Search results for: scientific modeling

Authors: Shashi Kant Verma, S. L. Sinha, D. K. Chandraker

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Numerical simulations of selected subchannel tracer (Potassium Nitrate) based experiments have been performed to study the capabilities of state-of-the-art of Computational Fluid Dynamics (CFD) codes. The Computational Fluid Dynamics (CFD) methodology can be useful for investigating the spacer effect on turbulent mixing to predict turbulent flow behavior such as Dimensionless mixing scalar distributions, radial velocity and vortices in the nuclear fuel assembly. A Gibson and Launder (GL) Reynolds stress model (RSM) has been selected as the primary turbulence model to be applied for the simulation case as it has been previously found reasonably accurate to predict flows inside rod bundles. As a comparison, the case is also simulated using a standard k-ε turbulence model that is widely used in industry. Despite being an isotropic turbulence model, it has also been used in the modeling of flow in rod bundles and to produce lateral velocities after thorough mixing of coolant fairly. Both these models have been solved numerically to find out fully developed isothermal turbulent flow in a 30º segment of a 54-rod bundle. Numerical simulation has been carried out for the study of natural mixing of a Tracer (Passive scalar) to characterize the growth of turbulent diffusion in an injected sub-channel and, afterwards on, cross-mixing between adjacent sub-channels. The mixing with water has been numerically studied by means of steady state CFD simulations with the commercial code STAR-CCM+. Flow enters into the computational domain through the mass inflow at the three subchannel faces. Turbulence intensity and hydraulic diameter of 1% and 5.9 mm respectively were used for the inlet. A passive scalar (Potassium nitrate) is injected through the mass fraction of 5.536 PPM at subchannel 2 (Upstream of the mixing section). Flow exited the domain through the pressure outlet boundary (0 Pa), and the reference pressure was 1 atm. Simulation results have been extracted at different locations of the mixing zone and downstream zone. The local mass fraction shows uniform mixing. The effect of the applied turbulence model is nearly negligible just before the outlet plane because the distributions look like almost identical and the flow is fully developed. On the other hand, quantitatively the dimensionless mixing scalar distributions change noticeably, which is visible in the different scale of the colour bars.

Keywords: single-phase flow, turbulent mixing, tracer, sub channel analysis

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Authors: Arash Dalili, Hamed Tahmouressi, Mina Hoorfar

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Advances in lab-on-a-chip (LOC) devices have led to significant advances in the manipulation, separation, and isolation of particles and cells. Among the different active and passive particle manipulation methods, dielectrophoresis (DEP) has been proven to be a versatile mechanism as it is label-free, cost-effective, simple to operate, and has high manipulation efficiency. DEP has been applied for a wide range of biological and environmental applications. A popular form of DEP devices is the continuous manipulation of particles by using co-planar slanted electrodes, which utilizes a sheath flow to focus the particles into one side of the microchannel. When particles enter the DEP manipulation zone, the negative DEP (nDEP) force generated by the slanted electrodes deflects the particles laterally towards the opposite side of the microchannel. The lateral displacement of the particles is dependent on multiple parameters including the geometry of the electrodes, the width, length and height of the microchannel, the size of the particles and the throughput. In this study, COMSOL Multiphysics® modeling along with experimental studies are used to investigate the effect of the aforementioned parameters. The electric field between the electrodes and the induced DEP force on the particles are modelled by COMSOL Multiphysics®. The simulation model is used to show the effect of the DEP force on the particles, and how the geometry of the electrodes (width of the electrodes and the gap between them) plays a role in the manipulation of polystyrene microparticles. The simulation results show that increasing the electrode width to a certain limit, which depends on the height of the channel, increases the induced DEP force. Also, decreasing the gap between the electrodes leads to a stronger DEP force. Based on these results, criteria for the fabrication of the electrodes were found, and soft lithography was used to fabricate interdigitated slanted electrodes and microchannels. Experimental studies were run to find the effect of the flow rate, geometrical parameters of the microchannel such as length, width, and height as well as the electrodes’ angle on the displacement of 5 um, 10 um and 15 um polystyrene particles. An empirical equation is developed to predict the displacement of the particles under different conditions. It is shown that the displacement of the particles is more for longer and lower height channels, lower flow rates, and bigger particles. On the other hand, the effect of the angle of the electrodes on the displacement of the particles was negligible. Based on the results, we have developed an optimum design (in terms of efficiency and throughput) for three size separation of particles.

Keywords: COMSOL Multiphysics, Dielectrophoresis, Microfluidics, Particle separation

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Authors: Surinder Pal, Ajay Gupta, Johny Khajuria

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Casting simulation helps visualize mold filling and casting solidification; predict related defects like cold shut, shrinkage porosity and hard spots; and optimize the casting design to achieve the desired quality with high yield. Flow and solidification of molten metals are, however, a very complex phenomenon that is difficult to simulate correctly by conventional computational techniques, especially when the part geometry is intricate and the required inputs (like thermo-physical properties and heat transfer coefficients) are not available. Simulation software is based on the process of modeling a real phenomenon with a set of mathematical formulas. It is, essentially, a program that allows the user to observe an operation through simulation without actually performing that operation. Simulation software is used widely to design equipment so that the final product will be as close to design specs as possible without expensive in process modification. Simulation software with real-time response is often used in gaming, but it also has important industrial applications. When the penalty for improper operation is costly, such as airplane pilots, nuclear power plant operators, or chemical plant operators, a mockup of the actual control panel is connected to a real-time simulation of the physical response, giving valuable training experience without fear of a disastrous outcome. The all casting simulation software has own requirements, like magma cast has only best for crack simulation. The latest generation software Auto CAST developed at IIT Bombay provides a host of functions to support method engineers, including part thickness visualization, core design, multi-cavity mold design with common gating and feeding, application of various feed aids (feeder sleeves, chills, padding, etc.), simulation of mold filling and casting solidification, automatic optimization of feeders and gating driven by the desired quality level, and what-if cost analysis. IIT Bombay has developed a set of applications for the foundry industry to improve casting yield and quality. Casting simulation is a fast and efficient solution for process for advanced tool which is the result of more than 20 years of collaboration with major industrial partners and academic institutions around the world. In this paper the process of casting simulation is studied.

Keywords: casting simulation software’s, simulation technique’s, casting simulation, processes

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Authors: Melanie Redding

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Historic widespread nitrate contamination of the Lower Yakima Valley aquifer in Washington State initiated a community-based effort to reduce nitrate concentrations to below-drinking water standards. This group commissioned studies on characterizing local nitrogen sources, deep soil assessments, drinking water, and assessing nitrate concentrations at the water table. Nitrate is the most prevalent groundwater contaminant with common sources from animal and human waste, fertilizers, plants and precipitation. It is challenging to address groundwater contamination when common sources, such as agriculture, on-site sewage systems, and animal production, are widespread. Remediation is not possible, so mitigation is essential. The Lower Yakima Valley is located over 175,000 acres, with a population of 56,000 residents. Approximately 25% of the population do not have access to safe, clean drinking water, and 20% of the population is at or below the poverty level. Agriculture is the primary economic land-use activity. Irrigated agriculture and livestock production make up the largest percentage of acreage and nitrogen load. Commodities include apples, grapes, hops, dairy, silage corn, triticale, alfalfa and cherries. These commodities are important to the economic viability of the residents of the Lower Yakima Valley, as well as Washington State. Mitigation of nitrate in groundwater is challenging. The goal is to ensure everyone has safe drinking water. There are no easy remedies due to the extensive and pervasiveness of the contamination. Monitoring at the water table indicates that 45% of the 30 spatially distributed monitoring wells exceeded the drinking water standard. This indicates that there are multiple sources that are impacting water quality. Washington State has several areas which have extensive groundwater nitrate contamination. The groundwater in these areas continues to degrade over time. However, the Lower Yakima Valley is being successful in addressing this health issue because of the following reasons: the community is engaged and committed; there is one common goal; there has been extensive public education and outreach to citizens; and generating credible data using sound scientific methods. Work in this area is continuing as an ambient groundwater monitoring network is established to assess the condition of the aquifer over time. Nitrate samples are being collected from 170 wells, spatially distributed across the aquifer. This research entails quarterly sampling for two years to characterize seasonal variability and then continue annually afterward. This assessment will provide the data to statistically determine trends in nitrate concentrations across the aquifer, over time. Thirty-three of these wells are monitoring wells that are screened across the aquifer. The water quality from these wells are indicative of activities at the land surface. Additional work is being conducted to identify land use management practices that are effective in limiting nitrate migration through the soil column. Tracking nitrate in the soil column every season is an important component of bridging land-use practices with the fate and transport of nitrate through the subsurface. Patience, tenacity, and the ability to think outside the box are essential for dealing with widespread nitrate contamination of groundwater.

