Search results for: edge detection algorithm

Authors: Yan Torres, Fernanda Simoes, Francisco Petrucci-Fonseca, Freddie-Jeanne Richard

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The non-invasive samples are an alternative of collecting genetic samples directly. Non-invasive samples are collected without the manipulation of the animal (e.g., scats, feathers and hairs). Nevertheless, the use of non-invasive samples has some limitations. The main issue is degraded DNA, leading to poorer extraction efficiency and genotyping. Those errors delayed for some years a widespread use of non-invasive genetic information. Possibilities to limit genotyping errors can be done using analysis methods that can assimilate the errors and singularities of non-invasive samples. Genotype matching and population estimation algorithms can be highlighted as important analysis tools that have been adapted to deal with those errors. Although, this recent development of analysis methods there is still a lack of empirical performance comparison of them. A comparison of methods with dataset different in size and structure can be useful for future studies since non-invasive samples are a powerful tool for getting information specially for endangered and rare populations. To compare the analysis methods, four different datasets used were obtained from the Dryad digital repository were used. Three different matching algorithms (Cervus, Colony and Error Tolerant Likelihood Matching - ETLM) are used for matching genotypes and two different ones for population estimation (Capwire and BayesN). The three matching algorithms showed different patterns of results. The ETLM produced less number of unique individuals and recaptures. A similarity in the matched genotypes between Colony and Cervus was observed. That is not a surprise since the similarity between those methods on the likelihood pairwise and clustering algorithms. The matching of ETLM showed almost no similarity with the genotypes that were matched with the other methods. The different cluster algorithm system and error model of ETLM seems to lead to a more criterious selection, although the processing time and interface friendly of ETLM were the worst between the compared methods. The population estimators performed differently regarding the datasets. There was a consensus between the different estimators only for the one dataset. The BayesN showed higher and lower estimations when compared with Capwire. The BayesN does not consider the total number of recaptures like Capwire only the recapture events. So, this makes the estimator sensitive to data heterogeneity. Heterogeneity in the sense means different capture rates between individuals. In those examples, the tolerance for homogeneity seems to be crucial for BayesN work properly. Both methods are user-friendly and have reasonable processing time. An amplified analysis with simulated genotype data can clarify the sensibility of the algorithms. The present comparison of the matching methods indicates that Colony seems to be more appropriated for general use considering a time/interface/robustness balance. The heterogeneity of the recaptures affected strongly the BayesN estimations, leading to over and underestimations population numbers. Capwire is then advisable to general use since it performs better in a wide range of situations.

Keywords: algorithms, genetics, matching, population

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Authors: N. Rambabu, H. Gururaj, Reynold Washington, Oommen George

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Motivation: Under the USAID Strengthening Health Outcomes through Private Sector (SHOPS-TB) initiative, Karnataka Health Promotion Trust (KHPT) with technical support of Abt associates is implementing a TB prevention and care model in Karnataka State, South India. KHPT is the interface agency between the public and private sectors, and providers and the target community facilitating early TB case detection and enhancing treatment compliance through private health care providers (pHCP) engagement in RNTCP. The project coverage is 0.84 million urban poor from 663 slums in 12 districts of Karnataka. Problem Statement: India with the highest burden of global TB (26%) and two million cases annually, accounts for approximately one fifth of the global incidence. WHO estimates 300,000 people die from TB annually in India. India expanded the coverage of Directly Observed Treatment, Short-course chemotherapy (DOTS) to the entire country as early as 2006. However, the performance of RNTCP has not been uniform across states. While the national annual new smear-positive (NSP) case notification rate is 53, it is much lower at 47 in Karnataka. A third of TB patients in India reside in urban slums. Approach: Under SHOPS, KHPT actively engages with communities through key opinion leaders and community structures. Interpersonal communication, by Outreach workers through house-to-house visits and at aggregation points, is the primary method used for communication about TB and its management and to increase demand for sputum examination and DOTS. pHCP are mapped, trained and mentored by KHPT. ORWs also provide patient and family counseling on TB treatment, side effects and adherence, screen close contacts of index patients especially children under 6 years of age and screen co-morbidities including HIV, diabetes and malnutrition and risk factors including alcoholism, tobacco use, occupational hazards making appropriate accompanied or documented referrals. A treatment ‘buddy’ system for the patients involving close friends or family members, ICT-based support, DOTS Prerana (inspiration) groups of TB patients, family members and community, DOTS Mitra (friend) helpline services are also used for care and support services. Results: The intervention educated 39988 slum dwellers, referred 1731 chest symptomatics, tested 1061 patients and initiated 248 patients on anti-TB treatment within three months of intervention through continuous community engagement. Conclusions: The intervention’s potential to increase access to preferred health care providers, reduce patient and health system delays in diagnosis and initiation of treatment, improve health seeking behaviour and enhance compliance of pHCPs to standard treatment protocols is being monitored. Initial results are promising.

Keywords: DOTS, KHPT, health outcomes, public and private sector

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Authors: Kseniya Gladun

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Tactile stimulation of a dorsal side of the wrist can have a strong impact on our attitude toward physical objects such as pleasant and unpleasant impact. This study explored different aspects of tactile perception to investigate atypical touch sensitivity in children with autism spectrum disorder (ASD). This study included 40 children with ASD and 40 healthy children aged 5 to 9 years. We recorded rsEEG (sampling rate of 250 Hz) during 20 min using EEG amplifier “Encephalan” (Medicom MTD, Taganrog, Russian Federation) with 19 AgCl electrodes placed according to the International 10–20 System. The electrodes placed on the left, and right mastoids served as joint references under unipolar montage. The registration of EEG v19 assignments was carried out: frontal (Fp1-Fp2; F3-F4), temporal anterior (T3-T4), temporal posterior (T5-T6), parietal (P3-P4), occipital (O1-O2). Subjects were passively touched by 4 types of tactile stimuli on the left wrist. Our stimuli were presented with a velocity of about 3–5 cm per sec. The stimuli materials and procedure were chosen for being the most "pleasant," "rough," "prickly" and "recognizable". Type of tactile stimulation: Soft cosmetic brush - "pleasant" , Rough shoe brush - "rough", Wartenberg pin wheel roller - "prickly", and the cognitive tactile stimulation included letters by finger (most of the patient’s name ) "recognizable". To designate the moments of the stimuli onset-offset, we marked the moment when the moment of the touch began and ended; the stimulation was manual, and synchronization was not precise enough for event-related measures. EEG epochs were cleaned from eye movements by ICA-based algorithm in EEGLAB plugin for MatLab 7.11.0 (Mathwork Inc.). Muscle artifacts were cut out by manual data inspection. The response to tactile stimuli was significantly different in the group of children with ASD and healthy children, which was also depended on type of tactile stimuli and the severity of ASD. Amplitude of Alpha rhythm increased in parietal region to response for only pleasant stimulus, for another type of stimulus ("rough," "thorny", "recognizable") distinction of amplitude was not observed. Correlation dimension D2 was higher in healthy children compared to children with ASD (main effect ANOVA). In ASD group D2 was lower for pleasant and unpleasant compared to the background in the right parietal area. Hilbert transform changes in the frequency of the theta rhythm found only for a rough tactile stimulation compared with healthy participants only in the right parietal area. Children with autism spectrum disorders and healthy children were responded to tactile stimulation differently with specific frequency distribution alpha and theta band in the right parietal area. Thus, our data supports the hypothesis that rsEEG may serve as a sensitive index of altered neural activity caused by ASD. Children with autism have difficulty in distinguishing the emotional stimuli ("pleasant," "rough," "prickly" and "recognizable").

