Search results for: human machine interface

Authors: Yi Zheng

Abstract:

Improving formability and mechanical properties have always been one of the challenges in the field of additive manufacturing (AM) of nickel-based superalloys. In this work, the effect of a coaxially coupled alternating magnetic field (AMF) on surface morphology and mechanical properties of plasma arc-based additive manufactured Inconel 718 deposit were investigated. Results show that the Lorentz force induced by AMF strongly alters the flow behavior of the plasma jet and the molten pool, suppressing the tendency of the liquid metal in the molten pool to flow down on the two sides face of the deposit, which in turn remarkably improved the surface accuracy of the thin-walled deposit. Furthermore, the electromagnetic stirring induced by AMF can effectively enhance the convection between the dendrites, which could not only contribute to the formation of finer dendrites but also alleviate the enrichment of the elements (i.e., Nb and Mo) at the solid-liquid interface and inhibits the precipitation of Laves phase. The smallest primary dendritic arm spacing (~13 μm) and lowest Laves phases area fraction (3.12%) were witnessed in the bottom region of the AMF-assisted deposit. The mechanical test confirmed that the deposit's micro-hardness and tensile properties were moderately improved compared with the counterpart without AMF.

Keywords: additive manufacturing, inconel 718, alternating magnetic field, laves phase

Procedia PDF Downloads 71

Authors: Steffy Maria Joseph, Abdu Rahiman V, Abdul Hameed K. M.

Abstract:

Online handwritten character recognition is a challenging field in Artificial Intelligence. The classification success rate of current techniques decreases when the dataset involves similarity and complexity in stroke styles, number of strokes and stroke characteristics variations. Malayalam is a complex south indian language spoken by about 35 million people especially in Kerala and Lakshadweep islands. In this paper, we consider the significant feature extraction for the similar stroke styles of Malayalam. This extracted feature set are suitable for the recognition of other handwritten south indian languages like Tamil, Telugu and Kannada. A classification scheme based on support vector machines (SVM) is proposed to improve the accuracy in classification and recognition of online malayalam handwritten characters. SVM Classifiers are the best for real world applications. The contribution of various features towards the accuracy in recognition is analysed. Performance for different kernels of SVM are also studied. A graphical user interface has developed for reading and displaying the character. Different writing styles are taken for each of the 44 alphabets. Various features are extracted and used for classification after the preprocessing of input data samples. Highest recognition accuracy of 97% is obtained experimentally at the best feature combination with polynomial kernel in SVM.

Keywords: SVM, matlab, malayalam, South Indian scripts, onlinehandwritten character recognition

Procedia PDF Downloads 559

Authors: Hafsa Tahir, Sanaullah Iqbal

Abstract:

Glactooligosaccharide (GOS) is a type of prebiotic which is mainly found in human milk. GOS belongs to those bacteria which stimulates the growth of beneficial bacteria in human intestines. The aim of the present study was to develop a value-added product by producing prebiotic (GOS) in buttermilk through trans galactosylation. Buttermilk is considered as an industrial waste which is discarded after the production of butter and cream. It contains protein, minerals, vitamins and a smaller amount of fat. Raw milk was pasteurized at 100º C for butter production and then trans galactosylation process was induced in the butter milk thus obtained to produce prebiotic GOS. Results showed that the enzyme (which was obtained from bacterial strain of Esecrshia coli and has a gene of Lactobacillus reuteri L103) concentration between 400-600µl/5ml can produce GOS in 30 minutes. Chemical analysis and sensory evaluation of plain and GOS containing buttermilk showed no remarkable difference in their composition. Furthermore, the shelf-life study showed that there was non-significant (P>0.05) difference in glass and pouch packaging of buttermilk. Buttermilk in pouch packaging maintained its stability for 6 days without the addition of preservatives. Therefore it is recommended that GOS enriched buttermilk which is generally considered as a processing waste in dairy manufacturing can be turned into a cost-effective nutritional functional food product. This will not only enhance the production efficiency of butter processing but also will create a new market opportunity for dairy manufacturers all over the world.

Keywords: buttermilk, galactooligosaccharide, shelf Life, transgalactosylation

Procedia PDF Downloads 280

Authors: Fahem Belkacemi, Lyes Ouali

Abstract:

Maintenance strategies and assessments continue to be a major concern for companies today. The socio-economic bets of their competitiveness are closely linked to the activities and quality of maintenance. This work deals with a study of a preventive maintenance plan to improve the availability of the production line within SAMSUNG HOME APPLIANCE “SAMHA”, Setif, Algeria. First, we applied the method of analysis of failure modes, their impact, and criticality to reduce downtime and identification of the most critical elements. Finally, to improve the availability of the production line, we carried out a study of the current preventive maintenance plan in the production line workshop at the company level and according to the history sheet of machine failures. We proposed a preventive maintenance plan to improve the availability of the production line.

