Search results for: thermal cycling machine

Authors: Mogale T. E., Ayisi K. K., Munjonji L., Kifle Y. G.

Abstract:

Grain sorghum and cowpea are important staple crops in many areas of South Africa, particularly the Limpopo Province. The two crops are produced under a wide range of unsustainable conventional methods, which reduces productivity in the long run. Climate-smart traditional methods such as intercropping can be adopted to ensure sustainable production of these important two crops in the province. A no-tillage field experiment was laid out in a randomised complete block design (RCBD) with four replications over two seasons in two distinct agro-ecological zones, Syferkuil and Ofcolacoin, the province to assess the productivity of sorghum-cowpea intercropped under two cowpea densities.LCi Ultra compact photosynthesis machine was used to collect photosynthetic rate data biweekly between 11h00 and 13h00 until physiological maturity. Biomass and grain yield of the component crops in binary and sole cultures were determined at harvest maturity from middle rows of 2.7 m2 area. The biomass was oven dried in the laboratory at 65oC till constant weight. To obtain grain yield, harvested sorghum heads and cowpea pods were threshed, cleaned, and weighed. Harvest index (HI) and land equivalent ratio (LER) of the two crops were calculated to assess intercrop productivity relative to sole cultures. Data was analysed using the statistical analysis software system (SAS) 9.4 version, followed by mean separation using the least significant difference method. The photosyntheticrate of sorghum-cowpea intercrop was influenced by cowpea density and sorghum cultivar. Photosynthetic rate under low density was higher compared to high density, but this was dependent on the growing conditions. Dry biomass accumulation, grain yield, and harvest index differed among the sorghum cultivars and cowpea in both binary and sole cultures at the two test locations during the 2018/19 and 2020/21 growing seasons. Cowpea grain and dry biomass yields werein excess of 60% under high density compared to low density in both binary and sole cultures. The results revealed that grain yield accumulation of sorghum cultivars was influenced by the density of the companion cowpea crop as well as the production season. For instant, at Syferkuil, Enforcer and Ns5511 accumulated high yield under low density, whereas, at Ofcolaco, the higher yield was recorded under high density. Generally, under low cowpea density, cultivar Enforcer produced relatively higher grain yield whereas, under higher density, Titan yield was superior. The partial and total LER varied with growing season and the treatments studied. The total LERs exceeded 1.0 at the two locations across seasons, ranging from 1.3 to 1.8. From the results, it can be concluded that resources were used more efficiently in sorghum-cowpea intercrop at both Syferkuil and Ofcolaco. Furthermore, intercropping system improved photosynthetic rate, grain yield, and dry matter accumulation of sorghum and cowpea depending on growing conditions and density of cowpea. Hence, the sorghum-cowpea intercropping system can be adopted as a climate-smart practice for sustainable production in the Limpopo province.

Keywords: cowpea, climate-smart, grain sorghum, intercropping

Procedia PDF Downloads 183

Authors: Dila Ram Bhandari

Abstract:

Throughout the 20th century, new governments were created where identities such as ethnic, religious, linguistic, caste, communal, tribal, and others played a part in the development of constitutions and the legal system of victim and criminal justice. Acute issues with extremism, poverty, environmental degradation, cybercrimes, human rights violations, crime against, and victimization of both individuals and groups have recently plagued South Asian nations. Everyday massive number of crimes are steadfast, these frequent crimes have made the lives of common citizens restless. Crimes are one of the major threats to society and also for civilization. Crime is a bone of contention that can create a societal disturbance. The old-style crime solving practices are unable to live up to the requirement of existing crime situations. Crime analysis is one of the most important activities of the majority of intelligent and law enforcement organizations all over the world. The South Asia region lacks such a regional coordination mechanism, unlike central Asia of Asia Pacific regions, to facilitate criminal intelligence sharing and operational coordination related to organized crime, including illicit drug trafficking and money laundering. There have been numerous conversations in recent years about using data mining technology to combat crime and terrorism. The Data Detective program from Sentient as a software company, uses data mining techniques to support the police (Sentient, 2017). The goals of this internship are to test out several predictive model solutions and choose the most effective and promising one. First, extensive literature reviews on data mining, crime analysis, and crime data mining were conducted. Sentient offered a 7-year archive of crime statistics that were daily aggregated to produce a univariate dataset. Moreover, a daily incidence type aggregation was performed to produce a multivariate dataset. Each solution's forecast period lasted seven days. Statistical models and neural network models were the two main groups into which the experiments were split. For the crime data, neural networks fared better than statistical models. This study gives a general review of the applied statistics and neural network models. A detailed image of each model's performance on the available data and generalizability is provided by a comparative analysis of all the models on a comparable dataset. Obviously, the studies demonstrated that, in comparison to other models, Gated Recurrent Units (GRU) produced greater prediction. The crime records of 2005-2019 which was collected from Nepal Police headquarter and analysed by R programming. In conclusion, gated recurrent unit implementation could give benefit to police in predicting crime. Hence, time series analysis using GRU could be a prospective additional feature in Data Detective.

Keywords: time series analysis, forecasting, ARIMA, machine learning

Procedia PDF Downloads 144

Authors: Noorh Albadi, Salha Khayyat

Abstract:

People's moods and well-being are affected by their environment. The built environment impacts one's level of activity and health. In order to enhance users' physical health, sustainable design strategies have been developed for the physical environment to improve users' health. This study aimed to determine whether adult resorts in Saudi Arabia meet standards that ensure physical wellness to identify the needed requirements. It will be significant to the Ministry of Tourism, Sports, developers, and designers. Physical activity affects human health physically and mentally. In Saudi Arabia, the percentage of people who practiced sports in the Kingdom in 2019 was 20.04% - males and females older than 15. On the other hand, there is a lack of physical activity in Saudi Arabia; 90% of the Kingdom's population spends more than two hours sitting down without moving, which puts them at risk of contracting a non-communicable disease. The lack of physical activity and movement led to an increase in the rate of obesity among Saudis by 59% in 2020 and consequently could cause chronic diseases or death. The literature generally endorses that leading an active lifestyle improves physical health and affects mental health. Therefore, the United Nations has set 17 sustainable development goals (SDGs) to ensure healthy lives and promote well-being for all ages. One of SDG3's targets is reducing mortality, which can be achieved by raising physical activity. In order to support sustainable design, many rating systems and strategies have been developed, such as WELL building, Leadership in Energy and Environmental Design, (LEED), Active design strategies, and RIPA plan of work. The survey was used to gather qualitative and quantitative information. It was designed based on the Active Design and WELL building theories targeting beach resorts visitors, professional and beginner athletes, and non-athletics to ask them about the beach resorts they visited in the Kingdom and whether they met the criteria of sports resorts and healthy and active design theories, in addition to gathering information about the preferences of physical activities in the Saudi society in terms of the type of activities that young people prefer, where they prefer to engage in and under any thermal and light conditions. The final section asks about the design of residential units in beach sports resorts, the data collected from 127 participants. Findings revealed that participants prefer outdoor activities in moderate weather and sunlight or the evening with moderate and sufficient lighting and that no beach sports resorts in the country are constructed to support sustainable design criteria for physical activity. Participants agreed that several measures that lessen tension at beach resorts and enhance movement and activity are needed by Saudi society. The study recommends designing resorts that meet the sustainable design criteria regarding physical activity in Saudi Arabia to increase physical activity to achieve psychological and physical benefits and avoid psychological and physical diseases related to physical inactivity.

Keywords: sustainable design, SDGs, active design strategies, well building, beach resort design

Procedia PDF Downloads 94

Authors: Seyed Hossein Hosseini, Seyed Majid Hashemzadeh

Abstract:

Solar energy is one of the remarkable renewable energy sources that have particular characteristics such as unlimited, no environmental pollution, and free access. Generally, solar energy can be used in thermal and photovoltaic (PV) types. The cost of installation of the PV system is very high. Additionally, due to dependence on environmental situations such as solar radiation and ambient temperature, electrical power generation of this system is unpredictable and without power electronics devices, there is no guarantee to maximum power delivery at the output of this system. Maximum power point tracking (MPPT) should be used to achieve the maximum power of a PV string. MPPT is one of the essential parts of the PV system which without this section, it would be impossible to reach the maximum amount of the PV string power and high losses are caused in the PV system. One of the noticeable challenges in the problem of MPPT is the partial shading conditions (PSC). In PSC, the output photocurrent of the PV module under the shadow is less than the PV string current. The difference between the mentioned currents passes from the module's internal parallel resistance and creates a large negative voltage across shaded modules. This significant negative voltage damages the PV module under the shadow. This condition is called hot-spot phenomenon. An anti-paralleled diode is inserted across the PV module to prevent the happening of this phenomenon. This diode is known as the bypass diode. Due to the performance of the bypass diode under PSC, the P-V curve of the PV string has several peaks. One of the P-V curve peaks that makes the maximum available power is the global peak. Model-based Global MPPT (GMPPT) methods can estimate the optimal point with higher speed than other GMPPT approaches. Centralized, modular, and interleaved DC-DC converter topologies are the significant structures that can be used for GMPPT at a PV string. there are some problems in the centralized structure such as current mismatch losses at PV sting, loss of power of the shaded modules because of bypassing by bypass diodes under PSC, needing to series connection of many PV modules to reach the desired voltage level. In the modular structure, each PV module is connected to a DC-DC converter. In this structure, by increasing the amount of demanded power from the PV string, the number of DC-DC converters that are used at the PV system will increase. As a result, the cost of the modular structure is very high. We can implement the model-based GMPPT through the multi-input interleaved boost DC-DC converter to increase the power extraction from the PV string and reduce hot-spot and current mismatch error in a PV string under different environmental condition and variable load circumstances. The interleaved boost DC-DC converter has many privileges than other mentioned structures, such as high reliability and efficiency, better regulation of DC voltage at DC link, overcome the notable errors such as module's current mismatch and hot spot phenomenon, and power switches voltage stress reduction.

