Search results for: models synthesis

Authors: K. Asanimoghadam, M. Salahi, A. Jamalian

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Data envelopment analysis is an approach to measure the efficiency of decision making units with multiple inputs and outputs. The structure of many decision making units also has decision-making subunits that are not considered in most data envelopment analysis models. Also, the inputs and outputs of the decision-making units usually are considered desirable, while in some real-world problems, the nature of some inputs or outputs are undesirable. In this thesis, we study the evaluation of the efficiency of two stage decision-making units, where some outputs are undesirable using two non-radial models, the SBM and the ASBM models. We formulate the nonlinear ASBM model as a second order cone optimization problem. Finally, we compare two models for both external and internal evaluation approaches for two real world example in the presence of undesirable outputs. The results show that, in both external and internal evaluations, the overall efficiency of ASBM model is greater than or equal to the overall efficiency value of the SBM model, and in internal evaluation, the ASBM model is more flexible than the SBM model.

Keywords: network DEA, conic optimization, undesirable output, SBM

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Authors: Shokrya Saleh A. Alshqaq, Abdullah Ali H. Ahmadini

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The Schwarz information criterion (SIC) is a popular tool for selecting the best variables in regression datasets. However, SIC is defined using an unbounded estimator, namely, the least-squares (LS), which is highly sensitive to outlying observations, especially bad leverage points. A method for robust variable selection based on SIC for linear regression models is thus needed. This study investigates the robustness properties of SIC by deriving its influence function and proposes a robust SIC based on the MM-estimation scale. The aim of this study is to produce a criterion that can effectively select accurate models in the presence of vertical outliers and high leverage points. The advantages of the proposed robust SIC is demonstrated through a simulation study and an analysis of a real dataset.

Keywords: influence function, robust variable selection, robust regression, Schwarz information criterion

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Authors: Daniel F. Sava, Anton Ficai, Bogdan S. Vasile, Georgeta Voicu, Ecaterina Andronescu

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The energy crisis and environmental contamination are very serious problems, therefore searching for better and sustainable renewable energy is a must. It is predicted that the global energy demand will double until 2050. Solar water splitting and photocatalysis are considered as one of the solutions to these issues. The use of oxide semiconductors for solar water splitting and photocatalysis started in 1972 with the experiments of Fujishima and Honda on TiO2 electrodes. Since then, the evolution of nanoscience and characterization methods leads to a better control of size, shape and properties of materials. Although the past decade advancements are astonishing, for these applications the properties have to be controlled at a much finer level, allowing the control of charge-carrier lives, energy level positions, charge trapping centers, etc. Graphene has attracted a lot of attention, since its discovery in 2004, due to the excellent electrical, optical, mechanical and thermal properties that it possesses. These properties make it an ideal support for photocatalysts, thus graphene composites with oxide semiconductors are of great interest. We present in this work the synthesis and characterization of graphene-related materials and oxide semiconductors and their different composites. These materials can be used in constructing devices for different applications (batteries, water splitting devices, solar cells, etc), thus showing their application flexibility. The synthesized materials are different morphologies and sizes of TiO2, ZnO and Fe2O3 that are obtained through hydrothermal, sol-gel methods and graphene oxide which is synthesized through a modified Hummer method and reduced with different agents. Graphene oxide and the reduced form could also be used as a single material for transparent conductive films. The obtained single materials and composites were characterized through several methods: XRD, SEM, TEM, IR spectroscopy, RAMAN, XPS and BET adsorption/desorption isotherms. From the results, we see the variation of the properties with the variation of synthesis parameters, size and morphology of the particles.

Keywords: composites, graphene, hydrothermal, renewable energy

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Authors: Rupan Das Chakraborty, Surendra K. Martha

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Hard carbons (HCs) are extensively used as anode materials for sodium-ion batteries due to their availability, low cost, and ease of synthesis. It possesses the ability to store Na ion between stacked sp2 carbon layers and micropores. In this work, hard carbons are synthesized from different concentrations (0.5M to 5M) of dextrose solutions by hydrothermal synthesis followed by high-temperature calcination at 1100 ⁰C in an inert atmosphere. Dextrose has been chosen as a precursor material as it is a eco-friendly and renewable source. Among all hard carbon derived from different concentrations of dextrose solutions, hard carbon derived from 3M dextrose solution delivers superior electrochemical performance compared to other hard carbons. Hard carbon derived from 3M dextrose solution (Dextrose derived Hard Carbon-3M) provides an initial reversible capacity of 257 mAh g-1 with a capacity retention of 83 % at the end of 100 cycles at 30 mA g-1). The carbons obtained from different dextrose concentration show very similar Cyclic Voltammetry and chargedischarging behavior at a scan rate of 0.05 mV s-1 the Cyclic Voltammetry curve indicate that solvent reduction and the solid electrolyte interface (SEI) formation start at E < 1.2 V (vs Na/Na+). Among all 3M dextrose derived electrode indicate as a promising anode material for Sodium-ion batteries (SIBs).

