Search results for: optimized network

Authors: Siddharth Ojha, Varun Dua

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Classified as one of the most important reasons of aerodynamic drag in the sedan automobiles is the fluid flow separation near the vehicle’s rear end. To retard the separation of flow, bump-shaped vortex generators are being tested for its implementation to the roof end of a sedan vehicle. Frequently used in the aircrafts to prevent the separation of fluid flow, vortex generators themselves produce drag, but they also substantially reduce drag by preventing flow separation at the downstream. The net effects of vortex generators can be calculated by summing the positive and negative impacts and effects. Since this effect depends on dimensions and geometry of vortex generators, those present on the vehicle roof are optimized for maximum efficiency and performance. The model was tested through ANSYS CFD analysis and modeling. The model was tested in the wind tunnel for observing it’s properties such as aerodynamic drag and flow separation and a major time lag was gained by employing vortex generators in the scaled model. Major conclusions which were recorded during the analysis were a substantial 24% reduction in the aerodynamic drag and 14% increase in the efficiency of the sedan automobile as the flow separation from the surface is delayed. This paper presents the results of optimization, the effect of vortex generators in the flow field and the mechanism by which these effects occur and are regulated.

Keywords: aerodynamics, aerodynamic devices, body, computational fluid dynamics (CFD), flow visualization

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Authors: Lawrence Mzukisi Madikizela, Luke Chimuka

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Removal of organics from wastewater offers a better water quality, therefore, the purpose of this work was to investigate the use of molecularly imprinted polymer (MIP) for the elimination of selected organics from water. A multi-template MIP for the adsorption of naproxen, ibuprofen and diclofenac was synthesized using a bulk polymerization method. A MIP was synthesized at 70°C by employing 2-vinylpyridine, ethylene glycol dimethacrylate, toluene and 1,1’-azobis-(cyclohexanecarbonitrile) as functional monomer, cross-linker, porogen and initiator, respectively. Thermogravimetric characterization indicated that the polymer backbone collapses at 250°C and scanning electron microscopy revealed the porous and roughness nature of the MIP after elution of templates. The performance of the MIP in aqueous solutions was evaluated by optimizing several adsorption parameters. The optimized adsorption conditions were 50 mg of MIP, extraction time of 10 min, a sample pH of 4.6 and the initial concentration of 30 mg/L. The imprinting factors obtained for naproxen, ibuprofen and diclofenac were 1.25, 1.42, and 2.01, respectively. The order of selectivity for the MIP was; diclofenac > ibuprofen > naproxen. MIP showed great swelling in water with an initial swelling rate of 2.62 g/(g min). The synthesized MIP proved to be able to adsorb naproxen, ibuprofen and diclofenac from contaminated deionized water, wastewater influent and effluent.

Keywords: adsorption, molecularly imprinted polymer, multi template, pharmaceuticals

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Authors: Shane D. Inder, Mehrdad Khamooshi

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Efficient energy storage is a crucial factor in facilitating the uptake of renewable energy resources. Among the many options available for energy storage systems required to balance imbalanced supply and demand cycles, compressed air energy storage (CAES) is a proven technology in grid-scale applications. This paper reviews the current state of micro scale CAES technology and describes a micro-scale advanced adiabatic CAES (A-CAES) system, where heat generated during compression is stored for use in the discharge phase. It will also describe a thermodynamic model, developed in EES (Engineering Equation Solver) to evaluate the performance and critical parameters of the discharge phase of the proposed system. Three configurations are explained including: single turbine without preheater, two turbines with preheaters, and three turbines with preheaters. It is shown that the micro-scale A-CAES is highly dependent upon key parameters including; regulator pressure, air pressure and volume, thermal energy storage temperature and flow rate and the number of turbines. It was found that a micro-scale AA-CAES, when optimized with an appropriate configuration, could deliver energy input to output efficiency of up to 70%.

Keywords: CAES, adiabatic compressed air energy storage, expansion phase, micro generation, thermodynamic

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Authors: Anita Vidacs, Robert Rajko, Csaba Vagvolgyi, Judit Krisch

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With the optimization of a new disinfectant the number of tests could be decreased and the cost of processing too. Good sanitizers are eco-friendly and allow no resistance evolvement of bacteria. The essential oils (EOs) are natural antimicrobials, and most of them have the Generally Recognized As Safe (GRAS) status. In our study, the effect of the EOs cinnamon, marjoram, and thyme was investigated against mixed species bacterial biofilms of Escherichia coli, Listeria monocytogenes, Pseudomonas putida, and Staphylococcus aureus. The optimal concentration of EOs, disinfection time and level of pH were evaluated with the aid of Response Surface Box-Behnken Design (RSD) on 1 day and 7 days old biofilms on metal, plastic, and wood surfaces. The variable factors were in the range of 1-3 times of minimum bactericide concentration (MBC); 10-110 minutes acting time and 4.5- 7.5 pH. The optimized EO disinfectant was compared to industrial used chemicals (HC-DPE, Hypo). The natural based disinfectants were applicable; the acting time was below 30 minutes. EOs were able to eliminate the biofilm from the used surfaces except from wood. The disinfection effect of the EO based natural solutions was in most cases equivalent or better compared to chemical sanitizers used in food industry.

