Search results for: combined analytical-numerical solution

Authors: Francis O. Nwawuru, Jeremiah N. Ezeora

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In this paper, a new algorithm for solving the system of split equilibrium and fixed point problems in real Hilbert spaces is considered. The equilibrium bifunction involves a nite family of pseudo-monotone mappings, which is an improvement over monotone operators. More so, it turns out that the solution of the finite family of nonexpansive mappings. The regularized parameters do not depend on Lipschitz constants. Also, the computations of the stepsize, which plays a crucial role in the convergence analysis of the proposed method, do require prior knowledge of the norm of the involved bounded linear map. Furthermore, to speed up the rate of convergence, an inertial term technique is introduced in the proposed method. Under standard assumptions on the operators and the control sequences, using a modified Halpern iteration method, we establish strong convergence, a desired result in applications. Finally, the proposed scheme is applied to solve some optimization problems. The result obtained improves numerous results announced earlier in this direction.

Keywords: equilibrium, Hilbert spaces, fixed point, nonexpansive mapping, extragradient method, regularized equilibrium

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Authors: Getachew Belay Kassahun

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Background: Locally established clinical laboratory reference intervals (RIs) are required to interpret laboratory test results for screening, diagnosis, and prognosis. The objective of this study was to establish a reference interval of clinical chemistry parameters among apparently healthy adolescents aged between 12 and 17 years in Mekelle, Tigrai, in the northern part of Ethiopia. Methods: Community-based cross-sectional study was employed from December 2018 to March 2019 in Mekelle City among 172 males and 172 females based on a Multi-stage sampling technique. Blood samples were tested for Fasting blood sugar (FBS), alanine amino transferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), Creatinine, urea, total protein, albumin (ALB), direct and indirect bilirubin (BIL.D and BIL.T) using 25 Bio system clinical chemistry analyzer. Results were analyzed using SPSS version 23 software and based on the Clinical Laboratory Standard Institute (CLSI)/ International Federation of Clinical Chemistry (IFCC) C 28-A3 Guideline which defines the reference interval as the 95% central range of 2.5th and 97.5th percentiles. Mann Whitney U test, descriptive statistics and box and whisker were statistical tools used for analysis. Results: This study observed statistically significant differences between males and females in ALP, ALT, AST, Urea and Creatinine Reference intervals. The established reference intervals for males and females, respectively, were: ALP (U/L) 79.48-492.12 versus 63.56-253.34, ALT (U/L) 4.54-23.69 versus 5.1-20.03, AST 15.7- 39.1 versus 13.3- 28.5, Urea (mg/dL) 9.33-24.99 versus 7.43-23.11, and Creatinine (mg/dL) 0.393-0.957 versus 0.301-0.846. The combined RIs for Total Protein (g/dL) were 6.08-7.85, ALB (g/dL) 4.42-5.46, FBS(mg/dL) 65-110, BIL.D (mg/dL) 0.033-0.532, and BIL.T (mg/dL) 0.106-0.812. Conclusions: The result showed a marked difference between sex and company-derived values for selected clinical chemistry parameters. Thus, the use of age and sex-specific locally established reference intervals for clinical chemistry parameters is recommended.

Keywords: reference interval, adolescent, clinical chemistry, Ethiopia

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Authors: M. L. Anitha, K. A. Radhakrishna Rao

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With the advent of modern computing technology, there is an increased demand for developing recognition systems that have the capability of verifying the identity of individuals. Recognition systems are required by several civilian and commercial applications for providing access to secured resources. Traditional recognition systems which are based on physical identities are not sufficiently reliable to satisfy the security requirements due to the use of several advances of forgery and identity impersonation methods. Recognizing individuals based on his/her unique physiological characteristics known as biometric traits is a reliable technique, since these traits are not transferable and they cannot be stolen or lost. Since the performance of biometric based recognition system depends on the particular trait that is utilized, the present work proposes a fusion approach which combines Inner knuckle print (IKP) trait of the middle, ring and index fingers with the geometrical features of hand. The hand image captured from a digital camera is preprocessed to find finger IKP as region of interest (ROI) and hand geometry features. Geometrical features are represented as the distances between different key points and IKP features are extracted by applying local binary pattern descriptor on the IKP ROI. The decision level AND fusion was adopted, which has shown improvement in performance of the combined scheme. The proposed approach is tested on the database collected at our institute. Proposed approach is of significance since both hand geometry and IKP features can be extracted from the palm region of the hand. The fusion of these features yields a false acceptance rate of 0.75%, false rejection rate of 0.86% for verification tests conducted, which is less when compared to the results obtained using individual traits. The results obtained confirm the usefulness of proposed approach and suitability of the selected features for developing biometric based recognition system based on features from palmar region of hand.

