Search results for: damage prediction models

Authors: Donatello Cardone

Abstract:

Bilinear hysteresis models are usually used to describe the cyclic behavior of high damping rubber bearings. However, they neglect a number of phenomena (such as the interaction between axial load and shear force, buckling and post-buckling behavior, cavitation, scragging effects, etc.) that can significantly influence the dynamic behavior of such isolation devices. In this work, an advanced hysteresis model is examined and properly calibrated using consolidated procedures. Results of preliminary numerical analyses, performed in OpenSees, are shown and compared with the results of experimental tests on high damping rubber bearings and simulation analyses using alternative nonlinear models. The findings of this study can provide an useful tool for the accurate evaluation of the seismic response of structures with rubber-based isolation systems.

Keywords: seismic isolation, high damping rubber bearings, numerical modeling, axial-shear force interaction

Procedia PDF Downloads 112

Authors: Lubna Azmi, Ila Shukla, Shyam Sundar Gupta, Padam Kant, Ch. V. Rao

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Gastric ulcer is a major global health threat, and it is the leading cause of stomach cancer death worldwide. Helicobacter pylori bacteriumis the most important etiologic factor for gastric ulcer. This infection is highly pervasive in South Asian developing countries, especially in India, Nepal, Srilanka etc. due to diversification in geographic area. Pathophysiology of gastric mucosal damage associated with non-invasive bacterium has not justified in detail, but it leads to change in histopathology, immunochemistry of the gastric and duodenal reason of host. The mechanism responsible for bacteria tissue tropism and mucosal damage in stomach variance during the disease is not clearly described and understood scientifically in treatment and control of pathogenic organisms. Polyphenols are secondary metabolites of plants and are generally involved in defense against aggression by pathogens. 2-(3,4-dihydroxyphenyl)-3,5,7-trihydroxychromen-4-one and 1-hydroxy-5,7-dimethoxy-2-naphthalene-carboxaldehyde are polyphenolic compound obtained from popular Indian medicinal plants ghavpatta (ArgeriaspeciosaLinn.f) andBael (Aeglemarmelos) have long been used in traditional Ayurvedic Indian medicine for various diseases. They have promising effects on ulcer, as detailed investigation has made in our laboratory. Therefore, the aim of present study is to explore membrane –dependent morphogenesis of H. pylori and associated apoptosis-mediated cell death. Based on this we analyzed immune gene expression in stomach of experimental animals with H. pylori, using quantitative reverse transcription polymerase chain reaction(q RT-PCR). This revealed rapid induction of prostaglandin, interferon I (INF-I), interferon II (INF-II) and INF-I associated genes in the infected animal. Ultrastructural changes associated with H. pylori will be taken for advanced studies. This investigation shows that the biomarkers eradicate H. pylori bacterium caused gastric ulcer which is a major risk factor for gastric cancer.

Keywords: gastric ulcer, Helicobacter pylori, immunochemistry, polyphenols

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Authors: Richa Shri, Varinder Singh, Kundan Singh Bora, Abhishek Bhanot, Rahul Kumar, Amit Kumar, Ravinder Kaur

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The term ‘neuroprotection’ means preserving/salvaging function and structure of neurons. Neuroprotection is an adjunctive treatment option for neurodegenerative disorders. Oxidative stress is considered a major culprit in neurodegenerative disorders; hence, management strategies include use of antioxidants. Our search for a neuroprotective agent began with Allium cepa L. or onions, (family Amaryllidaceae) - a potent antioxidant. We have investigated the neuroprotective potential of onions in experimental models of ischemic stroke, diabetic neuropathy, neuropathic pain, and dementia. In pre and post-ischemic stroke model, the methanol extract of outer scales of onion bulbs (MEOS) prevented memory loss and motor in-coordination; reduced oxidative stress and cerebral infarct size. This also prevented and ameliorated diabetic neuropathy in mice. The MEOS was fractionated to yield a flavonoid rich fraction (FRF) that successfully reversed ischemia-reperfusion induced neuronal damage, thereby demonstrating that the flavonoids are responsible for the activity. The FRF effectively ameliorated chronic constriction induced neuropathic pain in rats. The FRF was subjected to bioactivity-guided fractionated. It was seen that FRF is more effective as compared to the isolated components probably due to synergism among the constituents (i.e., quercetin and quercetin glucosides) in the FRF. The outer scales of onion bulbs have great potential for prevention as well as for treatment of neuronal disorders. Red onions, with higher amounts of flavonoids as compared to the white onions, produced more significant neuroprotection. Thus, the standardized FRF from the waste material of a commonly used vegetable, especially the red variety, may be developed as a valuable neuroprotective agent.