Keywords: solar energy, photovoltaic systems, interleaved boost converter, maximum power point tracking, model-based method, partial shading conditions

Procedia PDF Downloads 107

Authors: Abdelmessih Malak Sadek Labib

Abstract:

Ceramic materials are known for their stability in harsh environments and excellent electrical, mechanical, and thermal properties. They have been widely used in various applications despite the emergence of new materials such as plastics and composites. However, ceramics are often brittle, which can lead to catastrophic failure. The fragility of ceramics and the mechanisms behind their failure have been a topic of extensive research, particularly in load-bearing applications like veneers. Porcelain, a type of traditional pottery, is commonly used in such applications. Traditional pottery consists of clay, silica, and feldspar, and the presence of quartz in the ceramic body can lead to microcracks and stress concentrations. The mullite hypothesis suggests that the strength of porcelain can be improved by increasing the interlocking of mullite needles in the ceramic body. However, there is a lack of reports on Young's moduli in the literature, leading to erroneous conclusions about the mechanical behavior of porcelain. This project aims to investigate the role of quartz and mullite on the mechanical strength of various porcelains while considering factors such as particle size, flexural strength, and fractographic forces. Research Aim: The aim of this research project is to assess the role of quartz and mullite in enhancing the mechanical strength of different porcelains. The project will also explore the effect of reducing particle size on the properties of porcelain, as well as investigate flexural strength and fractographic techniques. Methodology: The methodology for this project involves using scientific expressions and a mix of modern English to ensure the understanding of all attendees. It will include the measurement of Young's modulus and the evaluation of the mechanical behavior of porcelains through various experimental techniques. Findings: The findings of this study will provide a realistic assessment of the role of quartz and mullite in strengthening and reducing the fragility of porcelain. The research will also contribute to a better understanding of the mechanical behavior of ceramics, specifically in load-bearing applications. Theoretical Importance: The theoretical importance of this research lies in its contribution to the understanding of the factors influencing the mechanical strength and fragility of ceramics, particularly porcelain. By investigating the interplay between quartz, mullite, and other variables, this study will enhance our knowledge of the properties and behavior of traditional ceramics. Data Collection and Analysis Procedures: Data for this research will be collected through experiments involving the measurement of Young's modulus and other mechanical properties of porcelains. The effects of quartz, mullite, particle size, flexural strength, and fractographic forces will be examined and analyzed using appropriate statistical techniques and fractographic analysis. Questions Addressed: This research project aims to address the following questions: (1) How does the presence of quartz and mullite affect the mechanical strength of porcelain? (2) What is the impact of reducing particle size on the properties of porcelain? (3) How do flexural strength and fractographic forces influence the behavior of porcelains? Conclusion: In conclusion, this research project aims to enhance the understanding of the role of quartz and mullite in strengthening and reducing the fragility of porcelain. By investigating the mechanical properties of porcelains and considering factors such as particle size, flexural strength, and fractographic forces, this study will contribute to the knowledge of traditional ceramics and their potential applications. The findings will have practical implications for the use of ceramics in various fields.

Keywords: stability, harsh environments, electrical, techniques, mechanical disadvantages, materials

Procedia PDF Downloads 45

Authors: Robert Pečenko, Karin Tomažič, Igor Planinc, Sabina Huč, Tomaž Hozjan

Abstract:

Timber is favourable structural material due to high strength to weight ratio, recycling possibilities, and green credentials. Despite being flammable material, it has relatively high fire resistance. Everyday engineering practice around the word is based on an outdated design of timber structures considering standard fire exposure, while modern principles of performance-based design enable use of advanced non-standard fire curves. In Europe, standard for fire design of timber structures EN 1995-1-2 (Eurocode 5) gives two methods, reduced material properties method and reduced cross-section method. In the latter, fire resistance of structural elements depends on the effective cross-section that is a residual cross-section of uncharred timber reduced additionally by so called zero strength layer. In case of standard fire exposure, Eurocode 5 gives a fixed value of zero strength layer, i.e. 7 mm, while for non-standard parametric fires no additional comments or recommendations for zero strength layer are given. Thus designers often implement adopted 7 mm rule also for parametric fire exposure. Since the latest scientific evidence suggests that proposed value of zero strength layer can be on unsafe side for standard fire exposure, its use in the case of a parametric fire is also highly questionable and more numerical and experimental research in this field is needed. Therefore, the purpose of the presented study is to use advanced calculation methods to investigate the thickness of zero strength layer and parametric charring rates used in effective cross-section method in case of parametric fire. Parametric studies are carried out on a simple solid timber beam that is exposed to a larger number of parametric fire curves Zero strength layer and charring rates are determined based on the numerical simulations which are performed by the recently developed advanced two step computational model. The first step comprises of hygro-thermal model which predicts the temperature, moisture and char depth development and takes into account different initial moisture states of timber. In the second step, the response of timber beam simultaneously exposed to mechanical and fire load is determined. The mechanical model is based on the Reissner’s kinematically exact beam model and accounts for the membrane, shear and flexural deformations of the beam. Further on, material non-linear and temperature dependent behaviour is considered. In the two step model, the char front temperature is, according to Eurocode 5, assumed to have a fixed temperature of around 300°C. Based on performed study and observations, improved levels of charring rates and new thickness of zero strength layer in case of parametric fires are determined. Thus, the reduced cross section method is substantially improved to offer practical recommendations for designing fire resistance of timber structures. Furthermore, correlations between zero strength layer thickness and key input parameters of the parametric fire curve (for instance, opening factor, fire load, etc.) are given, representing a guideline for a more detailed numerical and also experimental research in the future.

Keywords: advanced numerical modelling, parametric fire exposure, timber structures, zero strength layer

Procedia PDF Downloads 140

Authors: Luiza Melo De Lima, Tito Trindade, Jose M. Oliveira

Abstract:

The development of thermoplastic-based graphene nanocomposites has been of great interest not only to the scientific community but also to different industrial sectors. Due to the possible improvement of performance and weight reduction, thermoplastic nanocomposites are a great promise as a new class of materials. These nanocomposites are of relevance for the automotive industry, namely because the emission limits of CO2 emissions imposed by the European Commission (EC) regulations can be fulfilled without compromising the car’s performance but by reducing its weight. Thermoplastic polymers have some advantages over thermosetting polymers such as higher productivity, lower density, and recyclability. In the automotive industry, for example, poly(propylene) (PP) is a common thermoplastic polymer, which represents more than half of the polymeric raw material used in automotive parts. Graphene-based materials (GBM) are potential nanofillers that can improve the properties of polymer matrices at very low loading. In comparison to other composites, such as fiber-based composites, weight reduction can positively affect their processing and future applications. However, the properties and performance of GBM/polymer nanocomposites depend on the type of GBM and polymer matrix, the degree of dispersion, and especially the type of interactions between the fillers and the polymer matrix. In order to take advantage of the superior mechanical strength of GBM, strong interfacial strength between GBM and the polymer matrix is required for efficient stress transfer from GBM to the polymer. Thus, chemical compatibilizers and physicochemical modifications have been reported as important tools during the processing of these nanocomposites. In this study, PP-based nanocomposites were obtained by a simple melt blending technique, using a Brabender type mixer machine. Graphene nanoplatelets (GnPs) were applied as structural reinforcement. Two compatibilizers were used to improve the interaction between PP matrix and GnPs: PP graft maleic anhydride (PPgMA) and PPgMA modified with tertiary amine alcohol (PPgDM). The samples for tensile and Charpy impact tests were obtained by injection molding. The results suggested the GnPs presence can increase the mechanical strength of the polymer. However, it was verified that the GnPs presence can promote a decrease of impact resistance, turning the nanocomposites more fragile than neat PP. The compatibilizers’ incorporation increases the impact resistance, suggesting that the compatibilizers can enhance the adhesion between PP and GnPs. Compared to neat PP, Young’s modulus of non-compatibilized nanocomposite increase demonstrated that GnPs incorporation can promote a stiffness improvement of the polymer. This trend can be related to the several physical crosslinking points between the PP matrix and the GnPs. Furthermore, the decrease of strain at a yield of PP/GnPs, together with the enhancement of Young’s modulus, confirms that the GnPs incorporation led to an increase in stiffness but to a decrease in toughness. Moreover, the results demonstrated that incorporation of compatibilizers did not affect Young’s modulus and strain at yield results compared to non-compatibilized nanocomposite. The incorporation of these compatibilizers showed an improvement of nanocomposites’ mechanical properties compared both to those the non-compatibilized nanocomposite and to a PP sample used as reference.

Keywords: graphene nanoplatelets, mechanical properties, melt blending processing, poly(propylene)-based nanocomposites

Procedia PDF Downloads 163

Authors: Bruno Biagianti, Angela Tseng, Kathy Wannaviroj, Allison Corlett, Megan DuBois, Kyu Lee, Suma Jacob

Abstract:

Background: ASD is characterized by pervasive Sensory Processing Abnormalities (SPA) and social cognitive deficits that persist throughout the course of the illness and have been linked to functional abnormalities in specific neural systems that underlie the perception, processing, and representation of sensory information. SPA and social cognitive deficits are associated with difficulties in interpersonal relationships, poor development of social skills, reduced social interactions and lower academic performance. Importantly, they can hamper the effects of established evidence-based psychological treatments—including PEERS (Program for the Education and Enrichment of Relationship Skills), a parent/caregiver-assisted, 16-weeks social skills intervention—which nonetheless requires a functional brain capable of assimilating and retaining information and skills. As a matter of fact, some adolescents benefit from PEERS more than others, calling for strategies to increase treatment response rates. Objective: We will present interim data on CICADAS (Care Improving Cognition for ADolescents on the Autism Spectrum)—a clinician-assisted, closed-loop technology mobile application for adolescents with ASD. Via ten mobile assessments, CICADAS captures data on sensory processing abnormalities and associated cognitive deficits. These data populate a machine learning algorithm that tailors the delivery of ten neuroplasticity-based social cognitive training (NB-SCT) exercises targeting sensory processing abnormalities. Methods: In collaboration with the Autism Spectrum and Neurodevelopmental Disorders Clinic at the University of Minnesota, we conducted a fully remote, three-arm, randomized crossover trial with adolescents with ASD to document the acceptability of CICADAS and evaluate its potential as a stand-alone treatment or as a treatment enhancer of PEERS. Twenty-four adolescents with ASD (ages 11-18) have been initially randomized to 16 weeks of PEERS + CICADAS (Arm A) vs. 16 weeks of PEERS + computer games vs. 16 weeks of CICADAS alone (Arm C). After 16 weeks, the full battery of assessments has been remotely administered. Results: We have evaluated the acceptability of CICADAS by examining adherence rates, engagement patterns, and exit survey data. We found that: 1) CICADAS is able to serve as a treatment enhancer for PEERS, inducing greater improvements in sensory processing, cognition, symptom reduction, social skills and behaviors, as well as the quality of life compared to computer games; 2) the concurrent delivery of PEERS and CICADAS induces greater improvements in study outcomes compared to CICADAS only. Conclusion: While preliminary, our results indicate that the individualized assessment and treatment approach designed in CICADAS seems effective in inducing adaptive long-term learning about social-emotional events. CICADAS-induced enhancement of processing and cognition facilitates the application of PEERS skills in the environment of adolescents with ASD, thus improving their real-world functioning.