Keywords: preventive maintenance, DRUM line, AMDEC, availability

Procedia PDF Downloads 47

Authors: Trevor Malambo Simbayi, Diane Hildebrandt, Tonderayi Matambo

Abstract:

The rapid growth of population in recent decades has resulted in an increased need for energy to meet human activities. As energy demands increase, the need for other sources of energy other than fossil fuels, increases in turn. Furthermore, environmental concerns such as global warming due to the use of fossil fuels, depleting fossil fuel reserves and the rising cost of oil have contributed to an increased interest in renewables sources of energy. Biogas is a renewable source of energy produced through the process of anaerobic digestion (AD) and it offers a two-fold solution; it provides an environmentally friendly source of energy and its production helps to reduce the amount of organic waste taken to landfills. This research seeks to address the waste management problem caused by an aquatic weed called water hyacinth (Eichhornia crassipes) at the Hartbeespoort (Harties) Dam in the North West Province of South Africa, through biogas production of the weed. Water hyacinth is a category 1 invasive species and it is deemed to be the most problematic aquatic weed. This weed is said to double its size in the space of five days. Eutrophication in the Hartbeespoort Dam has manifested itself through the excessive algae bloom and water hyacinth infestation. A large amount of biomass from water hyacinth and algae are generated per annum from the two hundred hectare surface area of the dam exposed to the sun. This biomass creates a waste management problem. Water hyacinth when in full bloom can cover nearly half of the surface of Hartbeespoort Dam. The presence of water hyacinth in the dam has caused economic and environmental problems. Economic activities such as fishing, boating, and recreation, are hampered by the water hyacinth’s prolific growth. This research proposes the use of water hyacinth as a feedstock or substrate for biogas production in order to find an economic and environmentally friendly means of waste management for the communities living around the Hartbeespoort Dam. In order to achieve this objective, water hyacinth will be collected from the dam and it will be mechanically pretreated before anaerobic digestion. Pretreatment is required for lignocellulosic materials like water hyacinth because such materials are called recalcitrant solid materials. Cow manure will be employed as a source of microorganisms needed for biogas production to occur. Once the water hyacinth and the cow dung are mixed, they will be placed in laboratory anaerobic reactors. Biogas production will be monitored daily through the downward displacement of water. Characterization of the substrates (cow manure and water hyacinth) to determine the nitrogen, sulfur, carbon and hydrogen, total solids (TS) and volatile solids (VS). Liquid samples from the anaerobic digesters will be collected and analyzed for volatile fatty acids (VFAs) composition by means of a liquid gas chromatography machine.

Keywords: anaerobic digestion, biogas, waste management, water hyacinth

Procedia PDF Downloads 181

Authors: Muhammet Baldan, Emel Timuçin

Abstract:

Generally, absorption and bioavailability increase if solubility increases; therefore, it is crucial to predict them in drug discovery applications. Molecular descriptors and Molecular properties are traditionally used for the prediction of water solubility. There are various key descriptors that are used for this purpose, namely Drogan Descriptors, Morgan Descriptors, Maccs keys, etc., and each has different prediction capabilities with differentiating successes between different data sets. Another source for the prediction of solubility is structural features; they are commonly used for the prediction of solubility. However, there are little to no studies that combine three or more properties or descriptors for prediction to produce a more powerful prediction model. Unlike available models, we used a combination of those features in a random forest machine learning model for improved solubility prediction to better predict and, therefore, contribute to drug discovery systems.

Keywords: solubility, random forest, molecular descriptors, maccs keys

Procedia PDF Downloads 24

Authors: Shailendra M. Pradhan, Upendra M. Pradhan

Abstract:

Disasters are perennial features of human civilization. The recurring earthquakes, floods, cyclones, among others, that result in massive loss of lives and devastation, is a grim reminder of the fact that, despite all our success stories of development, and progress in science and technology, human society is perennially at risk to disasters. The apparent threat of climate change and global warming only severe our disaster risks. Darjeeling hills, situated along Eastern Himalayan region of India, and famous for its three Ts – tea, tourism and toy-train – is also equally notorious for its disasters. The recurring landslides and earthquakes, the cyclone Aila, and the Ambootia landslides, considered as the largest landslide in Asia, are strong evidence of the vulnerability of Darjeeling hills to natural disasters. Given its geographical location along the Hindu-Kush Himalayas, the region is marked by rugged topography, geo-physically unstable structure, high-seismicity, and fragile landscape, making it prone to disasters of different kinds and magnitudes. Most of the studies on disasters in Darjeeling hills are, however, scientific and geographical in orientation that focuses on the underlying geological and physical processes to the neglect of social and political conditions. This has created a tendency among the researchers and policy-makers to endorse and promote a particular type of discourse that does not consider the social and political aspects of disasters in Darjeeling hills. Disaster, this paper argues, is a complex phenomenon, and a result of diverse factors, both physical and human. The hazards caused by the physical and geological agents, and the vulnerabilities produced and rooted in political, economic, social and cultural structures of a society, together result in disasters. In this sense, disasters are as much a result of political and economic conditions as it is of physical environment. The human aspect of disasters, therefore, compels us to address intricating social and political challenges that ultimately determine our resilience and vulnerability to disasters. Set within the above milieu, the aims of the paper are twofold: a) to provide a political and sociological account of disasters in Darjeeling hills; and, b) to identify and address the root causes of its vulnerabilities to disasters. In situating disasters in Darjeeling Hills, the paper adopts the Pressure and Release Model (PAR) that provides a theoretical insight into the study of social and political aspects of disasters, and to examine myriads of other related issues therein. The PAR model conceptualises risk as a complex combination of vulnerabilities, on the one hand, and hazards, on the other. Disasters, within the PAR framework, occur when hazards interact with vulnerabilities. The root causes of vulnerability, in turn, could be traced to social and political structures such as legal definitions of rights, gender relations, and other ideological structures and processes. In this way, the PAR model helps the present study to identify and unpack the root causes of vulnerabilities and disasters in Darjeeling hills that have largely remained neglected in dominant discourses, thereby providing a more nuanced and sociologically sensitive understanding of disasters.

