Search results for: performance degradation

Authors: Dacheng Li, Bo Huang, Qinjin Han, Ming Li

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Remotely sensed imagery with the high spatial and temporal characteristics, which it is hard to acquire under the current land observation satellites, has been considered as a key factor for monitoring environmental changes over both global and local scales. On a basis of the limited high spatial-resolution observations, challenged studies called spatiotemporal fusion have been developed for generating high spatiotemporal images through employing other auxiliary low spatial-resolution data while with high-frequency observations. However, a majority of spatiotemporal fusion approaches yield to satisfactory assumption, empirical but unstable parameters, low accuracy or inefficient performance. Although the spatiotemporal fusion methodology via sparse representation theory has advantage in capturing reflectance changes, stability and execution efficiency (even more efficient when overcomplete dictionaries have been pre-trained), the retrieval of high-accuracy dictionary and its response to fusion results are still pending issues. In this paper, we employ additional image pairs (here each image-pair includes a Landsat Operational Land Imager and a Moderate Resolution Imaging Spectroradiometer acquisitions covering the partial area of Baotou, China) only into the coupled dictionary training process based on K-SVD (K-means Singular Value Decomposition) algorithm, and attempt to improve the fusion results of two existing sparse representation based fusion models (respectively utilizing one and two available image-pair). The results show that more eligible image pairs are probably related to a more accurate overcomplete dictionary, which generally indicates a better image representation, and is then contribute to an effective fusion performance in case that the added image-pair has similar seasonal aspects and image spatial structure features to the original image-pair. It is, therefore, reasonable to construct multi-dictionary training pattern for generating a series of high spatial resolution images based on limited acquisitions.

Keywords: spatiotemporal fusion, sparse representation, K-SVD algorithm, dictionary learning

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Authors: Gabriela Kalcikova, Igor Bosevski, Ula Rozman, Andreja Zgajnar Gotvajn

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Antibiotics are extensively used in human medicine and also in animal husbandry to prevent or control infections. Recently, a lot of attention has been put on veterinary antibiotics, because their global consumption is increasing and it is expected to be 106.600 tons in 2030. Most of veterinary antibiotics are introduced into the environment via animal manure, which is used as fertilizer. One of such veterinary antibiotics is tiamulin. It is used the form of fumarate for treatment of pig and poultry. It is used against prophylaxis of dysentery, pneumonia and mycroplasmal infections, but its environmental impact is practically unknown. Tiamulin has been found very persistent in animal manure and thus it is expected that can be, during rainfalls, transported into the aquatic environment and affect various organisms. For assessment of its environmental impact, it is necessary to evaluate its biodegradability and toxicity to various organisms from different levels of a food chain. Therefore, the aim of our study was to evaluate ready biodegradability and toxicity of tiamulin fumarate to various organisms. Bioassay used included luminescent bacterium Vibrio fischeri heterotrophic and nitrifying microorganisms of activated sludge, water flea Daphnia magna and duckweed Lemna minor. For each species, EC₅₀ values were calculated. Biodegradability test was used for determination of ready biodegradability and it provides information about biodegradability of tiamulin under the most common environmental conditions. Results of our study showed that tiamulin differently affects selected organisms. The most sensitive organisms were water fleas with 48hEC₅₀ = 14.2 ± 4.8 mg/L and duckweed with 168hEC₅₀ = 22.6 ± 0.8 mg/L. Higher concentrations of tiamulin (from 10 mg/L) significantly affected photosynthetic pigments content in duckweed and concentrations above 80 mg/L cause visible chlorosis. It is in agreement with previous studies showing significant effect of tiamulin on green algae and cyanobacteria. Tiamuline has a low effect on microorganisms. The lower toxicity was observed for heterotrophic microorganisms (30minEC₅₀ = 1656 ± 296 mg/L), than Vibrio fisheri (30minEC₅₀ = 492 ± 21) and the most sensitive organisms were nitrifying microorganisms (30minEC₅₀ = 183 ± 127 mg/L). The reason is most probably the mode of action of tiamulin being effective to gram-positive bacteria while gram-negative (e.g., Vibrio fisheri) are more tolerant to tiamulin. Biodegradation of tiamulin was very slow with a long lag-phase being 20 days. The maximal degradation reached 40 ± 2 % in 43 days of the test and tiamulin as other antibiotics (e.g. ciprofloxacin) are not easily biodegradable. Tiamulin is widely used antibiotic in veterinary medicine and thus present in the environment. According to our results, tiamulin can have negative effect on water fleas and duckweeds, but the concentrations are several magnitudes higher than that found in any environmental compartment. Tiamulin is low toxic to tested microorganisms, but it is very low biodegradable and thus possibly persistent in the environment.

Keywords: antibiotics, biodegradability, tiamulin, toxicity

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Authors: Dharminder Singh, Sanjeev Yadav, Pravakar Mohanty

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In this work, study of temperature profile in a pilot scale air bubbling fluidized bed (ABFB) gasifier for rice husk gasification was carried out. Effects of different factors such as multiple cyclones, gas cooling system, ventilate gas pipe length, and catalyst on temperature profile was examined. ABFB gasifier used in this study had two sections, one is bed section and the other is freeboard section. River sand was used as bed material with air as gasification agent, and conventional charcoal as start-up heating medium in this gasifier. Temperature of different point in both sections of ABFB gasifier was recorded at different ER value and ER value was changed by changing the feed rate of biomass (rice husk) and by keeping the air flow rate constant for long durational of gasifier operation. ABFB with double cyclone with gas coolant system and with short length ventilate gas pipe was found out to be optimal gasifier design to give temperature profile required for high gasification performance in long duration operation. This optimal design was tested with different ER values and it was found that ER of 0.33 was most favourable for long duration operation (8 hr continuous operation), giving highest carbon conversion efficiency. At optimal ER of 0.33, bed temperature was found to be stable at 700 °C, above bed temperature was found to be at 628.63 °C, bottom of freeboard temperature was found to be at 600 °C, top of freeboard temperature was found to be at 517.5 °C, gas temperature was found to be at 195 °C, and flame temperature was found to be 676 °C. Temperature at all the points showed fluctuations of 10 – 20 °C. Effect of catalyst i.e. dolomite (20% with sand bed) was also examined on temperature profile, and it was found that at optimal ER of 0.33, the bed temperature got increased to 795 °C, above bed temperature got decreased to 523 °C, bottom of freeboard temperature got decreased to 548 °C, top of freeboard got decreased to 475 °C, gas temperature got decreased to 220 °C, and flame temperature got increased to 703 °C. Increase in bed temperature leads to higher flame temperature due to presence of more hydrocarbons generated from more tar cracking at higher temperature. It was also found that the use of dolomite with sand bed eliminated the agglomeration in the reactor at such high bed temperature (795 °C).