Keywords: ASD, social skills, cognitive training, mobile app

Procedia PDF Downloads 182

Authors: Mohammad M. Khan, Pankaj Agarwal

Abstract:

The present investigation deals with the microstructures, mechanical and detailed wear characteristics of in situ TiC particles reinforced zinc aluminum-based metal matrix composites. The composites have been synthesized by liquid metallurgy route using vortex technique. The composite was found to be harder than the matrix alloy due to high hardness of the dispersoid particles therein. The former was also lower in ultimate tensile strength and ductility as compared to the matrix alloy. This could be explained to be due to the use of coarser size dispersoid and larger interparticle spacing. Reasonably uniform distribution of the dispersoid phase in the alloy matrix and good interfacial bonding between the dispersoid and matrix was observed. The composite exhibited predominantly brittle mode of fracture with microcracking in the dispersoid phase indicating effective easy transfer of load from matrix to the dispersoid particles. To study the wear behavior of the samples three different types of tests were performed namely: (i) sliding wear tests using a pin on disc machine under dry condition, (ii) high stress (two-body) abrasive wear tests using different combinations of abrasive media and specimen surfaces under the conditions of varying abrasive size, traversal distance and load, and (iii) low-stress (three-body) abrasion tests using a rubber wheel abrasion tester at various loads and traversal distances using different abrasive media. In sliding wear test, significantly lower wear rates were observed in the case of base alloy over that of the composites. This has been attributed to the poor room temperature strength as a result of increased microcracking tendency of the composite over the matrix alloy. Wear surfaces of the composite revealed the presence of fragmented dispersoid particles and microcracking whereas the wear surface of matrix alloy was observed to be smooth with shallow grooves. During high-stress abrasion, the presence of the reinforcement offered increased resistance to the destructive action of the abrasive particles. Microcracking tendency was also enhanced because of the reinforcement in the matrix. The negative effect of the microcracking tendency was predominant by the abrasion resistance of the dispersoid. As a result, the composite attained improved wear resistance than the matrix alloy. The wear rate increased with load and abrasive size due to a larger depth of cut made by the abrasive medium. The wear surfaces revealed fine grooves, and damaged reinforcement particles while subsurface regions revealed limited plastic deformation and microcracking and fracturing of the dispersoid phase. During low-stress abrasion, the composite experienced significantly less wear rate than the matrix alloy irrespective of the test conditions. This could be explained to be due to wear resistance offered by the hard dispersoid phase thereby protecting the softer matrix against the destructive action of the abrasive medium. Abraded surfaces of the composite showed protrusion of dispersoid phase. The subsurface regions of the composites exhibited decohesion of the dispersoid phase along with its microcracking and limited plastic deformation in the vicinity of the abraded surfaces.

Keywords: abrasive wear, liquid metallurgy, metal martix composite, SEM

Procedia PDF Downloads 130

Authors: Ja-Keoung Koo, Kensuke Nakamura, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Byung-Woo Hong

Abstract:

The machine learning techniques based on a convolutional neural network (CNN) have been actively developed and successfully applied to a variety of image analysis tasks including reconstruction, noise reduction, resolution enhancement, segmentation, motion estimation, object recognition. The classical visual information processing that ranges from low level tasks to high level ones has been widely developed in the deep learning framework. It is generally considered as a challenging problem to derive visual interpretation from high dimensional imagery data. A CNN is a class of feed-forward artificial neural network that usually consists of deep layers the connections of which are established by a series of non-linear operations. The CNN architecture is known to be shift invariant due to its shared weights and translation invariance characteristics. However, it is often computationally intractable to optimize the network in particular with a large number of convolution layers due to a large number of unknowns to be optimized with respect to the training set that is generally required to be large enough to effectively generalize the model under consideration. It is also necessary to limit the size of convolution kernels due to the computational expense despite of the recent development of effective parallel processing machinery, which leads to the use of the constantly small size of the convolution kernels throughout the deep CNN architecture. However, it is often desired to consider different scales in the analysis of visual features at different layers in the network. Thus, we propose a CNN model where different sizes of the convolution kernels are applied at each layer based on the random projection. We apply random filters with varying sizes and associate the filter responses with scalar weights that correspond to the standard deviation of the random filters. We are allowed to use large number of random filters with the cost of one scalar unknown for each filter. The computational cost in the back-propagation procedure does not increase with the larger size of the filters even though the additional computational cost is required in the computation of convolution in the feed-forward procedure. The use of random kernels with varying sizes allows to effectively analyze image features at multiple scales leading to a better generalization. The robustness and effectiveness of the proposed CNN based on random kernels are demonstrated by numerical experiments where the quantitative comparison of the well-known CNN architectures and our models that simply replace the convolution kernels with the random filters is performed. The experimental results indicate that our model achieves better performance with less number of unknown weights. The proposed algorithm has a high potential in the application of a variety of visual tasks based on the CNN framework. Acknowledgement—This work was supported by the MISP (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by IITP, and NRF-2014R1A2A1A11051941, NRF2017R1A2B4006023.

Keywords: deep learning, convolutional neural network, random kernel, random projection, dimensionality reduction, object recognition

Procedia PDF Downloads 262

Authors: Fabienne Samyn, Pauline Tranchard, Sophie Duquesne, Emilie Goncalves, Bruno Estebe, Serge Boubigot

Abstract:

Stringent fire safety regulations are enforced in the aeronautical industry due to the consequences that potential fire event on an aircraft might imply. This is so much true that the fire issue is considered right from the design of the aircraft structure. Due to the incorporation of an increasing amount of polymer matrix composites in replacement of more conventional materials like metals, the nature of the fire risks is changing. The choice of materials used is consequently of prime importance as well as the evaluation of its resistance to fire. The fire testing is mostly done using the so-called certification tests according to standards such as the ISO2685:1998(E). The latter describes a protocol to evaluate the fire resistance of structures located in fire zone (ability to withstand fire for 5min). The test consists in exposing an at least 300x300mm² sample to an 1100°C propane flame with a calibrated heat flux of 116kW/m². This type of test is time-consuming, expensive and gives access to limited information in terms of fire behavior of the materials (pass or fail test). Consequently, it can barely be used for material development purposes. In this context, the laboratory UMET in collaboration with industrial partners has developed a horizontal and a vertical small-scale instrumented fire benches for the characterization of the fire behavior of composites. The benches using smaller samples (no more than 150x150mm²) enables to cut downs costs and hence to increase sampling throughput. However, the main added value of our benches is the instrumentation used to collect useful information to understand the behavior of the materials. Indeed, measurements of the sample backside temperature are performed using IR camera in both configurations. In addition, for the vertical set up, a complete characterization of the degradation process, can be achieved via mass loss measurements and quantification of the gasses released during the tests. These benches have been used to characterize and study the fire behavior of aeronautical carbon/epoxy composites. The horizontal set up has been used in particular to study the performances and durability of protective intumescent coating on 2mm thick 2D laminates. The efficiency of this approach has been validated, and the optimized coating thickness has been determined as well as the performances after aging. Reductions of the performances after aging were attributed to the migration of some of the coating additives. The vertical set up has enabled to investigate the degradation process of composites under fire. An isotropic and a unidirectional 4mm thick laminates have been characterized using the bench and post-fire analyses. The mass loss measurements and the gas phase analyses of both composites do not present significant differences unlike the temperature profiles in the thickness of the samples. The differences have been attributed to differences of thermal conductivity as well as delamination that is much more pronounced for the isotropic composite (observed on the IR-images). This has been confirmed by X-ray microtomography. The developed benches have proven to be valuable tools to develop fire safe composites.

Keywords: aeronautical carbon/epoxy composite, durability, intumescent coating, small-scale ‘ISO 2685 like’ fire resistance test, X-ray microtomography

Procedia PDF Downloads 249

Authors: Hongmei Wang, Ziyun Xiang, Ying liu, Li Yu, Dongsheng Yue

Abstract:

Intro: The COVID-19 (Corona Virus Disease 2019) has greatly changed the global economic, political and financial ecology. The mutation of the coronavirus in the UK in December 2020 has brought new panic to the world. Deep learning was performed on Chest Computed tomography (CT) of COVID-19 and Influenza and describes their characteristics. The predominant features of COVID-19 pneumonia was ground-glass opacification, followed by consolidation. Lesion density: most lesions appear as ground-glass shadows, and some lesions coexist with solid lesions. Lesion distribution: the focus is mainly on the dorsal side of the periphery of the lung, with the lower lobe of the lungs as the focus, and it is often close to the pleura. Other features it has are grid-like shadows in ground glass lesions, thickening signs of diseased vessels, air bronchi signs and halo signs. The severe disease involves whole bilateral lungs, showing white lung signs, air bronchograms can be seen, and there can be a small amount of pleural effusion in the bilateral chest cavity. At the same time, this year's flu season could be near its peak after surging throughout the United States for months. Chest CT for Influenza infection is characterized by focal ground glass shadows in the lungs, with or without patchy consolidation, and bronchiole air bronchograms are visible in the concentration. There are patchy ground-glass shadows, consolidation, air bronchus signs, mosaic lung perfusion, etc. The lesions are mostly fused, which is prominent near the hilar and two lungs. Grid-like shadows and small patchy ground-glass shadows are visible. Deep neural networks have great potential in image analysis and diagnosis that traditional machine learning algorithms do not. Method: Aiming at the two major infectious diseases COVID-19 and influenza, which are currently circulating in the world, the chest CT of patients with two infectious diseases is classified and diagnosed using deep learning algorithms. The residual network is proposed to solve the problem of network degradation when there are too many hidden layers in a deep neural network (DNN). The proposed deep residual system (ResNet) is a milestone in the history of the Convolutional neural network (CNN) images, which solves the problem of difficult training of deep CNN models. Many visual tasks can get excellent results through fine-tuning ResNet. The pre-trained convolutional neural network ResNet is introduced as a feature extractor, eliminating the need to design complex models and time-consuming training. Fastai is based on Pytorch, packaging best practices for in-depth learning strategies, and finding the best way to handle diagnoses issues. Based on the one-cycle approach of the Fastai algorithm, the classification diagnosis of lung CT for two infectious diseases is realized, and a higher recognition rate is obtained. Results: A deep learning model was developed to efficiently identify the differences between COVID-19 and influenza using chest CT.