Keywords: community, groundwater, monitoring, nitrate

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Authors: Hamzeh Alipour, Masoumeh Bagheri, Abbasali Raz, Javad Dadgar Pakdel, Kourosh Azizi, Aboozar Soltani, Mohammad Djaefar Moemenbellah-Fard

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Background: Maggot debridement therapy is an appropriate, effective, and controlled method using sterilized larvae of Luciliasericata (L.sericata) to treat wounds. Netrin-A is an enzyme in the Laminins family which secreted from salivary gland of L.sericata with a central role in neural regeneration and angiogenesis. This study aimed to production of new recombinant Netrin-A protein of Luciliasericata larvae by baculovirus expression vector system (BEVS) in SF9. Material and methods: In the first step, gene structure was subjected to the in silico studies, which were include determination of Antibacterial activity, Prion formation risk, homology modeling, Molecular docking analysis, and Optimization of recombinant protein. In the second step, the Netrin-A gene was cloned and amplified in pTG19 vector. After digestion with BamH1 and EcoR1 restriction enzymes, it was cloned in pFastBac HTA vector. It was then transformed into DH10Bac competent cells, and the recombinant Bacmid was subsequently transfected into insect Sf9 cells. The expressed recombinant Netrin-A was thus purified in the Ni-NTA agarose. This protein evaluation was done using SDS-PAGE and western blot, respectively. Finally, its concentration was calculated with the Bradford assay method. Results: The Bacmid vector structure with Netrin-A was successfully constructed and then expressed as Netrin-A protein in the Sf9 cell lane. The molecular weight of this protein was 52 kDa with 404 amino acids. In the in silico studies, fortunately, we predicted that recombinant LSNetrin-A have Antibacterial activity and without any prion formation risk.This molecule hasa high binding affinity to the Neogenin and a lower affinity to the DCC-specific receptors. Signal peptide located between amino acids 24 and 25. The concentration of Netrin-A recombinant protein was calculated to be 48.8 μg/ml. it was confirmed that the characterized gene in our previous study codes L. sericata Netrin-A enzyme. Conclusions: Successful generation of the recombinant Netrin-A, a secreted protein in L.sericata salivary glands, and because Luciliasericata larvae are used in larval therapy. Therefore, the findings of the present study could be useful to researchers in future studies on wound healing.

Keywords: blowfly, BEVS, gene, immature insect, recombinant protein, Sf9

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Authors: Mónica P. Cala, Juan S. Carreño, Roland J.W. Meesters

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Recently, collection of biological fluids on special filter papers has become a popular micro-sampling technique. Especially, the dried blood spot (DBS) micro-sampling technique has gained much attention and is momently applied in various life sciences reserach areas. As a result of this popularity, DBS are not only intensively competing with the venous blood sampling method but are at this moment widely applied in numerous bioanalytical assays. In particular, in the screening of inherited metabolic diseases, pharmacokinetic modeling and in therapeutic drug monitoring. Recently, microsampling techniques were also introduced in “omics” areas, whereunder metabolomics. For a metabolic profiling study we applied micro-sampling of biological fluids (blood and plasma) from healthy controls and from women with breast cancer. From blood samples, dried blood and plasma samples were prepared by spotting 8uL sample onto pre-cutted 5-mm paper disks followed by drying of the disks for 100 minutes. Dried disks were then extracted by 100 uL of methanol. From liquid blood and plasma samples 40 uL were deproteinized with methanol followed by centrifugation and collection of supernatants. Supernatants and extracts were evaporated until dryness by nitrogen gas and residues derivated by O-methyxyamine and MSTFA. As internal standard C17:0-methylester in heptane (10 ppm) was used. Deconvolution and alignment of and full scan (m/z 50-500) MS data were done by AMDIS and SpectConnect (http://spectconnect.mit.edu) software, respectively. Statistical Data analysis was done by Principal Component Analysis (PCA) using R software. The results obtained from our preliminary study indicate that the use of dried blood/plasma on paper disks could be a powerful new tool in metabolic profiling. Many of the metabolites observed in plasma (liquid/dried) were also positively identified in whole blood samples (liquid/dried). Whole blood could be a potential substitute matrix for plasma in Metabolomic profiling studies as well also micro-sampling techniques for the collection of samples in clinical studies. It was concluded that the separation of the different sample methodologies (liquid vs. dried) as observed by PCA was due to different sample treatment protocols applied. More experiments need to be done to confirm obtained observations as well also a more rigorous validation .of these micro-sampling techniques is needed. The novelty of our approach can be found in the application of different biological fluid micro-sampling techniques for metabolic profiling.

Keywords: biofluids, breast cancer, metabolic profiling, micro-sampling

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Authors: Fazli Rahim Shinwari, Ulrich Dittmer

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Like many megacities around the world, Kabul is facing severe problems due to the rising frequency of urban flooding. Since 2001, Kabul is experiencing rapid population growth because of the repatriation of refugees and internal migration. Due to unplanned development, green areas inside city and hilly areas within and around the city are converted into new housing towns that had increased runoff. Trenches along the roadside comprise the unplanned drainage network of the city that drains the combined sewer flow. In rainy season overflow occurs, and after streets become dry, the dust particles contaminate the air which is a major cause of air pollution in Kabul city. In this study, a stormwater management model is introduced as a basis for a systematic approach to urban drainage planning in Kabul. For this purpose, Kabul city is delineated into 8 watersheds with the help of one-meter resolution LIDAR DEM. Storm, water management model, is developed for Wazirabad catchment by using available data and literature values. Due to lack of long term metrological data, the model is only run for hourly rainfall data of a rain event that occurred in April 2016. The rain event from 1st to 3rd April with maximum intensity of 3mm/hr caused huge flooding in Wazirabad Catchment of Kabul City. Model-estimated flooding at some points of the catchment as an actual measurement of flooding was not possible; results were compared with information obtained from local people, Kabul Municipality and Capital Region Independent Development Authority. The model helped to identify areas where flooding occurred because of less capacity of drainage system and areas where the main reason for flooding is due to blockage in the drainage canals. The model was used for further analysis to find a sustainable solution to the problem. The option to construct new canals was analyzed, and two new canals were proposed that will reduce the flooding frequency in Wazirabad catchment of Kabul city. By developing the methodology to develop a stormwater management model from digital data and information, the study had fulfilled the primary objective, and similar methodology can be used for other catchments of Kabul city to prepare an emergency and long-term plan for drainage system of Kabul city.