Keywords: biometrics, hand geometry features, inner knuckle print, recognition

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Authors: Kalimi Trinadh, Corinne Nouveau, A. S. Khanna, Karanveer S. Aneja, K. Ram Mohan Rao

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This study concerns improving the corrosion resistance of low alloy steel after plasma nitriding performed at variable time and temperature. Nitriding carried out in the temperature range of 450-550ᵒC for a various time period of 1-8 hrs. at 500Pa in a glow discharge plasma of H₂ and N₂ (80:20). The substrate was kept biased negatively at 250V. Following nitriding the X-ray diffraction studies shown that the phases formed were mainly γ′ (Fe₄N) and ε (Fe₂₋₃N). The ε (Fe₂₋₃N) phase found to be the dominating phase. Cross sections of the samples under scanning electron microscope point analyses revealed the presence of nitrogen in the surface region. For the assessment of corrosion resistance property, potentiodynamic polarization tests were performed in 3.5% NaCl solution. It has been shown that the plasma nitriding significantly improved the corrosion resistance when compared to the as-received steel. Furthermore, it has also been found that nitriding for 6h has more corrosion resistance than nitriding for the 8h duration. The hardness of the nitrided samples was measured by Vicker’s microhardness tester. The hardness of the nitrided steel was found to be improved much above the hardness of the steel in the as-received condition. It was found to be around two-fold of the initial hardness.

Keywords: corrosion, steel, plasma nitriding, X-ray diffraction

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Authors: Zhuanfang Fred Zhang, Tim C. Johnson, Yilin Fang, Chris E. Strickland

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A variety of geophysical techniques are available to image the opaque subsurface with little or no contact with the soil. It is common to conduct time-lapse surveys of different types for a given site for improved results of subsurface imaging. Regardless of the chosen survey methods, it is often a challenge to process the massive amount of survey data. The currently available software applications are generally based on the one-dimensional assumption for a desktop personal computer. Hence, they are usually incapable of imaging the three-dimensional (3D) processes/variables in the subsurface of reasonable spatial scales; the maximum amount of data that can be inverted simultaneously is often very small due to the capability limitation of personal computers. Presently, high-performance or integrating software that enables real-time integration of multi-process geophysical methods is needed. E4D-MP enables the integration and inversion of time-lapsed large-scale data surveys from geophysical methods. Using the supercomputing capability and parallel computation algorithm, E4D-MP is capable of processing data across vast spatiotemporal scales and in near real time. The main code and the modules of E4D-MP for inverting individual or combined data sets of time-lapse 3D electrical resistivity, spectral induced polarization, and gravity surveys have been developed and demonstrated for sub-surface imaging. E4D-MP provides capability of imaging the processes (e.g., liquid or gas flow, solute transport, cavity development) and subsurface properties (e.g., rock/soil density, conductivity) critical for successful control of environmental engineering related efforts such as environmental remediation, carbon sequestration, geothermal exploration, and mine land reclamation, among others.

Keywords: gravity survey, high-performance computing, sub-surface monitoring, electrical resistivity tomography

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Authors: Arturo Daag

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The Philippine Institute of Volcanology and Seismology (PHIVOLCS) is enhancing its liquefaction hazard map towards a detailed probabilistic approach using SDS and geophysical data. Target sites for liquefaction assessment are public schools in Metro Manila. Since target sites are in highly urbanized-setting, the objective of the project is to conduct both non-destructive geotechnical studies using Screw Driving Testing (SDFS) combined with geophysical data such as refraction microtremor array (ReMi), 3 component microtremor Horizontal to Vertical Spectral Ratio (HVSR), and ground penetrating RADAR (GPR). Initial test data was conducted in liquefaction impacted areas from the Mw 6.1 earthquake in Central Luzon last April 22, 2019 Province of Pampanga. Numerous accounts of liquefaction events were documented areas underlain by quaternary alluvium and mostly covered by recent lahar deposits. SDS estimated values showed a good correlation to actual SPT values obtained from available borehole data. Thus, confirming that SDS can be an alternative tool for liquefaction assessment and more efficient in terms of cost and time compared to SPT and CPT. Conducting borehole may limit its access in highly urbanized areas. In order to extend or extrapolate the SPT borehole data, non-destructive geophysical equipment was used. A 3-component microtremor obtains a subsurface velocity model in 1-D seismic shear wave velocity of the upper 30 meters of the profile (Vs30). For the ReMi, 12 geophone array with 6 to 8-meter spacing surveys were conducted. Microtremor data were computed through the Factor of Safety, which is the quotient of Cyclic Resistance Ratio (CRR) and Cyclic Stress Ratio (CSR). Complementary GPR was used to study the subsurface structure and used to inferred subsurface structures and groundwater conditions.

Keywords: screw drive testing, microtremor, ground penetrating RADAR, liquefaction

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Authors: Anuruddha Jagoda, Samiddhi Samarakoon, Anil Jasinghe

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In its simplest form, data quality can be defined as 'fitness for use' and it is a concept with multi-dimensions. Emergency Departments(ED) require information to treat patients and on the other hand it is the primary source of information regarding accidents, injuries, emergencies etc. Also, it is the starting point of various patient registries, databases and surveillance systems. This interventional study was carried out to improve data quality at the ED of the National Hospital of Sri Lanka (NHSL) by introducing an e health solution to improve data quality. The NHSL is the premier trauma care centre in Sri Lanka. The study consisted of three components. A research study was conducted to assess the quality of data in relation to selected five dimensions of data quality namely accuracy, completeness, timeliness, legibility and reliability. The intervention was to develop and deploy an electronic emergency department information system (eEDIS). Post assessment of the intervention confirmed that all five dimensions of data quality had improved. The most significant improvements are noticed in accuracy and timeliness dimensions.