Keywords: COVID-19, Fastai, influenza, transfer network

Procedia PDF Downloads 120

Authors: Hwang Ho Kim, Do Gyun Kim, Jin Young Choi, Sang Chul Park

Abstract:

Since tire production process is very complicated, company-wide management of it is very difficult, necessitating considerable amounts of capital and labors. Thus, productivity should be enhanced and maintained competitive by developing and applying effective production plans. Among major processes for tire manufacturing, consisting of mixing component preparation, building and curing, the mixing process is an essential and important step because the main component of tire, called compound, is formed at this step. Compound as a rubber synthesis with various characteristics plays its own role required for a tire as a finished product. Meanwhile, scheduling tire mixing process is similar to flexible job shop scheduling problem (FJSSP) because various kinds of compounds have their unique orders of operations, and a set of alternative machines can be used to process each operation. In addition, setup time required for different operations may differ due to alteration of additives. In other words, each operation of mixing processes requires different setup time depending on the previous one, and this kind of feature, called sequence dependent setup time (SDST), is a very important issue in traditional scheduling problems such as flexible job shop scheduling problems. However, despite of its importance, there exist few research works dealing with the tire mixing process. Thus, in this paper, we consider the scheduling problem for tire mixing process and suggest an efficient particle swarm optimization (PSO) algorithm to minimize the makespan for completing all the required jobs belonging to the process. Specifically, we design a particle encoding scheme for the considered scheduling problem, including a processing sequence for compounds and machine allocation information for each job operation, and a method for generating a tire mixing schedule from a given particle. At each iteration, the coordination and velocity of particles are updated, and the current solution is compared with new solution. This procedure is repeated until a stopping condition is satisfied. The performance of the proposed algorithm is validated through a numerical experiment by using some small-sized problem instances expressing the tire mixing process. Furthermore, we compare the solution of the proposed algorithm with it obtained by solving a mixed integer linear programming (MILP) model developed in previous research work. As for performance measure, we define an error rate which can evaluate the difference between two solutions. As a result, we show that PSO algorithm proposed in this paper outperforms MILP model with respect to the effectiveness and efficiency. As the direction for future work, we plan to consider scheduling problems in other processes such as building, curing. We can also extend our current work by considering other performance measures such as weighted makespan or processing times affected by aging or learning effects.

Keywords: compound, error rate, flexible job shop scheduling problem, makespan, particle encoding scheme, particle swarm optimization, sequence dependent setup time, tire mixing process

Procedia PDF Downloads 242

Authors: Clemens Bulitta, Sasan Sadrizadeh, Sebastian Buhl

Abstract:

Introduction: Ventilation technology in operating theatres (OT)is internationally regulated by dif-ferent standards, which define basic specifications for technical equipment and many times also the necessary operating and performance parameters. This confronts the operators of healthcare facilities with the question of finding the best ventilation and air conditioning system for the OT in order to achieve the goal of a large and robust surgicalworkzone with appropriate air quality and climate for patient safety and occupational health. Additionally, energy consumption and the potential need for clothing that limits transmission of bacteria must be considered as well as the total life cycle cost. However, the evaluation methodology of ventilation systems regarding these matters are still a topic of discussion. To date, there are neither any uniform standardized specifications nor any common validation criteria established. Thus, this study aimed to review data in the literature and add ourown research results to compare and assess the performance of different ventilations systems regarding infection preventive effects, energy efficiency, and staff comfort. Methods: We have conducted a comprehensive literature review on OT ventilation-related topics to understand the strengths and limitations of different ventilation systems. Furthermore, data from experimental assessments on OT ventilation systems at the University of Amberg-Weidenin Germany were in-cluded to comparatively assess the performance of Laminar Airflow (LAF), Turbulent Mixing Air-flow(TMA), and Temperature-controlled Airflow (TcAF) with regards to patient and occupational safety as well as staff comfort including indoor climate.CFD simulations from the Royal Institute of Technology in Sweden (KTH) were also studied to visualize the differences between these three kinds of ventilation systems in terms of the size of the surgical workzone, resilience to obstacles in the airflow, and energy use. Results: A variety of ventilation concepts are in use in the OT today. Each has its advantages and disadvantages, and thus one may be better suited than another depend-ing on the built environment and clinical workflow. Moreover, the proper functioning of OT venti-lation is also affected by multiple external and internal interfering factors. Based on the available data TcAF and LAF seem to provide the greatest effects regarding infection control and minimizing airborne risks for surgical site infections without the need for very tight surgical clothing systems. Resilience to obstacles, staff comfort, and energy efficiency seem to be favourable with TcAF. Conclusion: Based on literature data in current publications and our studies at the Technical Uni-versity of Applied Sciences Amberg-Weidenand the Royal Institute of Technoclogy, LAF and TcAF are more suitable for minimizing the risk for surgical site infections leading to improved clin-ical outcomes. Nevertheless, regarding the best management of thermal loads, atmosphere, energy efficiency, and occupational safety, overall results and data suggest that TcAF systems could pro-vide the economically most efficient and clinically most effective solution under routine clinical conditions.

Keywords: ventilation systems, infection control, energy efficiency, operating theatre, airborne infection risks

Procedia PDF Downloads 81

Authors: Hend Ben Tkhayat , Khaled Al Zahabi, Husam Younes

Abstract:

Introduction: Vildagliptin (VG), a dipeptidyl peptidase-4 inhibitor (DPP-4), was proven to be an active agent for the treatment of type 2 diabetes. VG works by enhancing and prolonging the activity of incretins which improves insulin secretion and decreases glucagon release, therefore lowering blood glucose level. It is usually used with various classes, such as insulin sensitizers or metformin. VG is currently only marketed as an immediate-release tablet that is administered twice daily. In this project, we aim to formulate an extended-release with a zero-order profile tableted lipid microparticles of VG that could be administered once daily ensuring the patient’s convenience. Method: The spray-congealing technique was used to prepare VG microparticles. Compritol® was heated at 10 oC above its melting point and VG was dispersed in the molten carrier using a homogenizer (IKA T25- USA) set at 13000 rpm. VG dispersed in the molten Compritol® was added dropwise to the molten Gelucire® 50/13 and PEG® (400, 6000, and 35000) in different ratios under manual stirring. The molten mixture was homogenized and Carbomer® amount was added. The melt was pumped through the two-fluid nozzle of the Buchi® Spray-Congealer (Buchi B-290, Switzerland) using a Pump drive (Master flex, USA) connected to a silicone tubing wrapped with silicone heating tape heated at the same temperature of the pumped mix. The physicochemical properties of the produced VG-loaded microparticles were characterized using Mastersizer, Scanning Electron Microscope (SEM), Differential Scanning Calorimeter (DSC) and X‐Ray Diffractometer (XRD). VG microparticles were then pressed into tablets using a single punch tablet machine (YDP-12, Minhua pharmaceutical Co. China) and in vitro dissolution study was investigated using Agilent Dissolution Tester (Agilent, USA). The dissolution test was carried out at 37±0.5 °C for 24 hours in three different dissolution media and time phases. The quantitative analysis of VG in samples was realized using a validated High-Pressure Liquid Chromatography (HPLC-UV) method. Results: The microparticles were spherical in shape with narrow distribution and smooth surface. DSC and XRD analyses confirmed the crystallinity of VG that was lost after being incorporated into the amorphous polymers. The total yields of the different formulas were between 70% and 80%. The VG content in the microparticles was found to be between 99% and 106%. The in vitro dissolution study showed that VG was released from the tableted particles in a controlled fashion. The adjustment of the hydrophilic/hydrophobic ratio of excipients, their concentration and the molecular weight of the used carriers resulted in tablets with zero-order kinetics. The Gelucire 50/13®, a hydrophilic polymer was characterized by a time-dependent profile with an important burst effect that was decreased by adding Compritol® as a lipophilic carrier to retard the release of VG which is highly soluble in water. PEG® (400,6000 and 35 000) were used for their gelling effect that led to a constant rate delivery and achieving a zero-order profile. Conclusion: Tableted spray-congealed lipid microparticles for extended-release of VG were successfully prepared and a zero-order profile was achieved.