Keywords: Allium cepa, antioxidant activity, flavonoid rich fraction, neuroprotection

Procedia PDF Downloads 138

Authors: Sahar M. El-Gowilly, Mai M. Helmy, Hanan M. El-Gowelli

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Methotrexate (MTX) is widely used in treatment of cancers and autoimmune diseases. However, nephrotoxicity is one of the most important side effects of MTX. The peroxisome proliferator-activated receptor gamma agonist, pioglitazone (PIO), is known to exert anti-inflammatory and reno-protective effects in various kidney injuries. The purpose of this study was to investigate the potential involvement of endothelial damage in MTX-induced renal injury and to elaborate the possible protective effect of PIO against MTX-induced nephropathy. Compared with saline-treated rats, treatment with MTX (7 mg/kg for 3 day) caused significant elevations in serum levels of urea and creatinine, increased renal nitrate/nitrite level and impaired renovascular responsiveness of isolated perfused kidney to endothelium-dependent vasodilations induced by acetylcholine (0.01-2.43 nmol) and isoprenaline (1µmol). These effects were abolished by concurrent treatment with PIO (2.5 mg/kg, for 5 days starting two days before MTX). Alternatively, MTX treatment did not affect endothelium-independent renovascular relaxation induced by sodium nitroprusside (1-30 μmole). The possibility that alterations in renal antioxidants, circulating cytokine and apoptotic factor (Fas) levels contributed to MTX-PIO interaction was assessed. PIO treatment abrogated renal oxidative stress (decreased reduced glutathione and catalase activity and increased malondialdehyde), elevated serum cytokine (interleukin-6, interleukin-10, tumor necrosis factor-alpha and transforming growth factor-beta1) and Fas induced by MTX. Histologically, MTX caused defused tubular cells swelling and vacuolization associated with endothelial damage in renal arterioles. These effects disappeared upon co-treated with PIO. Collectively, PIO abolished MTX-induced endothelium dysfunction and nephrotoxicity via ameliorating oxidative stress and rectifying cytokines and Fas abnormalities caused by MTX.

Keywords: methotrexate, pioglitazone, endothelium, kidney

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Authors: Amritpal Slaich, Mark Elshaw

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This paper explores the development of a decision support framework for the introduction of business intelligence (BI) through operational research techniques for application by SMEs. Aligned with the goals of the new Midlands Enterprise Initiative of improving the skill levels of the Midlands workforce and addressing high levels of regional unemployment, we have developed a framework to increase the level of business intelligence used by SMEs to improve business decision-making. Many SMEs in the Midlands fail due to the lack of high quality decision making. Our framework outlines how universities can: engage with SMEs in the use of BI through operational research techniques; develop appropriate and easy to use Excel spreadsheet models; and make use of a process to allow SMEs to feedback their findings of the models. Future work will determine how well the framework performs in getting SMEs to apply BI to improve their decision-making performance.

Keywords: SMEs, decision support framework, business intelligence, operational research techniques

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Authors: Sharmi Mukherjee, Anindita Chakraborty

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Cellular irradiation incites complex responses including arrest of cell cycle progression. This article accentuates the effects of radiation on cell cycle status of radiation non-targeted cells. Human Hepatoma HepG2 cells were exposed to increasing doses of γ radiations (1, 2, 4, 6 Gy) and their cell culture media was transferred to non-targeted HepG2 cells cultured in other Petri plates. These radiation non-targeted cells cultured in the ICCM (Irradiated cell conditioned media) were the bystander cells on which cell cycle analysis was performed using flow cytometry. An apparent decrease in the distribution of bystander cells at G0/G1 phase was observed with increased radiation doses upto 4 Gy representing a linear relationship. This was accompanied by a gradual increase in cellular distribution at G2/M phase. Interestingly the number of cells in G2/M phase at 1 and 2 Gy irradiation was not significantly different from each other. However, the percentage of G2 phase cells at 4 and 6 Gy doses were significantly higher than 2 Gy dose indicating the IC50 dose to be between 2 and 4 Gy. Cell cycle arrest is an indirect indicator of genotoxic damage in cells. In this study, bystander stress signals through the cell culture media of irradiated cells disseminated the radiation induced DNA damages in the non-targeted cells which resulted in arrest of the cell cycle progression at G2/M phase checkpoint. This implies that actual radiation biological effects represent a penumbra with effects encompassing a larger area than the actual beam. This article highlights the existence of genotoxic damages as bystander effects of γ rays in human Hepatoma cells by cell cycle analysis and opens up avenues for appraisal of bystander stress communications between tumor cells. Contemplation of underlying signaling mechanisms can be manipulated to maximize damaging effects of radiation with minimum dose and thus has therapeutic applications.

Keywords: bystander effect, cell cycle, genotoxic damage, hepatoma

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Authors: Ghanima Al-Sharrah, Haitham M. Lababidi, Yusuf I. Ali

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The main objective of the current work is to introduce sustainability factors in optimizing the supply chain model for process industries. The supply chain models are normally based on purely economic considerations related to costs and profits. To account for sustainability, two additional factors have been introduced; environment and risk. A supply chain for an entire petroleum organization has been considered for implementing and testing the proposed optimization models. The environmental and risk factors were introduced as indicators reflecting the anticipated impact of the optimal production scenarios on sustainability. The aggregation method used in extending the single objective function to multi-objective function is proven to be quite effective in balancing the contribution of each objective term. The results indicate that introducing sustainability factor would slightly reduce the economic benefit while improving the environmental and risk reduction performances of the process industries.