Keywords: biofilm, Box-Behnken design, disinfectant, essential oil

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Authors: Dimeji Abe, Nonso Okoye, Gideon Ikpimi, Prince Idemudia

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Multi-reservoir optimization operation has been a critical issue for river basin management. Water, as a scarce resource, is in high demand and the problems associated with the reservoir as its storage facility are enormous. The complexity in balancing the supply and demand of this prime resource has created the need to examine the best way to solve the problem using optimization techniques. The objective of this study is to evaluate the performance of the multi-objective meta-heuristic algorithm for the operation of Gariep Dam for satisfying ecological flow requirements. This study uses an evolutionary algorithm called backtrack search algorithm (BSA) to determine the best way to optimise the dam operations of hydropower production, flood control, and water supply without affecting the environmental flow requirement for the survival of aquatic bodies and sustain life downstream of the dam. To achieve this objective, the operations of the dam that corresponds to different tradeoffs between the objectives are optimized. The results indicate the best model from the algorithm that satisfies all the objectives without any constraint violation. It is expected that hydropower generation will be improved and more water will be available for ecological flow requirements with the use of the algorithm. This algorithm also provides farmers with more irrigation water as well to improve their business.

Keywords: BSA evolutionary algorithm, metaheuristics, optimization, river basin management

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Authors: Nurudeen Ayeni, Kasali Bello, Ovi Abayeh

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Study of the effect of dye–resin complexation on the degree of dye absorption were carried out using Procion Blue MX-R to dye cotton fabric in the presence hexamethylol melamine (MR 6) and its phosphate derivative (MPR 4) for resination. The highest degree of dye exhaustion was obtained at 400 C for 1 hour with the resinated fabric showing more affinity for the dye than the ordinary fiber. Improved fastness properties was recorded which show a relatively higher stability of dye–resin–cellulose network formed.

Keywords: cotton fabric, reactive dye, dyeing, resination

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Authors: R. Ghasemi, M. R. Rahimi Khoygani

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This paper proposes the designing direct adaptive neural controller to apply for a class of a nonlinear pendulum dynamic system. The radial basis function (RBF) neural adaptive controller is robust in presence of external and internal uncertainties. Both the effectiveness of the controller and robustness against disturbances are importance of this paper. The simulation results show the promising performance of the proposed controller.

Keywords: adaptive neural controller, nonlinear dynamical, neural network, RBF, driven pendulum, position control

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Authors: Sascha Kraus, Wolfgang Hora, Philipp Stieg, Thomas Niemand, Ferdinand Thies, Matthias Filser

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In most countries, small and medium-sized enterprises (SME) are the backbone of the economy due to their impact on job creation, innovation and wealth creation. Moreover, the ongoing globalization makes it inevitable – even for SME that traditionally focused on their domestic markets – to internationalize their business activities to realize further growth and survive in international markets. Thus, internationalization has become one of the most common growth strategies for SME and has received increasing scholarly attention over the last two decades. One the downside internationalization can be also regarded as the most complex strategy that a firm can undertake. Particularly for family firms, that are often characterized by limited financial capital, a risk-averse nature and limited growth aspirations, it could be argued that family firms are more likely to face greater challenges when taking the pathway to internationalization. Especially the triangulation of family, ownership, and management (so-called ‘familiness’) manifests in a unique behavior and decision-making process which is often characterized by the importance given to noneconomic goals and distinguishes a family firm from other businesses. Taking this into account, the concept of socio-emotional wealth (SEW) has been evolved to describe the behavior of family firms. In order to investigate how different internal and external firm characteristics shape internationalization success of family firms, we drew on a sample consisting of 297 small and medium-sized family firms from Germany, Austria, Switzerland, and Liechtenstein. Thus, we include SEW as essential family firm characteristic and added the two major intra-organizational characteristics, entrepreneurial orientation (EO), absorptive capacity (AC) as well as collaboration intensity (CI) and relational knowledge (RK) as two major external network characteristics. Based on previous research we assume that these characteristics are important to explain internationalization success of family firm SME. Regarding the data analysis, we applied a Fuzzy Set Qualitative Comparative Analysis (fsQCA), an approach that allows identifying configurations of firm characteristics, specifically used to study complex causal relationships where traditional regression techniques reach their limits. Results indicate that several combinations of these family firm characteristics can lead to international success, with no permanently required key characteristic. Instead, there are many roads to walk down for family firms to achieve internationalization success. Consequently, our data states that family owned SME are heterogeneous and internationalization is a complex and dynamic process. Results further show that network related characteristics occur in all sets, thus represent an essential element in the internationalization process of family owned SME. The contribution of our study is twofold, as we investigate different forms of international expansion for family firms and how to improve them. First, we are able to broaden the understanding of the intersection between family firm and SME internationalization with respect to major intra-organizational and network-related variables. Second, from a practical perspective, we offer family firm owners a basis for setting up internal capabilities to achieve international success.