Keywords: electronic health records, electronic emergency department information system, emergency department, data quality

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Authors: Elahe Moradi, Hossein Ali Khonakdar

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In the context of the growing interest in renewable polymers, this study presents an innovative approach to environmental conservation through the development of an eco-friendly structure. The research focused on enhancing the compatibility between two immiscible polymers, poly (lactic acid) (PLA) and poly (butylene adipate-co-terephthalate) (PBAT), using polyhedral oligomeric silsesquioxanes (POSS) nanoparticles with an epoxy functional group (Epoxy-POSS). This was achieved through a solution casting method. The study found that the modulus in the glassy region for blends containing Epoxy-POSS was significantly higher than that of the PLA/PBAT blend without Epoxy-POSS. However, in the transition and rubbery regions, the modulus of the Epoxy-POSS-containing blends was only marginally greater. From a mechanical properties’ perspective, the study demonstrated that the incorporation of POSS-EPOXY at varying concentrations enhanced the tensile strength of the PLA/PBAT blend by 30%, thereby acting as a reinforcement. This finding underscores the potential of this approach in the development of renewable polymers.

Keywords: Polyhedral oligomeric silsesquioxane, mechanical behavior, PLA, PBAT, nanocomposite

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Authors: Oluwatosin Olofintoye, Josiah Adeyemo, Gbemileke Shomade

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The relationship between rainfall and runoff is an important issue in surface water hydrology therefore the understanding and development of accurate rainfall-runoff models and their applications in water resources planning, management and operation are of paramount importance in hydrological studies. This paper reviews some of the previous works on the rainfall-runoff process modeling. The hydrologic applications of conceptual models and artificial neural networks (ANNs) for the precipitation-runoff process modeling were studied. Gradient training methods such as error back-propagation (BP) and evolutionary algorithms (EAs) are discussed in relation to the training of artificial neural networks and it is shown that application of EAs to artificial neural networks training could be an alternative to other training methods. Therefore, further research interest to exploit the abundant expert knowledge in the area of artificial intelligence for the solution of hydrologic and water resources planning and management problems is needed.

Keywords: artificial intelligence, artificial neural networks, evolutionary algorithms, gradient training method, rainfall-runoff model

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Authors: Mohammad Altaf Hossain

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A growing body of literature in urban studies has presented that urban precarity has been a lived experience for low-income groups of people in the cities of the Global South. It does not necessarily mean that cities in the Global North, where advanced capitalist economies exist, avoided the adverse realities of urban precarity. As a multifaceted condition, it creates other associated precariousness in lives -for example, economic deprivation, mental stress, and housing precarity. The interrelations between urbanity and precarity have been ubiquitous regardless of the developed and developing countries. People, mainly manual labourers with low incomes, go through uncertainties in every aspect of life. By analysing qualitative data and embracing structure-agency interaction, this paper intends to present how Bangladeshi migrants experience housing precarity in Dublin. Continued population growth and political economy factors such as labour market inequality, financialisation of the private rental sector, and the impact of cuts to government funding for social housing provision are combined to produce a housing supply crisis, affordability, and access in the city. As a result, low-income people practice informality in securing jobs and housing. The macro-structural components of this analysis include the Irish housing policy, the European labour market, the immigration policy, and the financialised housing market. The micro-structural components of South Asian communities’ experiences include social networks and social class. Access to social networks and practices of informality play a significant role in enabling them to negotiate urban precarity, including housing crises and income insecurity. In some cases, the collective agency of ethnic diaspora communities plays a vital role in negotiating with structural constraints.

Keywords: housing precarity, housing pathways, migration, agency, Dublin

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Authors: Anshika Kankane, Dongshik Kang

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Marine debris has a significant influence on coastal environments, damaging biodiversity, and causing loss and damage to marine and ocean sector. A functional cost-effective and automatic approach has been used to look up at this problem. Computer vision combined with a deep learning-based model is being proposed to identify and categorize marine debris of seven kinds on different beach locations of Japan. This research compares state-of-the-art deep learning models with a suggested model architecture that is utilized as a feature extractor for debris categorization. The model is being proposed to detect seven categories of litter using a manually constructed debris dataset, with the help of Mask R-CNN for instance segmentation and a shape matching network called HOGShape, which can then be cleaned on time by clean-up organizations using warning notifications of the system. The manually constructed dataset for this system is created by annotating the images taken by fixed KaKaXi camera using CVAT annotation tool with seven kinds of category labels. A pre-trained HOG feature extractor on LIBSVM is being used along with multiple templates matching on HOG maps of images and HOG maps of templates to improve the predicted masked images obtained via Mask R-CNN training. This system intends to timely alert the cleanup organizations with the warning notifications using live recorded beach debris data. The suggested network results in the improvement of misclassified debris masks of debris objects with different illuminations, shapes, viewpoints and litter with occlusions which have vague visibility.