Keywords: environmental indicators, optimization, risk, supply chain

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Authors: B. Schmid, C. Kexel, E. Djafarova

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Understanding consumers is elementary for practitioners in marketing. Consumers of sports events, the sports spectators, are a particularly complex consumer crowd. In order to identify and define their profiles different segmentation approaches can be found in literature, one of them being multidimensional segmentation. Multidimensional segmentation models correspond to the broad range of attitudes, behaviours, motivations and beliefs of sports spectators, other than earlier models. Moreover, in sports there are some well-researched disciplines (e.g. football or North American sports) where consumer profiles and marketing strategies are elaborate and others where no research at all can be found. For example, there is almost no research on athletics spectators. This paper explores the current state of research on sports spectators segmentation. An in-depth literature review provides the framework for a spectators segmentation in athletics. On this basis, additional potential consumer groups and implications for social media marketing will be explored. The findings are the basis for further research.

Keywords: multidimensional segmentation, social media, sports marketing, sports spectators segmentation

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Authors: Micheal Olaolu Arowolo, Muhammad Azam, Fei He, Mihail Popescu, Dong Xu

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As the quantity of biological articles rises, so does the number of biological route figures. Each route figure shows gene names and relationships. Annotating pathway diagrams manually is time-consuming. Advanced image understanding models could speed up curation, but they must be more precise. There is rich information in biological pathway figures. The first step to performing image understanding of these figures is to recognize gene names automatically. Classical optical character recognition methods have been employed for gene name recognition, but they are not optimized for literature mining data. This study devised a method to recognize an image bounding box of gene name as a photo using deep Siamese neural network models to outperform the existing methods using ResNet, DenseNet and Inception architectures, the results obtained about 84% accuracy.

Keywords: biological pathway, gene identification, object detection, Siamese network

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Authors: R. Pravin Kumar, L. Roopa

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Enzymes are molecular machines used in various industries such as pharmaceuticals, cosmetics, food and animal feed, paper and leather processing, biofuel, and etc. Nevertheless, this has been possible only by the breath-taking efforts of the chemists and biologists to evolve/engineer these mysterious biomolecules to work the needful. Main agenda of this enzyme engineering project is to derive screening and selection tools to obtain focused libraries of enzyme variants with desired qualities. The methodologies for this research include the well-established directed evolution, rational redesign and relatively less established yet much faster and accurate insilico methods. This concept was initiated as a Receptor Rependent-4Dimensional Quantitative Structure Activity Relationship (RD-4D-QSAR) to predict kinetic properties of enzymes and extended here to study transaminase by a 7D QSAR approach. Induced-fit scenarios were explored using Quantum Mechanics/Molecular Mechanics (QM/MM) simulations which were then placed in a grid that stores interactions energies derived from QM parameters (QMgrid). In this study, the mutated enzymes were immersed completely inside the QMgrid and this was combined with solvation models to predict descriptors. After statistical screening of descriptors, QSAR models showed > 90% specificity and > 85% sensitivity towards the experimental activity. Mapping descriptors on the enzyme structure revealed hotspots important to enhance the enantioselectivity of the enzyme.

Keywords: QMgrid, QM/MM simulations, RD-4D-QSAR, transaminase

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Authors: Levent Basayigit, Mert Dedeoglu, Fadime Ozogul

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In this study, an application was carried out to determine the Volcanic Soils by using remote sensing.  The study area was located on the Golcuk formation in Isparta-Turkey. The thermal bands of Landsat 7 image were used for processing. The implementation of the climate model that was based on the water index was used in ERDAS Imagine software together with pixel based image classification. Soil Moisture Index (SMI) was modeled by using the surface temperature (Ts) which was obtained from thermal bands and vegetation index (NDVI) derived from Landsat 7. Surface moisture values were grouped and classified by using scoring system. Thematic layers were compared together with the field studies. Consequently, different moisture levels for volcanic soils were indicator for determination and separation. Those thermal wavelengths are preferable bands for separation of volcanic soils using moisture and temperature models.

Keywords: Landsat 7, soil moisture index, temperature models, volcanic soils

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Authors: Paula Simões, Isabel Natário

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This paper reviews a number of theoretical aspects for implementing an explicit spatial perspective in econometrics for modelling non-continuous data, in general, and count data, in particular. It provides an overview of the several spatial econometric approaches that are available to model data that are collected with reference to location in space, from the classical spatial econometrics approaches to the recent developments on spatial econometrics to model count data, in a Bayesian hierarchical setting. Considerable attention is paid to the inferential framework, necessary for structural consistent spatial econometric count models, incorporating spatial lag autocorrelation, to the corresponding estimation and testing procedures for different assumptions, to the constrains and implications embedded in the various specifications in the literature. This review combines insights from the classical spatial econometrics literature as well as from hierarchical modeling and analysis of spatial data, in order to look for new possible directions on the processing of count data, in a spatial hierarchical Bayesian econometric context.