Keywords: entrepreneurial orientation, family firm, fsQCA, internationalization, socio-emotional wealth

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Authors: Carole Salis, Marie F. Wilson, Fabrizio Murgia, Cristian Lai, Franco Atzori, Giulia M. Orrù

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ANDASA is a knowledge management platform for the capitalization of knowledge and cultural assets for the artistic and cultural sectors. It was built based on the priorities expressed by the participating artists. Through mapping artistic activities and specificities, it enables to highlight various aspects of the artistic research and production. Such instrument will contribute to create networks and partnerships, as it enables to evidentiate who does what, in what field, using which methodology. The platform is accessible to network participants and to the general public.

Keywords: cultural promotion, knowledge representation, cultural maping, ICT

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Authors: Abolfazl Alishahi Ghalehjoughi

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Economic independence and security of Islamic world is the top priority. Economic dependence of Muslim countries on economies of non-Muslim imperialist countries results in political and cultural dependencies, and such dependencies will jeopardize the noble Islamic culture; because the will of a dependent country to implements the noble teachings of Islam would be faced with challenges. Solidarity of Muslim countries to achieve a uniformed and resistive economy-based Islamic economic system can improve ability of Islamic world to resist and counteract economic shocks produced by imperialists. Islam is the most complete religion in every aspect, from ideological and epistemological, to legislative and ethical, and economic aspect is no exception. Islam provides solutions to develop a flourishing economy for the whole Islamic nation. Knowledge of such solutions and identification of mechanisms to operationalise them in Islamic communities can highly contributed to establishment of the superior Islamic economy. Encourage of hard working, achievement and knowledge production, correction of consumption patterns, optimized management of import and export, avoiding Islamically prohibited income, economic discipline and equity, and promotion of interest free loan and the like are among the most important solutions to realize such resistive economy.

Keywords: resistive economy, cultural independence, Islam, solidarity

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Authors: Bruno RamaëL, GwenaëL Page, Catherine Knopf-Lenoir, Olivier Baledent, Anne-Virginie Salsac

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No routine technique currently exists for clinicians to measure the mechanical properties of vascular walls non-invasively. Most of the data available in the literature come from traction or dilatation tests conducted ex vivo on native blood vessels. The objective of the study is to develop a non-invasive characterization technique based on Magnetic Resonance Imaging (MRI) measurements of the deformation of vascular walls under pulsating blood flow conditions. The goal is to determine the mechanical properties of the vessels by inverse analysis, coupling imaging measurements and numerical simulations of the fluid-structure interactions. The hyperelastic properties are identified using Solidworks and Ansys workbench (ANSYS Inc.) solving an optimization technique. The vessel of interest targeted in the study is the common carotid artery. In vivo MRI measurements of the vessel anatomy and inlet velocity profiles was acquired along the facial vascular network on a cohort of 30 healthy volunteers: - The time-evolution of the blood vessel contours and, thus, of the cross-section surface area was measured by 3D imaging angiography sequences of phase-contrast MRI. - The blood flow velocity was measured using a 2D CINE MRI phase contrast (PC-MRI) method. Reference arterial pressure waveforms were simultaneously measured in the brachial artery using a sphygmomanometer. The three-dimensional (3D) geometry of the arterial network was reconstructed by first creating an STL file from the raw MRI data using the open source imaging software ITK-SNAP. The resulting geometry was then transformed with Solidworks into volumes that are compatible with Ansys softwares. Tetrahedral meshes of the wall and fluid domains were built using the ANSYS Meshing software, with a near-wall mesh refinement method in the case of the fluid domain to improve the accuracy of the fluid flow calculations. Ansys Structural was used for the numerical simulation of the vessel deformation and Ansys CFX for the simulation of the blood flow. The fluid structure interaction simulations showed that the systolic and diastolic blood pressures of the common carotid artery could be taken as reference pressures to identify the mechanical properties of the different arteries of the network. The coefficients of the hyperelastic law were identified using Ansys Design model for the common carotid. Under large deformations, a stiffness of 800 kPa is measured, which is of the same order of magnitude as the Young modulus of collagen fibers. Areas of maximum deformations were highlighted near bifurcations. This study is a first step towards patient-specific characterization of the mechanical properties of the facial vessels. The method is currently applied on patients suffering from facial vascular malformations and on patients scheduled for facial reconstruction. Information on the blood flow velocity as well as on the vessel anatomy and deformability will be key to improve surgical planning in the case of such vascular pathologies.

Keywords: identification, mechanical properties, arterial walls, MRI measurements, numerical simulations

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Authors: Joy Cao, Min Zhou

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Purpose: Acute Type A aortic dissection is a well-known cause of extremely high mortality rate. A highly accurate and cost-effective non-invasive predictor is critically needed so that the patient can be treated at earlier stage. Although various CFD approaches have been tried to establish some prediction frameworks, they are sensitive to uncertainty in both image segmentation and boundary conditions. Tedious pre-processing and demanding calibration procedures requirement further compound the issue, thus hampering their clinical applicability. Using the latest physics informed deep learning methods to establish an accurate and cost-effective predictor framework are amongst the main goals for a better Type A aortic dissection treatment. Methods: Via training a novel physics-informed deep residual network, with non-invasive 4D MRI displacement vectors as inputs, the trained model can cost-effectively calculate all these biomarkers: aortic blood pressure, WSS, and OSI, which are used to predict potential type A aortic dissection to avoid the high mortality events down the road. Results: The proposed deep learning method has been successfully trained and tested with both synthetic 3D aneurysm dataset and a clinical dataset in the aortic dissection context using Google colab environment. In both cases, the model has generated aortic blood pressure, WSS, and OSI results matching the expected patient’s health status. Conclusion: The proposed novel physics-informed deep residual network shows great potential to create a cost-effective, non-invasive predictor framework. Additional physics-based de-noising algorithm will be added to make the model more robust to clinical data noises. Further studies will be conducted in collaboration with big institutions such as Cleveland Clinic with more clinical samples to further improve the model’s clinical applicability.