Keywords: computer vision, debris, deep learning, fixed live camera images, histogram of gradients feature extractor, instance segmentation, manually annotated dataset, multiple template matching

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Authors: Fares A. Mayia, Mahmoud A. Yamany, Mushabbab A. Asiri

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In recent years, non-invasive Focused Ultrasound (FU) has been utilized for generating bubbles (cavities) to ablate target tissue by mechanical fractionation. Intensities >10 kW/cm² are required to generate the inertial cavities. The generation, rapid growth, and collapse of these inertial cavities cause tissue fractionation and the process is called Histotripsy. The ability to fractionate tissue from outside the body has many clinical applications including the destruction of the tumor mass. The process of tissue fractionation leaves a void at the treated site, where all the affected tissue is liquefied to particles at sub-micron size. The liquefied tissue will eventually be absorbed by the body. Histotripsy is a promising non-invasive treatment modality. This paper presents a technique for generating inertial cavities at lower intensities (< 1 kW/cm²). The technique (patent pending) is based on amplitude modulation (AM), whereby a low frequency signal modulates the amplitude of a higher frequency FU wave. Cavitation threshold is lower at low frequencies; the intensity required to generate cavitation in water at 10 kHz is two orders of magnitude lower than the intensity at 1 MHz. The Amplitude Modulation technique can operate in both continuous wave (CW) and pulse wave (PW) modes, and the percentage modulation (modulation index) can be varied from 0 % (thermal effect) to 100 % (cavitation effect), thus allowing a range of ablating effects from Hyperthermia to Histotripsy. Furthermore, changing the frequency of the modulating signal allows controlling the size of the generated cavities. Results from in vitro work demonstrate the efficacy of the new technique in fractionating soft tissue and solid calcium carbonate (Chalk) material. The technique, when combined with MR or Ultrasound imaging, will present a precise treatment modality for ablating diseased tissue without affecting the surrounding healthy tissue.

Keywords: focused ultrasound therapy, histotripsy, inertial cavitation, mechanical tissue ablation

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Authors: Nilesh Pancholi, Mangal Bhatt

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This paper highlights comparative results of traditional FMECA and multi-factor decision-making approach based on “Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)” for aluminum wire rolling mill plant. The suggested study is carried out to overcome the limitations of FMECA by assigning the scores against each failure modes in crisp values to evaluate the criticalities of the failure modes without uncertainty. The primary findings of the paper are that sudden impact on the rolls seems to be most critical failure cause and high contact stresses due to rolling & sliding action of mesh to be least critical failure cause. It is suggested to modify the current control practices with proper maintenance strategy based on achieved maintainability criticality index (MCI). The outcome of the study will be helpful in deriving optimized maintenance plan to maximize the performance of continuous process industry.

Keywords: reliability, maintenance, FMECA, TOPSIS, process industry

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Authors: Ksenia Siadkowska, Tytus Tulwin, Rafał Sochaczewski

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Selecting appropriate materials is presently a complex task as material databases cover tens of thousands of different types of materials. Product designing proceeds in numerous stages and in most of them there are open questions with not only one correct solution but better and worse ones. This paper overviews the Diesel engine body construction materials mentioned in the literature and discusses a certain practical method to select materials for a cylinder head and a Diesel engine block as a prototype. The engine body, depending on its purpose, is most frequently iron or aluminum. If it is important to optimize parts to achieve low weight, aluminum alloys are usually applied, especially in the automotive and aviation industries. In the latter case, weight is even more important so new types of magnesium alloys which are even lighter than aluminum ones are developed and used. However, magnesium alloys are, for example, more flammable and not enough strong so, for safety reasons, this type of material is not used solely in engine bodies. Acknowledgement: This work has been realized in the cooperation with The Construction Office of WSK "PZL-KALISZ" S.A." and is part of Grant Agreement No. POIR.01.02.00-00-0002/15 financed by the Polish National Centre for Research and Development.

Keywords: aluminum alloy, cylinder head, Diesel engine, materials selection

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Authors: Samiul Islam Hazarika, Domngam Boje, Ananta Kumar Atta

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It is well familiar that solvents play a significant role in modern chemistry. Solvents can change the reactivity and physicochemical properties of molecules in a solution. Keeping this in mind, we have designed and synthesized a mono-triazolyl-linked pyrenyl-appended xylofuranose derivative for the detection of metal ions with changing solvent systems. The incorporation of a sugar backbone in the sensor increases the water solubility and biocompatibility. The experimental study revealed that the xylofuranose-based fluorescence probe did not exhibit any specific selectivity towards metal ions in acetonitrile (CH₃CN) solvent. Whereas, we revealed that triazole-linked pyrenyl-appended xylofuranose-based fluorescent sensor would exhibit high selectivity and sensitivity towards Au³⁺ ions in CH₃CN-H₂O (1/1, v/v) system. This observation might be explained by the viscosity and polarity differences of CH₃CN and CH₃CN-H₂O solvent systems. The formation of the sensor-Au³⁺ complex was also established by high-resolution mass spectrometry (HRMS) data of the complex.

Keywords: triazole, furanose, fluorometric, solvent dependent

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Authors: Junaid Bin Aamir, Ma Fanhua

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Methanol is one of the most suitable alternative fuels for replacing gasoline in present and future spark-ignited engines. However, for pure methanol engines, cold start problems and misfires are observed under certain operating conditions. Hydrogen provides a solution for such problems. This paper experimentally investigated the effect of hydrogen on the performance of a pure methanol SI-engine at city driving conditions (1500 rpm speed and 1.18 excess air ratio). Hydrogen was used as a part of methanol reformed syngas (67% hydrogen by volume). 4% by mass of the total methanol converted to hydrogen and other constituent gases, was used in each cycle. Port fuel injection was used to inject methanol and hydrogen-rich syngas into the 4-cylinder engine. The results indicated an increase in brake thermal efficiency up to 5% with the addition of hydrogen, a decrease in brake specific fuel consumption up to 200 g/kWh, and a decrease in exhaust gas temperature by 100°C for all mean effective pressures. Hydrogen addition also decreased harmful exhaust emissions significantly. There was a reduction in THC emissions up to 95% and CO emissions up to 50%. NOx emissions were slightly increased (up to 15%), but they can be reduced to zero by lean burn strategy.