Keywords: autism, tactile stimulation, Hilbert transform, pediatric electroencephalography

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Authors: Elvis Medina, Alejandro Karelovic, Romel Jiménez

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Catalytic ammonia decomposition represents an attractive alternative due to its high H₂ content (17.8% w/w), a product stream free of COₓ, among others; however, challenges need to be addressed for its consolidation as an H₂ chemical storage technology, especially, those focused on the synthesis of efficient bimetallic catalytic systems, as an alternative to the price and scarcity of ruthenium, the most active catalyst reported. In this sense, from the perspective of rational catalyst design, adjusting the main catalytic activity descriptor, a screening of supported catalysts with different compositional settings of cobalt-molybdenum metals is presented to evaluate their effect on the catalytic decomposition rate of ammonia. Subsequently, a kinetic study on the supported monometallic Co and Mo catalysts, as well as on the bimetallic CoMo catalyst with the highest activity is shown. The synthesis of catalysts supported on γ-alumina was carried out using the Charge Enhanced Dry Impregnation (CEDI) method, all with a 5% w/w loading metal. Seeking to maintain uniform dispersion, the catalysts were oxidized and activated (In-situ activation) using a flow of anhydrous air and hydrogen, respectively, under the same conditions: 40 ml min⁻¹ and 5 °C min⁻¹ from room temperature to 600 °C. Catalytic tests were carried out in a fixed-bed reactor, confirming the absence of transport limitations, as well as an Approach to equilibrium (< 1 x 10⁻⁴). The reaction rate on all catalysts was measured between 400 and 500 ºC at 53.09 kPa NH3. The synergy theoretically (DFT) reported for bimetallic catalysts was confirmed experimentally. Specifically, it was observed that the catalyst composed mainly of 75 mol% cobalt proved to be the most active in the experiments, followed by the monometallic cobalt and molybdenum catalysts, in this order of activity as referred to in the literature. A kinetic study was performed at 10.13 – 101.32 kPa NH3 and at four equidistant temperatures between 437 and 475 °C the data were adjusted to an LHHW-type model, which considered the desorption of nitrogen atoms from the active phase surface as the rate determining step (RDS). The regression analysis were carried out under an integral regime, using a minimization algorithm based on SLSQP. The physical meaning of the parameters adjusted in the kinetic model, such as the RDS rate constant (k₅) and the lumped adsorption constant of the quasi-equilibrated steps (α) was confirmed through their Arrhenius and Van't Hoff-type behavior (R² > 0.98), respectively. From an energetic perspective, the activation energy for cobalt, cobalt-molybdenum, and molybdenum was 115.2, 106.8, and 177.5 kJ mol⁻¹, respectively. With this evidence and considering the volcano shape described by the ammonia decomposition rate in relation to the metal composition ratio, the synergistic behavior of the system is clearly observed. However, since characterizations by XRD and TEM were inconclusive, the formation of intermetallic compounds should be still verified using HRTEM-EDS. From this point onwards, our objective is to incorporate parameters into the kinetic expressions that consider both compositional and structural elements and explore how these can maximize or influence H₂ production.

Keywords: CEDI, hydrogen carrier, LHHW, RDS

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Authors: Ewa Bednorz, Marek Polrolniczak, Bartosz Czernecki, Arkadiusz Marek Tomczyk

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This study is meant as a contribution to the research of the upwelling phenomenon, which is one of the most pronounced examples of the sea-atmosphere coupling. The aim is to confirm the atmospheric forcing of the sea waters circulation and sea surface temperature along the variously oriented Baltic Sea coasts and to find out macroscale and regional circulation patterns triggering upwelling along different sections of this relatively small and semi-closed sea basin. The mean daily sea surface temperature data from the summer seasons (June–August) of the years 1982–2017 made the basis for the detection of upwelling cases. For the atmospheric part of the analysis, monthly indices of the Northern Hemisphere macroscale circulation patterns were used. Besides, in order to identify the local direction of airflow, the daily zonal and meridional regional circulation indices were constructed and introduced to the analysis. Finally, daily regional circulation patterns over the Baltic Sea region were distinguished by applying the principal component analysis to the gridded mean daily sea level pressure data. Within the Baltic Sea, upwelling is the most frequent along the zonally oriented northern coast of the Gulf of Finland, southern coasts of Sweden, and along the middle part of the western Gulf of Bothnia coast. Among the macroscale circulation patterns, the Scandinavian type (SCAND), with a primary circulation center located over Scandinavia, has the strongest impact on the horizontal flow of surface sea waters in the Baltic Sea, which triggers upwelling. An anticyclone center over Scandinavia in the positive phase of SCAND enhances the eastern airflow, which increases upwelling frequency along southeastern Baltic coasts. It was proved in the study that the zonal circulation has a stronger impact on upwelling occurrence than the meridional one, and it could increase/decrease a chance of upwelling formation by more than 70% in some coastal sections. Positive and negative phases of six distinguished regional daily circulation patterns made 12 different synoptic situations which were analyzed in the terms of their influence on the upwelling formation. Each of them revealed some impact on the frequency of upwelling in some coastal section of the Baltic Sea; however, two kinds of synoptic situations seemed to have the strongest influence, namely, the first kind representing pressure patterns enhancing the zonal flow and the second kind representing synoptic patterns with a cyclone/anticyclone centers over southern Scandinavia. Upwelling occurrence appeared to be particularly strongly reliant on the atmospheric conditions in some specific coastal sections, namely: the Gulf of Finland, the south eastern Baltic coasts (Polish and Latvian-Lithuanian section), and the western part of the Gulf of Bothnia. Concluding, it can be stated that atmospheric conditions strongly control the occurrence of upwelling within the Baltic Sea basin. Both local and macroscale circulation patterns expressed by the location of the pressure centers influence the frequency of this phenomenon; however, the impact strength varies, depending on the coastal region. Acknowledgment: This research was funded by the National Science Centre, Poland, grant number 2016/21/B/ST10/01440.

Keywords: Baltic Sea, circulation patterns, coastal upwelling, synoptic conditions

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Authors: Ibtissem Hosni, Lilia Bennaceur Farah, Saber Mohamed Naceur

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In order to assess the water availability in the soil, it is crucial to have information about soil distributed moisture content; this parameter helps to understand the effect of humidity on the exchange between soil, plant cover and atmosphere in addition to fully understanding the surface processes and the hydrological cycle. On the other hand, aerodynamic roughness length is a surface parameter that scales the vertical profile of the horizontal component of the wind speed and characterizes the surface ability to absorb the momentum of the airflow. In numerous applications of the surface hydrology and meteorology, aerodynamic roughness length is an important parameter for estimating momentum, heat and mass exchange between the soil surface and atmosphere. It is important on this side, to consider the atmosphere factors impact in general, and the natural erosion in particular, in the process of soil evolution and its characterization and prediction of its physical parameters. The study of the induced movements by the wind over soil vegetated surface, either spaced plants or plant cover, is motivated by significant research efforts in agronomy and biology. The known major problem in this side concerns crop damage by wind, which presents a booming field of research. Obviously, most models of soil surface require information about the aerodynamic roughness length and its temporal and spatial variability. We have used a bi-dimensional multi-scale (2D MLS) roughness description where the surface is considered as a superposition of a finite number of one-dimensional Gaussian processes each one having a spatial scale using the wavelet transform and the Mallat algorithm to describe natural surface roughness. We have introduced multi-layer aspect of the humidity of the soil surface, to take into account a volume component in the problem of backscattering radar signal. As humidity increases, the dielectric constant of the soil-water mixture increases and this change is detected by microwave sensors. Nevertheless, many existing models in the field of radar imagery, cannot be applied directly on areas covered with vegetation due to the vegetation backscattering. Thus, the radar response corresponds to the combined signature of the vegetation layer and the layer of soil surface. Therefore, the key issue of the numerical estimation of soil moisture is to separate the two contributions and calculate both scattering behaviors of the two layers by defining the scattering of the vegetation and the soil blow. This paper presents a synergistic methodology, and it is for estimating roughness and soil moisture from C-band radar measurements. The methodology adequately represents a microwave/optical model which has been used to calculate the scattering behavior of the aerodynamic vegetation-covered area by defining the scattering of the vegetation and the soil below.

Keywords: aerodynamic, bi-dimensional, vegetation, synergistic

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Authors: Yan Zhang

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Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Analytics-driven predictive maintenance is gaining increasing attention in many industries such as manufacturing, utilities, aerospace, etc., along with the emerging demand of Internet of Things (IoT) applications and the maturity of technologies that support Big Data storage and processing. This study aims to build an end-to-end analytics solution that includes both real-time machine condition monitoring and machine learning based predictive analytics capabilities. The goal is to showcase a general predictive maintenance solution architecture, which suggests how the data generated from field machines can be collected, transmitted, stored, and analyzed. We use a publicly available aircraft engine run-to-failure dataset to illustrate the streaming analytics component and the batch failure prediction component. We outline the contributions of this study from four aspects. First, we compare the predictive maintenance problems from the view of the traditional reliability centered maintenance field, and from the view of the IoT applications. When evolving to the IoT era, predictive maintenance has shifted its focus from ensuring reliable machine operations to improve production/maintenance efficiency via any maintenance related tasks. It covers a variety of topics, including but not limited to: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure. Second, we review the state-of-art technologies that enable a machine/device to transmit data all the way through the Cloud for storage and advanced analytics. These technologies vary drastically mainly based on the power source and functionality of the devices. For example, a consumer machine such as an elevator uses completely different data transmission protocols comparing to the sensor units in an environmental sensor network. The former may transfer data into the Cloud via WiFi directly. The latter usually uses radio communication inherent the network, and the data is stored in a staging data node before it can be transmitted into the Cloud when necessary. Third, we illustrate show to formulate a machine learning problem to predict machine fault/failures. By showing a step-by-step process of data labeling, feature engineering, model construction and evaluation, we share following experiences: (1) what are the specific data quality issues that have crucial impact on predictive maintenance use cases; (2) how to train and evaluate a model when training data contains inter-dependent records. Four, we review the tools available to build such a data pipeline that digests the data and produce insights. We show the tools we use including data injection, streaming data processing, machine learning model training, and the tool that coordinates/schedules different jobs. In addition, we show the visualization tool that creates rich data visualizations for both real-time insights and prediction results. To conclude, there are two key takeaways from this study. (1) It summarizes the landscape and challenges of predictive maintenance applications. (2) It takes an example in aerospace with publicly available data to illustrate each component in the proposed data pipeline and showcases how the solution can be deployed as a live demo.