Keywords: type-a aortic dissection, deep residual networks, blood flow modeling, data-driven modeling, non-invasive diagnostics, deep learning, artificial intelligence.

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Authors: Vikas Chawla, Buta Singh Sidhu, Amita Rani, Amit Handa

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The main objective of the present work is microstructural and mechanical characterization of the conventional and nanostructured TiAlN and AlCrN coatings deposited on T-11 boiler steel. In case of conventional coatings, Al-Cr and Ti-Al metallic powders were deposited using plasma spray process followed by gas nitriding of the surface which was done in the lab with optimized parameters after conducting several trials on plasma-sprayed coated specimens. The physical vapor deposition process (PAPVD) was employed for depositing nanostructured TiAlN and AlCrN coatings. The field emission scanning electron microscopy (FE-SEM) with energy dispersive X-ray analysis (EDAX) attachment, X-ray diffraction (XRD) analysis, atomic force microscopy (AFM) analysis and the X-Ray mapping analysis techniques have been used to study surface and cross-sectional morphology of the coatings. The surface roughness and micro-hardness were also measured. A good adhesion of the conventional thick TiAlN and AlCrN coatings was found. The coatings under study are recommended for the applications to super-heater and re-heater tubes of the boilers based upon the outcomes of the research work.

Keywords: nanostructure, physical vapour deposition, oxides, thin films, electron microscopy

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Authors: S. Santalunai, T. Thosdeekoraphat, C. Thongsopa

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This paper presents the effect of electric field distribution which is an electric field intensity analysis. Consideration of the dielectric heating of grains and insects, the rice and rice weevils are utilized for dielectric heating analysis. Furthermore, this analysis compares the effect of electric field distribution in rice and rice weevil. In this simulation, two copper plates are used to generate the electric field for dielectric heating system and put the rice materials between the copper plates. The simulation is classified in two cases, which are case I one rice weevil is placed in the rice and case II two rice weevils are placed at different position in the rice. Moreover, the probes are located in various different positions on plate. The power feeding on this plate is optimized by using CST EM studio program of 1000 watt electrical power at 39 MHz resonance frequency. The results of two cases are indicated that the most electric field distribution and intensity are occurred on the rice and rice weevils at the near point of the probes. Moreover, the heat is directed to the rice weevils more than the rice. When the temperature of rice and rice weevils are calculated and compared, the rice weevils has the temperature more than rice is about 41.62 Celsius degrees. These results can be applied for the dielectric heating applications to eliminate insect.

Keywords: capacitor copper plates, electric field distribution, dielectric heating, grains

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Authors: Akhilesh G. Naik, Dipankar Pal

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In today’s scenario, the complexity of digital signal processing (DSP) applications and various microcontroller architectures have been increasing to such an extent that the traditional approaches to multiplier design in most processors are becoming outdated for being comparatively slow. Modern processing applications require suitable pipelined approaches, and therefore, algorithms that are friendlier with pipelined architectures. Traditional algorithms like Wallace Tree, Radix-4 Booth, Radix-8 Booth, Dadda architectures have been proven to be comparatively slow for pipelined architectures. These architectures, therefore, need to be optimized or combined with other architectures amongst them to enhance its performances and to be made suitable for pipelined hardware/architectures. Recently, Vedic algorithm mathematically has proven to be efficient by appearing to be less complex and with fewer steps for its output establishment and have assumed renewed importance. This paper describes and shows how the Vedic algorithm can be better suited for pipelined architectures and also can be combined with traditional architectures and algorithms for enhancing its ability even further. In this paper, we also established that for complex applications on DSP and other microcontroller architectures, using Vedic approach for multiplication proves to be the best available and efficient option.

Keywords: Wallace Tree, Radix-4 Booth, Radix-8 Booth, Dadda, Vedic, Single-Stage Karatsuba (SSK), Looped Karatsuba (LK)

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Authors: My Ali El Khakani

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Single-wall Carbon nanotubes (SWCNTs) possess an unprecedented combination of unique properties that make them highly promising for suitable for a new generation of photovoltaic (PV) devices. Prior to discussing the integration of SWCNTs films into effective PV devices, we will briefly highlight our work on the synthesis of SWCNTs by means of the KrF pulsed laser deposition technique, their purification and transfer onto n-silicon substrates to form p-n junctions. Some of the structural and optoelectronic properties of SWCNTs relevant to PV applications will be emphasized. By varying the SWCNTs film density (µg/cm2), we were able to point out the existence of an optimum value that yields the highest photoconversion efficiency (PCE) of ~10%. Further control of the doping of the p-SWCNTs films, through their exposure to nitric acid vapors, along with the insertion of an optimized hole-extraction-layer in the p-SWCNTs/n-Si hybrid devices permitted to achieve a PCE value as high as 14.2%. Such a high PCE value demonstrates the full potential of these p-SWCNTs/n-Si devices for sunlight photoconversion. On the other hand, by examining both the optical transmission and electrical conductance of the SWCNTs’ films, we established a figure of merit (FOM) that was shown to correlate well with the PCE performance. Such a direct relationship between the FOM and the PCE can be used as a guide for further PCE enhancement of these novel p-SWCNTs/n-Si PV devices.