Keywords: dextrose derived hard carbon, anode, sodium-ion battery, electrochemical performance

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Authors: Angie Quevedo, Juan Bussi, Nestor Tancredi, Juan Fajardo-Díaz, Florentino López-Urías, Emilio Muñóz-Sandoval

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In this work, we study the carbon nanotubes (CNTs) formation by catalytic chemical vapor deposition (CCVD) over a catalyst with 20 % of Ni supported over La₂Zr₂O₇ (Ni20LZO). The high C solubility of Ni made it one of the most used in CNTs synthesis. Nevertheless, Ni presents also sintering and coalescence at high temperature. These troubles can be reduced by choosing a suitable support. We propose La₂Zr₂O₇ as for this matter since the incorporation of Ni by co-precipitation and calcination at 900 °C allows a good dispersion and interaction of the active metal (in the oxidized form, NiO) with this support. The CCVD was performed using 1 g of Ni20LZO at 950 °C during 30 min in Ar:H₂ atmosphere (2.5 L/min). The precursor, benzylamine, was added by a nebulizer-sprayer. X ray diffraction study shows the phase separation of NiO and La₂Zr₂O₇ after the calcination and the reduction to Ni after the synthesis. Raman spectra show D and G bands with a ID/IG ratio of 0.75. Elemental study verifies the incorporation of 1% of N. Thermogravimetric analysis shows the oxidation process start at around 450 °C. Future studies will determine the application potential of the samples.

Keywords: N doped carbon nanotubes, catalytic chemical vapor deposition, nickel catalyst, bimetallic oxide

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Authors: Sahar Shahsavaripour Ghazanfarpour

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As our daily life depends on quality of built services by systems and using devices in our environment; so education and model of software′s quality will be so important. By daily growth in software′s systems and using them so much, progressing process and requirements′ evaluation in primary level of progress especially architecture level in software get more important. Modern driver architecture changes an in dependent model of a level into some specific models that their purpose is reducing number of software changes into an executive model. Process of designing software engineering is mid-automated. The needed quality attribute in designing architecture and quality attribute in representation are in architecture models. The main problem is the relationship between needs, and elements in some aspect with implicit models and input sources in process. It’s because there is no detection ability. The MART profile is use to describe real-time properties and perform plat form modeling.

Keywords: MDA, DW, OMG, UML, AKB, software architecture, ontology, evaluation

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Authors: Edgar Willy Rimarachin Valderrama, Eduardo Rojas Parra

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Thermal energy from solar radiation is widely present in power plants, food drying, chemical reactors, heating and cooling systems, water treatment processes, hydrogen production, and others. In the case of power plants, one of the technologies available to transform solar energy into thermal energy is by solar rotary kilns where a bed of granular matter is heated through concentrated radiation obtained from an arrangement of heliostats. Numerical modeling is a useful approach to study the behavior of granular beds in solar rotary kilns. This technique, once validated with small-scale experiments, can be used to simulate large-scale processes for industrial applications. This study gives a comprehensive classification of numerical models used to simulate the movement and heat transfer for beds of granular media within solar rotary furnaces. In general, there exist three categories of models: 1) continuum, 2) discrete, and 3) multiphysics modeling. The continuum modeling considers zero-dimensional, one-dimensional and fluid-like models. On the other hand, the discrete element models compute the movement of each particle of the bed individually. In this kind of modeling, the heat transfer acts during contacts, which can occur by solid-solid and solid-gas-solid conduction. Finally, the multiphysics approach considers discrete elements to simulate grains and a continuous modeling to simulate the fluid around particles. This classification allows to compare the advantages and disadvantages for each kind of model in terms of accuracy, computational cost and implementation.