Keywords: alternative fuels, hydrogen, methanol, performance, spark ignition engines

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Authors: Effort M. Magoso

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This study analyses urban institutional arrangements and their implications on allocation of wetlands for development purposes in Zimbabwe using a case study of Harare. It was driven by the need to get to the root of the current urban assault on wetlands. The study sought to analyse institutions that influence wetlands governance in Harare, to ascertain level of wetlands loss and to determine the adequacy of the legal and regulatory framework for governing wetlands. Theories of common property resources and of institutions are the paradigms that undergird this study. A qualitative research methodology was employed, while in-depth interviews, observations and document review were used to gather data. The study found out that unchecked infrastructure developments are taking place in the city’s wetlands. Urban institutional arrangements in Harare were exposed as having negative implications on the protection of wetlands. It is the key argument of this study that good institutional arrangements are priceless in the protection of commons such as wetlands. This study also recommends a new framework that has environmentalists and technocrats as the final decision maker in land allocation as the solution to protect wetlands from undue anthropogenic activities.

Keywords: institutional arrangements, common property resources, wetlands, institutions

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Authors: Ogundele Lasun Tunde, Ayeku Oluwagbemiga Patrick

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Heavy metals are released into the sediments in aquatic environment from both natural and anthropogenic sources and they are considered as worldwide issue due to their deleterious ecological risks and food chain disruption. In this study, sediments samples were collected at three major sites (Awoye, Abereke and Ayetoro) along Ondo coastal area using VanVeen grab sampler. The concentrations of As, Cd, Cr, Cu, Fe, Mn, Ni, Pb, V and Zn were determined by employing Atomic Absorption Spectroscopy (AAS). The combined concentrations data were subjected to Positive Matrix Factorization (PMF) receptor approach for source identification and apportionment. The probable risks that might be posed by heavy metals in the sediment were estimated by potential and integrated ecological risks indices. Among the measured heavy metals, Fe had the average concentrations of 20.38 ± 2.86, 23.56 ± 4.16 and 25.32 ± 4.83 lg/g at Abereke, Awoye and Ayetoro sites, respectively. The PMF resulted in identification of four sources of heavy metals in the sediments. The resolved sources and their percentage contributions were oil exploration (39%), industrial waste/sludge (35%), detrital process (18%) and Mn-sources (8%). Oil exploration activities and industrial wastes are the major sources that contribute heavy metals into the coastal sediments. The major pollutants that posed ecological risks to the local aquatic ecosystem are As, Pb, Cr and Cd (40 B Ei ≤ 80) classifying the sites as moderate risk. The integrate risks values of Awoye, Abereke and Ayetoro are 231.2, 234.0 and 236.4, respectively suggesting that the study areas had a moderate ecological risk. The study showed the suitability of PMF receptor model for source identification of heavy metals in the sediments. Also, the intensive anthropogenic activities and natural sources could largely discharge heavy metals into the study area, which may increase the heavy metal contents of the sediments and further contribute to the associated ecological risk, thus affecting the local aquatic ecosystem.

Keywords: positive matrix factorization, sediments, heavy metals, sources, ecological risks

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Authors: O’Day Luke

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Purpose: To validate virtual non-contrast reconstructions generated from dual-layer spectral computed tomography (DL-CT) data as an alternative for the acquisition of a dedicated true non-contrast dataset during multiphase contrast studies. Material and methods: Thirty-three patients underwent a routine multiphase clinical CT examination, using Dual-Layer Spectral CT, from March to August 2021. True non-contrast (TNC) and virtual non-contrast (VNC) datasets, generated from both portal venous and arterial phase imaging were evaluated. For every patient in both true and virtual non-contrast datasets, a region-of-interest (ROI) was defined in aorta, liver, fluid (i.e. gallbladder, urinary bladder), kidney, muscle, fat and spongious bone, resulting in 693 ROIs. Differences in attenuation for VNC and TNV images were compared, both separately and combined. Consistency between VNC reconstructions obtained from the arterial and portal venous phase was evaluated. Results: Comparison of CT density (HU) on the VNC and TNC images showed a high correlation. The mean difference between TNC and VNC images (excluding bone results) was 5.5 ± 9.1 HU and > 90% of all comparisons showed a difference of less than 15 HU. For all tissues but spongious bone, the mean absolute difference between TNC and VNC images was below 10 HU. VNC images derived from the arterial and the portal venous phase showed a good correlation in most tissue types. The aortic attenuation was somewhat dependent however on which dataset was used for reconstruction. Bone evaluation with VNC datasets continues to be a problem, as spectral CT algorithms are currently poor in differentiating bone and iodine. Conclusion: Given the increasing availability of DL-CT and proven accuracy of virtual non-contrast processing, VNC is a promising tool for generating additional data during routine contrast-enhanced studies. This study shows the utility of virtual non-contrast scans as an alternative for true non-contrast studies during multiphase CT, with potential for dose reduction, without loss of diagnostic information.