Keywords: carbon nanotubes (CNTs), CNTs-silicon hybrid devices, photoconversion, photovoltaic devices, pulsed laser deposition

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Authors: Osamede Asowata

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Optimized gain in respect to output power of stand-alone photovoltaic (PV) systems is one of the major focus of PV in recent times. This is evident in its low carbon emission and efficiency. Power failure or outage from commercial providers, in general, does not promote development to public and private sector; these basically limit the development of industries. The need for a well-structured PV system is of importance for an efficient and cost effective monitoring system. The purpose of this paper is to validate the maximum power point of an off-grid PV system taking into consideration the most effective tilt and orientation angles for PV's in the southern hemisphere. This paper is based on analyzing the system using a solar charger with maximum power point tracking (MPPT) from a pulse width modulation (PWM) perspective. The power conditioning device chosen is a solar charger with MPPT. The practical setup consists of a PV panel that is set to an orientation angle of 0°N, with a corresponding tilt angle of 36°, 26°, and 16°. Preliminary results include regression analysis (normal probability plot) showing the maximum power point in the system as well the best tilt angle for maximum power point tracking.

Keywords: poly-crystalline PV panels, solar chargers, tilt and orientation angles, maximum power point tracking, MPPT, Pulse Width Modulation (PWM).

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Authors: Irene Yi

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Machine learning, natural language processing, and neural network models of language are becoming more and more prevalent in the fields of technology and linguistics today. Training data for machines are at best, large corpora of human literature and at worst, a reflection of the ugliness in society. Computational linguistics is a growing field dealing with such issues of data collection for technological development. Machines have been trained on millions of human books, only to find that in the course of human history, derogatory and sexist adjectives are used significantly more frequently when describing females in history and literature than when describing males. This is extremely problematic, both as training data, and as the outcome of natural language processing. As machines start to handle more responsibilities, it is crucial to ensure that they do not take with them historical sexist and misogynistic notions. This paper gathers data and algorithms from neural network models of language having to deal with syntax, semantics, sociolinguistics, and text classification. Computational analysis on such linguistic data is used to find patterns of misogyny. Results are significant in showing the existing intentional and unintentional misogynistic notions used to train machines, as well as in developing better technologies that take into account the semantics and syntax of text to be more mindful and reflect gender equality. Further, this paper deals with the idea of non-binary gender pronouns and how machines can process these pronouns correctly, given its semantic and syntactic context. This paper also delves into the implications of gendered grammar and its effect, cross-linguistically, on natural language processing. Languages such as French or Spanish not only have rigid gendered grammar rules, but also historically patriarchal societies. The progression of society comes hand in hand with not only its language, but how machines process those natural languages. These ideas are all extremely vital to the development of natural language models in technology, and they must be taken into account immediately.

Keywords: computational analysis, gendered grammar, misogynistic language, neural networks

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Authors: Laura N. Bolivar T.

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The tourism importance for regional development is mainly highlighted by the collaborative, cooperating and competitive relationships of the involved agents. The fostering of associativity processes, in particular, the cluster approach emphasizes the beneficial outcomes from the concentration of enterprises, where innovation and entrepreneurship flourish and shape the dynamics for tourism empowerment. Considering the department of Huila, it is located in the south-west of Colombia and holds the biggest coffee production in the country, although it barely contributes to the national GDP. Hence, its economic development strategy is looking for more dynamism and Huila could be consolidated as a leading destination for cultural, ecological and heritage tourism, if at least the public policy making processes for the tourism management of La Tatacoa Desert, San Agustin Park and Bambuco’s National Festival, were implemented in a more efficient manner. In this order of ideas, this study attempts to address the potential restrictions and beneficial factors for the consolidation of the tourism sector of Huila-Colombia as a cluster and how could it impact its regional development. Therefore, a set of theoretical frameworks such as the Tourism Routes Approach, the Tourism Breeding Environment, the Community-based Tourism Method, among others, but also a collection of international experiences describing tourism clustering processes and most outstanding problematics, is analyzed to draw up learning points, structure of proceedings and success-driven factors to be contrasted with the local characteristics in Huila, as the region under study. This characterization involves primary and secondary information collection methods and comprises the South American and Colombian context together with the identification of involved actors and their roles, main interactions among them, major tourism products and their infrastructure, the visitors’ perspective on the situation and a recap of the related needs and benefits regarding the host community. Considering the umbrella concepts, the theoretical and the empirical approaches, and their comparison with the local specificities of the tourism sector in Huila, an array of shortcomings is analytically constructed and a series of guidelines are proposed as a way to overcome them and simultaneously, raise economic development and positively impact Huila’s well-being. This non-exhaustive bundle of guidelines is focused on fostering cooperating linkages in the actors’ network, dealing with Information and Communication Technologies’ innovations, reinforcing the supporting infrastructure, promoting the destinations considering the less known places as well, designing an information system enabling the tourism network to assess the situation based on reliable data, increasing competitiveness, developing participative public policy-making processes and empowering the host community about the touristic richness. According to this, cluster dynamics would drive the tourism sector to meet articulation and joint effort, then involved agents and local particularities would be adequately assisted to cope with the current changing environment of globalization and competition.