Keywords: spatial data analysis, spatial econometrics, Bayesian hierarchical models, count data

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Authors: Sahar M. El-Gowilly, Mai M. Helmy, Hanan M. El-Gowelli

Abstract:

Methotrexate (MTX) is widely used in treatment of cancers and autoimmune diseases. However, nephrotoxicity is one of its most important side effects. The peroxisome proliferator-activated receptor gamma agonist, pioglitazone, is known to exert antiinflammatory and reno-protective effects in various kidney injuries. The purpose of this study was to investigate the potential involvement of endothelial damage in MTX-induced renal injury and to elaborate the possible protective effect of pioglitazone against MTX-induced endothelial impairment. Compared with saline-treated rats, treatment with MTX (7 mg/kg for 3 day) caused significant elevations in serum levels of urea and creatinine, increased renal nitrate/nitrite level and impaired renovascular responsiveness of isolated perfused kidney to endothelium-dependent vasodilations induced by acetylcholine (0.01-2.43 nmol) and isoprenaline (1µmol). These effects were abolished by concurrent treatment with pioglitazone (2.5 mg/kg, for 5 days starting two days before MTX). Alternatively, MTX treatment did not affect endothelium-independent renovascular relaxation induced by sodium nitroprusside (0.001-10 μmole). The possibility that alterations in renal antioxidants, circulating cytokine and apoptotic factor (Fas) levels contributed to MTX-pioglitazone interaction was assessed. Pioglitazone treatment abrogated renal oxidative stress (decreased reduced glutathione and catalase activity and increased malondialdehyde), elevated serum cytokine (interleukin-6, interleukin-10, tumor necrosis factor-alpha and transforming growth factor-beta1) and Fas induced by MTX. Histologically, MTX caused defused tubular cells swelling and vacuolization associated with endothelial damage in renal arterioles. These effects disappeared upon co-treated with pioglitazone. Collectively, pioglitazone abolished MTX-induced endothelium dysfunction and nephrotoxicity via ameliorating oxidative stress and rectifying cytokines and Fas abnormalities caused by MTX.

Keywords: methotrexate, pioglitazone, endothelium, kidney

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Authors: Rob van den Dam

Abstract:

Today’s Internet of Things (IoT) comprises more than a billion intelligent devices, connected via wired/wireless communications. The expected proliferation of hundreds of billions more places us at the threshold of a transformation sweeping across the communications industry. Yet, we found that the IoT architecture and solutions that currently work for billions of devices won’t necessarily scale to tomorrow’s hundreds of billions of devices because of high cost, lack of privacy, not future-proof, lack of functional value and broken business models. As the IoT scales exponentially, decentralized networks have the potential to reduce infrastructure and maintenance costs to manufacturers. Decentralization also promises increased robustness by removing single points of failure that could exist in traditional centralized networks. By shifting the power in the network from the center to the edges, devices gain greater autonomy and can become points of transactions and economic value creation for owners and users. To validate the underlying technology vision, IBM jointly developed with Samsung Electronics the autonomous decentralized peer-to- peer proof-of-concept (PoC). The primary objective of this PoC was to establish a foundation on which to demonstrate several capabilities that are fundamental to building a decentralized IoT. Though many commercial systems in the future will exist as hybrid centralized-decentralized models, the PoC demonstrated a fully distributed proof. The PoC (a) validated the future vision for decentralized systems to extensively augment today’s centralized solutions, (b) demonstrated foundational IoT tasks without the use of centralized control, (c) proved that empowered devices can engage autonomously in marketplace transactions. The PoC opens the door for the communications and electronics industry to further explore the challenges and opportunities of potential hybrid models that can address the complexity and variety of requirements posed by the internet that continues to scale. Contents: (a) The new approach for an IoT that will be secure and scalable, (b) The three foundational technologies that are key for the future IoT, (c) The related business models and user experiences, (d) How such an IoT will create an 'Economy of Things', (e) The role of users, devices, and industries in the IoT future, (f) The winners in the IoT economy.

Keywords: IoT, internet, wired, wireless

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Authors: Gu Pang, Bartosz Gebka

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We forecast the demand of total container throughput at the Indonesia’s largest seaport, Tanjung Priok Port. We propose four univariate forecasting models, including SARIMA, the additive Seasonal Holt-Winters, the multiplicative Seasonal Holt-Winters and the Vector Error Correction Model. Our aim is to provide insights into whether forecasting the total container throughput obtained by historical aggregated port throughput time series is superior to the forecasts of the total throughput obtained by summing up the best individual terminal forecasts. We test the monthly port/individual terminal container throughput time series between 2003 and 2013. The performance of forecasting models is evaluated based on Mean Absolute Error and Root Mean Squared Error. Our results show that the multiplicative Seasonal Holt-Winters model produces the most accurate forecasts of total container throughput, whereas SARIMA generates the worst in-sample model fit. The Vector Error Correction Model provides the best model fits and forecasts for individual terminals. Our results report that the total container throughput forecasts based on modelling the total throughput time series are consistently better than those obtained by combining those forecasts generated by terminal-specific models. The forecasts of total throughput until the end of 2018 provide an essential insight into the strategic decision-making on the expansion of port's capacity and construction of new container terminals at Tanjung Priok Port.