Keywords: granular beds, numerical models, rotary kilns, solar thermal applications

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Authors: Piotr Fiszeder, Marcin Fałdziński, Peter Molnár

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The dynamic conditional correlation model of Engle (2002) is one of the most popular multivariate volatility models. However, this model is based solely on closing prices. It has been documented in the literature that the high and low price of the day can be used in an efficient volatility estimation. We, therefore, suggest a model which incorporates high and low prices into the dynamic conditional correlation framework. Empirical evaluation of this model is conducted on three datasets: currencies, stocks, and commodity exchange-traded funds. The utilisation of realized variances and covariances as proxies for true variances and covariances allows us to reach a strong conclusion that our model outperforms not only the standard dynamic conditional correlation model but also a competing range-based dynamic conditional correlation model.

Keywords: volatility, DCC model, high and low prices, range-based models, covariance forecasting

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Authors: Cinthia Campoverde, Mateo Benavidez, Victor Arias, Milton Torres

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This article evaluates and compares three propagation models: the Okumura-Hata model, the Ericsson 9999 model, and the SUI model. The inclusion of vegetation attenuation in the area is also taken into account. These mathematical models aim to predict the power loss between a transmitting antenna (Tx) and a receiving antenna (Rx). The study was conducted in the open areas of the Livestock Department at the Escuela Superior Politécnica de Chimborazo (ESPOCH) University, located in the city of Riobamba, Ecuador. The necessary parameters for each model were calculated, considering LTE technology. The transmitting antenna belongs to the mobile phone company ”TUENTI” in Band 2, operating at a frequency of 1940 MHz. The reception power data in the area were empirically measured using the ”Network Cell Info” application. A total of 170 samples were collected, distributed across 19 radius, forming concentric circles around the transmitting antenna. The results demonstrate that the Okumura Hata urban model provides the best fit to the measured data.

Keywords: propagation models, reception power, LTE, power losses, correction factor

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Authors: Ahmed Fradi

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Nowadays, recent technological advances in the acquisition, modeling, and processing of three-dimensional (3D) objects data lead to the creation of models stored in huge databases, which are used in various domains such as computer vision, augmented reality, game industry, medicine, CAD (Computer-aided design), 3D printing etc. On the other hand, the industry is currently benefiting from powerful modeling tools enabling designers to easily and quickly produce 3D models. The great ease of acquisition and modeling of 3D objects make possible to create large 3D models databases, then, it becomes difficult to navigate them. Therefore, the indexing of 3D objects appears as a necessary and promising solution to manage this type of data, to extract model information, retrieve an existing model or calculate similarity between 3D objects. The objective of the proposed research is to develop a framework allowing easy and fast access to 3D objects in a CAD models database with specific indexing algorithm to find objects similar to a reference model. Our main objectives are to study existing methods of similarity calculation of 3D objects (essentially shape-based methods) by specifying the characteristics of each method as well as the difference between them, and then we will propose a new approach for indexing and comparing 3D models, which is suitable for our case study and which is based on some previously studied methods. Our proposed approach is finally illustrated by an implementation, and evaluated in a professional context.

Keywords: CAD, 3D object retrieval, shape based retrieval, similarity calculation

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Authors: Dmitry V. Fomichev, Vladimir V. Solonin

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This paper presents the findings from a numerical simulation of the flow in 37-rod fuel assembly models spaced by a double-wire trapezoidal wrapping as applied to the BREST-OD-300 experimental nuclear reactor. Data on a high static pressure distribution within the models, and equations for determining the fuel bundle flow friction factors have been obtained. Recommendations are provided on using the closing turbulence models available in the ANSYS Fluent. A comparative analysis has been performed against the existing empirical equations for determining the flow friction factors. The calculated and experimental data fit has been shown. An analysis into the experimental data and results of the numerical simulation of the BREST-OD-300 fuel rod assembly hydrodynamic performance are presented.

Keywords: BREST-OD-300, ware-spaces, fuel assembly, computation fluid dynamics

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Authors: Tariq Mansoor, Lubos Hes, Vladimir Bajzik

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Socks comfort has great significance in our daily life. This significance even increased when we have undergone a work of low or high activity. It causes the sweating of our body with different rates. In this study, plain socks with differential fibre composition were wetted to saturated level. Then after successive intervals of conditioning, these socks are characterized by thermal resistance in dry and wet states. Theoretical thermal resistance is predicted by using combined filling coefficients and thermal conductivity of wet polymers instead of dry polymer (fibre) in different models. By this modification, different mathematical models could predict thermal resistance at different moisture levels. Furthermore, predicted thermal resistance by different models has reasonable correlation range between (0.84 -0.98) with experimental results in both dry (lab conditions moisture) and wet states. "This work is supported by Technical University of Liberec under SGC-2019. Project number is 21314".