Keywords: innovative strategy, local development, network of tourism actors, tourism cluster

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Authors: Yadgar Tayfur, Antony Darby, Tim Ibell, Mark Evernden, John Orr

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Fabric form-work is a technique to cast concrete structures with a great advantage of saving concrete material of up to 40%. This technique is particularly associated with the optimized concrete structures that usually have smaller cross-section dimensions than equivalent prismatic members. However, this can make the structural system produced from these members prone to smaller serviceability safety margins. Therefore, it is very important to understand the serviceability issue of non-prismatic concrete structures. In this paper, an analytical computer-based model to optimize concrete beams and to predict load-deflection behaviour of both prismatic and non-prismatic concrete beams is presented. The model was developed based on the method of sectional analysis and integration of curvatures. Results from the analytical model were compared to load-deflection behaviour of a number of beams with different geometric and material properties from other researchers. The results of the comparison show that the analytical program can accurately predict the load-deflection response of concrete beams with medium reinforcement ratios. However, it over-estimates deflection values for lightly reinforced specimens. Finally, the analytical program acceptably predicted load-deflection behaviour of on-prismatic concrete beams.

Keywords: fabric-formed concrete, continuous beams, optimisation, serviceability

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Authors: Long Pham, Julia Blanke

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An increasingly popular way of establishing citizen engagement within communities is through ‘city apps’. Currently, most of these mobile applications seem to be extensions of the existing communication media, sometimes merely replicating the information available on the classical city web sites, and therefore provide minimal additional impact on citizen behavior and engagement. In order to overcome this challenge, we propose to use a holistic behavioral model to generate dynamic and contextualized app content based on optimizing well defined city-related performance goals constrained by the proposed behavioral model. In this paper, we will show how the data collected by the CorkCitiEngage project in the Irish city of Cork can be utilized to calibrate aspects of the proposed model enabling the design of a personalized citizen engagement app aiming at positively influencing people’s behavior towards more active participation in their communities. We will focus on the important aspect of intentions to act, which is essential for understanding the reasons behind the common value-action-gap being responsible for the mismatch between good intentions and actual observable behavior, and will discuss how customized app design can be based on a rigorous model of behavior optimized towards maximizing well defined city-related performance goals.

Keywords: city apps, holistic behaviour model, intention to act, value-action-gap, citizen engagement

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Authors: Netsanet Kebede Hundessa

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Substrate modification of thin film composite (TFC) membranes with various crosslinkers is typically necessary for organic solvent nanofiltration (OSN) applications. This modification is aimed at enhancing membrane stability and solvent resistance, but it often results in a decline in permeance. This study introduces a distinct approach by developing a coordination-crosslinked polyimide substrate, which differs from the covalently-crosslinked substrates traditionally used. This developed substrate achieves enhanced solvent resistance, improved hydrophilicity, and optimized porous microstructure simultaneously. The study investigates the effects of an alkaline coagulation bath, subsequent ion exchange, and further solvent activation. The resulting TFC membrane successfully overcomes the typical permeability-selectivity trade-off of OSN membranes. It demonstrates significantly improved solvent permeance (1.5–2 times higher than previously reported data) with values of 65.2 LMH/bar for methanol, 33.1 LMH/bar for ethanol, and 59.1 LMH/bar for acetone while maintaining competitive solute rejection (>98% for Rose Bengal). This research is expected to provide a new direction for developing high-performance OSN composite membranes and other separation applications.

Keywords: metal coordinatiom, thin film composite membrane, organic solvent nanofiltration, solvent activation

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Authors: Sunil Chandra, Himanshu Rawat, Vikas Gusain, Triparna Barman

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Forest fires are a recurrent phenomenon in the Himalayan region owing to the presence of vulnerable forest types, topographical gradients, climatic weather conditions, and anthropogenic pressure. The present study focuses on the identification of forest fire-affected areas in a small part of the West Himalayan region using a differential normalized burnt ratio method and spectral unmixing methods. The study area has a rugged terrain with the presence of sub-tropical pine forest, montane temperate forest, and sub-alpine forest and scrub. The major reason for fires in this region is anthropogenic in nature, with the practice of human-induced fires for getting fresh leaves, scaring wild animals to protect agricultural crops, grazing practices within reserved forests, and igniting fires for cooking and other reasons. The fires caused by the above reasons affect a large area on the ground, necessitating its precise estimation for further management and policy making. In the present study, two approaches have been used for carrying out a burnt area analysis. The first approach followed for burnt area analysis uses a differenced normalized burnt ratio (dNBR) index approach that uses the burnt ratio values generated using the Short-Wave Infrared (SWIR) band and Near Infrared (NIR) bands of the Sentinel-2 image. The results of the dNBR have been compared with the outputs of the spectral mixing methods. It has been found that the dNBR is able to create good results in fire-affected areas having homogenous forest stratum and with slope degree <5 degrees. However, in a rugged terrain where the landscape is largely influenced by the topographical variations, vegetation types, tree density, the results may be largely influenced by the effects of topography, complexity in tree composition, fuel load composition, and soil moisture. Hence, such variations in the factors influencing burnt area assessment may not be effectively carried out using a dNBR approach which is commonly followed for burnt area assessment over a large area. Hence, another approach that has been attempted in the present study utilizes a spectral mixing method where the individual pixel is tested before assigning an information class to it. The method uses a neural network approach utilizing Sentinel-2 bands. The training and testing data are generated from the Sentinel-2 data and the national field inventory, which is further used for generating outputs using ML tools. The analysis of the results indicates that the fire-affected regions and their severity can be better estimated using spectral unmixing methods, which have the capability to resolve the noise in the data and can classify the individual pixel to the precise burnt/unburnt class.