Keywords: air bubbling fluidized bed gasifier, bed temperature, charcoal heating, dolomite, flame temperature, rice husk

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Authors: Dimitris Varsamis, Christos Talagkozis, Alkiviadis Tsimpiris, Paris Mastorocostas

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The aim of this work is the parallel implementation of k-means in MATLAB, in order to reduce the execution time. Specifically, a new function in MATLAB for serial k-means algorithm is developed, which meets all the requirements for the conversion to a function in MATLAB with parallel computations. Additionally, two different variants for the definition of initial values are presented. In the sequel, the parallel approach is presented. Finally, the performance tests for the computation times respect to the numbers of features and classes are illustrated.

Keywords: K-means algorithm, clustering, parallel computations, Matlab

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Authors: Pranay Mathur, Carlo Michelassi, Simi Karatha, Gilda Pedoto

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Industrial plant uptime is top most importance for reliable, profitable & sustainable operation. Trip and failed start has major impact on plant reliability and all plant operators focussed on efforts required to minimise the trips & failed starts. The performance of these CTQs are measured with 2 metrics, MTBT(Mean time between trips) and SR (Starting reliability). These metrics helps to identify top failure modes and identify units need more effort to improve plant reliability. Baker Hughes Trip reduction program structured to reduce these unwanted trip 1. Real time machine operational parameters remotely available and capturing the signature of malfunction including related boundary condition. 2. Real time alerting system based on analytics available remotely. 3. Remote access to trip logs and alarms from control system to identify the cause of events. 4. Continuous support to field engineers by remotely connecting with subject matter expert. 5. Live tracking of key CTQs 6. Benchmark against fleet 7. Break down to the cause of failure to component level 8. Investigate top contributor, identify design and operational root cause 9. Implement corrective and preventive action 10. Assessing effectiveness of implemented solution using reliability growth models. 11. Develop analytics for predictive maintenance With this approach , Baker Hughes team is able to support customer in achieving their Reliability Key performance Indicators for monitored units, huge cost savings for plant operators. This Presentation explains these approach while providing successful case studies, in particular where 12nos. of LNG and Pipeline operators with about 140 gas compressing line-ups has adopted these techniques and significantly reduce the number of trips and improved MTBT

Keywords: reliability, availability, sustainability, digital infrastructure, weibull, effectiveness, automation, trips, fail start

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Authors: Chen Liu, Dawit Negussey

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EPS (Expanded Polystyrene) geofoam as light-weight material in geotechnical applications are made of pre-expanded resin beads that form fused cellular micro-structures. The strength and deformation properties of geofoam blocks are determined by unconfined compression of small test samples between rigid loading plates. Applied loads are presumed to be supported uniformly over the entire mating end areas. Predictions of field performance on the basis of such laboratory tests widely over-estimate actual post-construction settlements and exaggerate predictions of long-term creep deformations. This investigation examined the development of contact pressures at a large number of discrete points at low and large strain levels for different densities of geofoam. Development of pressure patterns for fine and coarse interface material textures as well as for molding skin and hot wire cut geofoam surfaces were examined. The lab testing showed that I-Scan tactile sensors are useful for detailed observation of contact pressures at a large number of discrete points simultaneously. At low strain level (1%), the lower density EPS block presents low variations in localized stress distribution compared to higher density EPS. At high strain level (10%), the dense geofoam reached the sensor cut-off limit. The imprint and pressure patterns for different interface textures can be distinguished with tactile sensing. The pressure sensing system can be used in many fields with real-time pressure detection. The research findings provide a better understanding of EPS geofoam behavior for improvement of design methods and performance prediction of critical infrastructures, which will be anticipated to guide future improvements in design and rapid construction of critical transportation infrastructures with geofoam in geotechnical applications.

Keywords: geofoam, pressure distribution, tactile pressure sensors, interface

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Authors: Seyed Mehdi Ghezi, Hesam Hasanpoor

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This scientific article explores enhancement methods for ensemble learning with the aim of improving the performance of classifiers in the analysis and classification of brain signals. The research approach in this field consists of two main parts, each with its own strengths and weaknesses. The choice of approach depends on the specific research question and available resources. By combining these approaches and leveraging their respective strengths, researchers can enhance the accuracy and reliability of classification results, consequently advancing our understanding of the brain and its functions. The first approach focuses on utilizing machine learning methods to identify the best features among the vast array of features present in brain signals. The selection of features varies depending on the research objective, and different techniques have been employed for this purpose. For instance, the genetic algorithm has been used in some studies to identify the best features, while optimization methods have been utilized in others to identify the most influential features. Additionally, machine learning techniques have been applied to determine the influential electrodes in classification. Ensemble learning plays a crucial role in identifying the best features that contribute to learning, thereby improving the overall results. The second approach concentrates on designing and implementing methods for selecting the best classifier or utilizing meta-classifiers to enhance the final results in ensemble learning. In a different section of the research, a single classifier is used instead of multiple classifiers, employing different sets of features to improve the results. The article provides an in-depth examination of each technique, highlighting their advantages and limitations. By integrating these techniques, researchers can enhance the performance of classifiers in the analysis and classification of brain signals. This advancement in ensemble learning methodologies contributes to a better understanding of the brain and its functions, ultimately leading to improved accuracy and reliability in brain signal analysis and classification.

Keywords: ensemble learning, brain signals, classification, feature selection, machine learning, genetic algorithm, optimization methods, influential features, influential electrodes, meta-classifiers

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Authors: Lilian Ogechi Iheukwumere-Esotu, Akilu Yunusa-Kaltungo, Paul Chan

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Industrial assets are prone to degradation and eventual failures due to repetitive loads and harsh environments in which they operate. These failures often lead to costly downtimes, which may involve loss of critical assets and/or human lives. The rising pressures from stakeholders for optimized systems’ outputs have further placed strains on business organizations. Traditional means of combating such failures are by adopting strategies capable of predicting, controlling, and/or reducing the likelihood of systems’ failures. Turnarounds, shutdowns, and outages (TSOs) projects are popular maintenance management activities conducted over a certain period of time. However, despite the critical and significant cost implications of TSOs, the management of the interface of knowledge between academia and industry to our best knowledge has not been fully explored in comparison to other aspects of industrial operations. This is perhaps one of the reasons for the limited knowledge transfer between academia and industry, which has affected the outcomes of most TSOs. Prior to now, the study of knowledge development trends as a failure analysis tool in the management of TSOs projects have not gained the required level of attention. Hence, this review provides useful references and their implications for future studies in this field. This study aims to harmonize the existing research trends of TSOs through a systematic review of more than 3,000 research articles published over 7 decades (1940- till date) which were extracted using very specific research criteria and later streamlined using nominated inclusion and exclusion parameters. The information obtained from the analysis were then synthesized and coded into 8 parameters, thereby allowing for a transformation into actionable outputs. The study revealed a variety of information, but the most critical findings can be classified into 4 folds: (1) Empirical validation of available conceptual frameworks and models is still a far cry in practice, (2) traditional project management views for managing uncertainties are still dominant, (3) Inconsistent approaches towards the adoption and promotion of knowledge management systems which supports creation, transfer and application of knowledge within and outside the project organization and, (4) exploration of social practices in industrial maintenance project environments are under-represented within the existing body of knowledge. Thus, the intention of this study is to depict the usefulness of a framework which incorporates fact findings emanating from careful analysis and illustrations of evidence based results as a suitable approach which can tackle reoccurring failures in industrial maintenance projects.