Keywords: vildagliptin, spray congealing, microparticles, controlled release

Procedia PDF Downloads 106

Authors: Anna Martin, Raffael Osen

Abstract:

The demand for fish from aquaculture is constantly growing. Concurrently, due to a shortage of fishmeal caused by extensive overfishing, fishmeal substitution by plant proteins is getting increasingly important for the production of sustainable aquafeed. Several research studies evaluated the impact of plant protein meals, concentrates or isolates on fish health and fish feed quality. However, these protein raw materials often require elaborate and expensive manufacturing and their availability is limited. Rapeseed press cake (RPC) – a side product of de-oiling processes – exhibits a high potential as a plant-based fishmeal alternative in fish feed for carnivorous species due to its availability, low costs and protein content. In order to produce aquafeed with RPC, it is important to systematically assess i) inclusion levels of RPC with similar pellet qualities compared to fishmeal containing formulations and ii) how extrusion parameters can be adjusted to achieve targeted pellet qualities. However, the effect of RPC on extrusion system parameters and pellet quality has only scarcely been investigated. Therefore, the aim of this study was to evaluate the impact of feed formulation, extruder barrel temperature (90, 100, 110 °C) and screw speed (200, 300, 400 rpm) on extrusion system parameters and the physical properties of fish feed pellets. A co-rotating pilot-scale twin screw extruder was used to produce five iso-nitrogenous feed formulations: a fish meal based reference formulation including 16 g/100g fishmeal and four formulations in which fishmeal was substituted by RPC to 25, 50, 75 or 100 %. Extrusion system parameters, being product temperature, pressure at the die, specific mechanical energy (SME) and torque, were monitored while samples were taken. After drying, pellets were analyzed regarding to optical appearance, sectional and longitudinal expansion, sinking velocity, bulk density, water stability, durability and specific hardness. In our study, the addition of minor amounts of RPC already had high impact on pellet quality parameters, especially on expansion but only marginally affected extrusion system parameters. Increasing amounts of RPC reduced sectional expansion, sinking velocity, bulk density and specific hardness and increased longitudinal expansion compared to a reference formulation without RPC. Water stability and durability were almost not affected by RPC addition. Moreover, pellets with rapeseed components showed a more coarse structure than pellets containing only fishmeal. When the adjustment of barrel temperature and screw speed was investigated, it could be seen that the increase of extruder barrel temperature led to a slight decrease of SME and die pressure and an increased sectional expansion of the reference pellets but did almost not affect rapeseed containing fish feed pellets. Also changes in screw speed had little effects on the physical properties of pellets however with raised screw speed the SME and the product temperature increased. In summary, a one-to-one substitution of fishmeal with RPC without the adjustment of extrusion process parameters does not result in fish feed of a designated quality. Therefore, a deeper knowledge of raw materials and their behavior under thermal and mechanical stresses as applied during extrusion is required.

Keywords: extrusion, fish feed, press cake, rapeseed

Procedia PDF Downloads 122

Authors: Amir E. Amirzadeh, Richard K. Strand

Abstract:

Moisture-induced problems are a significant concern for building owners, architects, construction managers, and building engineers, as they can have substantial impacts on building enclosures' durability and performance. Computational analyses, such as hygrothermal and thermal analysis, can provide valuable information and demonstrate the expected relative performance of building enclosure systems but are not grounded in absolute certainty. This paper evaluates the hygrothermal performance of common enclosure systems in hospitals in cold climates. The study aims to investigate the impact of exterior wall systems on hospitals, focusing on factors such as durability, construction deficiencies, and energy performance. The study primarily examines the impact of air leakage and vapor retarding layers relative to energy consumption. While these factors have been studied in residential and commercial buildings, there is a lack of information on their impact on hospitals in a holistic context. The study integrates various research studies and professional experience in hospital building design to achieve its objective. The methodology involves surveying and observing exterior wall assemblies, reviewing common exterior wall assemblies and details used in hospital construction, performing simulations and numerical analyses of various variables, validating the model and mechanism using available data from industry and academia, visualizing the outcomes of the analysis, and developing a mechanism to demonstrate the relative performance of exterior wall systems for hospitals under specific conditions. The data sources include case studies from real-world projects and peer-reviewed articles, industry standards, and practices. This research intends to integrate and analyze the in-situ and as-designed performance and durability of building enclosure assemblies with numerical analysis. The study's primary objective is to provide a clear and precise roadmap to better visualize and comprehend the correlation between the durability and performance of common exterior wall systems used in the construction of hospitals and the energy consumption of these buildings under certain static and dynamic conditions. As the construction of new hospitals and renovation of existing ones have grown over the last few years, it is crucial to understand the effect of poor detailing or construction deficiencies on building enclosure systems' performance and durability in healthcare buildings. This study aims to assist stakeholders involved in hospital design, construction, and maintenance in selecting durable and high-performing wall systems. It highlights the importance of early design evaluation, regular quality control during the construction of hospitals, and understanding the potential impacts of improper and inconsistent maintenance and operation practices on occupants, owner, building enclosure systems, and Heating, Ventilation, and Air Conditioning (HVAC) systems, even if they are designed to meet the project requirements.

Keywords: hygrothermal analysis, building enclosure, hospitals, energy efficiency, optimization and visualization, uncertainty and decision making

Procedia PDF Downloads 44

Authors: Weili Jiang, Simon Cadavid Lopera, Klaus Drechsler

Abstract:

Fuel cell vehicle has become the most competitive solution for the transportation sector in the hydrogen economy. Type IV pressure vessel is currently the most popular and widely developed technology for the on-board storage, based on their high reliability and relatively low cost. Due to the stringent requirement on mechanical performance, the pressure vessel is subject to great amount of composite material, a major cost driver for the hydrogen tanks. Evidently, the optimization of composite layup design shows great potential in reducing the overall material usage, yet requires comprehensive understanding on underlying mechanisms as well as the influence of different design parameters on mechanical performance. Given the type of materials and manufacturing processes by which the type IV pressure vessels are manufactured, the design and optimization are a nuanced subject. The manifold of stacking sequence and fiber orientation variation possibilities have an out-standing effect on vessel strength due to the anisotropic property of carbon fiber composites, which make the design space high dimensional. Each variation of design parameters requires computational resources. Using finite element analysis to evaluate different designs is the most common method, however, the model-ing, setup and simulation process can be very time consuming and result in high computational cost. For this reason, it is necessary to build a reliable automation scheme to set up and analyze the di-verse composite layups. In this research, the simulation process of different tank designs regarding various parameters is conducted and automatized in a commercial finite element analysis framework Abaqus. Worth mentioning, the modeling of the composite overwrap is automatically generated using an Abaqus-Python scripting interface. The prediction of the winding angle of each layer and corresponding thickness variation on dome region is the most crucial step of the modeling, which is calculated and implemented using analytical methods. Subsequently, these different composites layups are simulated as axisymmetric models to facilitate the computational complexity and reduce the calculation time. Finally, the results are evaluated and compared regarding the ultimate tank strength. By automatically modeling, evaluating and comparing various composites layups, this system is applicable for the optimization of the tanks structures. As mentioned above, the mechanical property of the pressure vessel is highly dependent on composites layup, which requires big amount of simulations. Consequently, to automatize the simulation process gains a rapid way to compare the various designs and provide an indication of the optimum one. Moreover, this automation process can also be operated for creating a data bank of layups and corresponding mechanical properties with few preliminary configuration steps for the further case analysis. Subsequently, using e.g. machine learning to gather the optimum by the data pool directly without the simulation process.

Keywords: type IV pressure vessels, carbon composites, finite element analy-sis, automation of simulation process

Procedia PDF Downloads 101

Authors: T. Ghatauray, J. Ingram, P. Holborn

Abstract:

The transition from a ‘carbon rich’ fossil fuel dependent to a ‘sustainable’ and ‘renewable’ hydrogen based society will see the deployment of hydrogen fuel cells (HFC) in transport applications and in the generation of heat and power for buildings, as part of a decentralised power network. Many deployments will be low power HFCs for domestic combined heat and power (CHP) and commercial ‘transportable’ HFCs for environmental situations, such as lighting and telephone towers. For broad commercialisation of small fuel cells to be achieved there needs to be significant confidence in their safety in both domestic and environmental applications. Low power HFCs are housed in protective steel enclosures. Standard enclosures have plain rectangular ventilation openings intended for thermal management of electronics and not the dispersion of a buoyant gas. Degradation of the HFC or supply pipework in use could lead to a low-level leak and a build-up of hydrogen gas in the enclosure. Hydrogen’s wide flammable range (4-75%) is a significant safety concern, with ineffective enclosure ventilation having the potential to cause flammable mixtures to develop with the risk of explosion. Mechanical ventilation is effective at managing enclosure hydrogen concentrations, but drains HFC power and is vulnerable to failure. This is undesirable in low power and remote installations and reliable passive ventilation systems are preferred. Passive ventilation depends upon buoyancy driven flow, with the size, shape and position of ventilation openings critical for producing predictable flows and maintaining low buoyant gas concentrations. With environmentally sited enclosures, ventilation openings with pressed horizontal and angled louvres are preferred to protect the HFC and electronics inside. There is an economic cost to adding louvres, but also a safety concern. A question arises over whether the use of pressed louvre vents impairs enclosure passive ventilation performance, when compared to same opening area plain vents. Comparison small enclosure (0.144m³) tests of same opening area pressed louvre and plain vents were undertaken. A displacement ventilation arrangement was incorporated into the enclosure with opposing upper and lower ventilation openings. A range of vent areas were tested. Helium (used as a safe analogue for hydrogen) was released from a 4mm nozzle at the base of the enclosure to simulate a hydrogen leak at leak rates from 1 to 10 lpm. Helium sensors were used to record concentrations at eight heights in the enclosure. The enclosure was otherwise empty. These tests determined that the use of pressed and angled louvre ventilation openings on the enclosure impaired the passive ventilation flow and increased helium concentrations in the enclosure. High-level stratified buoyant gas layers were also found to be deeper than with plain vent openings and were within the flammable range. The presence of gas within the flammable range is of concern, particularly as the addition of the fuel cell and electronics in the enclosure would further reduce the available volume and increase concentrations. The opening area of louvre vents would need to be greater than equivalent plain vents to achieve comparable ventilation flows or alternative schemes would need to be considered.