Keywords: urban hydrology, storm water management, modeling, SWMM, GEO-SWMM, GIS, identification of flood vulnerable areas, urban flooding analysis, sustainable urban drainage

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Authors: Tomilola J. Ajayi

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A category of nanovalves system containing the α-cyclodextrin (α-CD) ring on a stalk tethered to the pores of mesoporous silica nanoparticles (MSN) is theoretically and computationally modelled. This functions to control opening and blocking of the MSN pores for efficient targeted drug release system. Modeling of the nanovalves is based on the interaction between α-CD and the stalk (p-anisidine) in relation to pH variation. Conformational analysis was carried out prior to the formation of the inclusion complex, to find the global minimum of both neutral and protonated stalk. B3LYP/6-311G**(d, p) basis set was employed to attain all theoretically possible conformers of the stalk. Six conformers were taken into considerations, and the dihedral angle (θ) around the reference atom (N17) of the p-anisidine stalk was scanned from 0° to 360° at 5° intervals. The most stable conformer was obtained at a dihedral angle of 85.3° and was fully optimized at B3LYP/6-311G**(d, p) level of theory. The most stable conformer obtained from conformational analysis was used as the starting structure to create the inclusion complexes. 9 complexes were formed by moving the neutral guest into the α-CD cavity along the Z-axis in 1 Å stepwise while keeping the distance between dummy atom and OMe oxygen atom on the stalk restricted. The dummy atom and the carbon atoms on α-CD structure were equally restricted for orientation A (see Scheme 1). The generated structures at each step were optimized with B3LYP/6-311G**(d, p) methods to determine their energy minima. Protonation of the nitrogen atom on the stalk occurs at acidic pH, leading to unsatisfactory host-guest interaction in the nanogate; hence there is dethreading. High required interaction energy and conformational change are theoretically established to drive the release of α-CD at a certain pH. The release was found to occur between pH 5-7 which agreed with reported experimental results. In this study, we applied the theoretical model for the prediction of the experimentally observed pH-responsive nanovalves which enables blocking, and opening of mesoporous silica nanoparticles pores for targeted drug release system. Our results show that two major factors are responsible for the cargo release at acidic pH. The higher interaction energy needed for the complex/nanovalve formation to exist after protonation as well as conformational change upon protonation are driving the release due to slight pH change from 5 to 7.

Keywords: nanovalves, nanogate, mesoporous silica nanoparticles, cargo

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Authors: X. Li, Y. Liu, H. Wong, B. Pardoen, A. Fabbri, F. McGregor, E. Liu

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The target of this paper is to investigate how saturated poroelastic soils subject to freezing temperatures behave and how different boundary conditions can intervene and affect the thermo-hydro-mechanical (THM) responses, based on a particular but classical configuration of a finite homogeneous soil layer studied by Terzaghi. The essential relations on the constitutive behavior of a freezing soil are firstly recalled: ice crystal - liquid water thermodynamic equilibrium, hydromechanical constitutive equations, momentum balance, water mass balance, and the thermal diffusion equation, in general, non-linear case where material parameters are state-dependent. The system of equations is firstly linearized, assuming all material parameters to be constants, particularly the permeability of liquid water, which should depend on the ice content. Two analytical solutions solved by the classic Laplace transform are then developed, accounting for two different sets of boundary conditions. Afterward, the general non-linear equations with state-dependent parameters are solved using a commercial code COMSOL based on finite elements method to obtain numerical results. The validity of this numerical modeling is partially verified using the analytical solution in the limiting case of state-independent parameters. Comparison between the results given by the linearized analytical solutions and the non-linear numerical model reveals that the above-mentioned linear computation will always underestimate the liquid pore pressure and displacement, whatever the hydraulic boundary conditions are. In the nonlinear model, the faster growth of ice crystals, accompanying the subsequent reduction of permeability of freezing soil layer, makes a longer duration for the depressurization of water liquid and slower settlement in the case where the ground surface is swiftly covered by a thin layer of ice, as well as a bigger global liquid pressure and swelling in the case of the impermeable ground surface. Nonetheless, the analytical solutions based on linearized equations give a correct order-of-magnitude estimate, especially at moderate temperature variations, and remain a useful tool for preliminary design checks.

Keywords: chemical potential, cryosuction, Laplace transform, multiphysics coupling, phase transformation, thermodynamic equilibrium

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Authors: Daniela Velichová

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Plenty of ideas on how to adopt active learning methods in education are available nowadays. Mathematics is a subject where the active involvement of students is required in particular in order to achieve desirable results regarding sustainable knowledge and deep understanding. The present article is based on the outcomes of an Erasmus+ project DrIVE-MATH, that was aimed at developing a novel and integrated framework to teach maths classes in engineering courses at the university level. It is fundamental for students from the early years of their academic life to have agile minds. They must be prepared to adapt to their future working environments, where enterprises’ views are always evolving, where all collaborate in teams, and relations between peers are thought for the well-being of the whole - workers and company profit. This reality imposes new requirements on higher education in terms of adaptation of different pedagogical methods, such as project-based and active-learning methods used within the course curricula. Active learning methodologies are regarded as an effective way to prepare students to meet the challenges posed by enterprises and to help them in building critical thinking, analytic reasoning, and insight to the solved complex problems from different perspectives. Fostering learning-by-doing activities in the pedagogical process can help students to achieve learning independence, as they could acquire deeper conceptual understanding by experimenting with the abstract concept in a more interesting, useful, and meaningful way. Clear information about learning outcomes and goals might help students to take more responsibility for their learning results. Active learning methods implemented by the project team members in their teaching practice, eduScrum and Jigsaw in particular, proved to provide better scientific and soft skills support to students than classical teaching methods. EduScrum method enables teachers to generate a working environment that stimulates students' working habits and self-initiative as they become aware of their responsibilities within the team, their own acquired knowledge, and their abilities to solve problems independently, though in collaboration with other team members. This method enhances collaborative learning, as students are working in teams towards a common goal - knowledge acquisition, while they are interacting with each other and evaluated individually. Teams consisting of 4-5 students work together on a list of problems - sprint; each member is responsible for solving one of them, while the group leader – a master, is responsible for the whole team. A similar principle is behind the Jigsaw technique, where the classroom activity makes students dependent on each other to succeed. Students are divided into groups, and assignments are split into pieces, which need to be assembled by the whole group to complete the (Jigsaw) puzzle. In this paper, analysis of students’ perceptions concerning the achievement of deeper conceptual understanding in mathematics and the development of soft skills, such as self-motivation, critical thinking, flexibility, leadership, responsibility, teamwork, negotiation, and conflict management, is presented. Some new challenges are discussed as brought by introducing active learning methods in the basic mathematics courses. A few examples of sprints developed and used in teaching basic maths courses at technical universities are presented in addition.

Keywords: active learning methods, collaborative learning, conceptual understanding, eduScrum, Jigsaw, soft skills

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Authors: Zahirah Dhurmeea, Iker Zudaire, Heidi Pethybridge, Emmanuel Chassot, Maria Cedras, Natacha Nikolic, Jerome Bourjea, Wendy West, Chandani Appadoo, Nathalie Bodin