Keywords: industrial maintenance, knowledge management, maintenance projects, systematic review, TSOs

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Authors: Bogale Tadesse, Krutik Parikh, Ndagha Mkandawire, Boris Albijanic, Nimal Subasinghe

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It is well established that the floatability and selectivity of mineral particles are highly dependent on the particle size. Generally, a particle size of 10 micron is considered as the critical size below which both flotation selectivity and recovery decline sharply. It is widely accepted that the majority of ultrafine particles, including highly liberated valuable minerals, will be lost in tailings during a conventional flotation process. This is highly undesirable particularly in the processing of finely disseminated complex and refractory ores where there is a requirement for fine grinding in order to liberate the valuable minerals. In addition, the continuing decline in ore grade worldwide necessitates intensive processing of low grade mineral deposits. Recent advances in comminution allow the economic grinding of particles down to 10 micron sizes to enhance the probability of liberating locked minerals from low grade ores. Thus, it is timely that the flotation of fine and ultrafine particles is improved in order to reduce the amount of valuable minerals lost as slimes. It is believed that the use of fine bubbles in flotation increases the bubble-particle collision efficiency and hence the flotation performance. Electroflotation, where bubbles are generated by the electrolytic breakdown of water to produce oxygen and hydrogen gases, leads to the formation of extremely finely dispersed gas bubbles with dimensions varying from 5 to 95 micron. The sizes of bubbles generated by this method are significantly smaller than those found in conventional flotation (> 600 micron). In this study, microbubbles generated by electrolysis of water were injected into a bench top flotation cell to assess the performance electroflotation in enhancing the flotation of fine and ultrafine pyrite particles of sizes ranging from 5 to 53 micron. The design of the cell and the results from optimization of the process variables such as current density, pH, percent solid and particle size will be presented at this conference.

Keywords: electroflotation, fine bubbles, pyrite, ultrafine particles

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Authors: G. Alston Rush, Gus A. Rush III, M. F. Horstemeyer

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This study compares the shell-facemask responses of four commonly used American football helmets, under the National Operating Committee on Standards for Athletic Equipment (NOCSAE) drop impact test method, to show that the test standard would more accurately simulate in-use conditions by modification to include the facemask. In our study, the need for a more vigorous systematic approach to football helmet testing procedures is emphasized by comparing the Head Injury Criterion (HIC), the Gadd Severity Index (SI), and peak acceleration values for different helmets at different locations on the helmet under modified NOCSAE standard drop tower tests. Drop tests were performed on the Rawlings Quantum Plus, Riddell 360, Schutt Ion 4D, and Xenith X2 helmets at eight impact locations, impact velocities of 5.46 and 4.88 meters per second, and helmet configurations with and without facemasks. Analysis of NOCSAE drop test results reveal significant differences (p < 0.05) for when the facemasks were attached to helmets, as compared to the NOCSAE Standard, without facemask configuration. The boundary conditions of the facemask attachment can have up to a 50% decrease (p < 0.001) in helmet performance with respect to peak acceleration. While generally, all helmets with the facemasks gave greater HIC, SI, and acceleration values than helmets without the facemasks, significant helmet dependent variations were observed across impact locations and impact velocities. The variations between helmet responses could be attributed to the unique design features of each helmet tested, which include different liners, chin strap attachments, and faceguard attachment systems. In summary, these comparative drop test results revealed that the current NOCSAE standard test methods need improvement by attaching the facemasks to helmets during testing. The modified NOCSAE football helmet standard test gives a more accurate representation of a helmet’s performance and its ability to mitigate the on-field impact.

Keywords: football helmet testing, gadd severity index, head injury criterion, mild traumatic brain injury

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Authors: Assabo Mohamed, Bile Mohamed, Ali Farah, Isman Souleiman, Olga Alos Ramos, Marie Cadet

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The life of a nuclear power plant is regularly punctuated by short or long period outages to carry out maintenance operations and/or nuclear fuel reloading. During these stops periods, it is essential to conserve all the secondary circuit equipment to avoid corrosion priming. This kind of circuit is one of the main components of a nuclear reactor. Indeed, the conservation materials on shutdown of a nuclear unit improve circuit performance and reduce the maintenance cost considerably. This study is a part of the optimization of the dry preservation of equipment from the water station of the nuclear reactor. The main objective is to provide tools to guide Electricity Production Nuclear Centre (EPNC) in order to achieve the criteria required by the chemical specifications of conservation materials. A theoretical model of drying exchangers of water station is developed by the software Engineering Equation Solver (EES). It used to size requirements and air quality needed for dry conservation of equipment. This model is based on heat transfer and mass transfer governing the drying operation. A parametric study is conducted to know the influence of aerothermal factor taking part in the drying operation. The results show that the success of dry conservation of equipment of the secondary circuit of nuclear reactor depends strongly on the draining, the quality of drying air and the flow of air injecting in the secondary circuit. Finally, theoretical case study performed on EES highlights the importance of mastering the entire system to balance the air system to provide each exchanger optimum flow depending on its characteristics. From these results, recommendations to nuclear power plants can be formulated to optimize drying practices and achieve good performance in the conservation of material from the water at the stop position.

Keywords: dry conservation, optimization, sizing, water station

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Authors: H. B. Grobler, K. Du Plooy, P. Kruger, S. Ellis

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Objectives: The primary objective of the study was to determine the common mental disorders (CMD) identified by professional South African rugby players and its correlation with their mental toughness, as a first step towards developing such a programme within a larger research project. Design: Survey research, within the theoretical perspective of field theory, was conducted, utilising an adaptation of an already existing mental health questionnaire. The aim was to obtain feedback from as many possible professional South African rugby players in order to make certain generalizations and come to conclusions with regard to the current mental health experiences of these rugby players. Methods: Non-randomized sampling was done, linking it with internet research in the form of the online completion of a questionnaire. A sample of 215 rugby players participated and completed the online questionnaire. Permission was obtained to make use of an existing questionnaire, previously used by the specific authors with retired professional rugby players. A section on mental toughness was added. Data were descriptively analysed by means of the SPSS software platform. Results: Results indicated that the most significant problem that the players are experiencing, is a problem with alcohol (47.9%). Other problems that featured are distress (16.3%), sleep disturbances (7%), as well as anxiety and depression (4.2%). 4.7% of the players indicated that they smoke. 3.3% of the players experience themselves as not being mentally tough. A positive correlation between mental toughness and sound sleep (0.262) was found while a negative correlation was found between mental toughness and the following: anxiety/depression (-0.401), anxiety/depression positive (-0.423), distress (-0.259) and common mental disorder problems in general (-0.220). Conclusions: Although the presence of CMD at first glance do not seem significantly high amongst all the players, it must be considered that if one player in a team experiences the presence of CMD, it will have an impact on his mental toughness and most likely on his performance, as well as on the performance of the whole team. It is therefore important to ensure mental health in the whole team, by addressing individual CMD problems. A mental health support programme is therefore needed to be implemented to the benefit of these players within the South African context.