Keywords: enclosure, fuel cell, helium, hydrogen safety, louvre vent, passive ventilation

Procedia PDF Downloads 253

Authors: Fernando Martinez Soto, Gaetano Di Mino

Abstract:

The application of rubber into bituminous mixtures requires attention and care during mixing and compaction. Rubber modifies the properties because it reacts in the internal structure of bitumen at high temperatures changing the performance of the mixture (interaction process of solvents with binder-rubber aggregate). The main change is the increasing of the viscosity and elasticity of the binder due to the larger sizes of the rubber particles by dry process but, this positive effect is counteracted by short mixing times, compared to wet technology, and due to the transport processes, curing time and post-compaction of the mixtures. Therefore, negative effects as swelling of rubber particles, rebounding effect of the specimens and thermal changes by different expansion of the structure inside the mixtures, can change the mechanical properties of the rubberized blends. Based on the dry technology, different asphalt-rubber binders using devulcanized or natural rubber (truck and bus tread rubber), have served to demonstrate these effects and how to solve them into two dense-gap graded rubber modified asphalt concrete mixes (RUMAC) to enhance the stability, workability and durability of the compacted samples by Superpave gyratory compactor method. This paper specifies the procedures developed in the Department of Civil Engineering of the University of Palermo during September 2016 to March 2017, for characterizing the post-compaction and mix-stability of the one conventional mixture (hot mix asphalt without rubber) and two gap-graded rubberized asphalt mixes according granulometry for rail sub-ballast layers with nominal size of Ø22.4mm of aggregates according European standard. Thus, the main purpose of this laboratory research is the application of ambient ground rubber from scrap tires processed at conventional temperature (20ºC) inside hot bituminous mixtures (160-220ºC) as a substitute for 1.5%, 2% and 3% by weight of the total aggregates (3.2%, 4.2% and, 6.2% respectively by volumetric part of the limestone aggregates of bulk density equal to 2.81g/cm³) considered, not as a part of the asphalt binder. The reference bituminous mixture was designed with 4% of binder and ± 3% of air voids, manufactured for a conventional bitumen B50/70 at 160ºC-145ºC mix-compaction temperatures to guarantee the workability of the mixes. The proportions of rubber proposed are #60-40% for mixtures with 1.5 to 2% of rubber and, #20-80% for mixture with 3% of rubber (as example, a 60% of Ø0.4-2mm and 40% of Ø2-4mm). The temperature of the asphalt cement is between 160-180 ºC for mixing and 145-160 ºC for compaction, according to the optimal values for viscosity using Brookfield viscometer and 'ring and ball' - penetration tests. These crumb rubber particles act as a rubber-aggregate into the mixture, varying sizes between 0.4mm to 2mm in a first fraction, and 2-4mm as second proportion. Ambient ground rubber with a specific gravity of 1.154g/cm³ is used. The rubber is free of loose fabric, wire, and other contaminants. It was found optimal results in real beams and cylindrical specimens with each HMA mixture reducing the swelling effect. Different factors as temperature, particle sizes of rubber, number of cycles and pressures of compaction that affect the interaction process are explained.

Keywords: crumb-rubber, gyratory compactor, rebounding effect, superpave mix-design, swelling, sub-ballast railway

Procedia PDF Downloads 221

Authors: Alaa Fezai, Anuj Sharma, Wolfgang Mueller-Hirsch, André Zimmermann

Abstract:

Viscoelastic materials have been widely used in the automotive industry over the last few decades with different functionalities. Besides their main application as a simple and efficient surface damping treatment, they may ensure optimal operating conditions for on-board electronics as thermal interface or sealing layers. The dynamic behavior of viscoelastic materials is generally dependent on many environmental factors, the most important being temperature and strain rate or frequency. Prior to the reliability analysis of systems including viscoelastic layers, it is, therefore, crucial to accurately predict the dynamic and lifetime behavior of these materials. This includes the identification of the dynamic material parameters under critical temperature and frequency conditions along with a precise damage localization and identification methodology. The goal of this work is twofold. The first part aims at applying an inverse viscoelastic material-characterization approach for a wide frequency range and under different temperature conditions. For this sake, dynamic measurements are carried on a single lap joint specimen using an electrodynamic shaker and an environmental chamber. The specimen consists of aluminum beams assembled to adapter plates through a viscoelastic adhesive layer. The experimental setup is reproduced in finite element (FE) simulations, and frequency response functions (FRF) are calculated. The parameters of both the generalized Maxwell model and the fractional derivatives model are identified through an optimization algorithm minimizing the difference between the simulated and the measured FRFs. The second goal of the current work is to guarantee an on-line detection of the damage, i.e., delamination in the viscoelastic bonding of the described specimen during frequency monitored end-of-life testing. For this purpose, an inverse technique, which determines the damage location and size based on the modal frequency shift and on the change of the mode shapes, is presented. This includes a preliminary FE model-based study correlating the delamination location and size to the change in the modal parameters and a subsequent experimental validation achieved through dynamic measurements of specimen with different, pre-generated crack scenarios and comparing it to the virgin specimen. The main advantage of the inverse characterization approach presented in the first part resides in the ability of adequately identifying the material damping and stiffness behavior of soft viscoelastic materials over a wide frequency range and under critical temperature conditions. Classic forward characterization techniques such as dynamic mechanical analysis are usually linked to limitations under critical temperature and frequency conditions due to the material behavior of soft viscoelastic materials. Furthermore, the inverse damage detection described in the second part guarantees an accurate prediction of not only the damage size but also its location using a simple test setup and outlines; therefore, the significance of inverse numerical-experimental approaches in predicting the dynamic behavior of soft bonding layers applied in automotive electronics.

Keywords: damage detection, dynamic characterization, inverse approaches, vibration testing, viscoelastic layers

Procedia PDF Downloads 182

Authors: Viktor D. Dubrovin, Masoud Mollaee, Jie Zong, Xiushan Zhu, Nasser Peyghambarian

Abstract:

Glasses doped with high rare-earth (RE) elements concentration attracted considerable attention since the middle of the 20th century due to their particular magneto-optical properties. Such glasses exhibit the Faraday effect in which the polarization plane of a linearly polarized light beam is rotated by the interaction between the incident light and the magneto-optical material. That effect found application in optical isolators that are useful for laser systems, which can prevent back reflection of light into lasers or optical amplifiers and reduce signal instability and noise. Glasses are of particular interest since they are cost-effective and can be formed into fibers, thus breaking the limits of traditional bulk optics requiring optical coupling for use with fiber-optic systems. The advent of high-power fiber lasers operating near 2µm revealed a necessity in the development of all fiber isolators for this region. Ce³⁺, Pr³⁺, Dy³⁺, and Tb³⁺ ions provide the biggest contribution to the Verdet constant value of optical materials among the RE. It is known that Pr³⁺ and Tb³⁺ ions have strong absorption bands near 2 µm, thus making Dy³⁺ and Ce³⁺ the only prospective candidates for fiber isolator operating in that region. Due to the high tendency of Ce³⁺ ions pass to Ce⁴⁺ during the synthesis, glasses with high cerium content usually suffers from Ce⁴⁺ ions absorption extending from visible to IR. Additionally, Dy³⁺ (₆H¹⁵/²) same as Ho³⁺ (⁵I₈) ions, have the largest effective magnetic moment (µeff = 10.6 µB) among the RE ions that starts to play the key role if the operating region is far from 4fⁿ→ 4fⁿ⁻¹5 d¹ electric-dipole transition relevant to the Faraday Effect. Considering the high effective magnetic moment value of Er³⁺ ions (µeff = 9.6 µB) that is 3rd after Dy³⁺/ Ho³⁺ and Tb³⁺, it is possible to assume that Er³⁺ doped glasses should exhibit Verdet constant value near 2µm that is comparable with one of Dy doped glasses. Thus, partial replacement of Dy³⁺ on Er³⁺ ions has been performed, keeping the overall concentration of Re₂O₃ equal to 70 wt.% (30.6 mol.%). Al₂O₃-B₂O₃-SiO₂-30.6RE₂O₃ (RE= Er, Dy) glasses had been synthesized, and their thermal, spectral, optical, structural, and magneto-optical properties had been studied. Glasses synthesis had been conducted in Pt crucibles for 3h at 1500 °C. The obtained melt was poured into preheated up to 400 °C mold and annealed from 800 oC to room temperature for 12h with 1h dwell. The mass of obtained glass samples was about 200g. Shown that the difference between crystallization and glass transition temperature is about 150 oC, even taking into account the fact that high content of RE₂O₃ leads to glass network depolymerization. Verdet constant of Al₂O₃-B₂O₃-SiO₂-30.6RE₂O₃ glasses for wavelength 1950 nm can reach more than 5.9 rad/(T*m), which is among the highest number reported for a paramagnetic glass at this wavelength. The refractive index value was found to be equal to 1.7545 at 633 nm. Our experimental results show that Al₂O₃-B₂O₃-SiO₂-30.6RE₂O₃ glasses with high Dy₂O₃ content are expected to be promising material for use as highly effective Faraday isolators and modulators of electromagnetic radiation in the 2μm region.

Keywords: oxide glass, magneto-optical, dysprosium, erbium, Faraday rotator, boron-aluminosilicate system

Procedia PDF Downloads 94

Authors: Wenfa Yu, Thuva Gnanasampanthan, John Finlay, Jessica Clarke, Charlotte Anderson, Tony Clare, Axel Rosenhahn

Abstract:

Marine biofouling is a worldwide problem at vast economic and ecological costs. Historically it was combated with toxic coatings such as tributyltin. As those coatings being banned nowadays, finding environmental friendly antifouling solution has become an urgent topic. In this study antifouling coatings consisted of natural occurring polysaccharides hyaluronic acid (HA), alginic acid (AA), chitosan (Ch) and polyelectrolyte polyethylenimine (PEI) are constructed into polyelectrolyte multilayers (PEMs) in a Layer-by-Layer (LbL) method. LbL PEM construction is a straightforward way to assemble biomacromolecular coatings on surfaces. Advantages about PEM include ease of handling, highly diverse PEM composition, precise control over the thickness and so on. PEMs have been widely employed in medical application and there are numerous studies regarding their protein adsorption, elasticity and cell adhesive properties. With the adjustment of coating composition, termination layer charge, coating morphology and cross-linking method, it is possible to prepare low marine biofouling coatings with PEMs. In this study, using spin coating technology, PEM construction was achieved at smooth multilayers with roughness as low as 2nm rms and highly reproducible thickness around 50nm. To obtain stability in sea water, the multilayers were covalently cross-linked either thermally or chemically. The cross-linking method affected surface energy, which was reflected in water contact angle, thermal cross-linking led to hydrophobic surfaces and chemical cross-linking generated hydrophilic surfaces. The coatings were then evaluated regarding its protein resistance and biological species resistance. While the hydrophobic thermally cross-linked PEM had low resistance towards proteins, the resistance of chemically cross-linked PEM strongly depended on the PEM termination layer and the charge of the protein, opposite charge caused high adsorption and same charge low adsorption, indicating electrostatic interaction plays a crucial role in the protein adsorption processes. Ulva linza was chosen as the biological species for antifouling performance evaluation. Despite of the poor resistance towards protein adsorption, thermally cross-linked PEM showed good resistance against Ulva spores settlement, the chemically cross-linked multilayers showed poor resistance regardless of the termination layer. Marine species adhesion is a complex process, although it involves proteins as bioadhesives, protein resistance its own is not a fully indicator for its antifouling performance. The species will pre select the surface, responding to cues like surface energy, chemistry, or charge and so on. Thus making it difficult for one single factors to determine its antifouling performance. Preparing PEM coating is a comprehensive work involving choosing polyelectrolyte combination, determining termination layer and the method for cross-linking. These decisions will affect PEM properties such as surface energy, charge, which is crucial, since biofouling is a process responding to surface properties in a highly sensitive and dynamic way.