Keywords: Darjeeling, disasters, PAR, vulnerabilities

Procedia PDF Downloads 262

Authors: Ow Tze Weng, Suhaila Isaak, Yusmeeraz Yusof

Abstract:

The trend of health care screening devices in the world is increasingly towards the favor of portability and wearability, especially in the most common electrocardiogram (ECG) monitoring system. This is because these wearable screening devices are not restricting the patient’s freedom and daily activities. While the demand of low power and low cost biomedical system on chip (SoC) is increasing in exponential way, the front end ECG sensors are still suffering from flicker noise for low frequency cardiac signal acquisition, 50 Hz power line electromagnetic interference, and the large unstable input offsets due to the electrode-skin interface is not attached properly. In this paper, a high performance CMOS amplifier for ECG sensors that suitable for low power wearable cardiac screening is proposed. The amplifier adopts the highly stable folded cascode topology and later being implemented into RC feedback circuit for low frequency DC offset cancellation. By using 0.13 µm CMOS technology from Silterra, the simulation results show that this front end circuit can achieve a very low input referred noise of 1 pV/√Hz and high common mode rejection ratio (CMRR) of 174.05 dB. It also gives voltage gain of 75.45 dB with good power supply rejection ratio (PSSR) of 92.12 dB. The total power consumption is only 3 µW and thus suitable to be implemented with further signal processing and classification back end for low power biomedical SoC.

Keywords: CMOS, ECG, amplifier, low power

Procedia PDF Downloads 234

Authors: Ron Cottam, Nils Langloh, Willy Ranson, Roger Vounckx

Abstract:

We start by formally describing the requirements for environmental-reaction survival computation in a natural temporally-demanding medium, and develop this into a more general model of the evolutionary context as a computational machine. The effect of this development is to replace deterministic logic by a modified form which exhibits a continuous range of dimensional fractal diffuseness between the isolation of perfectly ordered localization and the extended communication associated with nonlocality as represented by pure causal chaos. We investigate the appearance of life and consciousness in the derived general model, and propose a representation of Nature within which all localizations have the character of quasi-quantal entities. We compare our conclusions with Heisenberg’s uncertainty principle and nonlocal teleportation, and maintain that computability is the principal influence on evolution in the model we propose.

Keywords: computability, evolution, life, localization, modeling, nonlocality

Procedia PDF Downloads 387

Authors: Tauseef Ahmad, Muhammad Ishaq, Mathew Eapen, Ahyoung Park, Sam Karpiniec, Vanni Caruso, Rajaraman Eri

Abstract:

Fucoidans are complex, fucose-rich sulfated polymers discovered in brown seaweeds. Fucoidans are popular around the world, particularly in the nutraceutical and pharmaceutical industries, due to their promising medicinal properties. Fucoidans have been shown to have a variety of biological activities, including anti-inflammatory effects. They are known to inhibit inflammatory processes through a variety of mechanisms, including enzyme inhibition and selectin blockade. Inflammation is a part of the complicated biological response of living systems to damaging stimuli, and it plays a role in the pathogenesis of a variety of disorders, including arthritis, inflammatory bowel disease, cancer, and allergies. In the current investigation, various fucoidan extracts from Undaria pinnatifida, Fucus vesiculosus, Macrocystis pyrifera, Ascophyllum nodosum, and Laminaria japonica were assessed for inhibition of pro-inflammatory cytokine production (TNF-α, IL-1β, and IL-6) in LPS induced human macrophage cell line (THP-1) and human peripheral blood mononuclear cells (PBMCs). Furthermore, we also sought to catalogue these extracts based on their anti-inflammatory effects in the current in-vitro cell model. Materials and Methods: To assess the cytotoxicity of fucoidan extracts, MTT (3-[4,5-dimethylthiazol-2-yl]-2,5, -diphenyltetrazolium bromide) cell viability assay was performed. Furthermore, a dose-response for fucoidan extracts was performed in LPS induced THP-1 cells and PBMCs after pre-treatment for 24 hours, and levels of TNF-α, IL-1β, and IL-6 cytokines were measured using Enzyme-Linked Immunosorbent Assay (ELISA). Results: The MTT cell viability assay demonstrated that fucoidan extracts exhibited no evidence of cytotoxicity in THP-1 cells or PBMCs after 48 hours of incubation. The results of the sandwich ELISA revealed that all fucoidan extracts suppressed cytokine production in LPS-stimulated PBMCs and human THP-1 cells in a dose-dependent manner. Notably, at lower concentrations, the lower molecular fucoidan (5-30 kDa) extract from Macrocystis pyrifera was a highly efficient inhibitor of pro-inflammatory cytokines. Fucoidan extracts from all species including Undaria pinnatifida, Fucus vesiculosus, Macrocystis pyrifera, Ascophyllum nodosum, and Laminaria japonica exhibited significant anti-inflammatory effects. These findings on several fucoidan extracts provide insight into strategies for improving their efficacy against inflammation-related diseases. Conclusion: In the current research, we have successfully catalogued several fucoidan extracts based on their efficiency in LPS-induced macrophages and PBMCs in downregulating the key pro-inflammatory cytokines (TNF-, IL-1 and IL-6), which are prospective targets in human inflammatory illnesses. Further research would provide more information on the mechanism of action, allowing it to be tested for therapeutic purposes as an anti-inflammatory medication.