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Scientific advice on the status of fish stocks relies on indicators that are based on strong assumptions on biological parameters such as condition, maturity and fecundity. Currently, information on the biology of albacore tuna, Thunnus alalunga, in the Indian Ocean is scarce. Consequently, many parameters used in stock assessment models for Indian Ocean albacore originate largely from other studied stocks or species of tuna. Inclusion of incorrect biological data in stock assessment models would lead to inappropriate estimates of stock status used by fisheries manager’s to establish future catch allowances. The reproductive biology of albacore tuna in the western Indian Ocean was examined through analysis of the sex ratio, spawning season, length-at-maturity (L50), spawning frequency, fecundity and fish condition. In addition, the total lipid content (TL) and lipid class composition in the gonads, liver and muscle tissues of female albacore during the reproductive cycle was investigated. A total of 923 female and 867 male albacore were sampled from 2013 to 2015. A bias in sex-ratio was found in favour of females with fork length (LF) <100 cm. Using histological analyses and gonadosomatic index, spawning was found to occur between 10°S and 30°S, mainly to the east of Madagascar from October to January. Large females contributed more to reproduction through their longer spawning period compared to small individuals. The L50 (mean ± standard error) of female albacore was estimated at 85.3 ± 0.7 cm LF at the vitellogenic 3 oocyte stage maturity threshold. Albacore spawn on average every 2.2 days within the spawning region and spawning months from November to January. Batch fecundity varied between 0.26 and 2.09 million eggs and the relative batch fecundity (mean  standard deviation) was estimated at 53.4 ± 23.2 oocytes g-1 of somatic-gutted weight. Depending on the maturity stage, TL in ovaries ranged from 7.5 to 577.8 mg g-1 of wet weight (ww) with different proportions of phospholipids (PL), wax esters (WE), triacylglycerol (TAG) and sterol (ST). The highest TL were observed in immature (mostly TAG and PL) and spawning capable ovaries (mostly PL, WE and TAG). Liver TL varied from 21.1 to 294.8 mg g-1 (ww) and acted as an energy (mainly TAG and PL) storage prior to reproduction when the lowest TL was observed. Muscle TL varied from 2.0 to 71.7 g-1 (ww) in mature females without a clear pattern between maturity stages, although higher values of up to 117.3 g-1 (ww) was found in immature females. TL results suggest that albacore could be viewed predominantly as a capital breeder relying mostly on lipids stored before the onset of reproduction and with little additional energy derived from feeding. This study is the first one to provide new information on the reproductive development and classification of albacore in the western Indian Ocean. The reproductive parameters will reduce uncertainty in current stock assessment models which will eventually promote sustainability of the fishery.

Keywords: condition, size-at-maturity, spawning behaviour, temperate tuna, total lipid content

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Authors: Leonid Zhukov, Dmytro Petrenko

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Optical thermometry has no alternative in many cases of industrial most effective continuous temperature control. Classical optical thermometry technologies can be used on available for pyrometers controlled objects with stable radiation characteristics and transmissivity of the intermediate medium. Without using temperature corrections, it is possible in the case of a “black” body for energy pyrometry and the cases of “black” and “grey” bodies for spectral ratio pyrometry or with using corrections – for any colored bodies. Consequently, with increasing the number of operating waves, optical thermometry possibilities to reduce methodical errors significantly expand. That is why, in recent 25-30 years, research works have been reoriented on more perfect spectral (multicolor) thermometry technologies. There are two physical material substances, i.e., substance (controlled object) and electromagnetic field (thermal radiation), to be operated in optical thermometry. Heat is transferred by radiation; therefore, radiation has the energy, entropy, and temperature. Optical thermometry was originating simultaneously with the developing of thermal radiation theory when the concept and the term "radiation temperature" was not used, and therefore concepts and terms "conditional temperatures" or "pseudo temperature" of controlled objects were introduced. They do not correspond to the physical sense and definitions of temperature in thermodynamics, molecular-kinetic theory, and statistical physics. Launched by the scientific thermometric society, discussion about the possibilities of temperature measurements of objects, including colored bodies, using the temperatures of their radiation is not finished. Are the information about controlled objects transferred by their radiation enough for temperature measurements? The positive and negative answers on this fundamental question divided experts into two opposite camps. Recent achievements of spectral thermometry develop events in her favour and don’t leave any hope for skeptics. This article presents the results of investigations and developments in the field of spectral thermometry carried out by the authors in the Department of Thermometry and Physics-Chemical Investigations. The authors have many-year’s of experience in the field of modern optical thermometry technologies. Innovative technologies of optical continuous temperature control have been developed: symmetric-wave, two-color compensative, and based on obtained nonlinearity equation of spectral emissivity distribution linear, two-range, and parabolic. Тhe technologies are based on direct measurements of physically substantiated and proposed by Prof. L. Zhukov, radiation temperatures with the next calculation of the controlled object temperature using this radiation temperatures and corresponding mathematical models. Тhe technologies significantly increase metrological characteristics of continuous contactless and light-guide temperature control in energy, metallurgical, ceramic, glassy, and other productions. For example, under the same conditions, the methodical errors of proposed technologies are less than the errors of known spectral and classical technologies in 2 and 3-13 times, respectively. Innovative technologies provide quality products obtaining at the lowest possible resource-including energy costs. More than 600 publications have been published on the completed developments, including more than 100 domestic patents, as well as 34 patents in Australia, Bulgaria, Germany, France, Canada, the USA, Sweden, and Japan. The developments have been implemented in the enterprises of USA, as well as Western Europe and Asia, including Germany and Japan.

Keywords: emissivity, radiation temperature, object temperature, spectral thermometry

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Authors: Bitewulign Mekonnen

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

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

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Authors: Maryam Hajibaba, Mohsen Soltanpour, Mehrnoosh Abbasian, S. Abbas Haghshenas

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This paper aims to evaluate the main role of mangroves forests on the unique hydrodynamic characteristics of the Khuran Strait (KS) in the Persian Gulf. Investigation of hydrodynamic conditions of KS is vital to predict and estimate sedimentation and erosion all over the protected areas north of Qeshm Island. KS (or Tang-e-Khuran) is located between Qeshm Island and the Iranian mother land and has a minimum width of approximately two kilometers. Hydrodynamics of the strait is dominated by strong tidal currents of up to 2 m/s. The bathymetry of the area is dynamic and complicated as 1) strong currents do exist in the area which lead to seemingly sand dune movements in the middle and southern parts of the strait, and 2) existence a vast area with mangrove coverage next to the narrowest part of the strait. This is why ordinary modeling schemes with normal mesh resolutions are not capable for high accuracy estimations of current fields in the KS. A comprehensive set of measurements were carried out with several components, to investigate the hydrodynamics and morpho-dynamics of the study area, including 1) vertical current profiling at six stations, 2) directional wave measurements at four stations, 3) water level measurements at six stations, 4) wind measurements at one station, and 5) sediment grab sampling at 100 locations. Additionally, a set of periodic hydrographic surveys was included in the program. The numerical simulation was carried out by using Delft3D – Flow Module. Model calibration was done by comparing water levels and depth averaged velocity of currents against available observational data. The results clearly indicate that observed data and simulations only fit together if a realistic perspective of the mangrove area is well captured by the model bathymetry data. Generating unstructured grid by using RGFGRID and QUICKIN, the flow model was driven with water level time-series at open boundaries. Adopting the available field data, the key role of mangrove area on the hydrodynamics of the study area can be studied. The results show that including the accurate geometry of the mangrove area and consideration of its sponge-like behavior are the key aspects through which a realistic current field can be simulated in the KS.