Keywords: SARIMA, Seasonal Holt-Winters, Vector Error Correction Model, container throughput

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Authors: Sushma Tejwani, Shoruba Dinakaran, Kushal Kacha, K. Bhujang Shetty

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Introduction and aim: It is not unusual to find patients with glaucomatous damage and normal intraocular pressure, and to label a patient as Normal tension glaucoma (NTG) majority of clinicians depend on office Intraocular pressures (IOP) recordings; hence, the concern is that whether we are missing the late night or early morning spikes in this group of patients. Also, ischemia to the optic nerve is one of the presumed causes of damage in these patients, however demonstrating the same has been a challenge. The aim of this study was to evaluate IOP variations and patterns in a series of patients with open angles, glaucomatous discs or fields but normal office IOP, and in addition to identify ischemic factors for true NTG patients. Materials & Methods: This was an observational cross- sectional study from a tertiary care centre. The patients that underwent full day DVT from Jan 2012 to April 2014 were studied. All patients underwent IOP measurement on Goldmann applanation tonometry every 3 hours for 24 hours along with a recording of the blood pressure (BP). Further patients with normal IOP throughout the 24- hour period were evaluated with a cardiologist for echocardiography and carotid Doppler. Results: There were 47 patients and a maximum number of patients studied was in the age group of 50-70 years. A biphasic IOP peak was noted for almost all the patients. Out of the 47 patients, 2 were excluded from analysis as they were on treatment.20 patients (42%) were diagnosed on DVT to have an IOP spike and were then diagnosed as open angle glaucoma and another 25 (55%) were diagnosed to have normal tension glaucoma and were subsequently advised a carotid Doppler and a cardiologists consult. Another interesting finding was that 9 patients had a nocturnal dip in their BP and 3 were found to have carotid artery stenosis. Conclusion: A continuous 24-hour monitoring of the IOP and BP is a very useful albeit mildly cumbersome tool which provides a wealth of information in cases of glaucoma presenting with normal office pressures. It is of great value in differentiating between normal tension glaucoma patients & open angle glaucoma patients. It also helps in timely diagnosis & possible intervention due to referral to a cardiologist in cases of carotid artery stenosis.

Keywords: carotid artery disease in NTG, diurnal variation of IOP, ischemia in glaucoma, normal tension glaucoma

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Authors: Muhammad Fezan Afzal, Asad Abbas, Imran Khan, Salma Imtiaz

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Software Product Line (SPL) paradigm is used for the development of the family of software products that share common and variable features. Feature model is a domain of SPL that consists of common and variable features with predefined relationships and constraints. Multiple SPLs consist of a number of similar common and variable features, such as mobile phones and Tabs. Reusability of common and variable features from the different domains of SPL is a complex task due to the external relationships and constraints of features in the feature model. To increase the reusability of feature model resources from domain engineering, it is required to manage the commonality of features at the level of SPL application development. In this research, we have proposed an approach that combines multiple SPLs into a single domain and converts them to a common feature model. Extracting the common features from different feature models is more effective, less cost and time to market for the application development. For extracting features from multiple SPLs, the proposed framework consists of three steps: 1) find the variation points, 2) find the constraints, and 3) combine the feature models into a single feature model on the basis of variation points and constraints. By using this approach, reusability can increase features from the multiple feature models. The impact of this research is to reduce the development of cost, time to market and increase products of SPL.

Keywords: software product line, feature model, variability management, multi-SPLs

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Authors: Shivani Sharma, Prashant Saxena, Inamul Hasan Madar

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Since the time of Darwin, it has been considered that genetic change is the direct indicator of variation in phenotype. But a few studies in system biology in the past years have proposed that epigenetic developmental processes also affect the phenotype thus shifting the focus from a linear genotype-phenotype map to a non-linear G-P map. In this paper, we attempt at explaining the evolution of colour vision in mammals by taking LWS/ Long-wave sensitive gene under consideration.

Keywords: evolution, phenotypes, epigenetics, LWS gene, G-P map

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Authors: Pablo Enrique Sartor Del Giudice

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Penalty shootouts often decide the outcome of important soccer matches. Although usually referred to as ”lotteries”, there is evidence that some national teams and clubs consistently perform better than others. The outcomes are therefore not explained just by mere luck, and therefore there are ways to improve the average performance of players, naturally at the expense of some sort of effort. In this article we study the payoff of player performance improvements in terms of the performance of the team as a whole. To do so we develop an analytical model with static individual performances, as well as Monte Carlo models that take into account the known influence of partial score and round number on individual performances. We find that within a range of usual values, the team performance improves above 70% faster than individual performances do. Using these models, we also estimate that the new ABBA penalty shootout ordering under test reduces almost all the known bias in favor of the first-shooting team under the current ABAB system.

Keywords: football, penalty shootouts, Montecarlo simulation, ABBA

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Authors: U. Ali Rahoma, Nabil Esawy, Fawzia Ibrahim Moursy, A. H. Hassan, Samy A. Khalil, Ashraf S. Khamees