Keywords: fucoidan, PBMCs, THP-1, TNF-α, IL-1β, IL-6, inflammation

Procedia PDF Downloads 47

Authors: Hana Gebremariam Liliso

Abstract:

This study investigates the factors contributing to the deterioration of thick asphalt pavements, such as rutting and cracking. We focus on the combined influence of traffic loads and pavement structure. This study uses a three-dimensional finite element model with a Mohr-Coulomb failure criterion to analyze the stress levels near the pavement's surface under realistic conditions. Our model considers various factors, including tire-pavement contact stresses, asphalt properties, moving loads, and dynamic analysis. This research suggests that cracking tends to occur between dual tires. Some key discoveries include the risk of cracking increases as temperatures rise; surface cracking at high temperatures is associated with distortional deformation; using a uniform contact stress distribution underestimates the risk of failure compared to realistic three-dimensional tire contact stress, particularly at high temperatures; the risk of failure is higher near the surface when there is a negative temperature gradient in the asphalt layer; and debonding beneath the surface layer leads to increased shear stress and premature failure around the interface.

Keywords: asphalt pavement, surface failure, 3d finite element model, multiaxial stress states, Mohr-Coulomb failure criterion

Procedia PDF Downloads 42

Authors: Alaa Koaik, Bruno Jurkiewiez, Sylvain Bel

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Recently, the use of GFRP pultruded profiles increased in the domain of civil engineering especially in the construction of sandwiched slabs and footbridges. However, under heavy loads, the risk of using these profiles increases due to their high deformability and instability as a result of their weak stiffness and orthotropic nature. A practical solution proposes the assembly of these profiles with concrete slabs to create a stiffer hybrid element to support higher loads. The connection of these two elements is established either by traditional means of steel studs (bolting in our case) or bonding technique. These two techniques have their advantages and disadvantages regarding the mechanical behavior and in-situ implementation. This paper presents experimental results of interface characterization and bending behavior of two hybrid beams, PB7 and PB8, designed and constructed using both connection techniques. The results obtained are exploited to design and build a hybrid footbridge BPBP1 which is tested within service limits (elastic domain). Analytical methods are also developed to analyze the behavior of these structures in the elastic range and the ultimate phase. Comparisons show acceptable differences mainly due to the sensitivity of the GFRP moduli as well as the non-linearity of concrete elements.

Keywords: analytical model, concrete, flexural behavior, GFRP pultruded profile, hybrid structure, interconnection slip, push-out

Procedia PDF Downloads 214

Authors: Sumrina Kousar

Abstract:

Objective: The aim of this study was to observe the effect of fortification of expressed human breast milk with olive oil and skimmed milk in improving weight gain in very low birth weight neonates and shortening their length of hospital stay. Study Design and place: A randomized controlled trial was carried out at the Combined Military Hospital Lahore from March 2018 to March 2019. Methods: Neonates admitted with very low birth weight and gestational age of < 34 weeks were included in the study. Sixty babies were enrolled using non-probability consecutive sampling; a random number table was used to allocate them into a fortification group and a control group. The control group received expressed milk alone, while olive oil 1 ml twice daily and skimmed milk 1 gram in every third feed were added to expressed milk in the fortification group. Data was analyzed on SPSS 20. Proportions were compared by applying the chi-square test. An independent sample t-test was applied for comparing means. A p-value of ≤ 0.05 was considered significant. Results: The study comprised of 60 neonates, with 30 in each of the groups. Weight gain was 24.83±5.63 in the fortification group and 11.72±3.95 in the control group (p =< 0.001). Mean hospital stay was 20.5716.511 in the fortification group and 27.678.89 in the control group (p =< 0.043). Conclusion: Olive oil and skimmed milk fortification of breast milk was effective for weight gain and reducing the length of hospital stay in very low birth weight neonates.

Keywords: fortification, olive oil, skimmed milk, weight gain

Procedia PDF Downloads 152

Authors: Amós García Montaño

Abstract:

In a democracy, a public policy must be developed within the regulatory framework and considering citizen participation in its planning, design, execution, and evaluation stages, necessary factors to have both legal support and sufficient legitimacy for its operation. However, the complexity and magnitude of certain public problems results in difficulties for the generation of consensus among society members, leading to unstable and unsuccessful scenarios for the exercise of the right to citizen participation and the generation of effective and efficient public policies. This is the case of public policies against corruption, an issue that in Mexico is difficult to define and generates conflicting opinions. To provide a possible solution to this delicate reality, this paper analyzes the principle of reasonableness as a tool for identifying the basic elements that guarantee a fundamental level of the exercise of the right to citizen participation in the fight against corruption, adopting elements of human rights indicator methodologies. In this sense, the relevance of having a legal framework that establishes obligations to incorporate proactive and transversal citizen participation in the matter is observed. It is also noted the need to monitor the operation of various citizen participation mechanisms in the decision-making processes of the institutions involved in the fight and prevention of corruption, which lead to an increase in the improvement of the perception of the citizen role as a relevant actor in this field. It is concluded that the principle of reasonableness is presented as a very useful tool for the identification of basic elements that facilitate the fulfillment of human rights commitments in the field of public policies.

Keywords: anticorruption, public participation, public policies, reasonableness

Procedia PDF Downloads 71

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 105

Authors: Uzu-Kuei Hsu, Chang-Hsien Tai, Kai-Wun Jin

Abstract:

When sinters are filled into the cooler from the sintering machine, and the non-uniform distribution of the sinters leads to uneven cooling. This causes the temperature difference of the sinters leaving the cooler to be so large that it results in the conveyors being deformed by the heat. The present work applies CFD method to investigate the thermo flowfield phenomena in a sinter cooler by the Porous Media Model. Using the obtained experimental data to simulate porosity (Ε), permeability (κ), inertial coefficient (F), specific heat (Cp) and effective thermal conductivity (keff) of the sinter packed beds. The physical model is a similar geometry whose Darcy numbers (Da) are similar to the sinter cooler. Using the Cooling Index (CI) and Uniformity Index (UI) to analyze the thermo flowfield in the sinter packed bed obtains the cooling performance of the sinter cooler.