Keywords: Khuran Strait, Persian Gulf, tide, current, Delft3D

Procedia PDF Downloads 211

Authors: Nicholas Aerne, John P. Parmigiani

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There is an increasing desire for renewable and sustainable energy sources to replace fossil fuels. This desire is the result of several factors. First, is the role of fossil fuels in climate change. Scientific data clearly shows that global warming is occurring. It has also been concluded that it is highly likely human activity; specifically, the combustion of fossil fuels, is a major cause of this warming. Second, despite the current surplus of petroleum, fossil fuels are a finite resource and will eventually become scarce and alternatives, such as clean or renewable energy will be needed. Third, operations to obtain fossil fuels such as fracking, off-shore oil drilling, and strip mining are expensive and harmful to the environment. Given these environmental impacts, there is a need to replace fossil fuels with renewable energy sources as a primary energy source. Various sources of renewable energy exist. Many familiar sources obtain renewable energy from the sun and natural environments of the earth. Common examples include solar, hydropower, geothermal heat, ocean waves and tides, and wind energy. Often obtaining significant energy from these sources requires physically-large, sophisticated, and expensive equipment (e.g., wind turbines, dams, solar panels, etc.). Other sources of renewable energy are from the man-made environment. An example is municipal water distribution systems. The movement of water through the pipelines of these systems typically requires the reduction of hydraulic pressure through the use of pressure reducing valves. These valves are needed to reduce upstream supply-line pressures to levels suitable downstream users. The energy associated with this reduction of pressure is significant but is currently not harvested and is simply lost. While the integrity of municipal water supplies is of paramount importance, one can certainly envision means by which this lost energy source could be safely accessed. This paper provides a technical description and analysis of one such means by the technology company InPipe Energy to generate hydroelectricity by harvesting energy from municipal water distribution pressure reducing valve stations. Specifically, InPipe Energy proposes to install hydropower turbines in parallel with existing pressure reducing valves in municipal water distribution systems. InPipe Energy in partnership with Oregon State University has evaluated this approach and built a prototype system at the O. H. Hinsdale Wave Research Lab. The Oregon State University evaluation showed that the prototype system rapidly and safely initiates, maintains, and ceases power production as directed. The outgoing water pressure remained constant at the specified set point throughout all testing. The system replicates the functionality of the pressure reducing valve and ensures accurate control of down-stream pressure. At a typical water-distribution-system pressure drop of 60 psi the prototype, operating at an efficiency 64%, produced approximately 5 kW of electricity. Based on the results of this study, this proposed method appears to offer a viable means of producing significant amounts of clean renewable energy from existing pressure reducing valves.

Keywords: pressure reducing valve, renewable energy, sustainable energy, water supply

Procedia PDF Downloads 205

Authors: Ehsan Moradi, Parisa Paikhaste, Amir Alam Beigi, Seyedeh Somayeh Bathaei

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The need for entrepreneurial sustainability is as important as the entrepreneurship category itself. By transferring competencies in a sustainable entrepreneurship framework, entrepreneurship education can make a significant contribution to the effectiveness of businesses, especially for start-up entrepreneurs. This study analyzes the essential competencies of students in the development of sustainable entrepreneurship. It is an applied causal study in terms of nature and field in terms of data collection. The main purpose of this research project is to study and explain the dimensions of sustainability entrepreneurship competencies among agricultural students. The statistical population consists of 730 junior and senior undergraduate students of the Campus of Agriculture and Natural Resources, University of Tehran. The sample size was determined to be 120 using the Cochran's formula, and the convenience sampling method was used. Face validity, structure validity, and diagnostic methods were used to evaluate the validity of the research tool and Cronbach's alpha and composite reliability to evaluate its reliability. Structural equation modeling (SEM) was used by the confirmatory factor analysis (CFA) method to prepare a measurement model for data processing. The results showed that seven key dimensions play a role in shaping sustainable entrepreneurial development competencies: systems thinking competence (STC), embracing diversity and interdisciplinary (EDI), foresighted thinking (FTC), normative competence (NC), action competence (AC), interpersonal competence (IC), and strategic management competence (SMC). It was found that acquiring skills in SMC by creating the ability to plan to achieve sustainable entrepreneurship in students through the relevant mechanisms can improve entrepreneurship in students by adopting a sustainability attitude. While increasing students' analytical ability in the field of social and environmental needs and challenges and emphasizing curriculum updates, AC should pay more attention to the relationship between the curriculum and its content in the form of entrepreneurship culture promotion programs. In the field of EDI, it was found that the success of entrepreneurs in terms of sustainability and business sustainability of start-up entrepreneurs depends on their interdisciplinary thinking. It was also found that STC plays an important role in explaining the relationship between sustainability and entrepreneurship. Therefore, focusing on these competencies in agricultural education to train start-up entrepreneurs can lead to sustainable entrepreneurship in the agricultural higher education system.

Keywords: sustainable entrepreneurship, entrepreneurship education, competency, agricultural higher education

Procedia PDF Downloads 144

Authors: Nathaniel Bayode Eniolorunda, Murtala Abubakar Gada, Sheikh Danjuma Abubakar

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The challenge of erosion in the study area is persistent, suggesting the need for a better understanding of the mechanisms that drive it. Thus, the study evolved a predictive erosion model (RUSLE_Sok), deploying Remote Sensing (RS) and Geographical Information System (GIS) tools. The nature and pattern of the factors of erosion were characterized, while soil losses were quantified. Factors’ impacts were also measured, and the morphometry of gullies was described. Data on the five factors of RUSLE and distances to settlements, rivers and roads (K, R, LS, P, C, DS DRd and DRv) were combined and processed following standard RS and GIS algorithms. Harmonized World Soil Data (HWSD), Shuttle Radar Topographical Mission (SRTM) image, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Sentinel-2 image accessed and processed within the Google Earth Engine, road network and settlements were the data combined and calibrated into the factors for erosion modeling. A gully morphometric study was conducted at some purposively selected sites. Factors of soil erosion showed low, moderate, to high patterns. Soil losses ranged from 0 to 32.81 tons/ha/year, classified into low (97.6%), moderate (0.2%), severe (1.1%) and very severe (1.05%) forms. The multiple regression analysis shows that factors statistically significantly predicted soil loss, F (8, 153) = 55.663, p < .0005. Except for the C-Factor with a negative coefficient, all other factors were positive, with contributions in the order of LS>C>R>P>DRv>K>DS>DRd. Gullies are generally from less than 100m to about 3km in length. Average minimum and maximum depths at gully heads are 0.6 and 1.2m, while those at mid-stream are 1 and 1.9m, respectively. The minimum downstream depth is 1.3m, while that for the maximum is 4.7m. Deeper gullies exist in proximity to rivers. With minimum and maximum gully elevation values ranging between 229 and 338m and an average slope of about 3.2%, the study area is relatively flat. The study concluded that major erosion influencers in the study area are topography and vegetation cover and that the RUSLE_Sok well predicted soil loss more effectively than ordinary RUSLE. The adoption of conservation measures such as tree planting and contour ploughing on sloppy farmlands was recommended.

Keywords: RUSLE_Sok, Sokoto, google earth engine, sentinel-2, erosion

Procedia PDF Downloads 78

Authors: Noah Odion, Honest Jimu, Blessing Atinuke Afuape

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Introduction: Motor control and gait abnormalities are commonly observed in individuals with autism spectrum disorder (ASD), affecting their mobility and coordination. Understanding the underlying neurological and biomechanical factors is essential for designing effective interventions. This study focuses on developing microchip-integrated wearable devices to capture real-time movement data from individuals with autism. By applying computational models to the collected data, we aim to analyze motor control patterns and gait abnormalities, bridging a crucial knowledge gap in autism-related motor dysfunction. Methods: We designed microchip-enabled wearable devices capable of capturing precise kinematic data, including joint angles, acceleration, and velocity during movement. A cross-sectional study was conducted on individuals with ASD and a control group to collect comparative data. Computational modelling was applied using machine learning algorithms to analyse motor control patterns, focusing on gait variability, balance, and coordination. Finite element models were also used to simulate muscle and joint dynamics. The study employed descriptive and analytical methods to interpret the motor data. Results: The wearable devices effectively captured detailed movement data, revealing significant gait variability in the ASD group. For example, gait cycle time was 25% longer, and stride length was reduced by 15% compared to the control group. Motor control analysis showed a 30% reduction in balance stability in individuals with autism. Computational models successfully predicted movement irregularities and helped identify motor control deficits, particularly in the lower limbs. Conclusions: The integration of microchip-based wearable devices with computational models offers a powerful tool for diagnosing and treating motor control deficits in autism. These results have significant implications for patient care, providing objective data to guide personalized therapeutic interventions. The findings also contribute to the broader field of neuroscience by improving our understanding of the motor dysfunctions associated with ASD and other neurodevelopmental disorders.