Keywords: common mental disorders, mental toughness, professional athletes, rugby players

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Authors: Johannes Günther

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Time Sensitive Networking (TSN), standardized in the IEEE 802.1 standard, has been lent increasing attention in the context of mission critical systems. Such mission critical systems, e.g., in the automotive domain, aviation, industrial, and smart factory domain, are responsible for coordinating complex functionalities in real time. In many of these contexts, a reliable data exchange fulfilling hard time constraints and quality of service (QoS) conditions is of critical importance. TSN standards are able to provide guarantees for deterministic communication behaviour, which is in contrast to common best-effort approaches. Therefore, the superior QoS guarantees of TSN may aid in the development of new technologies, which rely on low latencies and specific bandwidth demands being fulfilled. TSN extends existing Ethernet protocols with numerous standards, providing means for synchronization, management, and overall real-time focussed capabilities. These additional QoS guarantees, as well as management mechanisms, lead to an increased attack surface for potential malicious attackers. As TSN guarantees certain deadlines for priority traffic, an attacker may degrade the QoS by delaying a packet beyond its deadline or even execute a denial of service (DoS) attack if the delays lead to packets being dropped. However, thus far, security concerns have not played a major role in the design of such standards. Thus, while TSN does provide valuable additional characteristics to existing common Ethernet protocols, it leads to new attack vectors on networks and allows for a range of potential attacks. One answer to these security risks is to deploy defense mechanisms according to a moving target defense (MTD) strategy. The core idea relies on the reduction of the attackers' knowledge about the network. Typically, mission-critical systems suffer from an asymmetric disadvantage. DoS or QoS-degradation attacks may be preceded by long periods of reconnaissance, during which the attacker may learn about the network topology, its characteristics, traffic patterns, priorities, bandwidth demands, periodic characteristics on links and switches, and so on. Here, we implemented and tested several MTD-like defense strategies against different attacker models of varying capabilities and budgets, as well as collaborative attacks of multiple attackers within a network, all within the context of TSN networks. We modelled the networks and tested our defense strategies on an OMNET++ testbench, with networks of different sizes and topologies, ranging from a couple dozen hosts and switches to significantly larger set-ups.

Keywords: network security, time sensitive networking, moving target defense, cyber security

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Authors: Belen Montero, Carmen Ramirez, Maite Rico, Rebeca Bouza, Irene Derungs

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Starch is a promising polymer for producing biocomposite materials because it is renewable, completely biodegradable and easily available at a low cost. Thermoplastic starches (TPS) can be obtained after the disruption and plasticization of native starch with a plasticizer. In this work, the solvent casting method was used to obtain TPS films from wheat starch plasticized with glycerol and reinforced with nanocellulose (CNC). X-ray diffraction analysis was used to follow the evolution of the crystallinity. The native wheat starch granules have shown a profile corresponding to A-type crystal structures typical for cereal starches. When TPS films are analyzed a high amorphous halo centered on 19º is obtained, indicating the plasticization process is completed. SEM imaging was made in order to analyse the morphology. The image from the raw wheat starch granules shows a bimodal granule size distribution with some granules in large round disk-shape forms (A-type) and the others as smaller spherical particles (B-type). The image from the neat TPS surface shows a continuous surface. No starch aggregates or swollen granules can be seen so, the plasticization process is complete. In the surfaces of reinforced TPS films aggregates are seen as the CNC concentration in the matrix increases. The CNC influence on the mechanical properties of TPS films has been studied by dynamic mechanical analysis. A direct relation exists between the storage modulus values, E’, and the CNC content in reinforced TPS films: higher is the content of nanocellulose in the composite, higher is the value of E’. This reinforcement effect can be explained by the appearance of a strong and crystalline nanoparticle-TPS interphase. Thermal stability of films was analysed by TGA. It has not observed any influence on the behaviour related to the thermal degradation of films with the incorporation of the CNC. Finally, the resistance to the water absorption films was analysed following the standard UNE-EN ISO 1998:483. The percentage of water absorbed by the samples at each time was calculated. The addition of 5 wt % of CNC to the TPS matrix leads to a significant improvement in the moisture resistance of the starch based material decreasing their diffusivity. It has been associated to the formation of a nanocrystal network that prevents swelling of the starch and therefore water absorption and to the high crystallinity of cellulose compared to starch. As a conclusion, the wheat film reinforced with 5 wt % of cellulose nanocrystals seems to be a good alternative for short-life applications into the packaging industry, because of its greatest rigidity, thermal stability and moisture sorption resistance.

Keywords: biocomposites, nanocellulose, starch, wheat

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Authors: Fumihiro Ima, Shinichi Watanabe, Shingo Maeda, Haruna Imai, Hiroki Niimi

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It is important to know growth rate of brain tumors before surgery because it influences treatment planning including not only surgical resection strategy but also adjuvant therapy after surgery. Amide proton transfer (APT) imaging is an emerging molecular magnetic resonance imaging (MRI) technique based on chemical exchange saturation transfer without administration of contrast medium. The underlying assumption in APT imaging of tumors is that there is a close relationship between the proliferative activity of the tumor and mobile protein synthesis. We aimed to evaluate the diagnostic performance of APT imaging of pre-and post-treatment brain tumors. Ten patients with brain tumor underwent conventional and APT-weighted sequences on a 3.0 Tesla MRI before clinical intervention. The maximum and the minimum APT-weighted signals (APTWmax and APTWmin) in each solid tumor region were obtained and compared before and after clinical intervention. All surgical specimens were examined for histopathological diagnosis. Eight of ten patients underwent adjuvant therapy after surgery. Histopathological diagnosis was glioma in 7 patients (WHO grade 2 in 2 patients, WHO grade 3 in 3 patients and WHO grade 4 in 2 patients), meningioma WHO grade1 in 2 patients and primary lymphoma of the brain in 1 patient. High-grade gliomas showed significantly higher APTW-signals than that in low-grade gliomas. APTWmax in one huge parasagittal meningioma infiltrating into the skull bone was higher than that in glioma WHO grade 4. On the other hand, APTWmax in another convexity meningioma was the same as that in glioma WHO grade 3. Diagnosis of primary lymphoma of the brain was possible with APT imaging before pathological confirmation. APTW-signals in residual tumors decreased dramatically within one year after adjuvant therapy in all patients. APT imaging demonstrated excellent diagnostic performance for the planning of surgery and adjuvant therapy of brain tumors.