Keywords: porous media, sinter, cooling index (CI), uniformity index (UI), CFD

Procedia PDF Downloads 383

Authors: Murat Sarı, Emin Gundogar, Mumtaz Ipek

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Nowadays, in a furniture sector, competition of market share (portion) and production variety and changeability enforce the firm to reengineer operations on manufacturing line to increase the productivity. In this study, spring mattress manufacturing line of the furniture manufacturing firm is analyzed analytically. It’s intended to search and find the bottlenecks of production to balance the semi-finished material flow. There are four base points required to investigate in bottleneck elimination process. These are bottlenecks of Method, Material, Machine and Man (work force) resources, respectively. Mentioned bottlenecks are investigated and varied scenarios are created for recruitment of manufacturing system. Probable near optimal alternatives are determined by system models built in Arena simulation software.

Keywords: bottleneck search, buffer stock, furniture sector, simulation

Procedia PDF Downloads 347

Authors: Bunty Tomar, Shiva S.

Abstract:

Fabrication of steel and copper-based functionally graded material (FGM) through cold metal transfer-based wire arc additive manufacturing is a novel exploration. Components combining Cu and steel show significant usage in many industrial applications as they combine high corrosion resistance, ductility, thermal conductivity, and wear resistance to excellent mechanical properties. Joining steel and copper is challenging due to the mismatch in their thermo-mechanical properties. In this experiment, a functionally graded material (FGM) structure of pure copper (Cu) and 316L stainless steel (SS) was successfully developed using cold metal transfer-based wire arc additive manufacturing (CMT-WAAM). The interface of the fabricated samples was characterized under optical microscopy, field emission scanning electron microscopy, and X-ray diffraction techniques. Detailed EBSD and TEM analysis was performed to analyze the grain orientation, strain distribution, grain boundary misorientations, and formation of metastable and intermetallic phases. Mechanical characteristics of deposits was also analyzed using tensile and wear testing. This works paves the way to use CMT-WAAM to fabricate steel/copper FGMs.

Keywords: wire arc additive manufacturing (waam), cold metal transfer (cmt), metals and alloys, mechanical properties, characterization

Procedia PDF Downloads 64

Authors: Nebi Gedik

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One of the significant and continual public health problems in the world is breast cancer. Early detection is very important to fight the disease, and mammography has been one of the most common and reliable methods to detect the disease in the early stages. However, it is a difficult task, and computer-aided diagnosis (CAD) systems are needed to assist radiologists in providing both accurate and uniform evaluation for mass in mammograms. In this study, a multiresolution statistical method to classify mammograms as normal and abnormal in digitized mammograms is used to construct a CAD system. The mammogram images are represented by wave atom transform, and this representation is made by certain groups of coefficients, independently. The CAD system is designed by calculating some statistical features using each group of coefficients. The classification is performed by using support vector machine (SVM).

Keywords: wave atom transform, statistical features, multi-resolution representation, mammogram

Procedia PDF Downloads 209

Authors: Mwafak Shakoor

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The purpose of this study was to reduce patient waiting times, improve system throughput and improve resources utilization in radiology department. A discrete event simulation model was developed using Arena simulation software to investigate different alternatives to improve the overall system delivery based on adding resource scenarios due to the linkage between patient waiting times and resource availability. The study revealed that there is no addition investment need to procure additional scanner but hospital management deploy managerial tactics to enhance machine utilization and reduce the long waiting time in the department.

Keywords: discrete event simulation, radiology department, arena, waiting time, healthcare modeling, computed tomography

Procedia PDF Downloads 578

Authors: Jeansou Moun

Abstract:

This study argues that constructivism and structuralism, which have been the two important schools of mathematical philosophy since the mid-19th century, can and should be synthesized into structure-constructivism. In fact, the philosophy of mathematics is divided into more than ten schools depending on the point of view. However, the biggest trend is Platonism which claims that mathematical objects are "abstract entities" that exists independently of the human mind and material objects. Its opposite is constructivism. According to the latter, mathematical objects are products of the construction of the human mind. However, whether the basis of the construction is a logical device, a symbolic system, or an empirical perception, it is subdivided into logicism, formalism, and intuitionism. However, these three schools themselves are further subdivided into various variants, and among them, structuralism, which emerged in the mid-20th century, is receiving the most attention. On the other hand, structuralism which emphasizes structure instead of individual objects, is divided into non-eliminative structuralism, which supports the a priori of structure, and non-eliminative structuralism, which rejects any abstract entity. In this context, it is believed that the structure itself is not an a priori entity but a result of the construction of the cognitive subject and that no object has ever been given to us in its full meaning from the outset. In other words, concepts are progressively structured through a dialectical cycle between sensory perception, imagination (abstraction), concepts, judgments, and reasoning. Symbols are needed for formal operation. However, without concrete manipulation, the formal operation cannot have any meaning. However, when formal structurization is achieved, the reality (object) itself is also newly structured. This is the "structure-constructivism".