Keywords: motor control, gait abnormalities, autism, wearable devices, microchips, computational modeling, kinematic analysis, neurodevelopmental disorders

Procedia PDF Downloads 25

Authors: Prashant Gupta

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Throughout India, temples have served as cultural centers, commerce hubs, art galleries, educational institutions, and social centers in addition to being places of worship since centuries. Across the country, there are over two million temples, which are crucial economic hubs, attracting devotees and tourists worldwide. In India, we have 53 temples per each 100,000 Indians. As per NSSO survey, the temple economy is worth about $40 billion and 2.32 per cent of GDP based on major temple’s survey, which only includes formal sector. It could be much larger as an actual estimation has not been done yet. In India, 43.1% of total economy represents informal sector. Over 10 billion domestic tourists visit to new destinations every year within India. Even 20 per cent of the 90 million foreign tourists visited Madurai and Mahabalipuram temples which became the most visited tourist spot in 2022. Recently the current central government in power have started revitalizing the ancient Indian civilization by reconstructing and beautifying the major temples of India i.e., Kashi Vishwanath Corridor, Mahakaleshwara Temple, Kedarnath, Ayodhya etc. The reason researcher chose Kashi as a case study because it is known as a Spiritual Capital of India, which is also the abode for the spread of Hinduism, Buddhism, Jainism and Sikkism, which are core Sanatan Dharmic practices. 17,800 Million INR Amount was spend to redevelop Kashi Vishwanath Corridor since 2019. RESEARCH OBJECTIVES 1. To assess historical contribution of temples in socio economic development and revival of Indic Civilization. 2. To examine the role of temples redevelopment for informal sector businesses. 3. To identify the sub-sectors of informal sector businesses 4. To identify products and services of informal businesses for investigation of marketing strategies and business development. PROPOSED METHODS AND PROCEDURES This study will follow a mixed approach, employing both qualitative and quantitative methods of research. To conduct the study, data will be collected from 500 informal business owners through structured questionnaire and interview instruments. The informal business owners will be selected using a systematic random sampling technique. In addition, documents from government offices of the last 10 years of tax collection will be reviewed to substantiate the study. To analyze the study, descriptive and econometric analysis techniques will be employed. EXPECTED CONTRIBUTION OF THE PROPOSED STUDY By studying the contribution of temple re-development on informal business creation and growth, the study will be beneficial to the informal business owners and the government. For the government, scientific and empirical evidence on the contribution of temple re-development for informal business creation and growth to give evidence the study will give based infrastructural development and boosting tax collection. For informal businesses, the study will give them a detailed insight on the nature of their business and the possible future growth potential of their business, and the alternative products and services supplying to their customers in the future. Studying informal businesses will help to identify the key products and services which are majorly profitable and possess potential to multiply and grow through correct product marketing strategies and business development.

Keywords: business development, informal sector businesses, services and products marketing, temple economics

Procedia PDF Downloads 83

Authors: Valery Gulyayev, Sergey Glazunov, Elena Andrusenko, Nataliya Shlyun

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Enhancement of technology and techniques for drilling deep directed oil and gas bore-wells are of essential industrial significance because these wells make it possible to increase their productivity and output. Generally, they are used for drilling in hard and shale formations, that is why their drivage processes are followed by the emergency and failure effects. As is corroborated by practice, the principal drilling drawback occurring in drivage of long curvilinear bore-wells is conditioned by the need to obviate essential force hindrances caused by simultaneous action of the gravity, contact and friction forces. Primarily, these forces depend on the type of the technological regime, drill string stiffness, bore-hole tortuosity and its length. They can lead to the Eulerian buckling of the drill string and its sticking. To predict and exclude these states, special mathematic models and methods of computer simulation should play a dominant role. At the same time, one might note that these mechanical phenomena are very complex and only simplified approaches (‘soft string drag and torque models’) are used for their analysis. Taking into consideration that now the cost of directed wells increases essentially with complication of their geometry and enlargement of their lengths, it can be concluded that the price of mistakes of the drill string behavior simulation through the use of simplified approaches can be very high and so the problem of correct software elaboration is very urgent. This paper deals with the problem of simulating the regimes of drilling deep curvilinear bore-wells with prescribed imperfect geometrical trajectories of their axial lines. On the basis of the theory of curvilinear flexible elastic rods, methods of differential geometry, and numerical analysis methods, the 3D ‘stiff-string drag and torque model’ of the drill string bending and the appropriate software are elaborated for the simulation of the tripping in and out regimes and drilling operations. It is shown by the computer calculations that the contact and friction forces can be calculated and regulated, providing predesigned trouble-free modes of operation. The elaborated mathematic models and software can be used for the emergency situations prognostication and their exclusion at the stages of the drilling process design and realization.

Keywords: curvilinear drilling, drill string tripping in and out, contact forces, resistance forces

Procedia PDF Downloads 147

Authors: Yangzhi Li

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Network transfer of information and performance customization is now a viable method of digital industrial production in the era of Industry 4.0. Robot platforms and network platforms have grown more important in digital design and construction. The pressing need for novel building techniques is driven by the growing labor scarcity problem and increased awareness of construction safety. Robotic approaches in construction research are regarded as an extension of operational and production tools. Several technological theories related to robot autonomous recognition, which include high-performance computing, physical system modeling, extensive sensor coordination, and dataset deep learning, have not been explored using intelligent construction. Relevant transdisciplinary theory and practice research still has specific gaps. Optimizing high-performance computing and autonomous recognition visual guidance technologies improves the robot's grasp of the scene and capacity for autonomous operation. Intelligent vision guidance technology for industrial robots has a serious issue with camera calibration, and the use of intelligent visual guiding and identification technologies for industrial robots in industrial production has strict accuracy requirements. It can be considered that visual recognition systems have challenges with precision issues. In such a situation, it will directly impact the effectiveness and standard of industrial production, necessitating a strengthening of the visual guiding study on positioning precision in recognition technology. To best facilitate the handling of complicated components, an approach for the visual recognition of parts utilizing machine learning algorithms is proposed. This study will identify the position of target components by detecting the information at the boundary and corner of a dense point cloud and determining the aspect ratio in accordance with the guidelines for the modularization of building components. To collect and use components, operational processing systems assign them to the same coordinate system based on their locations and postures. The RGB image's inclination detection and the depth image's verification will be used to determine the component's present posture. Finally, a virtual environment model for the robot's obstacle-avoidance route will be constructed using the point cloud information.

Keywords: robotic construction, robotic assembly, visual guidance, machine learning

Procedia PDF Downloads 87

Authors: Monika Sobolewska, Aleksandra Majchrzak, Bartlomiej Chojnacki, Katarzyna Baruch, Adam Pilch

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The main purpose of the study is to perform the measurement and analysis of noise harmfulness in public utility facilities. The World Health Organization reports that the number of people suffering from hearing impairment is constantly increasing. The most alarming is the number of young people occurring in the statistics. The majority of scientific research in the field of hearing protection and noise prevention concern industrial and road traffic noise as the source of health problems. As the result, corresponding standards and regulations defining noise level limits are enforced. However, there is another field uncovered by profound research – leisure time. Public utility facilities such as clubs, shopping malls, sport facilities or concert halls – they all generate high-level noise, being out of proper juridical control. Among European Union Member States, the highest legislative act concerning noise prevention is the Environmental Noise Directive 2002/49/EC. However, it omits the problem discussed above and even for traffic, railway and aircraft noise it does not set limits or target values, leaving these issues to the discretion of the Member State authorities. Without explicit and uniform regulations, noise level control at places designed for relaxation and entertainment is often in the responsibility of people having little knowledge of hearing protection, unaware of the risk the noise pollution poses. Exposure to high sound levels in clubs, cinemas, at concerts and sports events may result in a progressive hearing loss, especially among young people, being the main target group of such facilities and events. The first step to change this situation and to raise the general awareness is to perform reliable measurements the results of which will emphasize the significance of the problem. This project presents the results of more than hundred measurements, performed in most types of public utility facilities in Poland. As the most suitable measuring instrument for such a research, personal noise dosimeters were used to collect the data. Each measurement is presented in the form of numerical results including equivalent and peak sound pressure levels and a detailed description considering the type of the sound source, size and furnishing of the room and the subjective sound level evaluation. In the absence of a straight reference point for the interpretation of the data, the limits specified in EU Directive 2003/10/EC were used for comparison. They set the maximum sound level values for workers in relation to their working time length. The analysis of the examined problem leads to the conclusion that during leisure time, people are exposed to noise levels significantly exceeding safe values. As the hearing problems are gradually progressing, most people underplay the problem, ignoring the first symptoms. Therefore, an effort has to be made to specify the noise regulations for public utility facilities. Without any action, in the foreseeable future the majority of Europeans will be dealing with serious hearing damage, which will have a negative impact on the whole societies.