Keywords: Internet of Things, machine learning, predictive maintenance, streaming data

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Authors: G. Singer, M. Golan

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Providing students with learning disabilities (LD) with extra time to grant them equal access to the exam is a necessary but insufficient condition to compensate for their LD; there should also be a clear indication that the additional time was actually used. For example, if students with LD use more time than students without LD and yet receive lower grades, this may indicate that a different accommodation is required. If they achieve higher grades but use the same amount of time, then the effectiveness of the accommodation has not been demonstrated. The main goal of this study is to evaluate the effect of including parameters related to LD and extended exam time, along with other commonly-used characteristics (e.g., student background and ability measures such as high-school grades), on the ability of ordinal decision-tree algorithms to predict exam performance. We use naturally-occurring data collected from hundreds of undergraduate engineering students. The sub-goals are i) to examine the improvement in prediction accuracy when the indicator of exam performance includes 'actual time used' in addition to the conventional indicator (exam grade) employed in most research; ii) to explore the effectiveness of extended exam time on exam performance for different courses and for LD students with different profiles (i.e., sets of characteristics). This is achieved by using the patterns (i.e., subgroups) generated by the algorithms to identify pairs of subgroups that differ in just one characteristic (e.g., course or type of LD) but have different outcomes in terms of exam performance (grade and time used). Since grade and time used to exhibit an ordering form, we propose a method based on ordinal decision-trees, which applies a weighted information-gain ratio (WIGR) measure for selecting the classifying attributes. Unlike other known ordinal algorithms, our method does not assume monotonicity in the data. The proposed WIGR is an extension of an information-theoretic measure, in the sense that it adjusts to the case of an ordinal target and takes into account the error severity between two different target classes. Specifically, we use ordinal C4.5, random-forest, and AdaBoost algorithms, as well as an ensemble technique composed of ordinal and non-ordinal classifiers. Firstly, we find that the inclusion of LD and extended exam-time parameters improves prediction of exam performance (compared to specifications of the algorithms that do not include these variables). Secondly, when the indicator of exam performance includes 'actual time used' together with grade (as opposed to grade only), the prediction accuracy improves. Thirdly, our subgroup analyses show clear differences in the effect of extended exam time on exam performance among different courses and different student profiles. From a methodological perspective, we find that the ordinal decision-tree based algorithms outperform their conventional, non-ordinal counterparts. Further, we demonstrate that the ensemble-based approach leverages the strengths of each type of classifier (ordinal and non-ordinal) and yields better performance than each classifier individually.

Keywords: actual exam time usage, ensemble learning, learning disabilities, ordinal classification, time extension

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Authors: Pavel Bulai, Taras Pitlik, Tatsiana Kulahava, Timofei Lipski

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The method of Electrocardiography (ECG) interpretation for post-exercise recovery tracking was developed. Metabolic indices (aerobic and anaerobic) were designed using ECG-derived features. This study reports the associations between aerobic and anaerobic indices and classical parameters of the person’s physiological state, including blood biochemistry, glycogen concentration and VO2max changes. During the study 9 participants, healthy, physically active medium trained men and women, which trained 2-4 times per week for at least 9 weeks, fulfilled (i) ECG monitoring using Apple Watch Series 4 (AWS4); (ii) blood biochemical analysis; (iii) maximal oxygen consumption (VO2max) test, (iv) bioimpedance analysis (BIA). ECG signals from a single-lead wrist-wearable device were processed with detection of QRS-complex. Aerobic index (AI) was derived as the normalized slope of QR segment. Anaerobic index (ANI) was derived as the normalized slope of SJ segment. Biochemical parameters, glycogen content and VO2max were evaluated eight times within 3-60 hours after training. ECGs were recorded 5 times per day, plus before and after training, cycloergometry and BIA. The negative correlation between AI and blood markers of the muscles functional status including creatine phosphokinase (r=-0.238, p < 0.008), aspartate aminotransferase (r=-0.249, p < 0.004) and uric acid (r = -0.293, p<0.004) were observed. ANI was also correlated with creatine phosphokinase (r= -0.265, p < 0.003), aspartate aminotransferase (r = -0.292, p < 0.001), lactate dehydrogenase (LDH) (r = -0.190, p < 0.050). So, when the level of muscular enzymes increases during post-exercise fatigue, AI and ANI decrease. During recovery, the level of metabolites is restored, and metabolic indices rising is registered. It can be concluded that AI and ANI adequately reflect the physiology of the muscles during recovery. One of the markers of an athlete’s physiological state is the ratio between testosterone and cortisol (TCR). TCR provides a relative indication of anabolic-catabolic balance and is considered to be more sensitive to training stress than measuring testosterone and cortisol separately. AI shows a strong negative correlation with TCR (r=-0.437, p < 0.001) and correctly represents post-exercise physiology. In order to reveal the relation between the ECG-derived metabolic indices and the state of the cardiorespiratory system, direct measurements of VO2max were carried out at various time points after training sessions. The negative correlation between AI and VO2max (r = -0.342, p < 0.001) was obtained. These data testifying VO2max rising during fatigue are controversial. However, some studies have revealed increased stroke volume after training, that agrees with findings. It is important to note that post-exercise increase in VO2max does not mean an athlete’s readiness for the next training session, because the recovery of the cardiovascular system occurs over a substantially longer period. Negative correlations registered for ANI with glycogen (r = -0.303, p < 0.001), albumin (r = -0.205, p < 0.021) and creatinine (r = -0.268, p < 0.002) reflect the dehydration status of participants after training. Correlations between designed metabolic indices and physiological parameters revealed in this study can be considered as the sufficient evidence to use these indices for assessing the state of person’s aerobic and anaerobic metabolic systems after training during fatigue, recovery and supercompensation.

Keywords: aerobic index, anaerobic index, electrocardiography, supercompensation

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Authors: Shenlun Chen, Leonard Wee

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Tumor grading is an essential reference for colorectal cancer (CRC) staging and survival prognostication. The widely used World Health Organization (WHO) grading system defines histological grade of CRC adenocarcinoma based on the density of glandular formation on whole slide images (WSI). Tumors are classified as well-, moderately-, poorly- or un-differentiated depending on the percentage of the tumor that is gland forming; >95%, 50-95%, 5-50% and <5%, respectively. However, manually grading WSIs is a time-consuming process and can cause observer error due to subjective judgment and unnoticed regions. Furthermore, pathologists’ grading is usually coarse while a finer and continuous differentiation grade may help to stratifying CRC patients better. In this study, a deep learning based automatic differentiation grading algorithm was developed and evaluated by survival analysis. Firstly, a gland segmentation model was developed for segmenting gland structures. Gland regions of WSIs were delineated and used for differentiation annotating. Tumor regions were annotated by experienced pathologists into high-, medium-, low-differentiation and normal tissue, which correspond to tumor with clear-, unclear-, no-gland structure and non-tumor, respectively. Then a differentiation prediction model was developed on these human annotations. Finally, all enrolled WSIs were processed by gland segmentation model and differentiation prediction model. The differentiation grade can be calculated by deep learning models’ prediction of tumor regions and tumor differentiation status according to WHO’s defines. If multiple WSIs were possessed by a patient, the highest differentiation grade was chosen. Additionally, the differentiation grade was normalized into scale between 0 to 1. The Cancer Genome Atlas, project COAD (TCGA-COAD) project was enrolled into this study. For the gland segmentation model, receiver operating characteristic (ROC) reached 0.981 and accuracy reached 0.932 in validation set. For the differentiation prediction model, ROC reached 0.983, 0.963, 0.963, 0.981 and accuracy reached 0.880, 0.923, 0.668, 0.881 for groups of low-, medium-, high-differentiation and normal tissue in validation set. Four hundred and one patients were selected after removing WSIs without gland regions and patients without follow up data. The concordance index reached to 0.609. Optimized cut off point of 51% was found by “Maxstat” method which was almost the same as WHO system’s cut off point of 50%. Both WHO system’s cut off point and optimized cut off point performed impressively in Kaplan-Meier curves and both p value of logrank test were below 0.005. In this study, gland structure of WSIs and differentiation status of tumor regions were proven to be predictable through deep leaning method. A finer and continuous differentiation grade can also be automatically calculated through above models. The differentiation grade was proven to stratify CAC patients well in survival analysis, whose optimized cut off point was almost the same as WHO tumor grading system. The tool of automatically calculating differentiation grade may show potential in field of therapy decision making and personalized treatment.