Keywords: philosophy of mathematics, platonism, logicism, formalism, constructivism, structuralism, structure-constructivism

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Authors: Warda Khan, Remo Lachmann, Adarsh S. Garakahally

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The increasing complexity of software and its applicability in a wide range of industries, e.g., automotive, call for enhanced quality assurance techniques. Test automation is one option to tackle the prevailing challenges by supporting test engineers with fast, parallel, and repetitive test executions. A high degree of test automation allows for a shift from mundane (manual) testing tasks to a more analytical assessment of the software under test. However, a high initial investment of test resources is required to establish test automation, which is, in most cases, a limitation to the time constraints provided for quality assurance of complex software systems. Hence, a computer-aided creation of automated test cases is crucial to increase the benefit of test automation. This paper proposes the application of machine learning for the generation of automated test cases. It is based on supervised learning to analyze test specifications and existing test implementations. The analysis facilitates the identification of patterns between test steps and their implementation with test automation components. For the test case generation, this approach exploits historical data of test automation projects. The identified patterns are the foundation to predict the implementation of unknown test case specifications. Based on this support, a test engineer solely has to review and parameterize the test automation components instead of writing them manually, resulting in a significant time reduction for establishing test automation. Compared to other generation approaches, this ML-based solution can handle different writing styles, authors, application domains, and even languages. Furthermore, test automation tools require expert knowledge by means of programming skills, whereas this approach only requires historical data to generate test cases. The proposed solution is evaluated using various multi-label evaluation criteria (EC) and two small-sized real-world systems. The most prominent EC is ‘Subset Accuracy’. The promising results show an accuracy of at least 86% for test cases, where a 1:1 relationship (Multi-Class) between test step specification and test automation component exists. For complex multi-label problems, i.e., one test step can be implemented by several components, the prediction accuracy is still at 60%. It is better than the current state-of-the-art results. It is expected the prediction quality to increase for larger systems with respective historical data. Consequently, this technique facilitates the time reduction for establishing test automation and is thereby independent of the application domain and project. As a work in progress, the next steps are to investigate incremental and active learning as additions to increase the usability of this approach, e.g., in case labelled historical data is scarce.

Keywords: machine learning, multi-class, multi-label, supervised learning, test automation

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Authors: Agboka Komi Mensah, John Odindi, Elfatih M. Abdel-Rahman, Onisimo Mutanga, Henri Ez Tonnang

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Ecosystems are networks of organisms and populations that form a community of various species interacting within their habitats. Such habitats are defined by abiotic and biotic conditions that establish the initial limits to a population's growth, development, and reproduction. The habitat’s conditions explain the context in which species interact to access resources such as food, water, space, shelter, and mates, allowing for feeding, dispersal, and reproduction. Dispersal is an essential life-history strategy that affects gene flow, resource competition, population dynamics, and species distributions. Despite the importance of dispersal in population dynamics and survival, understanding the mechanism underpinning the dispersal of organisms remains challenging. For instance, when an organism moves into an ecosystem for survival and resource competition, its progression is highly influenced by extrinsic factors such as its physiological state, climatic variables and ability to evade predation. Therefore, greater spatial detail is necessary to understand organism dispersal dynamics. Understanding organisms dispersal can be addressed using empirical and mechanistic modelling approaches, with the adopted approach depending on the study's purpose Cellular automata (CA) is an example of these approaches that have been successfully used in biological studies to analyze the dispersal of living organisms. Cellular automata can be briefly described as occupied cells by an individual that evolves based on proper decisions based on a set of neighbours' rules. However, in the ambit of modelling individual organisms dispersal at the landscape scale, we lack user friendly tools that do not require expertise in mathematical models and computing ability; such as a visual analytics framework for tracking and forecasting the dispersal behaviour of organisms. The term "visual analytics" (VA) describes a semiautomated approach to electronic data processing that is guided by users who can interact with data via an interface. Essentially, VA converts large amounts of quantitative or qualitative data into graphical formats that can be customized based on the operator's needs. Additionally, this approach can be used to enhance the ability of users from various backgrounds to understand data, communicate results, and disseminate information across a wide range of disciplines. To support effective analysis of the dispersal of organisms at the landscape scale, we therefore designed Pydisp which is a free visual data analytics tool for spatiotemporal dispersal modeling built in Python. Its user interface allows users to perform a quick and interactive spatiotemporal analysis of species dispersal using bioecological and climatic data. Pydisp enables reuse and upgrade through the use of simple principles such as Fuzzy cellular automata algorithms. The potential of dispersal modeling is demonstrated in a case study by predicting the dispersal of Fopius arisanus (Sonan), endoparasitoids to control Bactrocera dorsalis (Hendel) (Diptera: Tephritidae) in Kenya. The results obtained from our example clearly illustrate the parasitoid's dispersal process at the landscape level and confirm that dynamic processes in an agroecosystem are better understood when designed using mechanistic modelling approaches. Furthermore, as demonstrated in the example, the built software is highly effective in portraying the dispersal of organisms despite the unavailability of detailed data on the species dispersal mechanisms.

Keywords: cellular automata, fuzzy logic, landscape, spatiotemporal

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Authors: Laura Gantley, Vanessa M. Conn, Stuart Webb, Kirsty Kirk, Marta Gabryelska, Duncan Holds, Brett W. Stringer, Simon J. Conn

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Trinucleotide repeat disorders comprise ~20 severe, inherited human neuromuscular and neurodegenerative disorders, which are a result of an abnormal expansion of repetitive sequences in the DNA. The most common of these, Huntington’s disease, results from the expansion of the CAG repeat region in exon 1 of the HTT gene via an unknown mechanism. Non-coding RNAs have been implicated in the initiation and progression of many diseases; thus, we focus on one circular RNA (circRNA) molecule arising from non-canonical splicing (back splicing) of HTT pre-mRNA. This circRNA and its mouse orthologue were transgenically overexpressed in human cells (SHSY-5Y and HEK293T) and mouse cells (Mb1), respectively. High-content imaging and flow cytometry demonstrated the overexpression of this circRNA reduces cell proliferation, reduces nuclear size independent of cellular size, and alters cell cycle progression. Analysis of protein by western blot and immunofluorescence demonstrated no change to HTT protein levels but altered nuclear-cytoplasmic distribution without impacting the expansion of the HTT repeat region. As these phenotypic and genotypic changes are found in Huntington’s disease patients, these results may suggest that this circRNA may play a functional role in the progression of Huntington’s disease.