Keywords: hearing protection, noise level limits, noise prevention, noise regulations, public utility facilities

Procedia PDF Downloads 225

Authors: Torsten Hermanns, Thoufik Al Khawli, Wolfgang Schulz

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In laser cutting as well as in long pulsed laser drilling of metals, it can be demonstrated that the ablation shape (the shape of cut faces respectively the hole shape) that is formed approaches a so-called asymptotic shape such that it changes only slightly or not at all with further irradiation. These findings are already known from the ultrashort pulse (USP) ablation of dielectric and semiconducting materials. The explanation for the occurrence of an asymptotic shape in laser cutting and long pulse drilling of metals is identified, its underlying mechanism numerically implemented, tested and clearly confirmed by comparison with experimental data. In detail, there now is a model that allows the simulation of the temporal (pulse-resolved) evolution of the hole shape in laser drilling as well as the final (asymptotic) shape of the cut faces in laser cutting. This simulation especially requires much less in the way of resources, such that it can even run on common desktop PCs or laptops. Individual parameters can be adjusted using sliders – the simulation result appears in an adjacent window and changes in real time. This is made possible by an application-specific reduction of the underlying ablation model. Because this reduction dramatically decreases the complexity of calculation, it produces a result much more quickly. This means that the simulation can be carried out directly at the laser machine. Time-intensive experiments can be reduced and set-up processes can be completed much faster. The high speed of simulation also opens up a range of entirely different options, such as metamodeling. Suitable for complex applications with many parameters, metamodeling involves generating high-dimensional data sets with the parameters and several evaluation criteria for process and product quality. These sets can then be used to create individual process maps that show the dependency of individual parameter pairs. This advanced simulation makes it possible to find global and local extreme values through mathematical manipulation. Such simultaneous optimization of multiple parameters is scarcely possible by experimental means. This means that new methods in manufacturing such as self-optimization can be executed much faster. However, the software’s potential does not stop there; time-intensive calculations exist in many areas of industry. In laser welding or laser additive manufacturing, for example, the simulation of thermal induced residual stresses still uses up considerable computing capacity or is even not possible. Transferring the principle of reduced models promises substantial savings there, too.

Keywords: asymptotic ablation shape, interactive process simulation, laser drilling, laser cutting, metamodeling, reduced modeling

Procedia PDF Downloads 216

Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: cost prediction, machine learning, project management, random forest, neural networks

Procedia PDF Downloads 60

Authors: Ciaran Friel, Jerry Suls, Mark Butler, Patrick Robles, Samantha Gordon, Frank Vicari, Karina W. Davidson

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Physical activity guidelines focus on increasing moderate-intensity activity for older adults, but adherence to recommendations remains low. This is despite the fact that scientific evidence supports that any increase in physical activity is positively correlated with health benefits. Behavior change techniques (BCTs) have demonstrated effectiveness in reducing sedentary behavior and promoting physical activity. This pilot study uses a Personalized Trials (N-of-1) design to evaluate the efficacy of using four BCTs to promote an increase in low-intensity physical activity (2,000 steps of walking per day) in adults aged 45-75 years old. The 4 BCTs tested were goal setting, action planning, feedback, and self-monitoring. BCTs were tested in random order and delivered by text message prompts requiring participant engagement. The study recruited health system employees in the target age range, without mobility restrictions and demonstrating interest in increasing their daily activity by a minimum of 2,000 steps per day for a minimum of five days per week. Participants were sent a Fitbit® fitness tracker with an established study account and password. Participants were recommended to wear the Fitbit device 24/7 but were required to wear it for a minimum of ten hours per day. Baseline physical activity was measured by Fitbit for two weeks. In the 8-week intervention phase of the study, participants received each of the four BCTs, in random order, for a two-week period. Text message prompts were delivered daily each morning at a consistent time. All prompts required participant engagement to acknowledge receipt of the BCT message. Engagement is dependent upon the BCT message and may have included recording that a detailed plan for walking has been made or confirmed a daily step goal (action planning, goal setting). Additionally, participants may have been directed to a study dashboard to view their step counts or compare themselves to their baseline average step count (self-monitoring, feedback). At the end of each two-week testing interval, participants were asked to complete the Self-Efficacy for Walking Scale (SEW_Dur), a validated measure that assesses the participant’s confidence in walking incremental distances, and a survey measuring their satisfaction with the individual BCT that they tested. At the end of their trial, participants received a personalized summary of their step data in response to each individual BCT. The analysis will examine the novel individual-level heterogeneity of treatment effect made possible by N-of-1 design and pool results across participants to efficiently estimate the overall efficacy of the selected behavioral change techniques in increasing low-intensity walking by 2,000 steps, five days per week. Self-efficacy will be explored as the likely mechanism of action prompting behavior change. This study will inform the providers and demonstrate the feasibility of an N-of-1 study design to effectively promote physical activity as a component of healthy aging.

Keywords: aging, exercise, habit, walking

Procedia PDF Downloads 93

Authors: Kumar Shashvat, Amol P. Bhondekar

Abstract:

In the midst of the five senses, odor is the most reminiscent and least understood. Odor testing has been mysterious and odor data fabled to most practitioners. The delinquent of recognition and classification of odor is important to achieve. The facility to smell and predict whether the artifact is of further use or it has become undesirable for consumption; the imitation of this problem hooked on a model is of consideration. The general industrial standard for this classification is color based anyhow; odor can be improved classifier than color based classification and if incorporated in machine will be awfully constructive. For cataloging of odor for peas, trees and cashews various discriminative approaches have been used Discriminative approaches offer good prognostic performance and have been widely used in many applications but are incapable to make effectual use of the unlabeled information. In such scenarios, generative approaches have better applicability, as they are able to knob glitches, such as in set-ups where variability in the series of possible input vectors is enormous. Generative models are integrated in machine learning for either modeling data directly or as a transitional step to form an indeterminate probability density function. The algorithms or models Linear Discriminant Analysis and Naive Bayes Classifier have been used for classification of the odor of cashews. Linear Discriminant Analysis is a method used in data classification, pattern recognition, and machine learning to discover a linear combination of features that typifies or divides two or more classes of objects or procedures. The Naive Bayes algorithm is a classification approach base on Bayes rule and a set of qualified independence theory. Naive Bayes classifiers are highly scalable, requiring a number of restraints linear in the number of variables (features/predictors) in a learning predicament. The main recompenses of using the generative models are generally a Generative Models make stronger assumptions about the data, specifically, about the distribution of predictors given the response variables. The Electronic instrument which is used for artificial odor sensing and classification is an electronic nose. This device is designed to imitate the anthropological sense of odor by providing an analysis of individual chemicals or chemical mixtures. The experimental results have been evaluated in the form of the performance measures i.e. are accuracy, precision and recall. The investigational results have proven that the overall performance of the Linear Discriminant Analysis was better in assessment to the Naive Bayes Classifier on cashew dataset.