Keywords: colorectal cancer, differentiation, survival analysis, tumor grading

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Authors: Valentin Père, Mathieu Milhé, Fabien Baillon, Jean-Louis Dirion

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Microgrids offer potential solutions to meet the need for local grid stability and increase isolated networks autonomy with the integration of intermittent renewable energy production and storage facilities. In such a context, sizing production and storage for a given network is a complex task, highly depending on input data such as power load profile and renewable resource availability. This work aims at developing an operating cost computation methodology for different microgrid designs based on the use of deep reinforcement learning (RL) algorithms to tackle the optimal operation problem in stochastic environments. RL is a data-based sequential decision control method based on Markov decision processes that enable the consideration of random variables for control at a chosen time scale. Agents trained via RL constitute a promising class of Energy Management Systems (EMS) for the operation of microgrids with energy storage. Microgrid sizing (or design) is generally performed by minimizing investment costs and operational costs arising from the EMS behavior. The latter might include economic aspects (power purchase, facilities aging), social aspects (load curtailment), and ecological aspects (carbon emissions). Sizing variables are related to major constraints on the optimal operation of the network by the EMS. In this work, an islanded mode microgrid is considered. Renewable generation is done with photovoltaic panels; an electrochemical battery ensures short-term electricity storage. The controllable unit is a hydrogen tank that is used as a long-term storage unit. The proposed approach focus on the transfer of agent learning for the near-optimal operating cost approximation with deep RL for each microgrid size. Like most data-based algorithms, the training step in RL leads to important computer time. The objective of this work is thus to study the potential of Batch-Constrained Q-learning (BCQ) for the optimal sizing of microgrids and especially to reduce the computation time of operating cost estimation in several microgrid configurations. BCQ is an off-line RL algorithm that is known to be data efficient and can learn better policies than on-line RL algorithms on the same buffer. The general idea is to use the learned policy of agents trained in similar environments to constitute a buffer. The latter is used to train BCQ, and thus the agent learning can be performed without update during interaction sampling. A comparison between online RL and the presented method is performed based on the score by environment and on the computation time.

Keywords: batch-constrained reinforcement learning, control, design, optimal

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Authors: Caixia Gao, Chuanrong Li, Lingli Tang, Lingling Ma, Yonggang Qian, Ning Wang

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Knowledge of land surface temperature (LST) is of crucial important in energy balance studies and environment modeling. Satellite thermal infrared (TIR) imagery is the primary source for retrieving LST at the regional and global scales. Due to the combination of atmosphere and land surface of received radiance by TIR sensors, atmospheric effect correction has to be performed to remove the atmospheric transmittance and upwelling radiance. Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG) provides measurements every 15 minutes in 12 spectral channels covering from visible to infrared spectrum at fixed view angles with 3km pixel size at nadir, offering new and unique capabilities for LST, LSE measurements. However, due to its high temporal resolution, the atmosphere correction could not be performed with radiosonde profiles or reanalysis data since these profiles are not available at all SEVIRI TIR image acquisition times. To solve this problem, a two-part six-parameter semi-empirical diurnal temperature cycle (DTC) model has been applied to the temporal interpolation of ECMWF reanalysis data. Due to the fact that the DTC model is underdetermined with ECMWF data at four synoptic times (UTC times: 00:00, 06:00, 12:00, 18:00) in one day for each location, some approaches are adopted in this study. It is well known that the atmospheric transmittance and upwelling radiance has a relationship with water vapour content (WVC). With the aid of simulated data, the relationship could be determined under each viewing zenith angle for each SEVIRI TIR channel. Thus, the atmospheric transmittance and upwelling radiance are preliminary removed with the aid of instantaneous WVC, which is retrieved from the brightness temperature in the SEVIRI channels 5, 9 and 10, and a group of the brightness temperatures for surface leaving radiance (Tg) are acquired. Subsequently, a group of the six parameters of the DTC model is fitted with these Tg by a Levenberg-Marquardt least squares algorithm (denoted as DTC model 1). Although the retrieval error of WVC and the approximate relationships between WVC and atmospheric parameters would induce some uncertainties, this would not significantly affect the determination of the three parameters, td, ts and β (β is the angular frequency, td is the time where the Tg reaches its maximum, ts is the starting time of attenuation) in DTC model. Furthermore, due to the large fluctuation in temperature and the inaccuracy of the DTC model around sunrise, SEVIRI measurements from two hours before sunrise to two hours after sunrise are excluded. With the knowledge of td , ts, and β, a new DTC model (denoted as DTC model 2) is accurately fitted again with these Tg at UTC times: 05:57, 11:57, 17:57 and 23:57, which is atmospherically corrected with ECMWF data. And then a new group of the six parameters of the DTC model is generated and subsequently, the Tg at any given times are acquired. Finally, this method is applied to SEVIRI data in channel 9 successfully. The result shows that the proposed method could be performed reasonably without assumption and the Tg derived with the improved method is much more consistent with that from radiosonde measurements.

Keywords: atmosphere correction, diurnal temperature cycle model, land surface temperature, SEVIRI

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Authors: Faheema Khan

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To investigate the ability of sensitive and tolerant genotype of Glycine max to adapt to a saline environment in a field, we examined the growth performance, water relation and activities of antioxidant enzymes in relation to photosynthetic rate, chlorophyll a fluorescence, photosynthetic pigment concentration, protein and proline in plants exposed to salt stress. Ten soybean genotypes (Pusa-20, Pusa-40, Pusa-37, Pusa-16, Pusa-24, Pusa-22, BRAGG, PK-416, PK-1042, and DS-9712) were selected and grown hydroponically. After 3 days of proper germination, the seedlings were transferred to Hoagland’s solution (Hoagland and Arnon 1950). The growth chamber was maintained at a photosynthetic photon flux density of 430 μmol m−2 s−1, 14 h of light, 10 h of dark and a relative humidity of 60%. The nutrient solution was bubbled with sterile air and changed on alternate days. Ten-day-old seedlings were given seven levels of salt in the form of NaCl viz., T1 = 0 mM NaCl, T2=25 mM NaCl, T3=50 mM NaCl, T4=75 mM NaCl, T5=100 mM NaCl, T6=125 mM NaCl, T7=150 mM NaCl. The investigation showed that genotype Pusa-24, PK-416 and Pusa-20 appeared to be the most salt-sensitive. genotypes as inferred from their significantly reduced length, fresh weight and dry weight in response to the NaCl exposure. Pusa-37 appeared to be the most tolerant genotype since no significant effect of NaCl treatment on growth was found. We observed a greater decline in the photosynthetic variables like photosynthetic rate, chlorophyll fluorescence and chlorophyll content, in salt-sensitive (Pusa-24) genotype than in salt-tolerant Pusa-37 under high salinity. Numerous primers were verified on ten soybean genotypes obtained from Operon technologies among which 30 RAPD primers shown high polymorphism and genetic variation. The Jaccard’s similarity coefficient values for each pairwise comparison between cultivars were calculated and similarity coefficient matrix was constructed. The closer varieties in the cluster behaved similar in their response to salinity tolerance. Intra-clustering within the two clusters precisely grouped the 10 genotypes in sub-cluster as expected from their physiological findings.Salt tolerant genotype Pusa-37, was further analysed by 2-Dimensional gel electrophoresis to analyse the differential expression of proteins at high salt stress. In the Present study, 173 protein spots were identified. Of these, 40 proteins responsive to salinity were either up- or down-regulated in Pusa-37. Proteomic analysis in salt-tolerant genotype (Pusa-37) led to the detection of proteins involved in a variety of biological processes, such as protein synthesis (12 %), redox regulation (19 %), primary and secondary metabolism (25 %), or disease- and defence-related processes (32 %). In conclusion, the soybean plants in our study responded to salt stress by changing their protein expression pattern. The photosynthetic, biochemical and molecular study showed that there is variability in salt tolerance behaviour in soybean genotypes. Pusa-24 is the salt-sensitive and Pusa-37 is the salt-tolerant genotype. Moreover this study gives new insights into the salt-stress response in soybean and demonstrates the power of genomic and proteomic approach in plant biology studies which finally could help us in identifying the possible regulatory switches (gene/s) controlling the salt tolerant genotype of the crop plants and their possible role in defence mechanism.