Keywords: cell biology, circular RNAs, Huntington’s disease, molecular biology, neurodegenerative disorders

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Authors: M. Abdullah Al Faruque, Ram Balachandar

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An experimental study with four different types of bed conditions was carried out to understand the effect of roughness in open channel flow at two different Reynolds numbers. The bed conditions include a smooth surface and three different roughness conditions which were generated using sand grains with a median diameter of 2.46 mm. The three rough conditions include a surface with distributed roughness, a surface with continuously distributed roughness and a sand bed with a permeable interface. A commercial two-component fibre-optic LDA system was used to conduct the velocity measurements. The variables of interest include the mean velocity, turbulence intensity, the correlation between the streamwise and the wall normal turbulence, Reynolds shear stress and velocity triple products. Quadrant decomposition was used to extract the magnitude of the Reynolds shear stress of the turbulent bursting events. The effect of roughness was evident throughout the flow depth. The results show that distributed roughness has the greatest roughness effect followed by the sand bed and the continuous roughness. Compared to the smooth bed, the streamwise turbulence intensity reduces but the vertical turbulence intensity increases at a location very close to the bed due to the introduction of roughness. Although the same sand grain is used to create the three different rough bed conditions, the difference in the turbulence intensity is an indication that the specific geometry of the roughness has an influence on turbulence structure.

Keywords: open channel flow, smooth and rough bed, Reynolds number, turbulence

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Authors: Anna Makuch, Grzegorz Kokot, Konstanty Skalski, Jakub Banczorowski

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Cancellous bone is a porous composite of a hierarchical structure and anisotropic properties. The biological tissue is considered to be a viscoelastic material, but many studies based on a nanoindentation method have focused on their elasticity and microhardness. However, the response of many organic materials depends not only on the load magnitude, but also on its duration and time course. Depth Sensing Indentation (DSI) technique has been used for examination of creep in polymers, metals and composites. In the indentation tests on biological samples, the mechanical properties are most frequently determined for animal tissues (of an ox, a monkey, a pig, a rat, a mouse, a bovine). However, there are rare reports of studies of the bone viscoelastic properties on microstructural level. Various rheological models were used to describe the viscoelastic behaviours of bone, identified in the indentation process (e. g Burgers model, linear model, two-dashpot Kelvin model, Maxwell-Voigt model). The goal of the study was to determine the influence of creep effect on the mechanical properties of human cancellous bone in indentation tests. The aim of this research was also the assessment of the material properties of bone structures, having in mind the energy aspects of the curve (penetrator loading-depth) obtained in the loading/unloading cycle. There was considered how the different holding times affected the results within trabecular bone.As a result, indentation creep (CIT), hardness (HM, HIT, HV) and elasticity are obtained. Human trabecular bone samples (n=21; mean age 63±15yrs) from the femoral heads replaced during hip alloplasty were removed and drained from alcohol of 1h before the experiment. The indentation process was conducted using CSM Microhardness Tester equipped with Vickers indenter. Each sample was indented 35 times (7 times for 5 different hold times: t1=0.1s, t2=1s, t3=10s, t4=100s and t5=1000s). The indenter was advanced at a rate of 10mN/s to 500mN. There was used Oliver-Pharr method in calculation process. The increase of hold time is associated with the decrease of hardness parameters (HIT(t1)=418±34 MPa, HIT(t2)=390±50 MPa, HIT(t3)= 313±54 MPa, HIT(t4)=305±54 MPa, HIT(t5)=276±90 MPa) and elasticity (EIT(t1)=7.7±1.2 GPa, EIT(t2)=8.0±1.5 GPa, EIT(t3)=7.0±0.9 GPa, EIT(t4)=7.2±0.9 GPa, EIT(t5)=6.2±1.8 GPa) as well as with the increase of the elastic (Welastic(t1)=4.11∙10-7±4.2∙10-8Nm, Welastic(t2)= 4.12∙10-7±6.4∙10-8 Nm, Welastic(t3)=4.71∙10-7±6.0∙10-9 Nm, Welastic(t4)= 4.33∙10-7±5.5∙10-9Nm, Welastic(t5)=5.11∙10-7±7.4∙10-8Nm) and inelastic (Winelastic(t1)=1.05∙10-6±1.2∙10-7 Nm, Winelastic(t2) =1.07∙10-6±7.6∙10-8 Nm, Winelastic(t3)=1.26∙10-6±1.9∙10-7Nm, Winelastic(t4)=1.56∙10-6± 1.9∙10-7 Nm, Winelastic(t5)=1.67∙10-6±2.6∙10-7)) reaction of materials. The indentation creep increased logarithmically (R2=0.901) with increasing hold time: CIT(t1) = 0.08±0.01%, CIT(t2) = 0.7±0.1%, CIT(t3) = 3.7±0.3%, CIT(t4) = 12.2±1.5%, CIT(t5) = 13.5±3.8%. The pronounced impact of creep effect on the mechanical properties of human cancellous bone was observed in experimental studies. While the description elastic-inelastic, and thus the Oliver-Pharr method for data analysis, may apply in few limited cases, most biological tissues do not exhibit elastic-inelastic indentation responses. Viscoelastic properties of tissues may play a significant role in remodelling. The aspect is still under an analysis and numerical simulations. Acknowledgements: The presented results are part of the research project founded by National Science Centre (NCN), Poland, no.2014/15/B/ST7/03244.