Keywords: hyaluronic acid, polyelectrolyte multilayers, protein resistance, Ulva linza zoospores

Procedia PDF Downloads 146

Authors: Eliana Bergamin

Abstract:

This philosophical inquiry, influenced by the seminal works of Annemarie Mol and Jeannette Pols, critically examines the transformative impact of artificial intelligence (AI) on emotional caregiving practices within virtual healthcare. Rooted in the traditions of philosophy of care, philosophy of emotions, and applied philosophy, this study seeks to unravel nuanced shifts in the moral and emotional fabric of healthcare mediated by AI-powered technologies. Departing from traditional empirical studies, the approach embraces the foundational principles of care ethics and phenomenology, offering a focused exploration of the ethical and existential dimensions of AI-mediated emotional caregiving. At its core, this research addresses the introduction of AI-powered technologies mediating emotional and care practices in the healthcare sector. By drawing on Mol and Pols' insights, the study offers a focused exploration of the ethical and existential dimensions of AI-mediated emotional caregiving. Anchored in ethnographic research within a pioneering private healthcare company in the Netherlands, this critical philosophical inquiry provides a unique lens into the dynamics of AI-mediated emotional practices. The study employs in-depth, semi-structured interviews with virtual caregivers and care receivers alongside ongoing ethnographic observations spanning approximately two and a half months. Delving into the lived experiences of those at the forefront of this technological evolution, the research aims to unravel subtle shifts in the emotional and moral landscape of healthcare, critically examining the implications of AI in reshaping the philosophy of care and human connection in virtual healthcare. Inspired by Mol and Pols' relational approach, the study prioritizes the lived experiences of individuals within the virtual healthcare landscape, offering a deeper understanding of the intertwining of technology, emotions, and the philosophy of care. In the realm of philosophy of care, the research elucidates how virtual tools, particularly those driven by AI, mediate emotions such as empathy, sympathy, and compassion—the bedrock of caregiving. Focusing on emotional nuances, the study contributes to the broader discourse on the ethics of care in the context of technological mediation. In the philosophy of emotions, the investigation examines how the introduction of AI alters the phenomenology of emotional experiences in caregiving. Exploring the interplay between human emotions and machine-mediated interactions, the nuanced analysis discerns implications for both caregivers and caretakers, contributing to the evolving understanding of emotional practices in a technologically mediated healthcare environment. Within applied philosophy, the study transcends empirical observations, positioning itself as a reflective exploration of the moral implications of AI in healthcare. The findings are intended to inform ethical considerations and policy formulations, bridging the gap between technological advancements and the enduring values of caregiving. In conclusion, this focused philosophical inquiry aims to provide a foundational understanding of the evolving landscape of virtual healthcare, drawing on the works of Mol and Pols to illuminate the essence of human connection, care, and empathy amid technological advancements.

Keywords: applied philosophy, artificial intelligence, healthcare, philosophy of care, philosophy of emotions

Procedia PDF Downloads 37

Authors: Simone Cinquemani, Lorenzo Mantovani, Aleksander Dabek

Abstract:

New technologies in agriculture are opening new challenges and new opportunities. Among these, certainly, robotics, vision, and artificial intelligence are the ones that will make a significant leap, compared to traditional agricultural techniques, possible. In particular, the indoor farming sector will be the one that will benefit the most from these solutions. Vertical farming is a new field of research where mechanical engineering can bring knowledge and know-how to transform a highly labor-based business into a fully autonomous system. The aim of the research is to develop a multi-purpose, modular, and perfectly integrated platform for crop production in indoor vertical farming. Activities will be based both on hardware development such as automatic tools to perform different activities on soil and plants, as well as research to introduce an extensive use of monitoring techniques based on machine learning algorithms. This paper presents the preliminary results of a research project of a vertical farm living lab designed to (i) develop and test vertical farming cultivation practices, (ii) introduce a very high degree of mechanization and automation that makes all processes replicable, fully measurable, standardized and automated, (iii) develop a coordinated control and management environment for autonomous multiplatform or tele-operated robots in environments with the aim of carrying out complex tasks in the presence of environmental and cultivation constraints, (iv) integrate AI-based algorithms as decision support system to improve quality production. The coordinated management of multiplatform systems still presents innumerable challenges that require a strongly multidisciplinary approach right from the design, development, and implementation phases. The methodology is based on (i) the development of models capable of describing the dynamics of the various platforms and their interactions, (ii) the integrated design of mechatronic systems able to respond to the needs of the context and to exploit the strength characteristics highlighted by the models, (iii) implementation and experimental tests performed to test the real effectiveness of the systems created, evaluate any weaknesses so as to proceed with a targeted development. To these aims, a fully automated laboratory for growing plants in vertical farming has been developed and tested. The living lab makes extensive use of sensors to determine the overall state of the structure, crops, and systems used. The possibility of having specific measurements for each element involved in the cultivation process makes it possible to evaluate the effects of each variable of interest and allows for the creation of a robust model of the system as a whole. The automation of the laboratory is completed with the use of robots to carry out all the necessary operations, from sowing to handling to harvesting. These systems work synergistically thanks to the knowledge of detailed models developed based on the information collected, which allows for deepening the knowledge of these types of crops and guarantees the possibility of tracing every action performed on each single plant. To this end, artificial intelligence algorithms have been developed to allow synergistic operation of all systems.

Keywords: automation, vertical farming, robot, artificial intelligence, vision, control

Procedia PDF Downloads 15

Authors: Amanda Kavner, Richard Lamb

Abstract:

Faster growth in science and technology of other nations may make staying globally competitive more difficult without shifting focus on how science is taught in US classes. An integral part of learning science involves visual and spatial thinking since complex, and real-world phenomena are often expressed in visual, symbolic, and concrete modes. The primary barrier to spatial thinking and visual literacy in Science, Technology, Engineering, and Math (STEM) fields is representational competence, which includes the ability to generate, transform, analyze and explain representations, as opposed to generic spatial ability. Although the relationship is known between the foundational visual literacy and the domain-specific science literacy, science literacy as a function of science learning is still not well understood. Moreover, the need for a more reliable measure is necessary to design resources which enhance the fundamental visuospatial cognitive processes behind scientific literacy. To support the improvement of students’ representational competence, first visualization skills necessary to process these science representations needed to be identified, which necessitates the development of an instrument to quantitatively measure visual literacy. With such a measure, schools, teachers, and curriculum designers can target the individual skills necessary to improve students’ visual literacy, thereby increasing science achievement. This project details the development of an artificial neural network capable of measuring science literacy using functional Near-Infrared Spectroscopy (fNIR) data. This data was previously collected by Project LENS standing for Leveraging Expertise in Neurotechnologies, a Science of Learning Collaborative Network (SL-CN) of scholars of STEM Education from three US universities (NSF award 1540888), utilizing mental rotation tasks, to assess student visual literacy. Hemodynamic response data from fNIRsoft was exported as an Excel file, with 80 of both 2D Wedge and Dash models (dash) and 3D Stick and Ball models (BL). Complexity data were in an Excel workbook separated by the participant (ID), containing information for both types of tasks. After changing strings to numbers for analysis, spreadsheets with measurement data and complexity data were uploaded to RapidMiner’s TurboPrep and merged. Using RapidMiner Studio, a Gradient Boosted Trees artificial neural network (ANN) consisting of 140 trees with a maximum depth of 7 branches was developed, and 99.7% of the ANN predictions are accurate. The ANN determined the biggest predictors to a successful mental rotation are the individual problem number, the response time and fNIR optode #16, located along the right prefrontal cortex important in processing visuospatial working memory and episodic memory retrieval; both vital for science literacy. With an unbiased measurement of science literacy provided by psychophysiological measurements with an ANN for analysis, educators and curriculum designers will be able to create targeted classroom resources to help improve student visuospatial literacy, therefore improving science literacy.