Keywords: odor classification, generative models, naive bayes, linear discriminant analysis

Procedia PDF Downloads 390

Authors: Huseyin Guler, Cigdem Kosar

Abstract:

The aim of this study is to propose Bridge estimator for seasonal unit root tests. Seasonality is an important factor for many economic time series. Some variables may contain seasonal patterns and forecasts that ignore important seasonal patterns have a high variance. Therefore, it is very important to eliminate seasonality for seasonal macroeconomic data. There are some methods to eliminate the impacts of seasonality in time series. One of them is filtering the data. However, this method leads to undesired consequences in unit root tests, especially if the data is generated by a stochastic seasonal process. Another method to eliminate seasonality is using seasonal dummy variables. Some seasonal patterns may result from stationary seasonal processes, which are modelled using seasonal dummies but if there is a varying and changing seasonal pattern over time, so the seasonal process is non-stationary, deterministic seasonal dummies are inadequate to capture the seasonal process. It is not suitable to use seasonal dummies for modeling such seasonally nonstationary series. Instead of that, it is necessary to take seasonal difference if there are seasonal unit roots in the series. Different alternative methods are proposed in the literature to test seasonal unit roots, such as Dickey, Hazsa, Fuller (DHF) and Hylleberg, Engle, Granger, Yoo (HEGY) tests. HEGY test can be also used to test the seasonal unit root in different frequencies (monthly, quarterly, and semiannual). Another issue in unit root tests is the lag selection. Lagged dependent variables are added to the model in seasonal unit root tests as in the unit root tests to overcome the autocorrelation problem. In this case, it is necessary to choose the lag length and determine any deterministic components (i.e., a constant and trend) first, and then use the proper model to test for seasonal unit roots. However, this two-step procedure might lead size distortions and lack of power in seasonal unit root tests. Recent studies show that Bridge estimators are good in selecting optimal lag length while differentiating nonstationary versus stationary models for nonseasonal data. The advantage of this estimator is the elimination of the two-step nature of conventional unit root tests and this leads a gain in size and power. In this paper, the Bridge estimator is proposed to test seasonal unit roots in a HEGY model. A Monte-Carlo experiment is done to determine the efficiency of this approach and compare the size and power of this method with HEGY test. Since Bridge estimator performs well in model selection, our approach may lead to some gain in terms of size and power over HEGY test.

Keywords: bridge estimators, HEGY test, model selection, seasonal unit root

Procedia PDF Downloads 342

Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

Procedia PDF Downloads 40

Authors: S. Delgado, C. Cerrada, R. S. Gómez

Abstract:

This research introduces an approach to voxelizing the surfaces of triangular meshes with efficiency and accuracy. Our method leverages parallel equidistant scan-lines and introduces a Gap Detection technique to address the limitations of existing approaches. We present a comprehensive study showcasing the method's effectiveness, scalability, and versatility in different scenarios. Voxelization is a fundamental process in computer graphics and simulations, playing a pivotal role in applications ranging from scientific visualization to virtual reality. Our algorithm focuses on enhancing the voxelization process, especially for complex models and high resolutions. One of the major challenges in voxelization in the Graphics Processing Unit (GPU) is the high cost of discovering the same voxels multiple times. These repeated voxels incur in costly memory operations with no useful information. Our scan-line-based method ensures that each voxel is detected exactly once when processing the triangle, enhancing performance without compromising the quality of the voxelization. The heart of our approach lies in the use of parallel, equidistant scan-lines to traverse the interiors of triangles. This minimizes redundant memory operations and avoids revisiting the same voxels, resulting in a significant performance boost. Moreover, our method's computational efficiency is complemented by its simplicity and portability. Written as a single compute shader in Graphics Library Shader Language (GLSL), it is highly adaptable to various rendering pipelines and hardware configurations. To validate our method, we conducted extensive experiments on a diverse set of models from the Stanford repository. Our results demonstrate not only the algorithm's efficiency, but also its ability to produce 26 tunnel free accurate voxelizations. The Gap Detection technique successfully identifies and addresses gaps, ensuring consistent and visually pleasing voxelized surfaces. Furthermore, we introduce the Slope Consistency Value metric, quantifying the alignment of each triangle with its primary axis. This metric provides insights into the impact of triangle orientation on scan-line based voxelization methods. It also aids in understanding how the Gap Detection technique effectively improves results by targeting specific areas where simple scan-line-based methods might fail. Our research contributes to the field of voxelization by offering a robust and efficient approach that overcomes the limitations of existing methods. The Gap Detection technique fills a critical gap in the voxelization process. By addressing these gaps, our algorithm enhances the visual quality and accuracy of voxelized models, making it valuable for a wide range of applications. In conclusion, "Closing the Gap: Efficient Voxelization with Equidistant Scan-lines and Gap Detection" presents an effective solution to the challenges of voxelization. Our research combines computational efficiency, accuracy, and innovative techniques to elevate the quality of voxelized surfaces. With its adaptable nature and valuable innovations, this technique could have a positive influence on computer graphics and visualization.

Keywords: voxelization, GPU acceleration, computer graphics, compute shaders

Procedia PDF Downloads 75

Authors: Yumei Han, Ling Li, Naiqing Song, Xiaoping Yang, Yuping Han

Abstract:

Educational think tank is agreeably regarded as significant part of a nation’s soft power to promote the scientific and democratic level of educational policy making, and it plays critical role of bridging educational research in higher institutions and educational policy making. This study explores the concept, functions and significance of educational think tank in China, and conceptualizes a three dimensional framework to analyze the approaches of transforming research-based higher institutions into effective educational think tanks to serve educational policy making in the nation wide. Since 2014, the Ministry of Education P.R. China has been promoting the strategy of developing new type of educational think tanks in higher institutions, and such a strategy has been put into the agenda for the 13th Five Year Plan for National Education Development released in 2017.In such context, increasing scholars conduct studies to put forth strategies of promoting the development and transformation of new educational think tanks to serve educational policy making process. Based on literature synthesis, policy text analysis, and analysis of theories about policy making process and relationship between educational research and policy-making, this study constructed a three dimensional conceptual framework to address the following questions: (a) what are the new features of educational think tanks in the new era comparing traditional think tanks, (b) what are the functional objectives of the new educational think tanks, (c) what are the organizational patterns and mechanism of the new educational think tanks, (d) in what approaches traditional research-based higher institutions can be developed or transformed into think tanks to effectively serve the educational policy making process. The authors adopted case study approach on five influential education policy study centers affiliated with top higher institutions in China and applied the three dimensional conceptual framework to analyze their functional objectives, organizational patterns as well as their academic pathways that researchers use to contribute to the development of think tanks to serve education policy making process.Data was mainly collected through interviews with center administrators, leading researchers and academic leaders in the institutions. Findings show that: (a) higher institution based think tanks mainly function for multi-level objectives, providing evidence, theoretical foundations, strategies, or evaluation feedbacks for critical problem solving or policy-making on the national, provincial, and city/county level; (b) higher institution based think tanks organize various types of research programs for different time spans to serve different phases of policy planning, decision making, and policy implementation; (c) in order to transform research-based higher institutions into educational think tanks, the institutions must promote paradigm shift that promotes issue-oriented field studies, large data mining and analysis, empirical studies, and trans-disciplinary research collaborations; and (d) the five cases showed distinguished features in their way of constructing think tanks, and yet they also exposed obstacles and challenges such as independency of the think tanks, the discourse shift from academic papers to consultancy report for policy makers, weakness in empirical research methods, lack of experience in trans-disciplinary collaboration. The authors finally put forth implications for think tank construction in China and abroad.

Keywords: education policy-making, educational research, educational think tank, higher institution

Procedia PDF Downloads 159