Keywords: dual-layer spectral computed tomography, virtual non-contrast, true non-contrast, clinical comparison

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Authors: A. H. Elkholy, D. M. Almutairi

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The successful and efficient execution of a forging process is dependent upon the correct analysis of loading and metal flow of blanks. This paper investigates the Upper Bound Technique (UBT) and its application in the analysis of open forging process when a possibility of blank bulging exists. The UBT is one of the energy rate minimization methods for the solution of metal forming process based on the upper bound theorem. In this regards, the kinematically admissible velocity field is obtained by minimizing the total forging energy rate. A computer program is developed in this research to implement the UBT. The significant advantages of this method is the speed of execution while maintaining a fairly high degree of accuracy and the wide prediction capability. The information from this analysis is useful for the design of forging processes and dies. Results for the prediction of forging loads and stresses, metal flow and surface profiles with the assured benefits in terms of press selection and blank preform design are outlined in some detail. The obtained predictions are ready for comparison with both laboratory and industrial results.

Keywords: forging, upper bound technique, metal forming, forging energy, forging die/platen

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Authors: Lam Pham, Peter Hubber, Russell Tytler

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Students’ conceptual understandings of chemistry concepts/phenomena involve capability to coordinate across the three levels of Johnston’s triangle model. This triplet model is based on reasoning about chemical phenomena across macro, sub-micro and symbolic levels. In chemistry education, there is a need for further examining inquiry-based approaches that enhance students’ conceptual learning and problem solving skills. This research adopted a directed inquiry pedagogy based on students constructing and coordinating representations, to investigate senior school students’ capabilities to flexibly move across Johnston’ levels when learning dilution and molar concentration concepts. The participants comprise 50 grade 11 and 20 grade 10 students and 4 chemistry teachers who were selected from 4 secondary schools located in metropolitan Melbourne, Victoria. This research into classroom practices used ethnographic methodology, involved teachers working collaboratively with the research team to develop representational activities and lesson sequences in the instruction of a unit on solution chemistry. The representational activities included challenges (Representational Challenges-RCs) that used ‘representational tools’ to assist students to move across Johnson’s three levels for dilution phenomena. In this report, the ‘representational tool’ called ‘cross and portion’ model was developed and used in teaching and learning the molar concentration concept. Students’ conceptual understanding and problem solving skills when learning with this model are analysed through group case studies of year 10 and 11 chemistry students. In learning dilution concepts, students in both group case studies actively conducted a practical experiment, used their own language and visualisation skills to represent dilution phenomena at macroscopic level (RC1). At the sub-microscopic level, students generated and negotiated representations of the chemical interactions between solute and solvent underpinning the dilution process. At the symbolic level, students demonstrated their understandings about dilution concepts by drawing chemical structures and performing mathematical calculations. When learning molar concentration with a ‘cross and portion’ model (RC2), students coordinated across visual and symbolic representational forms and Johnson’s levels to construct representations. The analysis showed that in RC1, Year 10 students needed more ‘scaffolding’ in inducing to representations to explicit the form and function of sub-microscopic representations. In RC2, Year 11 students showed clarity in using visual representations (drawings) to link to mathematics to solve representational challenges about molar concentration. In contrast, year 10 students struggled to get match up the two systems, symbolic system of mole per litre (‘cross and portion’) and visual representation (drawing). These conceptual problems do not lie in the students’ mathematical calculation capability but rather in students’ capability to align visual representations with the symbolic mathematical formulations. This research also found that students in both group case studies were able to coordinate representations when probed about the use of ‘cross and portion’ model (in RC2) to demonstrate molar concentration of diluted solutions (in RC1). Students mostly succeeded in constructing ‘cross and portion’ models to represent the reduction of molar concentration of the concentration gradients. In conclusion, this research demonstrated how the strategic introduction and coordination of chemical representations across modes and across the macro, sub-micro and symbolic levels, supported student reasoning and problem solving in chemistry.

Keywords: cross and portion, dilution, Johnston's triangle, molar concentration, representations

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Authors: L. Rakesh, T. Y. Heblekar

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This study entails the development and optimization of a mathematical model for a floating drum biogas reactor from first principles using thermal and empirical considerations. The model was derived on the basis of mass conservation, lumped mass heat transfer formulations and empirical biogas formation laws. The treatment leads to a system of coupled nonlinear ordinary differential equations whose solution mapped four-time independent controllable parameters to five output variables which adequately serve to describe the reactor performance. These equations were solved numerically using fourth order Runge-Kutta method for a range of input parameter values. Using the data so obtained an Artificial Neural Network with a single hidden layer was trained using Levenberg-Marquardt Damped Least Squares (DLS) algorithm. This network was then fine-tuned for optimal mapping by varying hidden layer size. This fast forward model was then employed as a health score generator in the Bacterial Foraging Optimization code. The optimal operating state of the simplified Biogas reactor was thus obtained.