Keywords: amides, magnetic resonance imaging, brain tumors, cell proliferation

Procedia PDF Downloads 139

Authors: Afshin Gharib

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Often, instructors prefer to bring into class a guest lecturer who can provide an “experiential” perspective on a particular topic. The assumption is that the personal experience brought into the classroom makes the material resonate more with students and that students would have a preference for material being taught from an experiential perspective. The question we asked in the present study was whether a guest lecture from an “experiential” expert with a disability (e.g. a guest suffering from cone-rod dystrophy lecturing on vision, or a dyslexic lecturing on the psychology of reading) would be more effective than the course instructor in capturing students attention and conveying information in an Introduction to Psychology class. Students in two sections of Introduction to Psychology (N = 25 in each section) listened to guest lecturers with disabilities lecturing on a topic related to their disability, one in the area of Sensation and Perception (the guest lecturer is vision impaired) and one in the area of Language Development (the guest lecturer is dyslexic). The Guest lecturers lectured on the same topic in both sections, however, each lecturer used their own experiences to highlight the topics they cover in one section but not the other (counterbalanced between sections), providing students in one section with experiential testimony. Following each of the 4 lectures (two experiential, two non-experiential) students rated the lecture on several dimensions including overall quality, level of engagement, and performance. In addition, students in both sections were tested on the same test items from the lecture material to ascertain degree of learning, and given identical “pop” quizzes two weeks after the exam to measure retention. It was hypothesized that students would find the experiential lectures from lecturers talking about their disabilities more engaging, learn more from them, and retain the material for longer. We found that students in fact preferred the course instructor to the guests, regardless of whether the guests included a discussion of their own disability in their lectures. Performance on the exam questions and the pop quiz items were not different between “experiential” and “non-experiential” lectures, suggesting that guest lecturers who discuss their own disabilities in lecture are not more effective in conveying material and students are not more likely to retain material delivered by “experiential” guests. In future research we hope to explore the reasons for students preference for their regular instructor over guest lecturers.

Keywords: guest lecturer, student perception, retention, experiential

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Authors: Ramesh Chinnasamy, Srishti Semalty, Vishnu S. Nair, Thirumurugan Vedagiri, Mahesh Ganeshan, Gautam Talukdar, Karthy Sivapushanam, Abhijit Das

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Snakes form an integral component of ecological systems. Human population explosion and associated acceleration of habitat destruction and degradation, has led to a rapid increase in human-snake encounters. The study aims at understanding the level of awareness, knowledge, and attitude of the people towards human-snake negative interaction and role of awareness programmes in the Moyar river valley, Tamil Nadu. The study area is part of the Mudumalai and the Sathyamangalam Tiger Reserves, which are significant wildlife corridors between the Western Ghats and the Eastern Ghats in the Nilgiri Biosphere Reserve. The data was collected using questionnaire covering 644 respondents spread across 18 villages between 2018 and 2019. The study revealed that 86.5% of respondents had strong negative perceptions towards snakes which were propelled by fear, superstitions, and threat of snakebite which was common and did not vary among different villages (F=4.48; p = <0.05) and age groups (X2 = 1.946; p = 0.962). Cobra 27.8% (n = 294) and rat snake 21.3% (n = 225) were the most sighted species and most snake encounter occurred during the monsoon season i.e., July 35.6 (n = 218), June 19.1% (n = 117) and August 18.4% (n = 113). At least 1 out of 5 respondents was reportedly bitten by snakes during their lifetime. The most common species of snakes that were the cause of snakebite were Saw scaled viper (32.6%, n = 42) followed by Cobra 17.1% (n = 22). About 21.3% (n = 137) people reported livestock loss due to pythons and other snakes 21.3% (n = 137). Most people, preferred medical treatment for snakebite (87.3%), whereas 12.7%, still believed in traditional methods. The majority (82.3%) used precautionary measure by keeping traditional items such as garlic, kerosene, and snake plant to avoid snakes. About 30% of the respondents expressed need for technical and monetary support from the forest department that could aid in reducing the human-snake conflict. It is concluded that the general perception in the study area is driven by fear and negative attitude towards snakes. Though snakes such as Cobra were widely worshiped in the region, there are still widespread myths and misconceptions that have led to the irrational killing of snakes. Awareness and innovative education programs rooted in the local context and language should be integrated at the village level, to minimize risk and the associated threat of snakebite among the people. Results from this study shall help policy makers to devise appropriate conservation measures to reduce human-snake conflicts in India.

Keywords: Envenomation, Health-Education, Human-Wildlife Conflict, Neglected Tropical Disease, Snakebite Mitigation, Traditional Practitioners

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Authors: Bahar Salavati Vie Le Sage, Ismaïl Ben Hariz, Flavien Viguier, Sirine Noura Kahil, Audrey Jacquin, Maxime Convert

Abstract:

The ballast layer plays a key role in railroad maintenance and the geometry of the track structure. Ballast also holds the track in place as the trains roll over it. Track ballast is packed between the sleepers and on the sides of railway tracks. An imbalance in ballast volume on the tracks can lead to safety issues as well as a quick degradation of the overall quality of the railway segment. If there is a lack of ballast in the track bed during the summer, there is a risk that the rails will expand and buckle slightly due to the high temperatures. Furthermore, the knowledge of the ballast quantities that will be excavated during renewal works is important for efficient ballast management. The volume of excavated ballast per meter of track can be calculated based on excavation depth, excavation width, volume of track skeleton (sleeper and rail) and sleeper spacing. Since 2012, SNCF has been collecting 3D points cloud data covering its entire railway network by using 3D laser scanning technology (LiDAR). This vast amount of data represents a modelization of the entire railway infrastructure, allowing to conduct various simulations for maintenance purposes. This paper aims to present an automated method for ballast volume estimation based on the processing of LiDAR data. The estimation of abnormal volumes in ballast on the tracks is performed by analyzing the cross-section of the track. Further, since the amount of ballast required varies depending on the track configuration, the knowledge of the ballast profile is required. Prior to track rehabilitation, excess ballast is often present in the ballast shoulders. Based on 3D laser scans, a Digital Terrain Model (DTM) was generated and automatic extraction of the ballast profiles from this data is carried out. The surplus in ballast is then estimated by performing a comparison between this ballast profile obtained empirically, and a geometric modelization of the theoretical ballast profile thresholds as dictated by maintenance standards. Ideally, this excess should be removed prior to renewal works and recycled to optimize the output of the ballast renewal machine. Based on these parameters, an application has been developed to allow the automatic measurement of ballast profiles. We evaluated the method on a 108 kilometers segment of railroad LiDAR scans, and the results show that the proposed algorithm detects ballast surplus that amounts to values close to the total quantities of spoil ballast excavated.

Keywords: ballast, railroad, LiDAR , cloud point, track ballast, 3D point

Procedia PDF Downloads 109

Authors: Sultan Muhammed Fatih Apaydin

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In this study, two types of robot were examined as being pioneer robot and follower robot for improving of the capabilities of tracking robots. Robots continue to tracking each other and measurement of the follow-up distance between them is very important for improvements to be applied. It was achieved that the follower robot follows the pioneer robot in line with intended goals. The tests were applied to the robots in various grounds and environments in point of performance and necessary improvements were implemented by measuring the results of these tests.