Keywords: glycine max, salt stress, RAPD, genomic and proteomic variability

Procedia PDF Downloads 420

Authors: Massimiliano Biagini, Marco Spinsanti, Gabriella De Angelis, Sara Tomei, Ilaria Ferlenghi, Maria Scarselli, Alessia Biolchi, Alessandro Muzzi, Brunella Brunelli, Silvana Savino, Marzia M. Giuliani, Isabel Delany, Paolo Costantino, Rino Rappuoli, Vega Masignani, Nathalie Norais

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The gram-negative bacterium Neisseria meningitidis serogroup B (MenB) is an exclusively human pathogen representing the major cause of meningitides and severe sepsis in infants and children but also in young adults. This pathogen is usually present in the 30% of healthy population that act as a reservoir, spreading it through saliva and respiratory fluids during coughing, sneezing, kissing. Among surface-exposed protein components of this diplococcus, factor H binding protein is a lipoprotein proved to be a protective antigen used as a component of the recently licensed Bexsero vaccine. fHbp is a highly variable meningococcal protein: to reflect its remarkable sequence variability, it has been classified in three variants (or two subfamilies), and with poor cross-protection among the different variants. Furthermore, the level of fHbp expression varies significantly among strains, and this has also been considered an important factor for predicting MenB strain susceptibility to anti-fHbp antisera. Different methods have been used to assess fHbp expression on meningococcal strains, however, all these methods use anti-fHbp antibodies, and for this reason, the results are affected by the different affinity that antibodies can have to different antigenic variants. To overcome the limitations of an antibody-based quantification, we developed a quantitative Mass Spectrometry (MS) approach. Selected Reaction Monitoring (SRM) recently emerged as a powerful MS tool for detecting and quantifying proteins in complex mixtures. SRM is based on the targeted detection of ProteoTypicPeptides (PTPs), which are unique signatures of a protein that can be easily detected and quantified by MS. This approach, proven to be highly sensitive, quantitatively accurate and highly reproducible, was used to quantify the absolute amount of fHbp antigen in total extracts derived from 105 clinical isolates, evenly distributed among the three main variant groups and selected to be representative of the fHbp circulating subvariants around the world. We extended the study at the genetic level investigating the correlation between the differential level of expression and polymorphisms present within the genes and their promoter sequences. The implications of fHbp expression on the susceptibility of the strain to killing by anti-fHbp antisera are also presented. To date this is the first comprehensive fHbp expression profiling in a large panel of Neisseria meningitidis clinical isolates driven by an antibody-independent MS-based methodology, opening the door to new applications in vaccine coverage prediction and reinforcing the molecular understanding of released vaccines.

Keywords: quantitative mass spectrometry, Neisseria meningitidis, vaccines, bexsero, molecular epidemiology

Procedia PDF Downloads 312

Authors: Andreas Kohlhepp, Gerrit Schatte, Wieland Christoph, Spliethoff Hartmut

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In recent years the research of heat transfer phenomena of water and other working fluids near the critical point experiences a growing interest for power engineering applications. To match the highly volatile characteristics of renewable energies, conventional power plants need to shift towards flexible operation. This requires speeding up the load change dynamics of steam generators and their heating surfaces near the critical point. In dynamic load transients, both a high heat flux with an unfavorable ratio to the mass flux and a high difference in fluid and wall temperatures, may cause problems. It may lead to deteriorated heat transfer (at supercritical pressures), dry-out or departure from nucleate boiling (at subcritical pressures), all cases leading to an extensive rise of temperatures. For relevant technical applications, the heat transfer coefficients need to be predicted correctly in case of transient scenarios to prevent damage to the heated surfaces (membrane walls, tube bundles or fuel rods). In transient processes, the state of the art method of calculating the heat transfer coefficients is using a multitude of different steady-state correlations for the momentarily existing local parameters for each time step. This approach does not necessarily reflect the different cases that may lead to a significant variation of the heat transfer coefficients and shows gaps in the individual ranges of validity. An algorithm was implemented to calculate the transient behavior of steam generators during load changes. It is used to assess existing correlations for transient heat transfer calculations. It is also desirable to validate the calculation using experimental data. By the use of a new full-scale supercritical thermo-hydraulic test rig, experimental data is obtained to describe the transient phenomena under dynamic boundary conditions as mentioned above and to serve for validation of transient steam generator calculations. Aiming to improve correlations for the prediction of the onset of deteriorated heat transfer in both, stationary and transient cases the test rig was specially designed for this task. It is a closed loop design with a directly electrically heated evaporation tube, the total heating power of the evaporator tube and the preheater is 1MW. To allow a big range of parameters, including supercritical pressures, the maximum pressure rating is 380 bar. The measurements contain the most important extrinsic thermo-hydraulic parameters. Moreover, a high geometric resolution allows to accurately predict the local heat transfer coefficients and fluid enthalpies.

Keywords: departure from nucleate boiling, deteriorated heat transfer, dryout, supercritical working fluid, transient operation of steam generators

Procedia PDF Downloads 218

Authors: Ibrahim Abdulla Labib, Medhat Mohamed Morsy, Gamal Hosny, Hanan Dawood Yassa, Gaber Hassan

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Amiodarone is a very effective drug, widely used for arrhythmia. Unfortunately it has many side effects involving many organs especially thyroid gland. The current work was conducted to elucidate the effect of amiodarone on the thyroid gland and the possible protective role of vitamin E. Fifty adult male albino rats weighed 200 – 250 grams were divided into five groups; ten rats each. Group I (Control): Five rats were sacrificed after three weeks and five rats were sacrificed after six weeks. Group II (Sham control): Each rat received sunflower oil orally; the solvent of vitamin E for three weeks. Group III (Amiodarone-treated): each rat received an oral dose of amiodarone; 150 mg/kg/day for three weeks. Group IV (Recovery): Each rat received amiodarone as group III then the drug was stopped for three weeks to evaluate recovery. Group V (Amiodarone + Vitamin E-treated): Each rat received amiodarone as group III followed by 100 mg/kg/day vitamin E orally for three weeks. Thyroid gland of the sacrificed animals were dissected out and prepared for light and electron microscopic studies. Amiodarone administration resulted in loss of normal follicular architecture as many follicles appeared either shrunken, empty or contained scanty pale colloid. Some follicles appeared lined by more than one layer of cells while others showed interruption of their membrane. Masson's Trichrome stained sections showed increased collagen fibers in between the thyroid follicles. Ultrastructurally, the apical border of the follicular cells showed few irregular detached microvilli. The nuclei of the follicular cells were almost irregular with chromatin condensation. The cytoplasm of most follicular cells revealed numerous dilated rough endoplasmic reticulum with numerous lysosomes. After three weeks of stopping amiodarone, the follicles were nearly regular in outline. Some follicles were filled with homogenous eosinophilic colloid and others had shrunken pale colloid or were empty. Some few follicles showed exfoliated cells in their lumina and others were still lined by more than one layer of follicular cells. Moderate amounts of collagen fibers were observed in-between thyroid follicles. Ultrastructurally, many follicular cells had rounded euchromatic nucleui, moderate number of lysosomes and moderately dilated rough endoplasmic reticulum. However, few follicular cells still showing irregular nucleui, dilated rough endoplasmic reticulum and many cytoplasmic vacuoles. Administration of vitamin E with amiodarone for three weeks resulted in obvious structural improvement. Most of the follicles were lined by a single layer of cuboidal cells and the lumina were filled with homogenous eosinophilic colloid with very few vacuolations. The majority of follicular cells had rounded nuclei with occasional detection of ballooned cells and dark nuclei. Scanty collagen fibers were detected among thyroid follicles. Ultrastructurally, most follicular cells exhibited rounded euchromatic nuclei with few short microvilli were projecting into the colloid. Few lysosomes were also noticed. It was concluded that amiodarone administration leads to many adverse histological changes in the thyroid gland. Some of these changes are reversible during the recovery period however concomitant vitamin E administration with amiodarone has a major protective role in preventing many of these changes.

Keywords: amiodarone, recovery, ultrastructure, vitamin E.

Procedia PDF Downloads 350

Authors: Nicoly Coelho, Eduardo Vieira Vilas Boas, Paulo Orestes Formigoni

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Faced with the remarkable developments in the various segments of modern engineering, provided by the increasing technological development, professionals of all educational areas need to overcome the difficulties generated due to the good understanding of those who are starting their academic journey. Aiming to overcome these difficulties, this article aims at an introduction to the basic study of numerical methods applied to fluid mechanics and thermodynamics, demonstrating the modeling and simulations with its substance, and a detailed explanation of the fundamental numerical solution for the use of finite difference method, using SCILAB, a free software easily accessible as it is free and can be used for any research center or university, anywhere, both in developed and developing countries. It is known that the Computational Fluid Dynamics (CFD) is a necessary tool for engineers and professionals who study fluid mechanics, however, the teaching of this area of knowledge in undergraduate programs faced some difficulties due to software costs and the degree of difficulty of mathematical problems involved in this way the matter is treated only in postgraduate courses. This work aims to bring the use of DFC low cost in teaching Transport Phenomena for graduation analyzing a small classic case of fundamental thermodynamics with Scilab® program. The study starts from the basic theory involving the equation the partial differential equation governing heat transfer problem, implies the need for mastery of students, discretization processes that include the basic principles of series expansion Taylor responsible for generating a system capable of convergence check equations using the concepts of Sassenfeld, finally coming to be solved by Gauss-Seidel method. In this work we demonstrated processes involving both simple problems solved manually, as well as the complex problems that required computer implementation, for which we use a small algorithm with less than 200 lines in Scilab® in heat transfer study of a heated plate in rectangular shape on four sides with different temperatures on either side, producing a two-dimensional transport with colored graphic simulation. With the spread of computer technology, numerous programs have emerged requiring great researcher programming skills. Thinking that this ability to program DFC is the main problem to be overcome, both by students and by researchers, we present in this article a hint of use of programs with less complex interface, thus enabling less difficulty in producing graphical modeling and simulation for DFC with an extension of the programming area of experience for undergraduates.