Keywords: categorical data, log linear modeling, neural network, shifting cultivation

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Authors: Akeel A. Shah, Tong Zhang

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Computational approaches to learning the properties of materials are commonplace, motivated by the need to screen or design materials for a given application, e.g., semiconductors and energy storage. Experimental approaches can be both time consuming and costly. Unfortunately, computational approaches such as ab-initio electronic structure calculations and classical or ab-initio molecular dynamics are themselves can be too slow for the rapid evaluation of materials, often involving thousands to hundreds of thousands of candidates. Machine learning assisted approaches have been developed to overcome the time limitations of purely physics-based approaches. These approaches, on the other hand, require large volumes of data for training (hundreds of thousands on many standard data sets such as QM7b). This means that they are limited by how quickly such a large data set of physics-based simulations can be established. At high fidelity, such as configuration interaction, composite methods such as G4, and coupled cluster theory, gathering such a large data set can become infeasible, which can compromise the accuracy of the predictions - many applications require high accuracy, for example band structures and energy levels in semiconductor materials and the energetics of charge transfer in energy storage materials. In order to circumvent this problem, multi-fidelity approaches can be adopted, for example the Δ-ML method, which learns a high-fidelity output from a low-fidelity result such as Hartree-Fock or density functional theory (DFT). The general strategy is to learn a map between the low and high fidelity outputs, so that the high-fidelity output is obtained a simple sum of the physics-based low-fidelity and correction, Although this requires a low-fidelity calculation, it typically requires far fewer high-fidelity results to learn the correction map, and furthermore, the low-fidelity result, such as Hartree-Fock or semi-empirical ZINDO, is typically quick to obtain, For high-fidelity outputs the result can be an order of magnitude or more in speed up. In this work, a new multi-fidelity approach is developed, based on a graph convolutional network (GCN) combined with semi-supervised learning. The GCN allows for the material or molecule to be represented as a graph, which is known to improve accuracy, for example SchNet and MEGNET. The graph incorporates information regarding the numbers of, types and properties of atoms; the types of bonds; and bond angles. They key to the accuracy in multi-fidelity methods, however, is the incorporation of low-fidelity output to learn the high-fidelity equivalent, in this case by learning their difference. Semi-supervised learning is employed to allow for different numbers of low and high-fidelity training points, by using an additional GCN-based low-fidelity map to predict high fidelity outputs. It is shown on 4 different data sets that a significant (at least one order of magnitude) increase in accuracy is obtained, using one to two orders of magnitude fewer low and high fidelity training points. One of the data sets is developed in this work, pertaining to 1000 simulations of quinone molecules (up to 24 atoms) at 5 different levels of fidelity, furnishing the energy, dipole moment and HOMO/LUMO.

Keywords: .materials screening, computational materials, machine learning, multi-fidelity, graph convolutional network, semi-supervised learning

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Authors: Subodh Kumar, Amit Varde

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Typical on-premises high-performance computing (HPC) environments consist of a fixed number and a fixed set of computing hardware. During the design of the HPC environment, the hardware components, including but not limited to CPU, Memory, GPU, and networking, are carefully chosen from select vendors for optimal performance. High capital cost for building the environment is a prime factor influencing the design environment. A class of software called “Job Schedulers” are critical to maximizing these resources and running multiple workloads to extract the maximum value for the high capital cost. In principle, schedulers work by preventing workloads and users from monopolizing the finite hardware resources by queuing jobs in a workload. A cloud-based HPC environment does not have the limitations of fixed (type of and quantity of) hardware resources. In theory, users and workloads could spin up any number and type of hardware resource. This paper discusses the limitations of using traditional scheduling algorithms for cloud-based HPC workloads. It proposes a new set of features, called “HPC optimizers,” for maximizing the benefits of the elasticity and scalability of the cloud with the goal of cost-performance optimization of the workload.