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The design of any solar energy conversion system requires the knowledge of solar radiation data obtained over a long period. Satellite data has been widely used to estimate solar energy where no ground observation of solar radiation is available, yet there are limitations on the temporal coverage of satellite data. Reanalysis is a “retrospective analysis” of the atmosphere parameters generated by assimilating observation data from various sources, including ground observation, satellites, ships, and aircraft observation with the output of NWP (Numerical Weather Prediction) models, to develop an exhaustive record of weather and climate parameters. The evaluation of the performance of reanalysis datasets (ERA-5) for North Africa against high-quality surface measured data was performed using statistical analysis. The estimation of global solar radiation (GSR) distribution over six different selected locations in North Africa during ten years from the period time 2011 to 2020. The root means square error (RMSE), mean bias error (MBE) and mean absolute error (MAE) of reanalysis data of solar radiation range from 0.079 to 0.222, 0.0145 to 0.198, and 0.055 to 0.178, respectively. The seasonal statistical analysis was performed to study seasonal variation of performance of datasets, which reveals the significant variation of errors in different seasons—the performance of the dataset changes by changing the temporal resolution of the data used for comparison. The monthly mean values of data show better performance, but the accuracy of data is compromised. The solar radiation data of ERA-5 is used for preliminary solar resource assessment and power estimation. The correlation coefficient (R2) varies from 0.93 to 99% for the different selected sites in North Africa in the present research. The goal of this research is to give a good representation for global solar radiation to help in solar energy application in all fields, and this can be done by using gridded data from European Centre for Medium-Range Weather Forecasts ECMWF and producing a new model to give a good result.

Keywords: solar energy, solar radiation, ERA-5, potential energy

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Authors: Paras Ram, Vikas Kumar

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The transmission of Malaria with seasonal were studied through the use of mathematical models. The data from the annual number of Malaria cases reported to the Division of Epidemiology, Ministry of Public Health, Thailand during the period 1997-2011 were analyzed. The transmission of Malaria with seasonal was studied by formulating a mathematical model which had been modified to describe different situations encountered in the transmission of Malaria. In our model, the population was separated into two groups: the human and vector groups, and then constructed a system of nonlinear differential equations. Each human group was divided into susceptible, infectious in hot season, infectious in rainy season, infectious in cool season and recovered classes. The vector population was separated into two classes only: susceptible and infectious vectors. The analysis of the models was given by the standard dynamical modeling.

Keywords: ferrofluid, magnetic field, porous medium, rotating disk, Neuringer-Rosensweig Model

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Authors: Julia Ive, Lucia Specia

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Understanding and detecting toxicity is an important problem to support safer human interactions online. Our work focuses on the important problem of contextual toxicity detection, where automated classifiers are tasked with determining whether a short textual segment (usually a sentence) is toxic within its conversational context. We use “toxicity” as an umbrella term to denote a number of variants commonly named in the literature, including hate, abuse, offence, among others. Detecting toxicity in context is a non-trivial problem and has been addressed by very few previous studies. These previous studies have analysed the influence of conversational context in human perception of toxicity in controlled experiments and concluded that humans rarely change their judgements in the presence of context. They have also evaluated contextual detection models based on state-of-the-art Deep Learning and Natural Language Processing (NLP) techniques. Counterintuitively, they reached the general conclusion that computational models tend to suffer performance degradation in the presence of context. We challenge these empirical observations by devising better contextual predictive models that also rely on NLP data augmentation techniques to create larger and better data. In our study, we start by further analysing the human perception of toxicity in conversational data (i.e., tweets), in the absence versus presence of context, in this case, previous tweets in the same conversational thread. We observed that the conclusions of previous work on human perception are mainly due to data issues: The contextual data available does not provide sufficient evidence that context is indeed important (even for humans). The data problem is common in current toxicity datasets: cases labelled as toxic are either obviously toxic (i.e., overt toxicity with swear, racist, etc. words), and thus context does is not needed for a decision, or are ambiguous, vague or unclear even in the presence of context; in addition, the data contains labeling inconsistencies. To address this problem, we propose to automatically generate contextual samples where toxicity is not obvious (i.e., covert cases) without context or where different contexts can lead to different toxicity judgements for the same tweet. We generate toxic and non-toxic utterances conditioned on the context or on target tweets using a range of techniques for controlled text generation(e.g., Generative Adversarial Networks and steering techniques). On the contextual detection models, we posit that their poor performance is due to limitations on both of the data they are trained on (same problems stated above) and the architectures they use, which are not able to leverage context in effective ways. To improve on that, we propose text classification architectures that take the hierarchy of conversational utterances into account. In experiments benchmarking ours against previous models on existing and automatically generated data, we show that both data and architectural choices are very important. Our model achieves substantial performance improvements as compared to the baselines that are non-contextual or contextual but agnostic of the conversation structure.

Keywords: contextual toxicity detection, data augmentation, hierarchical text classification models, natural language processing

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Authors: Charles Iledun Oyewole, Harira Shuaib

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While fulfilling the food security of an increasing population like Nigeria remains a major global concern, more than one-third of crop harvested is lost or wasted during harvesting or in postharvest operations. Reducing the harvest and postharvest losses, especially in developing countries, could be a sustainable solution to increase food availability, eliminate hunger and improve farmers’ livelihoods. Nigeria is one of the countries in sub-Saharan Africa with insufficient food production and high food import bill, which has had debilitating effects on the country’s economy. One of the goals of Nigeria’s agricultural development policy is to ensure that, the nation produces enough food and be less dependent on importation so as to ensure adequate and affordable food for all. Maize could fill the food gap in Nigeria’s effort to beat hunger and food insecurity. Maize is the most important cereal after rice and its production contributes immensely to food availability on the tables of many Nigerians. Maize grains constitute primary source of food for large percentage of the Nigerian populace, thus a considerable waste of this valuable food pre and post-harvest constitutes such a major agricultural bottleneck; that the reduction of pre and post-harvest losses is now a common food security strategy. In surveys conducted, as much as 60% maize outputs can be lost on the field and during the storage stage due to technical inefficiency. Field losses due to rodent damage alone can account for between 10% - 60% grain losses depending on the location. While the use of scientific storage methods can reduce losses below 2% in storage, timely harvesting of crop can check losses on the fields resulting from rodent damage or pest infestation. A push for increased crop production must be complemented by available and affordable post-harvest technologies that will reduce losses on farmers’ fields as well as in storage.