Keywords: artificial intelligence, artificial neural network, machine learning, science literacy, neuroscience

Procedia PDF Downloads 95

Authors: Vasundhara Arora, Anupam Jhobta, Suresh Thakur, Sanjiv Sharma

Abstract:

Aims and Objectives: Intracranial tuberculosis (TB) is one of the most devastating manifestations of TB and a challenging public health issue of considerable importance and magnitude world over. This study elaborates on the imaging spectrum of neurotuberculosis on magnetic resonance imaging (MRI) in 29 clinically suspected cases from a tertiary care hospital. Materials and Methods: The prospective hospital based evaluation of MR imaging features of neuro-tuberculosis in 29 clinically suspected cases was carried out in Department of Radio-diagnosis, Indira Gandhi Medical Hospital from July 2017 to August 2018. MR Images were obtained on a 1.5 T Magnetom Avanto machine and were analyzed to identify any abnormal meningeal enhancement or parenchymal lesions. Microbiological and Biochemical CSF analysis was performed in radio-logically suspected cases and the results were compared with the imaging data. Clinical follow up of the patients started on anti-tuberculous treatment was done to evaluate the response to treatment and clinical outcome. Results: Age range of patients in the study was between 1 year to 73 years. The mean age of presentation was 11.5 years. No significant difference in the distribution of cerebral tuberculosis was noted among the two genders. Imaging findings of neuro-tuberculosis obtained were varied and non specific ranging from lepto-meningeal enhancement, cerebritis to space occupying lesions such as tuberculomas and tubercular abscesses. Complications presenting as hydrocephalus (n= 7) and infarcts (n=9) was noted in few of these patients. 29 patients showed radiological suspicion of CNS tuberculosis with meningitis alone observed in 11 cases, tuberculomas alone were observed in 4 cases, meningitis with parenchymal tuberculomas in 11 cases. Tubercular abscess and cerebritis were observed in one case each. Tuberculous arachnoiditis was noted in one patient. Gene expert positivity was obtained in 11 out of 29 radiologically suspected patients; none of the patients showed culture positivity. Meningeal form of the disease alone showed higher positivity rate of gene Xpert (n=5) followed by combination of meningeal and parenchymal forms of disease (n=4). The parenchymal manifestation of disease alone showed least positivity rates (n= 3) with gene xpert testing. All 29 patients were started on anti tubercular treatment based on radiological suspicion of the disease with clinical improvement observed in 27 treated patients. Conclusions: In our study, higher incidence of neuro- tuberculosis was noted in paediatric population with predominance of the meningeal form of the disease. Gene Xpert positivity obtained was low due to paucibacillary nature of cerebrospinal fluid (CSF) with even lower positivity of CSF samples in parenchymal form of the manifestation. MRI showed high accuracy in detecting CNS lesions in neuro-tuberculosis. Hence, it can be concluded that MRI plays a crucial role in the diagnosis because of its inherent sensitivity and specificity and is an indispensible imaging modality. It caters to the need of early diagnosis owing to poor sensitivity of microbiological tests more so in the parenchymal manifestation of the disease.

Keywords: neurotuberculosis, tubercular abscess, tuberculoma, tuberculous meningitis

Procedia PDF Downloads 145

Authors: Reggie Chandra

Abstract:

Trafficaccidents and traffic signal optimization are correlated. However, 70-90% of the traffic signals across the USA are not synchronized. The reason behind that is insufficient resources to create and implement timing plans. In this work, we will discuss the use of a breakthrough Artificial Intelligence (AI) technology to optimize traffic flow and collect 24/7/365 accurate traffic data using a vehicle detection system. We will discuss what are recent advances in Artificial Intelligence technology, how does AI work in vehicles, pedestrians, and bike data collection, creating timing plans, and what is the best workflow for that. Apart from that, this paper will showcase how Artificial Intelligence makes signal timing affordable. We will introduce a technology that uses Convolutional Neural Networks (CNN) and deep learning algorithms to detect, collect data, develop timing plans and deploy them in the field. Convolutional Neural Networks are a class of deep learning networks inspired by the biological processes in the visual cortex. A neural net is modeled after the human brain. It consists of millions of densely connected processing nodes. It is a form of machine learning where the neural net learns to recognize vehicles through training - which is called Deep Learning. The well-trained algorithm overcomes most of the issues faced by other detection methods and provides nearly 100% traffic data accuracy. Through this continuous learning-based method, we can constantly update traffic patterns, generate an unlimited number of timing plans and thus improve vehicle flow. Convolutional Neural Networks not only outperform other detection algorithms but also, in cases such as classifying objects into fine-grained categories, outperform humans. Safety is of primary importance to traffic professionals, but they don't have the studies or data to support their decisions. Currently, one-third of transportation agencies do not collect pedestrian and bike data. We will discuss how the use of Artificial Intelligence for data collection can help reduce pedestrian fatalities and enhance the safety of all vulnerable road users. Moreover, it provides traffic engineers with tools that allow them to unleash their potential, instead of dealing with constant complaints, a snapshot of limited handpicked data, dealing with multiple systems requiring additional work for adaptation. The methodologies used and proposed in the research contain a camera model identification method based on deep Convolutional Neural Networks. The proposed application was evaluated on our data sets acquired through a variety of daily real-world road conditions and compared with the performance of the commonly used methods requiring data collection by counting, evaluating, and adapting it, and running it through well-established algorithms, and then deploying it to the field. This work explores themes such as how technologies powered by Artificial Intelligence can benefit your community and how to translate the complex and often overwhelming benefits into a language accessible to elected officials, community leaders, and the public. Exploring such topics empowers citizens with insider knowledge about the potential of better traffic technology to save lives and improve communities. The synergies that Artificial Intelligence brings to traffic signal control and data collection are unsurpassed.

Keywords: artificial intelligence, convolutional neural networks, data collection, signal control, traffic signal

Procedia PDF Downloads 137

Authors: Mayank Gupta, Siba Prasad Samal, Vasu Kakkirala

Abstract:

The internet world and its priorities have changed considerably in the last decade. Browsing on smart phones has increased manifold and is set to explode much more. Users spent considerable time browsing different websites, that gives a great deal of insight into user’s preferences. Instead of plain information classifying different aspects of browsing like Bookmarks, History, and Download Manager into useful categories would improve and enhance the user’s experience. Most of the classification solutions are server side that involves maintaining server and other heavy resources. It has security constraints and maybe misses on contextual data during classification. On device, classification solves many such problems, but the challenge is to achieve accuracy on classification with resource constraints. This on device classification can be much more useful in personalization, reducing dependency on cloud connectivity and better privacy/security. This approach provides more relevant results as compared to current standalone solutions because it uses content rendered by browser which is customized by the content provider based on user’s profile. This paper proposes a Naive Bayes based lightweight classification engine targeted for a resource constraint devices. Our solution integrates with Web Browser that in turn triggers classification algorithm. Whenever a user browses a webpage, this solution extracts DOM Tree data from the browser’s rendering engine. This DOM data is a dynamic, contextual and secure data that can’t be replicated. This proposal extracts different features of the webpage that runs on an algorithm to classify into multiple categories. Naive Bayes based engine is chosen in this solution for its inherent advantages in using limited resources compared to other classification algorithms like Support Vector Machine, Neural Networks, etc. Naive Bayes classification requires small memory footprint and less computation suitable for smartphone environment. This solution has a feature to partition the model into multiple chunks that in turn will facilitate less usage of memory instead of loading a complete model. Classification of the webpages done through integrated engine is faster, more relevant and energy efficient than other standalone on device solution. This classification engine has been tested on Samsung Z3 Tizen hardware. The Engine is integrated into Tizen Browser that uses Chromium Rendering Engine. For this solution, extensive dataset is sourced from dmoztools.net and cleaned. This cleaned dataset has 227.5K webpages which are divided into 8 generic categories ('education', 'games', 'health', 'entertainment', 'news', 'shopping', 'sports', 'travel'). Our browser integrated solution has resulted in 15% less memory usage (due to partition method) and 24% less power consumption in comparison with standalone solution. This solution considered 70% of the dataset for training the data model and the rest 30% dataset for testing. An average accuracy of ~96.3% is achieved across the above mentioned 8 categories. This engine can be further extended for suggesting Dynamic tags and using the classification for differential uses cases to enhance browsing experience.

Keywords: chromium, lightweight engine, mobile computing, Naive Bayes, Tizen, web browser, webpage classification

Procedia PDF Downloads 139

Authors: Serhat Tüzün, Tufan Demirel

Abstract:

Decision Support Systems (DSS) have been investigated by researchers and technologists for more than 35 years. This paper analyses the developments in the architecture and software of these systems, provides a systematic analysis for different Web-based DSS approaches and Intelligent Decision-making Technologies (IDT), with the suggestion for future studies. Decision Support Systems literature begins with building model-oriented DSS in the late 1960s, theory developments in the 1970s, and the implementation of financial planning systems and Group DSS in the early and mid-80s. Then it documents the origins of Executive Information Systems, online analytic processing (OLAP) and Business Intelligence. The implementation of Web-based DSS occurred in the mid-1990s. With the beginning of the new millennia, intelligence is the main focus on DSS studies. Web-based technologies are having a major impact on design, development and implementation processes for all types of DSS. Web technologies are being utilized for the development of DSS tools by leading developers of decision support technologies. Major companies are encouraging its customers to port their DSS applications, such as data mining, customer relationship management (CRM) and OLAP systems, to a web-based environment. Similarly, real-time data fed from manufacturing plants are now helping floor managers make decisions regarding production adjustment to ensure that high-quality products are produced and delivered. Web-based DSS are being employed by organizations as decision aids for employees as well as customers. A common usage of Web-based DSS has been to assist customers configure product and service according to their needs. These systems allow individual customers to design their own products by choosing from a menu of attributes, components, prices and delivery options. The Intelligent Decision-making Technologies (IDT) domain is a fast growing area of research that integrates various aspects of computer science and information systems. This includes intelligent systems, intelligent technology, intelligent agents, artificial intelligence, fuzzy logic, neural networks, machine learning, knowledge discovery, computational intelligence, data science, big data analytics, inference engines, recommender systems or engines, and a variety of related disciplines. Innovative applications that emerge using IDT often have a significant impact on decision-making processes in government, industry, business, and academia in general. This is particularly pronounced in finance, accounting, healthcare, computer networks, real-time safety monitoring and crisis response systems. Similarly, IDT is commonly used in military decision-making systems, security, marketing, stock market prediction, and robotics. Even though lots of research studies have been conducted on Decision Support Systems, a systematic analysis on the subject is still missing. Because of this necessity, this paper has been prepared to search recent articles about the DSS. The literature has been deeply reviewed and by classifying previous studies according to their preferences, taxonomy for DSS has been prepared. With the aid of the taxonomic review and the recent developments over the subject, this study aims to analyze the future trends in decision support systems.

Keywords: decision support systems, intelligent decision-making, systematic analysis, taxonomic review

Procedia PDF Downloads 254