Keywords: thermal resistance, mathematical model, plain socks, moisture loss rate

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Authors: Khryslyn Araño, Dela Cruz, Michael Leo, Dela Pena, Eden May, Leslie Joy Diaz

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Montmorillonite (MMT) is a very abundant clay mineral and is versatile such that it can be chemically or physically altered by changing the ions between the sheets of its layered structure. This clay mineral can be prepared into functional nanoparticles that can be used as fillers in other nanomaterials such as nanofibers to achieve special properties. In this study, two types of iron-modified MMT, Iron-MMT (FeMMT) and Zero Valent Iron-MMT (ZVIMMT) were synthesized via ion exchange technique. The modified clay was incorporated in polymer nanofibers which were produced using a process called electrospinning. ICP analysis confirmed that clay modification was successful where there is an observed decrease in the concentration of Na and an increase in the concentration of Fe after ion exchange. XRD analysis also confirmed that modification took place because of the changes in the d-spacing of Na-MMT from 11.5 Å to 13.6 Å and 12.6 Å after synthesis of FeMMT and ZVIMMT, respectively. SEM images of the electrospun nanofibers revealed that the ZVIMMT-filled fibers have a smaller average diameter than the FeMMT-filled fibers because of the lower resistance of the suspensions of the former to the elongation force from the applied electric field. The resistance to the electric field was measured by getting the bulk voltage of the suspensions.

Keywords: electrospinning, nanofibers, montmorillonite, materials science

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Authors: Pooja Kumari, Anandkumar Tengli

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Aurora kinase enzyme, a protein on overexpression, leads to metastasis and is extremely important for women’s health in terms of prevention or treatment. While creating a targeted technique, the aim of the work is to design purine molecules that inhibit in aurora kinase enzyme and helps to suppress breast cancer. Purine molecules attached to an amino acid in DNA block protein synthesis or halt the replication and metastasis caused by the aurora kinase enzyme. Various protein related to the overexpression of aurora protein was docked with purine molecule using Biovia Drug Discovery, the perpetual software. Various parameters like X-ray crystallographic structure, presence of ligand, Ramachandran plot, resolution, etc., were taken into consideration for selecting the target protein. A higher negative binding scored molecule has been taken for simulation studies. According to the available research and computational analyses, purine compounds may be powerful enough to demonstrate a greater affinity for the aurora target. Despite being clinically effective now, purines were originally meant to fight breast cancer by inhibiting the aurora kinase enzyme. In in-silico studies, it is observed that purine compounds have a moderate to high potency compared to other molecules, and our research into the literature revealed that purine molecules have a lower risk of side effects. The research involves the design, synthesis, and identification of active purine molecules against breast cancer. Purines are structurally similar to the normal metabolites of adenine and guanine; hence interfere/compete with protein synthesis and suppress the abnormal proliferation of cells/tissues. As a result, purine target metastasis cells and stop the growth of kinase; purine derivatives bind with DNA and aurora protein which may stop the growth of protein or inhibits replication and stop metastasis of overexpressed aurora kinase enzyme.

Keywords: aurora kinases, in silico studies, medicinal chemistry, combination therapies, chronic cancer, clinical translation

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Authors: Ghada A. Alfattni

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Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates. 

Keywords: imbalanced datasets, SMOTE, machine learning, logistic regression, support vector machine, nearest neighbour

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Authors: V. Vargas-Alejo, L. E. Montero-Moguel

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Understanding the concept of function has been important in mathematics education for many years. In this study, the models built by a group of five business administration and accounting undergraduate students when carrying out a population growth activity are analyzed. The theoretical framework is the Models and Modeling Perspective. The results show how the students included tables, graphics, and algebraic representations in their models. Using technology was useful to interpret, describe, and predict the situation. The first model, the students built to describe the situation, was linear. After that, they modified and refined their ways of thinking; finally, they created exponential growth. Modeling the activity was useful to deep on mathematical concepts such as covariation, rate of change, and exponential function also to differentiate between linear and exponential growth.