Keywords: high performance computing, HPC, cloud computing, optimization, schedulers

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Authors: Yeon Woo Seo, Haeyoung Choi, Jung Hyun Jeong

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Recently, the UC phosphors have attracted much attention owing to their wide applicability in areas such as biological fluorescence labeling, three-dimensional color displays, temperature sensor, solar cells, white light emitting diodes (WLEDs), fiber optic communication, anti-counterfeiting and other areas. The UC efficiency is mainly dependent on the host lattice and the interaction between the host lattice and doped ions. Up to date, various host matrices, such as oxides, fluorides, vanadates and phosphates, have been investigated as efficient UC luminescent hosts. Recently, oxide materials with low phonon energy have been investigated as the host matrices of UC materials due to their high chemical durability and physical stability. A series of Sr2CeO4: Tm3+/Yb3+ phosphors with different concentrations of Yb3+ ions have been successfully prepared using the high-energy ball milling method. In this study, we reported the UC luminescent properties of Tm3+/Yb3+ ions co-doped Sr2CeO4 phosphors under an excitation wavelength of 975 nm. Furthermore, the structural and morphological characteristics, as well as the UC luminescence mechanism were investigated in detail. The X-ray diffraction patterns confirmed their orthorhombic structure. Under 975 nm excitation, the emission peaks were observed at 478 nm (blue) and 652 nm (red), corresponding to the 1G4 → 3H6 and 1G4 → 3F4 transitions of Tm3+, respectively. The optimized doping concentration of Yb3+ ion was 10 mol%.

Keywords: Strontium Cerate, up-conversion, luminescence, Tm3+, Yb3+

Procedia PDF Downloads 269

Authors: S. Kadiyala, M. Berzins, D. Juba, W. Keyrouz

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This paper describes the Material Point Method (MPM) for simulating a single-track laser melting process on an IN718 solid plate. MPM, known for simulating challenging multiphysics problems, is used to model the intricate thermal, mechanical, and fluid interactions during the laser sintering process. This study analyzes the formation of single tracks, exploring the impact of varying laser parameters such as speed, power, and spot diameter on the melt pool and track formation. The focus is on MPM’s ability to accurately simulate and capture the transient thermo-mechanical and phase change phenomena, which are critical in predicting the cooling rates before and after solidification of the laser track and the final melt pool geometry. The simulation results are rigorously compared with experimental data (AMB2022 benchmarks), demonstrating the effectiveness of MPM in replicating the physical processes in laser sintering. This research highlights the potential of MPM in advancing the understanding and simulation of melt pool physics in metal additive manufacturing, paving the way for optimized process parameters and improved material performance.

Keywords: dditive manufacturing simulation, material point method, phase change, melt pool physics

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Authors: A. Sacko, H. Nyoni, T. A. M. Msagati, B. Ntsendwana

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Carbon based materials to target environmental pollutants have become increasingly recognized in science. Electrochemical methods using carbon based materials are notable methods for high sensitive detection of organic pollutants in air. It is therefore in this light that exfoliated graphite electrode was fabricated for electrochemical analysis of PAHs in urban atmospheric air. The electrochemical properties of the graphite electrode were studied using CV and EIS in the presence of acetate buffer supporting electrolyte with 2 Mm ferricyanide as a redox probe. The graphite electrode showed enhanced current response which confirms facile kinetics and enhanced sensitivity. However, the peak to peak (DE) separation increased as a function of scan rate. The EIS showed a high charger transfer resistance. The detection phenanthrene on the exfoliated graphite was studied in the presence of acetate buffer solution at PH 3.5 using DPV. The oxidation peak of phenanthrene was observed at 0.4 V. Under optimized conditions (supporting electrolyte, pH, deposition time, etc.). The detection limit observed was at 5x 10⁻⁸ M. Thus the results demonstrate with further optimization and modification lower concentration detection can be achieved.

Keywords: electrochemical detection, exfoliated graphite, PAHs (polycyclic aromatic hydrocarbons), urban air

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Authors: Rajiv Arora

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Biodiesel may be economical if produced from inexpensive feedstock which commonly contains high level of free fatty acids (FFA) as an inhibitor in production of methyl ester. In this study, a two-step process for biodiesel production from high FFA spent bleach earth oil in a batch reactor is developed. Oil sample extracted from spent bleaching earth (SBE) was utilized for biodiesel process. In the first step, FFA of the SBE oil was reduced to 1.91% through sulfuric acid catalyzed esterification. In the second step, the product prepared from the first esterification process was carried out transesterification with an alkaline catalyst. The influence of four variables on conversion efficiency to methyl ester, i.e., methanol/ SBE oil molar ratio, catalyst amount, reaction temperature and reaction time, was studied in the second stage. The optimum process variables in the transesterification were methanol/oil molar ratio 6:1, heterogeneous catalyst conc. 5 wt %, reaction temperature 65 °C and reaction time 60 minutes to produce biodiesel. Major fuel properties of SBE biodiesel were measured to comply with ASTM and EN standards. Therefore, an optimized process for production of biodiesel from a low-cost high FFA source was accomplished.

Keywords: biodiesel, esterification, free fatty acids, residual oil, spent bleaching earth, transesterification

Procedia PDF Downloads 180

Authors: Diego De Almeida Pereira, Diana Borchenko

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Artificial neural networks are used for many applications as they are able to learn complex nonlinear relationships between input and output data. As the number of neurons and layers in a neural network increases, it is possible to represent more complex behaviors. The present study proposes that artificial neural networks are a valuable tool for architecture and engineering professionals concerned with understanding how buildings influence human and social well-being based on theories of environmental psychology.

Keywords: environmental psychology, architecture, neural networks, human and social well-being

Procedia PDF Downloads 506