Keywords: biogas, floating drum reactor, neural network model, optimization

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Authors: M. Velasco, D. While, P. Raju, J. Krasniewicz, A. Amini, L. Hernandez-Munoz

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Web service adaptation involves the creation of adapters that solve Web services incompatibilities known as mismatches. Since the importance of Web services adaptation is increasing because of the frequent implementation and use of online Web services, this paper presents a literature review of web services to investigate the main methods of adaptation, their theoretical underpinnings and the metrics used to measure adapters performance. Eighteen publications were reviewed independently by two researchers. We found that adaptation techniques are needed to solve different types of problems that may arise due to incompatibilities in Web service interfaces, including protocols, messages, data and semantics that affect the interoperability of the services. Although adapters are non-invasive methods that can improve Web services interoperability and there are current approaches for service adaptation; there is, however, not yet one solution that fits all types of mismatches. Our results also show that only a few research projects incorporate theoretical frameworks and that metrics to measure adapters’ performance are very limited. We conclude that further research on software adaptation should improve current adaptation methods in different layers of the service interoperability and that an adaptation theoretical framework that incorporates a theoretical underpinning and measures of qualitative and quantitative performance needs to be created.

Keywords: Web Services Adapters, software adaptation, web services mismatches, web services interoperability

Procedia PDF Downloads 286

Authors: Aydın Kavak, Özkan Coruk, Adnan Aydıner

Abstract:

Long-term performance of pavement structures is significantly impacted by the stability of the underlying soils. In situ subgrades often do not provide enough support required to achieve acceptable performance under traffic loading and environmental demands. NovoCrete® is a powder binder-mineral for cement stabilized road pavements soils. NovoCrete® combined with Portland cement at optimum water content increases the crystallize formations during the hydration process, resulting in higher strengths, neutralizes pH levels, and provides water impermeability. These changes in soil properties may lead to transforming existing unsuitable in-situ materials into suitable fill materials. The main features of NovoCrete® are: They are applicable to all types of soil, reduce premature cracking and improve soil properties, creating base and subbase course layers with high bearing capacity by reducing hazardous materials. It can be used also for stabilization of recyclable aggregates and old asphalt pavement aggregate, etc. There are many applications in Germany, Turkey, India etc. In this paper, a few field application in Turkey will be discussed. In the road construction works, this binder material is used for cement stabilization works. In the applications 120-180 kg cement is used for 1 m3 of soil with a 2 % of binder NovoCrete® material for the stabilization. The results of a plate loading test in a road construction site show 1 mm deformation which is very small under 7 kg/cm2 loading. The modulus of subgrade reaction increase from 611 MN/m3 to 3673 MN/m3.The soaked CBR values for stabilized soils increase from 10-20 % to 150-200 %. According to these data weak subgrade soil can be used as a base or sub base after the modification. The potential reduction in the need for quarried materials will help conserve natural resources. The use of on-site or nearby materials in fills, will significantly reduce transportation costs and provide both economic and environmental benefits.

Keywords: soil, stabilization, cement, binder, Novocrete, additive

Procedia PDF Downloads 218

Authors: Abderafi Charki, David Bigaud

Abstract:

The aim of this oral speach is to present the PVMODREL© (PhotoVoltaic MODule RELiability) new software developed in the University of Angers. This new tool permits us to evaluate the lifetime and reliability of a PV module whatever its geographical location and environmental conditions. The electrical power output of a PV module decreases with time mainly as a result of the effects of corrosion, encapsulation discoloration, and solder bond failure. The failure of a PV module is defined as the point where the electrical power degradation reaches a given threshold value. Accelerated life tests (ALTs) are commonly used to assess the reliability of a PV module. However, ALTs provide limited data on the failure of a module and these tests are expensive to carry out. One possible solution is to conduct accelerated degradation tests. The Wiener process in conjunction with the accelerated failure time model makes it possible to carry out numerous simulations and thus to determine the failure time distribution based on the aforementioned threshold value. By this means, the failure time distribution and the lifetime (mean and uncertainty) can be evaluated. An example using the damp heat test is shown to demonstrate the usefulness PVMODREL.

Keywords: lifetime, reliability, PV Module, accelerated life testing, accelerated degradation testing

Procedia PDF Downloads 568

Authors: Henry Pariaman, Iwa Garniwa, Isti Surjandari, Bambang Sugiarto

Abstract:

Availability of a complex system of thermal power plant is strongly influenced by the reliability of spare parts and maintenance management policies. A reliability-centered maintenance (RCM) technique is an established method of analysis and is the main reference for maintenance planning. This method considers the consequences of failure in its implementation, but does not deal with further risk of down time that associated with failures, loss of production or high maintenance costs. Risk-based maintenance (RBM) technique provides support strategies to minimize the risks posed by the failure to obtain maintenance task considering cost effectiveness. Meanwhile, condition-based maintenance (CBM) focuses on monitoring the application of the conditions that allow the planning and scheduling of maintenance or other action should be taken to avoid the risk of failure prior to the time-based maintenance. Implementation of RCM, RBM, CBM alone or combined RCM and RBM or RCM and CBM is a maintenance technique used in thermal power plants. Implementation of these three techniques in an integrated maintenance will increase the availability of thermal power plants compared to the use of maintenance techniques individually or in combination of two techniques. This study uses the reliability, risks and conditions-based maintenance in an integrated manner to increase the availability of thermal power plants. The method generates MPI (Priority Maintenance Index) is RPN (Risk Priority Number) are multiplied by RI (Risk Index) and FDT (Failure Defense Task) which can generate the task of monitoring and assessment of conditions other than maintenance tasks. Both MPI and FDT obtained from development of functional tree, failure mode effects analysis, fault-tree analysis, and risk analysis (risk assessment and risk evaluation) were then used to develop and implement a plan and schedule maintenance, monitoring and assessment of the condition and ultimately perform availability analysis. The results of this study indicate that the reliability, risks and conditions-based maintenance methods, in an integrated manner can increase the availability of thermal power plants.