Keywords: mobile robot, remote and autonomous control, infra-red sensors, arduino

Procedia PDF Downloads 566

Authors: Mohammed Seyam, Faridah Othman, Ahmed El-Shafie

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The early warning of high stream flow events (HSF) and floods is an important aspect in the management of surface water and rivers systems. This process can be performed using either process-based models or data driven-based models such as artificial intelligence (AI) techniques. The main goal of this study is to develop efficient AI-based model for predicting the real-time hourly stream flow (Q) and apply it as early warning tool of HSF and floods in the downstream area of the Selangor River basin, taken here as a paradigm of humid tropical rivers in Southeast Asia. The performance of AI-based models has been improved through the integration of the lag time (Lt) estimation in the modelling process. A total of 8753 patterns of Q, water level, and rainfall hourly records representing one-year period (2011) were utilized in the modelling process. Six hydrological scenarios have been arranged through hypothetical cases of input variables to investigate how the changes in RF intensity in upstream stations can lead formation of floods. The initial SF was changed for each scenario in order to include wide range of hydrological situations in this study. The performance evaluation of the developed AI-based model shows that high correlation coefficient (R) between the observed and predicted Q is achieved. The AI-based model has been successfully employed in early warning throughout the advance detection of the hydrological conditions that could lead to formations of floods and HSF, where represented by three levels of severity (i.e., alert, warning, and danger). Based on the results of the scenarios, reaching the danger level in the downstream area required high RF intensity in at least two upstream areas. According to results of applications, it can be concluded that AI-based models are beneficial tools to the local authorities for flood control and awareness.

Keywords: floods, stream flow, hydrological modelling, hydrology, artificial intelligence

Procedia PDF Downloads 248

Authors: Ravi Verma

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Cryosorption pump is the best solution to achieve clean, vibration free ultra-high vacuum. Furthermore, the operation of cryosorption pump is free from the influence of electric and magnetic fields. Due to these attributes, this pump is used in the space simulation chamber to create the ultra-high vacuum. The cryosorption pump comprises of three parts (a) panel which is cooled with the help of cryogen or cryocooler, (b) an adsorbent which is used to adsorb the gas molecules, (c) an epoxy which holds the adsorbent and the panel together thereby aiding in heat transfer from adsorbent to the panel. The performance of cryosorption pump depends on the temperature of the adsorbent and hence, on the thermal conductivity of the epoxy. Therefore we have made an attempt to increase the thermal conductivity of epoxy adhesive by mixing nano-sized graphene filler particles. The thermal conductivity of epoxy-graphene composite adhesive is measured with the help of indigenously developed experimental setup in the temperature range from 4.5 K to 7 K, which is generally the operating temperature range of cryosorption pump for efficiently pumping of hydrogen and helium gas. In this article, we have presented the experimental results of epoxy-graphene composite adhesive in the temperature range from 4.5 K to 7 K. We have also proposed an analytical heat conduction model to find the thermal conductivity of the composite. In this case, the filler particles, such as graphene, are randomly distributed in a base matrix of epoxy. The developed model considers the complete spatial random distribution of filler particles and this distribution is explained by Binomial distribution. The results obtained by the model have been compared with the experimental results as well as with the other established models. The developed model is able to predict the thermal conductivity in both isotropic regions as well as in anisotropic region over the required temperature range from 4.5 K to 7 K. Due to the non-empirical nature of the proposed model, it will be useful for the prediction of other properties of composite materials involving the filler in a base matrix. The present studies will aid in the understanding of low temperature heat transfer which in turn will be useful towards the development of high performance cryosorption pump.

Keywords: composite adhesive, computational modelling, cryosorption pump, thermal conductivity

Procedia PDF Downloads 89

Authors: Yi Jen Wang, Yu Ju Chen

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Background: Heart rate variability (HRV) biofeedback can promote autonomic nervous function, sleep quality and reduce psychological stress. In HRV biofeedback training, it is hoped that through the guidance of machine video or audio, the patient can breathe slowly according to his own heart rate changes so that the heart and lungs can achieve resonance, thereby promoting the related effects of autonomic nerve function; while, it is also pointed out that if slow breathing of 6 times per minute can also guide the case to achieve the effect of cardiopulmonary resonance. However, there is no relevant research to explore the comparison of the effectiveness of cardiopulmonary resonance by using video or audio HRV biofeedback training and metronome-guided slow breathing. Purpose: To compare the promotion of autonomic nervous function performance between using HRV biofeedback and slow breathing guided by a metronome. Method: This research is a kind of experimental design with convenient sampling; the cases are randomly divided into the heart rate variability biofeedback training group and the slow breathing training group. The HRV biofeedback training group will conduct HRV biofeedback training in a four-week laboratory and use the home training device for autonomous training; while the slow breathing training group will conduct slow breathing training in the four-week laboratory using the mobile phone APP breathing metronome to guide the slow breathing training, and use the mobile phone APP for autonomous training at home. After two groups were enrolled and four weeks after the intervention, the autonomic nervous function-related performance was repeatedly measured. Using the chi-square test, student’s t-test and other statistical methods to analyze the results, and use p <0.05 as the basis for statistical significance. Results: A total of 27 subjects were included in the analysis. After four weeks of training, the HRV biofeedback training group showed significant improvement in the HRV indexes (SDNN, RMSSD, HF, TP) and sleep quality. Although the stress index also decreased, it did not reach statistical significance; the slow breathing training group was not statistically significant after four weeks of training, only sleep quality improved significantly, while the HRV indexes (SDNN, RMSSD, TP) all increased. Although HF and stress indexes decreased, they were not statistically significant. Comparing the difference between the two groups after training, it was found that the HF index improved significantly and reached statistical significance in the HRV biofeedback training group. Although the sleep quality of the two groups improved, it did not reach that level in a statistically significant difference. Conclusion: HRV biofeedback training is more effective in promoting autonomic nervous function than slow breathing training, but the effects of reducing stress and promoting sleep quality need to be explored after increasing the number of samples. The results of this study can provide a reference for clinical or community health promotion. In the future, it can also be further designed to integrate heart rate variability biological feedback training into the development of AI artificial intelligence wearable devices, which can make it more convenient for people to train independently and get effective feedback in time.

Keywords: autonomic nervous function, HRV biofeedback, heart rate variability, slow breathing

Procedia PDF Downloads 176

Authors: Mark Lin, Periklis Papadopoulos

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This paper explores the aerodynamics of the formula racecar when a ‘halo’ driver-protection device is added to the chassis. The halo protection device was introduced at the start of the 2018 racing season as a safety measure against foreign object impacts that a driver may encounter when driving an open-wheel racecar. In the one-year since its introduction, the device has received wide acclaim for protecting the driver on two separate occasions. The benefit of such a safety device certainly cannot be disputed. However, by adding the halo device to a car, it changes the airflow around the vehicle, and most notably, to the engine air-intake and the rear wing. These negative effects in the air supply to the engine, and equally to the downforce created by the rear wing are studied in this paper using numerical technique, and the resulting CFD outputs are presented and discussed. Comparing racecar design prior to and after the introduction of the halo device, it is shown that the design of the air intake and the rear wing has not followed suit since the addition of the halo device. The reduction of engine intake mass flow due to the halo device is computed and presented for various speeds the car may be going. Because of the location of the halo device in relation to the air intake, airflow is directed away from the engine, making the engine perform less than optimal. The reduction is quantified in this paper to show the correspondence to reduce the engine output when compared to a similar car without the halo device. This paper shows that through aerodynamic arguments, the engine in a halo car will not receive unobstructed, clean airflow that a non-halo car does. Another negative effect is on the downforce created by the rear wing. Because the amount of downforce created by the rear wing is influenced by every component that comes before it, when a halo device is added upstream to the rear wing, airflow is obstructed, and less is available for making downforce. This reduction in downforce is especially dramatic as the speed is increased. This paper presents a graph of downforce over a range of speeds for a car with and without the halo device. Acknowledging that although driver safety is paramount, the negative effect of this safety device on the performance of the car should still be well understood so that any possible redesign to mitigate these negative effects can be taken into account in next year’s rules regulation.