Keywords: covariation reasoning, exponential function, modeling, representations

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Authors: Mayowa A. Sofowora, Seraphim D. Eyono Obono

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In recent times the level of advancement in electronics and manufacturing technologies for portable electronic devices, especially for mobile devices such as cell phones, smartphones, personal digital assistants and tablet computers is unprecedented. Mobile devices have become indispensable to individuals, and businesses all over the world. The high level of manufacturing and production of mobile devices has led to the rapid release of newer and sleeker models with new features and capabilities. However, these newer models therefore render older models obsolete, and this pushes people to frequently replace their devices. The drawback of such frequent replacements is that a large number of devices are disposed and they end up as e-waste. The fact that e-waste constitutes a major hazard to human health and to the environment is the motivation behind this study whose aim is to develop a model of possible factors that affects the dependability of mobile devices which in turn leads to the obsolescence of these devices.

Keywords: dependability, mobile devices, obsolescence, e-waste

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Authors: Alan S. Hoback

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Most people see human faces in car front and back ends because of the process of pareidolia. 96 people were surveyed to see how many of them saw a face in the vehicle styling. Participants were aged 18 to 72 years. 94% of the participants saw faces in the front-end design of production models. All participants that recognized faces indicated that most styles showed some degree of an angry expression. It was found that women were more likely to see faces in inanimate objects. However, with respect to whether women were more likely to perceive anger in the vehicle design, the results need further clarification. Survey responses were correlated to the design features of vehicles to determine what cues the respondents were likely looking at when responding. Whether the features looked anthropomorphic was key to anger perception. Features such as the headlights which could represent eyes and the air intake that could represent a mouth had high correlations to trends in scores. Results are compared among models, makers, by groupings of body styles classifications for the top 12 brands sold in the US, and by year for the top 20 models sold in the US in 2016. All of the top models sold increased in perception of an angry expression over the last 20 years or since the model was introduced, but the relative change varied by body style grouping.

Keywords: aggressive driving, face recognition, road rage, vehicle styling

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Authors: Stephanie Watson, Fabrice Stephenson, Conrad Pilditch, Carolyn Lundquist

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Spatial data detailing both the distribution and density of functionally important marine species are needed to inform management decisions. Species distribution models (SDMs) have proven helpful in this regard; however, models often focus only on species occurrences derived from spatially expansive datasets and lack the resolution and detail required to inform regional management decisions. Boosted regression trees (BRT) were used to produce high-resolution SDMs (250 m) at two spatial scales predicting probability of occurrence, abundance (count per sample unit), density (count per km2) and uncertainty for seven coastal seafloor taxa that vary in habitat usage and distribution to examine prediction differences and implications for coastal management. We investigated if small scale regionally focussed models (82,000 km2) can provide improved predictions compared to data-rich national scale models (4.2 million km2). We explored the variability in predictions across model type (occurrence vs abundance) and model scale to determine if specific taxa models or model types are more robust to geographical variability. National scale occurrence models correlated well with broad-scale environmental predictors, resulting in higher AUC (Area under the receiver operating curve) and deviance explained scores; however, they tended to overpredict in the coastal environment and lacked spatially differentiated detail for some taxa. Regional models had lower overall performance, but for some taxa, spatial predictions were more differentiated at a localised ecological scale. National density models were often spatially refined and highlighted areas of ecological relevance producing more useful outputs than regional-scale models. The utility of a two-scale approach aids the selection of the most optimal combination of models to create a spatially informative density model, as results contrasted for specific taxa between model type and scale. However, it is vital that robust predictions of occurrence and abundance are generated as inputs for the combined density model as areas that do not spatially align between models can be discarded. This study demonstrates the variability in SDM outputs created over different geographical scales and highlights implications and opportunities for managers utilising these tools for regional conservation, particularly in data-limited environments.

Keywords: Benthic ecology, spatial modelling, multi-scalar modelling, marine conservation.

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Authors: Érick Aragão Ribeiro, George André Pereira Thé, José Marques Soares

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Graphical Human Interface, also known as supervision software, are increasingly present in industrial processes supported by Supervisory Control and Data Acquisition (SCADA) systems and so it is evident the need for qualified developers. In order to make engineering students able to produce high quality supervision software, method for the development must be created. In this paper we propose model, based on the international standards ISO/IEC 25010 and ISO/IEC 25040, for the development of graphical human interface. When compared with to other methods through experiments, the model here presented leads to improved quality indexes, therefore help guiding the decisions of programmers. Results show the efficiency of the models and the contribution to student learning. Students assessed the training they have received and considered it satisfactory.

