the conversion and molecular weight of the PLA. This model could be very useful tool to complement Ludovic® software to predict the large scale production process when using reactive extrusion.

Keywords: polymer, poly-lactic acid (PLA), ring opening polymerization (ROP), metal-catalyst, bio-degradable, renewable source, alternative energy (AE)

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Authors: Zahir Bakiri, Saci Nacefa

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In this paper a comparison in made between two models, with and without dispersion term, and focused on the characterization of the movement of the sludge blanket in the secondary sedimentation tank using the solid flux theory and the velocity settling. This allowed us develop a one-dimensional models, with and without dispersion based on a thorough experimental study carried out in situ and the application of online data which are the mass load flow, transfer concentration, and influent characteristic. On the other hand, in the proposed model, the new settling velocity law (double-exponential function) used is based on the Vesilind function.

Keywords: wastewater, activated sludge, sedimentation, settling velocity, settling models

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Authors: Jan Barak, Karel Frana, Joerg Stiller

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This work presents results of moist air condensation in heat exchanger. It describes theoretical knowledge and definition of moist air. Model with geometry of square canal was created for better understanding and post processing of condensation phenomena. Different approaches were examined on this model to find suitable software and model. Obtained knowledge was applied to geometry of real heat exchanger and results from experiment were compared with numerical results. One of the goals is to solve this issue without creating any user defined function in the applied code. It also contains summary of knowledge and outlook for future work.

Keywords: condensation, exchanger, experiment, validation

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Authors: S. Jigna, K. Nanda Kumar, T. Anna

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Being a major contributor to the Indian economy and a critical facilitator for the country’s digital India vision, the Indian telecommunications industry is also a major source of employment for the country. Since the last few years, the Indian telecommunication service providers (TSPs), however, are facing business challenges related to increasing competition, losses, debts, and decreasing revenue. The strategic use of digital technologies for a successful digital transformation has the potential to equip organizations to meet these business challenges. Despite an increased focus on digital transformation, the telecom service providers globally, including Indian TSPs, have seen limited success so far. The purpose of this research was thus to identify the factors that are critical for the digital transformation and to what extent they influence the successful digital transformation of the Indian TSPs. The literature review of more than 300 digital transformation-related articles, mostly from 2013-2019, demonstrated a lack of an empirical model consisting of factors for the successful digital transformation of the TSPs. This study theorizes a research framework grounded in multiple theories, and a research model consisting of 7 constructs that may be influencing business success during the digital transformation of the organization was proposed. The questionnaire survey of senior managers in the Indian telecommunications industry was seeking to validate the research model. Based on 294 survey responses, the validation of the Structural equation model using the statistical tool ADANCO 2.1.1 was found to be robust. Results indicate that Digital Capabilities, Digital Strategy, and Corporate Level Data Strategy in that order has a strong influence on the successful Business Performance, followed by IT Function Transformation, Digital Innovation, and Transformation Management respectively. Even though Digital Organization did not have a direct significance on Business Performance outcomes, it had a strong influence on IT Function Transformation, thus affecting the Business Performance outcomes indirectly. Amongst numerous practical and theoretical contributions of the study, the main contribution for the Indian TSPs is a validated reference for prioritizing the transformation initiatives in their strategic roadmap. Also, the main contribution to the theory is the possibility to use the research framework artifact of the present research for quantitative validation in different industries and geographies.

Keywords: corporate level data strategy, digital capabilities, digital innovation, digital strategy

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Authors: Jonathan Gong

Abstract:

Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: Yiannis Koumpouros, Effie Papageorgiou, Alexandra Karavasili, Foteini Koureta

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The objective of the present study was to develop a scale called PYTHEIA. The PYTHEIA is a self-reported measure for the assessment of rehabilitation and assistive robotics and other assistive technology devices. The development of PYTHEIA faced the absence of a valid instrument that can be used to evaluate the assistive robotic devices both as a whole, as well as any of their individual components or functionalities implemented. According to the results presented, PYTHEIA is a valid and reliable scale able to be applied to different target groups for the subjective evaluation of various assistive technology devices.

Keywords: rehabilitation, assistive technology, assistive robots, rehabilitation robots, scale, psychometric test, assessment, validation, user satisfaction

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Authors: Ruchika Goyal, Ashwani Kumar, Sandeep Jain

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The experimental data for anticancer potential of curcumin nanoparticles was calculated by means of eclectic data. The optimal descriptors were examined using Monte Carlo method based CORAL SEA software. The statistical quality of the model is following: n = 14, R² = 0.6809, Q² = 0.5943, s = 0.175, MAE = 0.114, F = 26 (sub-training set), n =5, R²= 0.9529, Q² = 0.7982, s = 0.086, MAE = 0.068, F = 61, Av Rm² = 0.7601, ∆R²m = 0.0840, k = 0.9856 and kk = 1.0146 (test set) and n = 5, R² = 0.6075 (validation set). This data can be used to build predictive QSAR models for anticancer activity.

Keywords: anticancer potential, curcumin, model, nanoparticles, optimal descriptors, QSAR

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Authors: Enrique Barra, Abel Carril, Aldo Gordillo, Joaquin Salvachua, Juan Quemada

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Computers and multimedia equipment have improved a lot in the last years. They have reduced costs and size while at the same time has increased their capabilities. These improvements allowed us to design and implement a portable recording system that also integrates the teacher´s tablet PC to capture what he/she writes on the slides and all that happens in it. This paper explains this system in detail and the validation of the recordings that we did after using it to record all the lectures of a course in our university called “Communications Software”. The results show that pupils used the recordings for different purposes and consider them useful for a variety of things, especially after missing a lecture.

Keywords: recording system, capture dynamic presentations, lecture recording

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Authors: Shaan Khosla, Jon Krohn

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In a recruiting context, machine learning models are valuable for recommendations: to predict the best candidates for a vacancy, to match the best vacancies for a candidate, and compile a set of similar candidates for any given candidate. While useful to create these models, validating their accuracy in a recommendation context is difficult due to a sparsity of data. In this report, we use network graph data to generate useful representations for candidates and vacancies. We use candidates and vacancies as network nodes and designate a bi-directional link between them based on the candidate interviewing for the vacancy. After using node2vec, the embeddings are used to construct a validation dataset with a ranked order, which will help validate new recommender systems.

Keywords: AI, machine learning, NLP, recruiting

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