Keywords: automotive aerodynamics, halo device, downforce. engine intake

Procedia PDF Downloads 110

Authors: Margit Kastner

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To overcome the negative consequences associated with large class sizes and to support students in developing the necessary competences (e.g., critical thinking, problem-solving, or team-work skills) a marketing course has been redesigned by implementing project-based learning with peer assessment (PBL&PA). This means that students can voluntarily take advantage of this supplementary offer and explore -in addition to attending the lecture where clicker questions are asked- a real-world problem, find a solution, and assess the results of peers while working in small collaborative groups. In order to handle this with little further effort, the process is technically supported by the university’s e-learning system in such a way that students upload their solution in form of an assignment which is then automatically distributed to peer groups who have to assess the work of three other groups. Finally, students’ work is graded automatically considering both, students’ contribution to the project and the conformity of the peer assessment. The purpose of this study is to evaluate students’ perception of PBL&PA using an online-questionnaire to collect the data. More specifically, it aims to discover students’ motivations for (not) working on a project and the benefits and problems students encounter. In addition to the survey, students’ performance was analyzed by comparing the final grades of those who participated in PBL&PA with those who did not participate. Among the 260 students who filled out the questionnaire, 47% participated in PBL&PA. Besides extrinsic motivations (bonus credits), students’ participation was often motivated by learning and social benefits. Reasons for not working on a project were connected to students’ organization and management of their studies (e.g., time constraints, no/wrong information) and teamwork concerns (e.g., missing engagement of peers, prior negative experiences). In addition, high workload and insufficient extrinsic motivation (bonus credits) were mentioned. With regards to benefits and problems students encountered during the project, students provided more positive than negative comments. Positive aspects most often stated were learning and social benefits while negative ones were mainly attached to the technical implementation. Interestingly, bonus credits were hardly named as a positive aspect meaning that intrinsic motivations have become more important when working on the project. Team aspects generated mixed feelings. In addition, students who voluntarily participated in PBL&PA were, in general, more active and utilized further course offers such as clicker questions. Examining students’ performance at the final exam revealed that students without participating in any of the offered active learning tasks performed poorest in the exam while students who used all activities were best. In conclusion, the goals of the implementation were met in terms of students’ perceived benefits and the positive impact on students’ exam performance. Since the comparison of the automatic grading with faculty grading showed valid results, it is possible to rely only on automatic grading in the future. That way, the additional workload for faculty will be within limits. Thus, the implementation of project-based learning with peer assessment can be recommended for large classes.

Keywords: automated grading, large classes, peer assessment, project-based learning

Procedia PDF Downloads 165

Authors: Hira Lal Gope, Hidekazu Fukai

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The aim of this study is to develop a system which can identify and sort peaberries automatically at low cost for coffee producers in developing countries. In this paper, the focus is on the classification of peaberries and normal coffee beans using image processing and machine learning techniques. The peaberry is not bad and not a normal bean. The peaberry is born in an only single seed, relatively round seed from a coffee cherry instead of the usual flat-sided pair of beans. It has another value and flavor. To make the taste of the coffee better, it is necessary to separate the peaberry and normal bean before green coffee beans roasting. Otherwise, the taste of total beans will be mixed, and it will be bad. In roaster procedure time, all the beans shape, size, and weight must be unique; otherwise, the larger bean will take more time for roasting inside. The peaberry has a different size and different shape even though they have the same weight as normal beans. The peaberry roasts slower than other normal beans. Therefore, neither technique provides a good option to select the peaberries. Defect beans, e.g., sour, broken, black, and fade bean, are easy to check and pick up manually by hand. On the other hand, the peaberry pick up is very difficult even for trained specialists because the shape and color of the peaberry are similar to normal beans. In this study, we use image processing and machine learning techniques to discriminate the normal and peaberry bean as a part of the sorting system. As the first step, we applied Deep Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) as machine learning techniques to discriminate the peaberry and normal bean. As a result, better performance was obtained with CNN than with SVM for the discrimination of the peaberry. The trained artificial neural network with high performance CPU and GPU in this work will be simply installed into the inexpensive and low in calculation Raspberry Pi system. We assume that this system will be used in under developed countries. The study evaluates and compares the feasibility of the methods in terms of accuracy of classification and processing speed.

Keywords: convolutional neural networks, coffee bean, peaberry, sorting, support vector machine

Procedia PDF Downloads 144

Authors: Yanqing Yi, Maria Cristina Lavagnolo, Alessandro Manzardo

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Construction and demolition waste (C&DW) is a major challenge in the European Union, emphasizing the urgent need for appropriate waste management processes. Selecting these solutions is challenging, as it requires identifying efficient C&DW management techniques that balance acceptable practices, regulatory compliance, resource conservation, economic viability, and environmental concerns. Techniques for analyzing many kinds of criteria allow for the use of multi-criteria analysis in life cycle assessment (LCA). Although LCA is commonly used to analyze environmental effects, the economic factor has not been fully integrated into the LCA approach in C&DW management. The life cycle costing (LCC) approach was designed to assess economic performance in the C&DW management process. The choice of an effective multi-criteria decision-making (MCDM) technique is critical for the C&DW system. This study seeks to propose a model that employs MCDM by considering LCA and LCC results, thereby augmenting both environmental and economic sustainability. A widely used compensatory MCDM technique, TOPSIS, has been chosen to identify the most effective C&DW management scheme by comparing and ranking various scenarios. Four waste management alternatives were examined in the Lombardy region of Italy, namely, (i) landfill; (ii) recycling for concrete production and road construction, incineration with energy recovery; (iii) recycling for road construction; (iv) recycling for concrete production and road construction. We determine that, with the implementation of various scenarios, the most suitable scenario emerges to be recycled for concrete production and road construction, with a score of 0.711/1; recycling for road construction, with a final score of 0.291/1, ranks second; recycling for concrete production and road construction, incineration with energy recovery scores 0.002/1, ranks third; and landfill (scores: 0/1) is the worst choice, indicating it has the highest environmental impact. Finally, suggestions were developed to improve the system's environmental performance.