Keywords: government policy, maize, population increase, storage, sustainable food production, yield, yield losses

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Authors: D. Romero Espitia

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Research on peacebuilding has taken a critical turn into examining the neoliberal and hegemonic conception of peace operations. Alternative peacebuilding models have been analyzed, but the scholarly discussion fails to bring them together or form connections between them. The objective of this paper is to rethink peacebuilding by extracting the positive aspects of the various peacebuilding models, connecting them with the local context, and therefore promote emancipation in contemporary peacebuilding efforts. Moreover, local ownership has been widely labelled as one, if not the core principle necessary for a successful peacebuilding project. Yet, definitions of what constitutes the 'local' remain debated. Through a qualitative review of literature, this paper unpacks the contemporary conception of peacebuilding in nexus with 'local ownership' as manifested through civil society. Using Colombia as a case study, this paper argues that a new peacebuilding framework, one that reconsiders the terms of engagement between international and national actors, is needed in order to foster effective peacebuilding efforts in contested transitional states.

Keywords: civil society, Colombia, emancipation, peacebuilding

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Authors: Moses Aron, Elias Mwakilama, Jimmy Namangale

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Spending more time on long queues for a basic service remains a common challenge to most developing countries, including Malawi. For health sector in particular, Out-Patient Department (OPD) experiences long queues. This puts the lives of patients at risk. However, using queuing analysis to under the nature of the problems and efficiency of service systems, such problems can be abated. Based on a kind of service, literature proposes different possible queuing models. However, unlike using generalized assumed models proposed by literature, use of real time case study data can help in deeper understanding the particular problem model and how such a model can vary from one day to the other and also from each case to another. As such, this study uses data obtained from one urban HC for BP, Pediatric and General OPD cases to investigate an average queuing time for patients within the system. It seeks to highlight the proper queuing model by investigating the kind of distributions functions over patient’s arrival time, inter-arrival time, waiting time and service time. Comparable with the standard set values by WHO, the study found that patients at this HC spend more waiting times than service times. On model investigation, different days presented different models ranging from an assumed M/M/1, M/M/2 to M/Er/2. As such, through sensitivity analysis, in general, a commonly assumed M/M/1 model failed to fit the data but rather an M/Er/2 demonstrated to fit well. An M/Er/3 model seemed to be good in terms of measuring resource utilization, proposing a need to increase medical personnel at this HC. However, an M/Er/4 showed to cause more idleness of human resources.

Keywords: health care, out-patient department, queuing model, sensitivity analysis

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Authors: Saurav S. Rath, Birendra K. David

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Generic pharmaceutical industry has to operate in strict timelines of product development and scale-up from lab to plant. Hence, detailed product & process understanding and implementation of appropriate mechanistic modelling and Quality-by-design (QbD) approaches are imperative in the product life cycle. This work provides example cases of such efforts in topical dosage products. Topical products are typically in the form of emulsions, gels, thick suspensions or even simple solutions. The efficacy of such products is determined by characteristics like rheology and morphology. Defining, and scaling up the right manufacturing process with a given set of ingredients, to achieve the right product characteristics presents as a challenge to the process engineer. For example, the non-Newtonian rheology varies not only with CPPs and CMAs but also is an implicit function of globule size (CQA). Hence, this calls for various mechanistic models, to help predict the product behaviour. This paper focusses on such models obtained from computational fluid dynamics (CFD) coupled with population balance modelling (PBM) and constitutive models (like shear, energy density). In a special case of the use of high shear homogenisers (HSHs) for the manufacture of thick emulsions/gels, this work presents some findings on (i) scale-up algorithm for HSH using shear strain, a novel scale-up parameter for estimating mixing parameters, (ii) non-linear relationship between viscosity and shear imparted into the system, (iii) effect of hold time on rheology of product. Specific examples of how this approach enabled scale-up across 1L, 10L, 200L, 500L and 1000L scales will be discussed.