Keywords: bone, creep, indentation, mechanical properties

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Authors: Dong-ping Yuan, Lin Gu, Jun Long, Jie Chen, Ni Jie, Ying-Li Shi

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Endometriosis is a common disease in women of reproductive age, whose classic characteristic is mononuclear cell infiltration into lesions. Shikonin is an anti-inflammatory phytocompound from Lithospermum erythrorhizon, whose potential therapeutic effects for the endometriosis remain unclear. The working hypothesis was that shikonin can inhibit the development of endometriosis by the inhibition of chemotactic effect. Shikonin significantly inhibited the growth of human endometrial tissue implanted into mice (P<0.05). No observable adverse effects were found. The mouse regulated upon activation normal T-cell expressed and secreted (mRANTES) level in peritoneal fluid of animal endometriosis model was higher than that in normal SCID mice (P<0.05), and decreased dramatically after shikonin treatment in a dose-dependent manner (P<0.05). Peritoneal fluid from NOD/SCID mice treated with shikonin inhibited monocytes chemotaxis, which could be abolished by mRANTES antibody. In vitro, shikonin significantly inhibited RANTES expression of U937 cells cultured alone or co-cultured with human methothelail cells and endometrial stromal cells, and inhibited RANTES-induced chemotaxis of U937 cells (P<0.05). The present results suggest that shikonin can inhibit the development of endometriosis by mechanisms that at least include the inhibition of RANTES expression and decreased migration of mononuclear cells to lesions. Shikonin may be a useful and safe new approach for treating endometriosis.

Keywords: endometriosis, shikonin, RANTES chemotaxis

Procedia PDF Downloads 381

Authors: K. Jagannath, Akhilesh Kotian, S. S. Sharma, Achutha Kini U., P. R. Prabhu

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Boiling process is characterized by the rapid formation of vapour bubbles at the solid–liquid interface (nucleate boiling) with pre-existing vapour or gas pockets. Computational fluid dynamics (CFD) is an important tool to study bubble dynamics. In the present study, CFD simulation has been carried out to determine the bubble detachment diameter and its terminal velocity. Volume of fluid method is used to model the bubble and the surrounding by solving single set of momentum equations and tracking the volume fraction of each of the fluids throughout the domain. In the simulation, bubble is generated by allowing water-vapour to enter a cylinder filled with liquid water through an inlet at the bottom. After the bubble is fully formed, the bubble detaches from the surface and rises up during which the bubble accelerates due to the net balance between buoyancy force and viscous drag. Finally when these forces exactly balance each other, it attains a constant terminal velocity. The bubble detachment diameter and the terminal velocity of the bubble are captured by the monitor function provided in FLUENT. The detachment diameter and the terminal velocity obtained is compared with the established results based on the shape of the bubble. A good agreement is obtained between the results obtained from simulation and the equations in comparison with the established results.

Keywords: bubble growth, computational fluid dynamics, detachment diameter, terminal velocity

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Authors: Rohana Abdullah, Md Nizam Abd Rahman, Seri Rahayu Kamat

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Operator efficiency aspect is gaining importance in ensuring optimized usage of resources especially in the semi-automated manufacturing environment. This paper addresses a case study done to solve operator efficiency and line balancing issue at a semiconductor assembly and test manufacturing. A Man-to-Machine (M2M) work study technique is used to study operator current utilization and determine the optimum allocation of the operators to the machines. Critical factors such as operator activity, activity frequency and operator competency level are considered to gain insight on the parameters that affects the operator utilization. Equipment standard time and overall equipment efficiency (OEE) information are also gathered and analyzed to achieve a balanced and optimized production.

Keywords: operator efficiency, optimized production, line balancing, industrial and manufacturing engineering

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Authors: Oyekan Kolawole Jamiu

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The right to life is a moral principle based on the belief that a human being has the right to life and, in particular, should not be unjustly killed by another human being. However, the most worrisome security challenge in Nigeria which has cut short the lives of innocent Nigerians is the activities of the dreaded terrorist group known as Boko Haram (which means Western Education is a sin). Between 2004 till date, over 15000 people have been gruesomely murdered by this terrorist group. However, despite the facts that suspected terrorists are arrested and paraded almost on a daily basis, cases of terrorism in our courts in Nigeria today have not been expeditiously dealt with by the judiciary. This paper examines the concept of right to life. The right to life is an inherent right for each and every person. From his or her birth; the individual is considered a living being that must be protected. The right to life connotes also right to live and grow in a healthy environment where there is appropriate health care, qualitative education and adequate security of lives and property. The paper also examines the fight against terrorism and the duty of the government to protect right to life of every individual even in the midst of the fight against terrorism. The paper further reviews the Terrorism Act 2011(as amended) and the clogs in the wheel of prosecution of suspected terrorists. The paper concludes that since terrorism is a new security challenge, to prevent conflict of interest, only one security agency should be trained and saddled with the responsibility of prosecuting suspected terrorist, Law should be enacted to compel intelligent gathering and sharing of information among security agencies and in addition, a special court should be established to deal expeditiously with cases of terrorism in Nigeria.

Keywords: terrorism, intelligent gathering, right to life, prosecution

Procedia PDF Downloads 331