Keywords: numerical methods, finite difference method, heat transfer, Scilab

Procedia PDF Downloads 385

Authors: Alessandro Evangelista, Vito M. Manghisi, Michele Gattullo, Enricoandrea Laviola

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In this work, we propose a custom visualization technique for Out-Of-View-Objects in Virtual and Augmented Reality applications using Head Mounted Displays. In the last two decades, Augmented Reality (AR) and Virtual Reality (VR) technologies experienced a remarkable growth of applications for navigation, interaction, and collaboration in different types of environments, real or virtual. Both environments can be potentially very complex, as they can include many virtual objects located in different places. Given the natural limitation of the human Field of View (about 210° horizontal and 150° vertical), humans cannot perceive objects outside this angular range. Moreover, despite recent technological advances in AR e VR Head-Mounted Displays (HMDs), these devices still suffer from a limited Field of View, especially regarding Optical See-Through displays, thus greatly amplifying the challenge of visualizing out-of-view objects. This problem is not negligible when the user needs to be aware of the number and the position of the out-of-view objects in the environment. For instance, during a maintenance operation on a construction site where virtual objects serve to improve the dangers' awareness. Providing such information can enhance the comprehension of the scene, enable fast navigation and focused search, and improve users' safety. In our research, we investigated how to represent out-of-view-objects in HMD User Interfaces (UI). Inspired by commercial video games such as Call of Duty Modern Warfare, we designed a customized Compass. By exploiting the Unity 3D graphics engine, we implemented our custom solution that can be used both in AR and VR environments. The Compass Bar consists of a graduated bar (in degrees) at the top center of the UI. The values of the bar range from -180 (far left) to +180 (far right), the zero is placed in front of the user. Two vertical lines on the bar show the amplitude of the user's field of view. Every virtual object within the scene is represented onto the compass bar as a specific color-coded proxy icon (a circular ring with a colored dot at its center). To provide the user with information about the distance, we implemented a specific algorithm that increases the size of the inner dot as the user approaches the virtual object (i.e., when the user reaches the object, the dot fills the ring). This visualization technique for out-of-view objects has some advantages. It allows users to be quickly aware of the number and the position of the virtual objects in the environment. For instance, if the compass bar displays the proxy icon at about +90, users will immediately know that the virtual object is to their right and so on. Furthermore, by having qualitative information about the distance, users can optimize their speed, thus gaining effectiveness in their work. Given the small size and position of the Compass Bar, our solution also helps lessening the occlusion problem thus increasing user acceptance and engagement. As soon as the lockdown measures will allow, we will carry out user-tests comparing this solution with other state-of-the-art existing ones such as 3D Radar, SidebARs and EyeSee360.

Keywords: augmented reality, situation awareness, virtual reality, visualization design

Procedia PDF Downloads 126

Authors: Yi H. Wong, Li L. Chan, Chee O. Leong, Stephen Ambu, Joon W. Mak, Priyadashi S. Sahu

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Background: Acanthamoeba is a free-living opportunistic protist which is ubiquitously distributed in the environment. Virulent Acanthamoeba can cause fatal encephalitis in immunocompromised patients and potential blinding keratitis in immunocompetent contact lens wearers. Approximately 24 species have been identified but only the A. castellanii, A. polyphaga and A. culbertsoni are commonly associated with human infections. Until to date, the precise molecular basis for Acanthamoeba pathogenesis remains unclear. Previous studies reported that Acanthamoeba virulence can be diminished through prolonged axenic culture but revived through serial mouse passages. As no clear explanation on this reversible pathogenesis is established, hereby, we postulate that the epigenetic regulators, DNA-methyltransferases (DNMT) and histone-deacetylases (HDAC), could possibly be involved in granting the virulence plasticity of Acanthamoeba spp. Methods: Four rounds of mouse passages were conducted to revive the virulence potential of the virulence-attenuated Acanthamoeba castellanii strain (ATCC 50492). Briefly, each mouse (n=6/group) was inoculated intraperitoneally with Acanthamoebae cells (2x 105 trophozoites/mouse) and incubated for 2 months. Acanthamoebae cells were isolated from infected mouse organs by culture method and subjected to subsequent mouse passage. In vitro cytopathic, encystment and gelatinolytic assays were conducted to evaluate the virulence characteristics of Acanthamoebae isolates for each passage. PCR primers which targeted on the 2 members (DNMT1 and DNMT2) and 5 members (HDAC1 to 5) of the DNMT and HDAC gene families respectively were custom designed. Quantitative real-time PCR (qPCR) was performed to detect and quantify the relative expression of the two gene families in each Acanthamoeba isolates. Beta-tubulin of A. castellanii (Genbank accession no: XP_004353728) was included as housekeeping gene for data normalisation. PCR mixtures were also analyzed by electrophoresis for amplicons detection. All statistical analyses were performed using the paired one-tailed Student’s t test. Results: Our pathogenicity tests showed that the virulence-reactivated Acanthamoeba had a higher degree of cytopathic effect on vero cells, a better resistance to encystment challenge and a higher gelatinolytic activity which was catalysed by serine protease. qPCR assay showed that DNMT1 expression was significantly higher in the virulence-reactivated compared to the virulence-attenuated Acanthamoeba strain (p ≤ 0.01). The specificity of primers which targeted on DNMT1 was confirmed by sequence analysis of PCR amplicons, which showed a 97% similarity to the published DNA-methyltransferase gene of A. castellanii (GenBank accession no: XM_004332804.1). Out of the five primer pairs which targeted on the HDAC family genes, only HDAC4 expression was significantly difference between the two variant strains. In contrast to DNMT1, HDAC4 expression was much higher in the virulence-attenuated Acanthamoeba strain. Conclusion: Our mouse passages had successfully restored the virulence of the attenuated strain. Our findings suggested that DNA-methyltransferase (DNMT1) and histone deacetylase (HDAC4) expressions are associated with virulence potential of Acanthamoeba spp.

Keywords: acanthamoeba, DNA-methyltransferase, histone deacetylase, virulence-associated proteins

Procedia PDF Downloads 287

Authors: Sudipto Sarkar, Anamika Paul

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Pseudoplastic (n<1, n is the power index) fluids have great importance in food, pharmaceutical and chemical process industries which require a lot of attention. Unfortunately, due to its complex flow behavior inadequate research works can be found even in laminar flow regime. A practical problem is solved in the present research work by numerical simulation where we tried to control the vortex shedding from a square cylinder using a horizontal splitter plate placed at the downstream flow region. The position of the plate is at the centerline of the cylinder with varying distance from the cylinder to calculate the critical gap-ratio. If the plate is placed inside this critical gap, the vortex shedding from the cylinder suppressed completely. The Reynolds number considered here is in unsteady laminar vortex shedding regime, Re = 100 (Re = U∞a/ν, where U∞ is the free-stream velocity of the flow, a is the side of the cylinder and ν is the maximum value of kinematic viscosity of the fluid). Flow behavior has been studied for three different gap-ratios (G/a = 2, 2.25 and 2.5, where G is the gap between cylinder and plate) and for a fluid with three different flow behavior indices (n =1, 0.8 and 0.5). The flow domain is constructed using Gambit 2.2.30 and this software is also used to generate the mesh and to impose the boundary conditions. For G/a = 2, the domain size is considered as 37.5a × 16a with 316 × 208 grid points in the streamwise and flow-normal directions respectively after a thorough grid independent study. Fine and equal grid spacing is used close to the geometry to capture the vortices shed from the cylinder and the boundary layer developed over the flat plate. Away from the geometry meshes are unequal in size and stretched out. For other gap-ratios, proportionate domain size and total grid points are used with similar kind of mesh distribution. Velocity inlet (u = U∞), pressure outlet (Neumann condition), symmetry (free-slip boundary condition) at upper and lower domain boundary conditions are used for the simulation. Wall boundary condition (u = v = 0) is considered both on the cylinder and the splitter plate surfaces. Discretized forms of fully conservative 2-D unsteady Navier Stokes equations are then solved by Ansys Fluent 14.5. SIMPLE algorithm written in finite volume method is selected for this purpose which is a default solver inculcate in Fluent. The results obtained for Newtonian fluid flow agree well with previous works supporting Fluent’s usefulness in academic research. A thorough analysis of instantaneous and time-averaged flow fields are depicted both for Newtonian and pseudoplastic fluid flow. It has been observed that as the value of n reduces the stretching of shear layers also reduce and these layers try to roll up before the plate. For flow with high pseudoplasticity (n = 0.5) the nature of vortex shedding changes and the value of critical gap-ratio reduces. These are the remarkable findings for laminar periodic vortex shedding regime in pseudoplastic flow environment.