Keywords: integrated maintenance techniques, availability, thermal power plant, MPI, FDT

Procedia PDF Downloads 787

Authors: Titus A. Beu

Abstract:

Many modern gene-delivery protocols rely on condensed complexes of DNA with polycations to introduce the genetic payload into cells by endocytosis. In particular, polyethyleneimine (PEI) stands out by a high buffering capacity (enabling the efficient condensation of DNA) and relatively simple fabrication. Realistic computational studies can offer essential insights into the formation process of DNA-PEI polyplexes, providing hints on efficient designs and engineering routes. We present comprehensive computational investigations of solvated PEI and DNA-PEI polyplexes involving calculations at three levels: ab initio, all-atom (AA), and coarse-grained (CG) molecular mechanics. In the first stage, we developed a rigorous AA CHARMM (Chemistry at Harvard Macromolecular Mechanics) force field (FF) for PEI on the basis of accurate ab initio calculations on protonated model pentamers. We validated this atomistic FF by matching the results of extensive molecular dynamics (MD) simulations of structural and dynamical properties of PEI with experimental data. In a second stage, we developed a CG MARTINI FF for PEI by Boltzmann inversion techniques from bead-based probability distributions obtained from AA simulations and ensuring an optimal match between the AA and CG structural and dynamical properties. In a third stage, we combined the developed CG FF for PEI with the standard MARTINI FF for DNA and performed comprehensive CG simulations of DNA-PEI complex formation and condensation. Various technical aspects which are crucial for the realistic modeling of DNA-PEI polyplexes, such as options of treating electrostatics and the relevance of polarizable water models, are discussed in detail. Massive CG simulations (with up to 500 000 beads) shed light on the mechanism and provide time scales for DNA polyplex formation independence of PEI chain size and protonation pattern. The DNA-PEI condensation mechanism is shown to primarily rely on the formation of DNA bundles, rather than by changes of the DNA-strand curvature. The gained insights are expected to be of significant help for designing effective gene-delivery applications.

Keywords: DNA condensation, gene-delivery, polyethylene-imine, molecular dynamics.

Procedia PDF Downloads 114

Authors: Diogo Pereira, Maria Moura, Joana Campos, João Nunes

Abstract:

As the Ukraine war highlights the European Economic Area’s vulnerability and external dependence on feed and food, agriculture gains significant importance. Transformative change is necessary to reach a sustainable and resilient agricultural sector. Agriculture is an important drive for bioeconomy and the equilibrium and survival of society and rural fires resilience. The pressure of (1) water stress, (2) nutrient cycle, and (3) social demographic evolution towards 70% of the population in Urban systems and the aging of the rural population, combined with climate change, exacerbates the problem and paradigm of rural and wildfires, especially in Portugal. The Portuguese territory is characterized by (1) 28% of marginal land, (2) the soil quality of 70% of the territory not being appropriate for agricultural activity, (3) a micro smallholding, with less than 1 ha per proprietor, with mainly familiar and traditional agriculture in the North and Centre regions, and (4) having the most vulnerable areas for rural fires in these same regions. The most important difference between the South, North and Centre of Portugal, referring to rural and wildfires, is the agricultural activity, which has a higher level in the South. In Portugal, rural and wildfires represent an average annual economic loss of around 800 to 1000 million euros. The WinBio model is an agrienvironmental metabolism design, with the capacity to create a new agri-food metabolism through Agricultural Organized Areas, a privatepublic partnership. This partnership seeks to grow agricultural activity in regions with (1) abandoned territory, (2) micro smallholding, (3) water and nutrient management necessities, and (4) low agri-food literacy. It also aims to support planning and monitoring of resource use efficiency and sustainability of territories, using agriculture as a barrier for rural and wildfires in order to protect rural population.

Keywords: agricultural organized areas, residues, climate change, drought, nutrients, rural and wild fires

Procedia PDF Downloads 72

Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 118

Authors: Jiaqi Huang, Lu Jin

Abstract:

Pervious concrete features with high water permeability rate. However, due to the lack of fine aggregates, the compressive strength is usually lower than other conventional concrete products. Optimization of pervious concrete mix design has long been recognized as an effective mechanism to achieve high compressive strength while maintaining desired permeability rate. In this paper, a Gibbs Sampling based algorithm is proposed to approximate the optimal mix design to achieve a high compressive strength of pervious concrete. We prove that the proposed algorithm efficiently converges to the set of global optimal solutions. The convergence rate and accuracy depend on a control parameter employed in the proposed algorithm. The simulation results show that, by using the proposed approach, the system converges to the optimal solution quickly and the derived optimal mix design achieves the maximum compressive strength while maintaining the desired permeability rate.

Keywords: convergence, Gibbs Sampling, high compressive strength, optimal mix design, pervious concrete

Procedia PDF Downloads 174