Keywords: life cycle assessment, life cycle costing, construction and demolition waste, multi-criteria decision making

Procedia PDF Downloads 71

Authors: Fumihiro Imai, Shinichi Watanabe, Shingo Maeda, Haruna Imai, Hiroki Niimi

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It is important to know the growth rate of brain tumors before surgery because it influences treatment planning, including not only surgical resection strategy but also adjuvant therapy after surgery. Amide proton transfer (APT) imaging is an emerging molecular magnetic resonance imaging (MRI) technique based on chemical exchange saturation transfer without the administration of a contrast medium. The underlying assumption in APT imaging of tumors is that there is a close relationship between the proliferative activity of the tumor and mobile protein synthesis. We aimed to evaluate the diagnostic performance of APT imaging of pre-and post-treatment brain tumors. Ten patients with brain tumor underwent conventional and APT-weighted sequences on a 3.0 Tesla MRI before clinical intervention. The maximum and the minimum APT-weighted signals (APTWmax and APTWmin) in each solid tumor region were obtained and compared before and after a clinical intervention. All surgical specimens were examined for histopathological diagnosis. Eight of ten patients underwent adjuvant therapy after surgery. Histopathological diagnosis was glioma in 7 patients (WHO grade 2 in 2 patients, WHO grade 3 in 3 patients, and WHO grade 4 in 2 patients), meningioma WHO grade 1 in 2 patients, and primary lymphoma of the brain in 1 patient. High-grade gliomas showed significantly higher APTW signals than that low-grade gliomas. APTWmax in one huge parasagittal meningioma infiltrating into the skull bone was higher than that in glioma WHO grade 4. On the other hand, APTWmax in another convexity meningioma was the same as that in glioma WHO grade 3. Diagnosis of primary lymphoma of the brain was possible with APT imaging before pathological confirmation. APTW signals in residual tumors decreased dramatically within one year after adjuvant therapy in all patients. APT imaging demonstrated excellent diagnostic performance for the planning of surgery and adjuvant therapy of brain tumors.

Keywords: amides, magnetic resonance imaging, brain tumors, cell proliferation

Procedia PDF Downloads 87

Authors: Da Tao, Tieyan Wang, Mingfu Qin

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While social robots have been increasingly developed and rapidly applied in our daily life, how robots should interact with humans is still an urgent problem to be explored. Appropriate use of interactive behavior is likely to create a good user experience in human-robot interaction situations, which in turn can improve people’s acceptance of robots. This paper aimed to systematically and quantitatively examine the effects of several important situational factors (i.e., interaction distance, interaction posture, and feedback style) on user experience during human-robot interaction. A three-factor mixed designed experiment was adopted in this study, where subjects were asked to interact with a social robot in different interaction situations by combinations of varied interaction distance, interaction posture, and feedback style. A set of data on users’ behavioral performance, subjective perceptions, and eye movement measures were tracked and collected, and analyzed by repeated measures analysis of variance. The results showed that the three situational factors showed no effects on behavioral performance in tasks during human-robot interaction. Interaction distance and feedback style yielded significant main effects and interaction effects on the proportion of fixation times. The proportion of fixation times on the robot is higher for negative feedback compared with positive feedback style. While the proportion of fixation times on the robot generally decreased with the increase of the interaction distance, it decreased more under the positive feedback style than under the negative feedback style. In addition, there were significant interaction effects on pupil diameter between interaction distance and posture. As interaction distance increased, mean pupil diameter became smaller in side interaction, while it became larger in frontal interaction. Moreover, the three situation factors had significant interaction effects on user acceptance of the interaction mode. The findings are helpful in the underlying mechanism of user experience in human-robot interaction situations and provide important implications for the design of robot behavioral expression and for optimal strategies to improve user experience during human-robot interaction.

Keywords: social robots, human-robot interaction, interaction posture, interaction distance, feedback style, user experience

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Authors: Elmabruk Laias, Abdalla M. Hanashi, Mohammed Alnas

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Worldwide Interoperability for Microwave Access (WiMAX) became one of the most challenging issues, since it was responsible for distributing available resources of the network among all users this leaded to the demand of constructing and designing high efficient scheduling algorithms in order to improve the network utilization, to increase the network throughput, and to minimize the end-to-end delay. In this study, the proposed algorithm focuses on an efficient mechanism to serve non-real time traffic in congested networks by considering channel status.

Keywords: WiMAX, Quality of Services (QoS), OPNE, Diff-Serv (DS).

Procedia PDF Downloads 286

Authors: Kyong-Ku Yun, Dae-Ae Kim, Kyeo-Re Lee, Kyong Namkung, Seung-Yeon Han

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Micro silica is collected as a by-product of the silicon and ferrosilicon alloy production in electric arc furnace using highly pure quartz, wood chips, coke and the like. It consists of about 85% of silicon which has spherical particles with an average particle size of 150 μm. The bulk density of micro silica varies from 150 to 700kg/m^3 and the fineness ranges from 150,000 to 300,000cm^2/g. An amorphous structure with a high silicon oxide content of micro silica induces an active reaction with calcium hydroxide (Ca(OH)₂) generated by the cement hydrate of a large surface area (about 20 m^² / g), and they are also known to form calcium, silicate, hydrate conjugate (C-S-H). Micro silica tends to act as a filler because of the fine particles and the spherical shape. These particles do not get covered by water and they fit well in the space between the relatively rough cement grains which does not freely fluidize concrete. On the contrary, water demand increases since micro silica particles have a tendency to absorb water because of the large surface area. The overall effect of micro silica depends on the amount of micro silica added with other parameters in the water-(cement + micro silica) ratio, and the availability of superplasticizer. In this research, it was studied on cellular sprayed concrete. This method involves a direct re-production of ready mixed concrete into a high performance at a job site. It could reduce the cost of construction by an adding a cellular and a micro silica into a ready mixed concrete truck in a field. Also, micro silica which is difficult with mixing due to high fineness in the field can be added and dispersed in concrete by increasing the fluidity of ready mixed concrete through the surface activity of cellular. Increased air content is converged to a certain level of air content by spraying and it also produces high-performance concrete by remixing of powders in the process of spraying. As it does not use a field mixing equipment the cost of construction decrease and it can be constructed after installing special spray machine in a commercial pump car. Therefore, use of special equipment is minimized, providing economic feasibility through the utilization of existing equipment. This study was carried out to evaluate a highly reliable method of confirming dispersion through a high performance cellular sprayed concrete. A mixture of 25mm coarse aggregate and river sand was applied to the concrete. In addition, by applying silica fume and foam, silica fume dispersion is confirmed in accordance with foam mixing, and the mean and standard deviation is obtained. Then variation coefficient is calculated to finally evaluate the dispersion. Comparison and analysis of before and after spraying were conducted on the experiment variables of 21L, 35L foam for each 7%, 14% silica fume respectively. Taking foam and silica fume as variables, the experiment proceed. Casting a specimen for each variable, a five-day sample is taken from each specimen for EDS test. In this study, it was examined by an experiment materials, plan and mix design, test methods, and equipment, for the evaluation of dispersion in accordance with micro silica and foam.

Keywords: micro silica, distribution, ready mixed concrete, foam

Procedia PDF Downloads 219