Keywords: software development models, software quality, supervision software, fuzzy logic

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Authors: Harsh Thakkar, Ganesh Iyer

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In this paper, we survey various user-centered or context-based biomedical health information retrieval systems. We present and discuss the performance of systems submitted in CLEF eHealth 2014 Task 3 for this purpose. We classify and focus on comparing the two most prevalent retrieval models in biomedical information retrieval namely: Language Model (LM) and Vector Space Model (VSM). We also report on the effectiveness of using external medical resources and ontologies like MeSH, Metamap, UMLS, etc. We observed that the LM based retrieval systems outperform VSM based systems on various fronts. From the results we conclude that the state-of-art system scores for MAP was 0.4146, P@10 was 0.7560 and NDCG@10 was 0.7445, respectively. All of these score were reported by systems built on language modeling approaches.

Keywords: clinical document retrieval, concept-based information retrieval, query expansion, language models, vector space models

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Authors: Mourad Ben Bennani

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Views of language and language learning have undergone a tremendous change over the last decades. Language is no longer seen as a set of structured rules. It is rather viewed as a tool of interaction and communication. This shift in views has resulted in change in viewing language learning, which gave birth to various approaches and methodologies of language teaching. Two of these approaches are content-based instruction and content and language integrated learning (CLIL). These are similar approaches which integrate content and foreign/second language learning through various methodologies and models as a result of different implementations around the world. This presentation deals with sociocultural view of CBI and CLIL. It also defines language and content as vital components of CBI and CLIL. Next it reviews the origins of CBI and the continuum perspectives and CLIL definitions and models featured in the literature. Finally it summarizes current aspects around research in program evaluation with a focus on the benefits and challenges of these innovative approaches for second language teaching.

Keywords: CBI, CLIL, CBI continuum, CLIL models

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Authors: Mabrouka Morri, Amel Soualmia, Philippe Belleudy

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Vegetation affects the mean and turbulent flow structure. It may increase flood risks and sediment transport. Therefore, it is important to develop analytical approaches for the bed shear stress on vegetated bed, to predict resistance caused by vegetation. In the recent years, experimental and numerical models have both been developed to model the effects of submerged vegetation on open-channel flow. In this paper, different analytic models are compared and tested using the criteria of deviation, to explore their capacity for predicting the mean velocity and select the suitable one that will be applied in real case of rivers. The comparison between the measured data in vegetated flume and simulated mean velocities indicated, a good performance, in the case of rigid vegetation, whereas, Huthoff model shows the best agreement with a high coefficient of determination (R2=80%) and the smallest error in the prediction of the average velocities.

Keywords: analytic models, comparison, mean velocity, vegetation

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Authors: Subir S. Rao

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Existing warehouse configurations use non-traditional aisle designs with a central P/D point in their models, which is mathematically simple but less practical. Many warehouses use multiple P/D points to avoid congestion for pickers, and different warehouses have different flow policies and infrastructure for using the P/D points. Many warehouses use multiple P/D points with non-traditional aisle designs in their analytical models. Standard warehouse models introduce one-sided multiple P/D points in a flying-V warehouse and minimize pick distance for a one-way travel between an active P/D point and a pick location with P/D points, assuming uniform flow rates. A simulation of the mathematical model generally uses four fixed configurations of P/D points which are on two different sides of the warehouse. It can be easily proved that if the source and destination P/D points are both chosen randomly, in a uniform way, then minimizing the one-way travel is the same as minimizing the two-way travel. Another warehouse configuration analytically models the warehouse for multiple one-sided P/D points while keeping the angle of the cross-aisles and picking aisles as a decision variable. The minimization of the one-way pick travel distance from the P/D point to the pick location by finding the optimal position/angle of the cross-aisle and picking aisle for warehouses having different numbers of multiple P/D points with variable flow rates is also one of the objectives. Most models of warehouses with multiple P/D points are one-way travel models and we extend these analytical models to minimize the two-way pick travel distance wherein the destination P/D is chosen optimally for the return route, which is not similar to minimizing the one-way travel. In most warehouse models, the return P/D is chosen randomly, but in our research, the return route P/D point is chosen optimally. Such warehouses are common in practice, where the flow rates at the P/D points are flexible and depend totally on the position of the picks. A good warehouse management system is efficient in consolidating orders over multiple P/D points in warehouses where the P/D is flexible in function. In the latter arrangement, pickers and shrink-wrap processes are not assigned to particular P/D points, which ultimately makes the P/D points more flexible and easy to use interchangeably for picking and deposits. The number of P/D points considered in this research uniformly increases from a single-central one to a maximum of each aisle symmetrically having a P/D point below it.