Keywords: computational fluid dynamics, morphology, quality-by-design, rheology

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Authors: Tigabu Dagne Akal

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While advances in computer and communications technology have made the network ubiquitous, they have also rendered networked systems vulnerable to malicious attacks devised from a distance. These attacks or intrusions start with attackers infiltrating a network through a vulnerable host and then launching further attacks on the local network or Intranet. Nowadays, system administrators and network professionals can attempt to prevent such attacks by developing intrusion detection tools and systems using data mining technology. In this study, the experiments were conducted following the Knowledge Discovery in Database Process Model. The Knowledge Discovery in Database Process Model starts from selection of the datasets. The dataset used in this study has been taken from Massachusetts Institute of Technology Lincoln Laboratory. After taking the data, it has been pre-processed. The major pre-processing activities include fill in missed values, remove outliers; resolve inconsistencies, integration of data that contains both labelled and unlabelled datasets, dimensionality reduction, size reduction and data transformation activity like discretization tasks were done for this study. A total of 21,533 intrusion records are used for training the models. For validating the performance of the selected model a separate 3,397 records are used as a testing set. For building a predictive model for intrusion detection J48 decision tree and the Naïve Bayes algorithms have been tested as a classification approach for both with and without feature selection approaches. The model that was created using 10-fold cross validation using the J48 decision tree algorithm with the default parameter values showed the best classification accuracy. The model has a prediction accuracy of 96.11% on the training datasets and 93.2% on the test dataset to classify the new instances as normal, DOS, U2R, R2L and probe classes. The findings of this study have shown that the data mining methods generates interesting rules that are crucial for intrusion detection and prevention in the networking industry. Future research directions are forwarded to come up an applicable system in the area of the study.

Keywords: intrusion detection, data mining, computer science, data mining

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Authors: Puneet Katyal, Punit Kumar

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A great deal of efforts has been done in the field of thermal effects in electrohydrodynamic lubrication (TEHL) during the last five decades. The focus was primarily on the development of an efficient numerical scheme to deal with the computational challenges involved in the solution of TEHL model; however, some important aspects related to the accurate description of lubricant properties such as viscosity, rheology and thermal conductivity in EHL point contact analysis remain largely neglected. A few studies available in this regard are based upon highly complex mathematical models difficult to formulate and execute. Using a simplified thermal EHL model for point contacts, this work sheds some light on the importance of accurate characterization of the lubricant properties and demonstrates that the computed TEHL characteristics are highly sensitive to lubricant properties. It also emphasizes the use of appropriate mathematical models with experimentally determined parameters to account for correct lubricant behaviour.

Keywords: TEHL, shear thinning, rheology, conductivity

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Authors: Seyed Hamed Hashemi Sohi, Gerald Jo Denoga

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Machining or metal cutting is one of the most widely used production processes in industry. The quality of the process and the resulting machined product depends on parameters like tool geometry, material, and cutting conditions. However, the relationships of these parameters to the cutting process are often based mostly on empirical knowledge. In this study, computer modeling and simulation using LS-DYNA software and a Smoothed Particle Hydrodynamic (SPH) methodology, was performed on the orthogonal metal cutting process to analyze three-dimensional deformation of AISI 1045 medium carbon steel during machining. The simulation was performed using the following constitutive models: the Power Law model, the Johnson-Cook model, and the Zerilli-Armstrong models (Z-A). The outcomes were compared against the simulated results obtained by Cenk Kiliçaslan using the Finite Element Method (FEM) and the empirical results of Jaspers and Filice. The analysis shows that the SPH method combined with the Zerilli-Armstrong constitutive model is a viable alternative to simulating the metal cutting process. The tangential force was overestimated by 7%, and the normal force was underestimated by 16% when compared with empirical values. The simulation values for flow stress versus strain at various temperatures were also validated against empirical values. The SPH method using the Z-A model has also proven to be robust against issues of time-scaling. Experimental work was also done to investigate the effects of friction, rake angle and tool tip radius on the simulation.

Keywords: metal cutting, smoothed particle hydrodynamics, constitutive models, experimental, cutting forces analyses

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Authors: Shalaka Kadam, Dinesh Apte, Sagar Mainkar

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The life cycle of products is becoming shorter and shorter due to increased competition in market, shorter product development time and increased product diversity. Short life cycles are normal in retail industry, style business, entertainment media, and telecom and semiconductor industry. The subject of accurate forecasting for demand of short lifecycle products is of special enthusiasm for many researchers and organizations. Due to short life cycle of products the amount of historical data that is available for forecasting is very minimal or even absent when new or modified products are launched in market. The companies dealing with such products want to increase the accuracy in demand forecasting so that they can utilize the full potential of the market at the same time do not oversupply. This provides the challenge to develop a forecasting model that can forecast accurately while handling large variations in data and consider the complex relationships between various parameters of data. Many statistical models have been proposed in literature for forecasting time series data. Traditional time series forecasting models do not work well for short life cycles due to lack of historical data. Also artificial neural networks (ANN) models are very time consuming to perform forecasting. We have studied the existing models that are used for forecasting and their limitations. This work proposes an effective and powerful forecasting approach for short life cycle time series forecasting. We have proposed an approach which takes into consideration different scenarios related to data availability for short lifecycle products. We then suggest a methodology which combines statistical analysis with structured judgement. Also the defined approach can be applied across domains. We then describe the method of creating a profile from analogous products. This profile can then be used for forecasting products with historical data of analogous products. We have designed an application which combines data, analytics and domain knowledge using point-and-click technology. The forecasting results generated are compared using MAPE, MSE and RMSE error scores. Conclusion: Based on the results it is observed that no one approach is sufficient for short life-cycle forecasting and we need to combine two or more approaches for achieving the desired accuracy.

Keywords: forecast, short life cycle product, structured judgement, time series

Procedia PDF Downloads 340