Keywords: CFD, pseudoplastic fluid flow, wake-boundary layer interactions, critical gap-ratio

Procedia PDF Downloads 110

Authors: Nutcha Taneepanichskul, Helen C. Hailes, Mark Miodownik

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Plastic waste has emerged as a critical global environmental challenge, primarily driven by the prevalent use of conventional plastics derived from petrochemical refining and manufacturing processes in modern packaging. While these plastics serve vital functions, their persistence in the environment post-disposal poses significant threats to ecosystems. Addressing this issue necessitates approaches, one of which involves the development of biodegradable plastics designed to degrade under controlled conditions, such as industrial composting facilities. It is imperative to note that compostable plastics are engineered for degradation within specific environments and are not suited for uncontrolled settings, including natural landscapes and aquatic ecosystems. The full benefits of compostable packaging are realized when subjected to industrial composting, preventing environmental contamination and waste stream pollution. Therefore, effective sorting technologies are essential to enhance composting rates for these materials and diminish the risk of contaminating recycling streams. In this study, it leverage hyperspectral imaging technology (HSI) coupled with advanced machine learning algorithms to accurately identify various types of plastics, encompassing conventional variants like Polyethylene terephthalate (PET), Polypropylene (PP), Low density polyethylene (LDPE), High density polyethylene (HDPE) and biodegradable alternatives such as Polybutylene adipate terephthalate (PBAT), Polylactic acid (PLA), and Polyhydroxyalkanoates (PHA). The dataset is partitioned into three subsets: a training dataset comprising uncontaminated conventional and biodegradable plastics, a validation dataset encompassing contaminated plastics of both types, and a testing dataset featuring real-world packaging items in both pristine and contaminated states. Five distinct machine learning algorithms, namely Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Logistic Regression, and Decision Tree Algorithm, were developed and evaluated for their classification performance. Remarkably, the Logistic Regression and CNN model exhibited the most promising outcomes, achieving a perfect accuracy rate of 100% for the training and validation datasets. Notably, the testing dataset yielded an accuracy exceeding 80%. The successful implementation of this sorting technology within recycling and composting facilities holds the potential to significantly elevate recycling and composting rates. As a result, the envisioned circular economy for plastics can be established, thereby offering a viable solution to mitigate plastic pollution.

Keywords: biodegradable plastics, sorting technology, hyperspectral imaging technology, machine learning algorithms

Procedia PDF Downloads 77

Authors: Aura Alejandra Burbano Patino, Mariela Agotegaray, Veronica Lassalle, Fernanda Horst

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Water pollution is of worldwide concern due to its importance as an essential resource for life. Industrial and urbanistic growth are anthropogenic activities that have caused an increase of undesirable compounds in water. In the last decade, emerging pollutants have become of great interest since, at very low concentrations (µg/L and ng/L), they exhibit a hazardous effect on wildlife, aquatic ecosystems, and human organisms. One group of emerging pollutants that are a matter of study are pharmaceuticals. Their high consumption rate and their inappropriate disposal have led to their detection in wastewater treatment plant influent, effluent, surface water, and drinking water. In consequence, numerous technologies have been developed to efficiently treat these pollutants. Adsorption appears like an easy and cost-effective technology. One of the most used adsorbents of emerging pollutants removal is carbon-based materials such as hydrochars. This study aims to use a magnetic hydrochar nanocomposite to be employed as an adsorbent for diclofenac removal. Kinetics models and the adsorption efficiency in real water samples were analyzed. For this purpose, a magnetic hydrochar nanocomposite was synthesized through the hydrothermal carbonization (HTC) technique hybridized to co-precipitation to add the magnetic component into the hydrochar, based on iron oxide nanoparticles. The hydrochar was obtained from sunflower husk residue as the precursor. TEM, TGA, FTIR, Zeta potential as a function of pH, DLS, BET technique, and elemental analysis were employed to characterize the material in terms of composition and chemical structure. Adsorption kinetics were carried out in distilled water and real water at room temperature, pH of 5.5 for distilled water and natural pH for real water samples, 1:1 adsorbent: adsorbate dosage ratio, contact times from 10-120 minutes, and 50% dosage concentration of DCF. Results have demonstrated that magnetic hydrochar presents superparamagnetic properties with a saturation magnetization value of 55.28 emu/g. Besides, it is mesoporous with a surface area of 55.52 m²/g. It is composed of magnetite nanoparticles incorporated into the hydrochar matrix, as can be proven by TEM micrographs, FTIR spectra, and zeta potential. On the other hand, kinetic studies were carried out using DCF models, finding percent removal efficiencies up to 85.34% after 80 minutes of contact time. In addition, after 120 minutes of contact time, desorption of emerging pollutants from active sites took place, which indicated that the material got saturated after that t time. In real water samples, percent removal efficiencies decrease up to 57.39%, ascribable to a possible mechanism of competitive adsorption of organic or inorganic compounds, ions for active sites of the magnetic hydrochar. The main suggested adsorption mechanism between the magnetic hydrochar and diclofenac include hydrophobic and electrostatic interactions as well as hydrogen bonds. It can be concluded that the magnetic hydrochar nanocomposite could be valorized into a by-product which appears as an efficient adsorbent for DCF removal as a model emerging pollutant. These results are being complemented by modifying experimental variables such as pollutant’s initial concentration, adsorbent: adsorbate dosage ratio, and temperature. Currently, adsorption assays of other emerging pollutants are being been carried out.

Keywords: environmental remediation, emerging pollutants, hydrochar, magnetite nanoparticles

Procedia PDF Downloads 188

Authors: Mattias Bergstrom

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Significance of the Study: This research will provide new insights into the risks with digital embedded infrastructure. Through this research, we will analyze each risk and its potential negation strategies, especially for AI and autonomous automation. Moreover, the analysis that is presented in this paper will convey valuable information for future research that can create more stable, secure, and efficient autonomous systems. To learn and understand the risks, a large IoT system was envisioned, and risks with hardware, tampering, and cyberattacks were collected, researched, and evaluated to create a comprehensive understanding of the potential risks. Potential solutions have then been evaluated on an open source IoT hardware setup. This list shows the identified passive and active risks evaluated in the research. Passive Risks: (1) Hardware failures- Critical Systems relying on high rate data and data quality are growing; SCADA systems for infrastructure are good examples of such systems. (2) Hardware delivers erroneous data- Sensors break, and when they do so, they don’t always go silent; they can keep going, just that the data they deliver is garbage, and if that data is not filtered out, it becomes disruptive noise in the system. (3) Bad Hardware injection- Erroneous generated sensor data can be pumped into a system by malicious actors with the intent to create disruptive noise in critical systems. (4) Data gravity- The weight of the data collected will affect Data-Mobility. (5) Cost inhibitors- Running services that need huge centralized computing is cost inhibiting. Large complex AI can be extremely expensive to run. Active Risks: Denial of Service- It is one of the most simple attacks, where an attacker just overloads the system with bogus requests so that valid requests disappear in the noise. Malware- Malware can be anything from simple viruses to complex botnets created with specific goals, where the creator is stealing computer power and bandwidth from you to attack someone else. Ransomware- It is a kind of malware, but it is so different in its implementation that it is worth its own mention. The goal with these pieces of software is to encrypt your system so that it can only be unlocked with a key that is held for ransom. DNS spoofing- By spoofing DNS calls, valid requests and data dumps can be sent to bad destinations, where the data can be extracted for extortion or to corrupt and re-inject into a running system creating a data echo noise loop. After testing multiple potential solutions. We found that the most prominent solution to these risks was to use a Peer 2 Peer consensus algorithm over a blockchain to validate the data and behavior of the devices (sensors, storage, and computing) in the system. By the devices autonomously policing themselves for deviant behavior, all risks listed above can be negated. In conclusion, an Internet middleware that provides these features would be an easy and secure solution to any future autonomous IoT deployments. As it provides separation from the open Internet, at the same time, it is accessible over the blockchain keys.