Keywords: non-traditional warehouse, V cross-aisle, multiple P/D point, pick travel distance

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Authors: Sanjay Bari, Vinod Ugale, Kamalkishor Patil

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Over the past several years the emergence of micro-organisms resistant to nearly all the class of antimicrobial agents has become a serious public health concern. In the present research, we report the synthesis and in-vitro antimicrobial activity of a new series of novel quinazolino-thiadiazoles 3 (a-j). The synthesized compounds were confirmed by melting point, IR, 1H-NMR, 13C NMR and Mass spectroscopy. In general, the results of the in-vitro antibacterial activity are encouraging, as out of 10 compounds tested, Compound 3f and 3i with a 4-chloro phenyl and 4-nitro phenyl at C-2 of thiadiazolyl of quinazolino-thiadiazoles, displayed the excellent antibacterial and antifungal activities against all the tested microorganisms (Bacterial and Fungal strain) with MIC values of 62.5 μg/mL. It is worth to mention that the combination of two biologically active moieties quinazoline and thiadiazole profoundly influences the biological activity. While evaluating the antimicrobial activity, it was observed that compounds having electron withdrawing groups on thiazole has shown profound activity in comparison to compounds having electron releasing groups. As a result of this study, it can be concluded that halogen substituent on thiazole ring increases antimicrobial activity. Possible improvements in the antimicrobial activity can be further achieved by slight modifications in the substituent’s and/or additional structural activity investigations to have good antimicrobial activity.

Keywords: antifungal, antimicrobial, quinazolino-thiazoles, synthesis

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Authors: Azadeh Jafari, Robert G. Owens

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In this study, a geometrical multiscale approach which means coupling together the 2-D Navier-Stokes equations, constitutive equations and 0-D lumped parameter models is investigated. A multiscale approach, suggest a natural way of coupling detailed local models (in the flow domain) with coarser models able to describe the dynamics over a large part or even the whole cardiovascular system at acceptable computational cost. In this study we introduce a new velocity correction scheme to decouple the velocity computation from the pressure one. To evaluate the capability of our new scheme, a comparison between the results obtained with Neumann outflow boundary conditions on the velocity and Dirichlet outflow boundary conditions on the pressure and those obtained using coupling with the lumped parameter model has been performed. Comprehensive studies have been done based on the sensitivity of numerical scheme to the initial conditions, elasticity and number of spectral modes. Improvement of the computational algorithm with stable convergence has been demonstrated for at least moderate Weissenberg number. We comment on mathematical properties of the reduced model, its limitations in yielding realistic and accurate numerical simulations, and its contribution to a better understanding of microvascular blood flow. We discuss the sophistication and reliability of multiscale models for computing correct boundary conditions at the outflow boundaries of a section of the cardiovascular system of interest. In this respect the geometrical multiscale approach can be regarded as a new method for solving a class of biofluids problems, whose application goes significantly beyond the one addressed in this work.

Keywords: geometrical multiscale models, haemorheology model, coupled 2-D navier-stokes 0-D lumped parameter modeling, computational fluid dynamics

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Authors: Zhihan Li

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Nowadays, most universities use the course enrollment system considering students’ registration orders. However, the students’ preference level to certain courses is also one important factor to consider. In this research, the possibility of applying a preference-first system has been discussed and analyzed compared to the order-first system. A bipartite graph is applied to resemble the relationship between students and courses they tend to register. With the graph set up, we apply Ford-Fulkerson (F.F.) Algorithm to maximize parings between two sets of nodes, in our case, students and courses. Two models are proposed in this paper: the one considered students’ order first, and the one considered students’ preference first. By comparing and contrasting the two models, we highlight the usability of models which potentially leads to better designs for school course registration systems.

Keywords: bipartite graph, Ford-Fulkerson (F.F.) algorithm, graph theory, maximum matching

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Authors: S. Curteanu, F. Leon, M. Gavrilescu, S. A. Floria

Abstract:

Optimization algorithms inspired from human behavior were applied in this approach, associated with neural networks models. The algorithms belong to human behaviors of learning and cooperation and human competitive behavior classes. For the first class, the main strategies include: random learning, individual learning, and social learning, and the selected algorithms are: simplified human learning optimization (SHLO), social learning optimization (SLO), and teaching-learning based optimization (TLBO). For the second class, the concept of learning is associated with competitiveness, and the selected algorithms are sports-inspired algorithms (with Football Game Algorithm, FGA and Volleyball Premier League, VPL) and Imperialist Competitive Algorithm (ICA). A real process, the synthesis of polyacrylamide-based multicomponent hydrogels, where some parameters are difficult to obtain experimentally, is considered as a case study. Reaction yield and swelling degree are predicted as a function of reaction conditions (acrylamide concentration, initiator concentration, crosslinking agent concentration, temperature, reaction time, and amount of inclusion polymer, which could be starch, poly(vinyl alcohol) or gelatin). The experimental results contain 175 data. Artificial neural networks are obtained in optimal form with biologically inspired algorithm; the optimization being perform at two level: structural and parametric. Feedforward neural networks with one or two hidden layers and no more than 25 neurons in intermediate layers were obtained with values of correlation coefficient in the validation phase over 0.90. The best results were obtained with TLBO algorithm, correlation coefficient being 0.94 for an MLP(6:9:20:2) – a feedforward neural network with two hidden layers and 9 and 20, respectively, intermediate neurons. Good results obtained prove the efficiency of the optimization algorithms. More than the good results, what is important in this approach is the simulation methodology, including neural networks and optimization biologically inspired algorithms, which provide satisfactory results. In addition, the methodology developed in this approach is general and has flexibility so that it can be easily adapted to other processes in association with different types of models.

Keywords: artificial neural networks, human behaviors of learning and cooperation, human competitive behavior, optimization algorithms

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Authors: Pradeep Lamichhane, Nima Pourali, E. V. Rebrov, Volker Hessel

Abstract:

Nitrogen fixation is critical for plants for the biosynthesis of protein and nucleic acid. Most of our atmosphere is nitrogen, yet plants cannot directly absorb it from the air, and natural nitrogen fixation is insufficient to meet the demands. This experiment used a fast-modulated surface-confined atmospheric pressure plasma created by a 6 kV (peak-peak) sinusoidal power source with a repetition frequency of 68 kHz to fix nitrogen. Plasmas have been proposed for excitation of nitrogen gas, which quickly oxidised to NOX. With different N2/O2 input ratios, the rate of NOX generation was investigated. The rate of NOX production was shown to be optimal for mixtures of 60–70% O2 with N2. To boost NOX production in plasma, metal oxide catalysts based on TiO2 were coated over the dielectric layer of a reactor. These results demonstrate that nitrogen activation was more advantageous in surface-confined plasma sources because micro-discharges formed on the sharp edges of the electrodes, which is a primary function attributed to NOX synthesis and is further enhanced by metal oxide catalysts. The energy-efficient and sustainable NOX synthesis described in this study will offer a fresh perspective for ongoing research on green nitrogen fixation techniques.

Keywords: nitrogen fixation, fast-modulated, surface-confined, sustainable

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Authors: Nikolett Deutsch, Éva Pintér, Péter Bagó, Miklós Hetényi

Abstract:

Strategic technology management has emerged and evolved parallelly with strategic management paradigms. It focuses on the opportunity for organizations operating mainly in technology-intensive industries to explore and exploit technological capabilities upon which competitive advantage can be obtained. As strategic technology management involves multifunction within an organization, requires broad and diversified knowledge, and must be developed and implemented with business objectives to enable a firm’s profitability and growth, excellence in strategic technology management provides unique opportunities for organizations in terms of building a successful future. Accordingly, a framework supporting the evaluation of the technological readiness level of management can significantly contribute to developing organizational competitiveness through a better understanding of strategic-level capabilities and deficiencies in operations. In the last decade, several innovation maturity assessment models have appeared and become designated management tools that can serve as references for future practical approaches expected to be used by corporate leaders, strategists, and technology managers to understand and manage technological capabilities and capacities. The aim of this paper is to provide a comprehensive review of the state-of-the-art innovation maturity frameworks, to investigate the critical lessons learned from their application, to identify the similarities and differences among the models, and identify the main aspects and elements valid for the field and critical functions of technology management. To this end, a systematic literature review was carried out considering the relevant papers and articles published in highly ranked international journals around the 27 most widely known innovation maturity models from four relevant digital sources. Key findings suggest that despite the diversity of the given models, there is still room for improvement regarding the common understanding of innovation typologies, the full coverage of innovation capabilities, and the generalist approach to the validation and practical applicability of the structure and content of the models. Furthermore, the paper proposes an initial structure by considering the maturity assessment of the technological capacities and capabilities - i.e., technology identification, technology selection, technology acquisition, technology exploitation, and technology protection - covered by strategic technology management.

Keywords: innovation capabilities, innovation maturity models, technology audit, technology management, technology management maturity models

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