Keywords: IoT, security, infrastructure, SCADA, blockchain, AI

Procedia PDF Downloads 106

Authors: Priyanka Sharma, Ranjita Das, Mohan C. Kalita, Debajit Thakur

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Background: Actinomycetes are the richest source of novel bioactive secondary metabolites such as antibiotics, enzymes and other therapeutically useful metabolites with diverse biological activities. The present study aims at the antimicrobial potential and genetic diversity of culturable Actinomycetes isolated from protected forest ecosystems of Assam which includes Kaziranga National Park (26°30˝-26°45˝N and 93°08˝-93°36˝E), Pobitora Wildlife Sanctuary (26º12˝-26º16˝N and 91º58˝-92º05˝E) and Gibbon Wildlife Sanctuary (26˚40˝-26˚45˝N and 94˚20˝-94˚25˝E) which are located in the North-eastern part of India. Northeast India is a part of the Indo-Burma mega biodiversity hotspot and most of the protected forests of this region are still unexplored for the isolation of effective antibiotic-producing Actinomycetes. Thus, there is tremendous possibility that these virgin forests could be a potential storehouse of novel microorganisms, particularly Actinomycetes, exhibiting diverse biological properties. Methodology: Soil samples were collected from different ecological niches of the protected forest ecosystems of Assam and Actinomycetes were isolated by serial dilution spread plate technique using five selective isolation media. Preliminary screening of Actinomycetes for an antimicrobial activity was done by spot inoculation method and the secondary screening by disc diffusion method against several test pathogens, including multidrug resistant Staphylococcus aureus (MRSA). The strains were further screened for the presence of antibiotic synthetic genes such as type I polyketide synthases (PKS-I), type II polyketide synthases (PKS-II) and non-ribosomal peptide synthetases (NRPS) genes. Genetic diversity of the Actinomycetes producing antimicrobial metabolites was analyzed through 16S rDNA-RFLP using Hinf1 restriction endonuclease. Results: Based on the phenotypic characterization, a total of 172 morphologically distinct Actinomycetes were isolated and screened for antimicrobial activity by spot inoculation method on agar medium. Among the strains tested, 102 (59.3%) strains showed activity against Gram-positive bacteria, 98 (56.97%) against Gram-negative bacteria, 92 (53.48%) against Candida albicans MTCC 227 and 130 (75.58%) strains showed activity against at least one of the test pathogens. Twelve Actinomycetes exhibited broad spectrum antimicrobial activity in the secondary screening. The taxonomic identification of these twelve strains by 16S rDNA sequencing revealed that Streptomyces was found to be the predominant genus. The PKS-I, PKS-II and NRPS genes detection indicated diverse bioactive products of these twelve Actinomycetes. Genetic diversity by 16S rDNA-RFLP indicated that Streptomyces was the dominant genus amongst the antimicrobial metabolite producing Actinomycetes. Conclusion: These findings imply that Actinomycetes from the protected forest ecosystems of Assam, India, are a potential source of bioactive secondary metabolites. These areas are as yet poorly studied and represent diverse and largely unscreened ecosystem for the isolation of potent Actinomycetes producing antimicrobial secondary metabolites. Detailed characterization of the bioactive Actinomycetes as well as purification and structure elucidation of the bioactive compounds from the potent Actinomycetes is the subject of ongoing investigation. Thus, to exploit Actinomycetes from such unexplored forest ecosystems is a way to develop bioactive products.

Keywords: Actinomycetes, antimicrobial activity, forest ecosystems, RFLP

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Authors: Omarova Gulnara, Assubayeva Saltanat, Tugambay Symbat, Bulegenov Kanat

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The article discusses the problem of developing a methodology for studying thin and nanoscale gold in ores and placers of primary deposits, which will allow us to develop schemes for revealing dispersed gold inclusions and thus improve its recovery rate to increase the gold reserves of the Republic of Kazakhstan. The type of studied gold, is characterized by a number of features. In connection with this, the conditions of its concentration and distribution in ore bodies and formations, as well as the possibility of reliably determining it by "traditional" methods, differ significantly from that of fine gold (less than 0.25 microns) and even more so from that of larger grains. The mineral composition of rocks (metasomatites) and gold ore and the mineralization associated with them were studied in detail on the Kalba ore field in Kazakhstan. Mineralized zones were identified, and samples were taken from them for analytical studies. The research revealed paragenetic relationships of newly formed mineral formations at the nanoscale, which makes it possible to clarify the conditions for the formation of deposits with a particular type of mineralization. This will provide significant assistance in developing a scheme for study. Typomorphic features of gold were revealed, and mechanisms of formation and aggregation of gold nanoparticles were proposed. The presence of a large number of particles isolated at the laboratory stage from concentrates of gravitational enrichment can serve as an indicator of the presence of even smaller particles in the object. Even the most advanced devices based on gravitational methods for gold concentration provide extraction of metal at a level of around 50%, while pulverized metal is extracted much worse, and gold of less than 1 micron size is extracted at only a few percent. Therefore, when particles of gold smaller than 10 microns are detected, their actual numbers may be significantly higher than expected. In particular, at the studied sites, enrichment of slurry and samples with volumes up to 1 m³ was carried out using a screw lock or separator to produce a final concentrate weighing up to several kilograms. Free gold particles were extracted from the concentrates in the laboratory using a number of processes (magnetic and electromagnetic separation, washing with bromoform in a cup to obtain an ultracontentrate, etc.) and examined under electron microscopes to investigate the nature of their surface and chemical composition. The main result of the study was the detection of gold nanoparticles located on the surface of loose metal grains. The most characteristic forms of gold secretions are individual nanoparticles and aggregates of different configurations. Sometimes, aggregates form solid dense films, deposits, and crusts, all of which are confined to the negative forms of the nano- and microrelief on the surfaces of golden. The results will provide significant knowledge about the prevalence and conditions for the distribution of fine and nanoscale gold in Kazakhstan deposits, as well as the development of methods for studying it, which will minimize losses of this type of gold during extraction. Acknowledgments: This publication has been produced within the framework of the Grant "Development of methodology for studying fine and nanoscale gold in ores of primary deposits, placers and products of their processing" (АР23485052, №235/GF24-26).

Keywords: electron microscopy, microminerology, placers, thin and nanoscale gold

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Authors: Solanki Ravirajsinh, Kudo Kuniaki, Sharma Ankit, Devi Sherine, Kuboshima Misaki, Tachi Shuntaro

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In software development life cycles, the absence of scalable CI/CD significantly impacts organizations, leading to increased overall maintenance costs, prolonged release delivery times, heightened manual efforts, and difficulties in meeting tight deadlines. Implementing CI/CD with standard serverless technologies using cloud services overcomes all the above-mentioned issues and helps organizations improve efficiency and faster delivery without the need to manage server maintenance and capacity. By integrating scalable CI/CD with scalable automation testing, productivity, quality, and agility are enhanced while reducing the need for repetitive work and manual efforts. Implementing scalable CI/CD for development using cloud services like ECS (Container Management Service), AWS Fargate, ECR (to store Docker images with all dependencies), Serverless Computing (serverless virtual machines), Cloud Log (for monitoring errors and logs), Security Groups (for inside/outside access to the application), Docker Containerization (Docker-based images and container techniques), Jenkins (CI/CD build management tool), and code management tools (GitHub, Bitbucket, AWS CodeCommit) can efficiently handle the demands of diverse development environments and are capable of accommodating dynamic workloads, increasing efficiency for faster delivery with good quality. CI/CD pipelines encourage collaboration among development, operations, and quality assurance teams by providing a centralized platform for automated testing, deployment, and monitoring. Scalable CI/CD streamlines the development process by automatically fetching the latest code from the repository every time the process starts, building the application based on the branches, testing the application using a scalable automation testing framework, and deploying the builds. Developers can focus more on writing code and less on managing infrastructure as it scales based on the need. Serverless CI/CD eliminates the need to manage and maintain traditional CI/CD infrastructure, such as servers and build agents, reducing operational overhead and allowing teams to allocate resources more efficiently. Scalable CI/CD adjusts the application's scale according to usage, thereby alleviating concerns about scalability, maintenance costs, and resource needs. Creating scalable automation testing using cloud services (ECR, ECS Fargate, Docker, EFS, Serverless Computing) helps organizations run more than 500 test cases in parallel, aiding in the detection of race conditions, performance issues, and reducing execution time. Scalable CI/CD offers flexibility, dynamically adjusting to varying workloads and demands, allowing teams to scale resources up or down as needed. It optimizes costs by only paying for the resources as they are used and increases reliability. Scalable CI/CD pipelines employ automated testing and validation processes to detect and prevent errors early in the development cycle.

Keywords: achieve parallel execution, cloud services, scalable automation testing, scalable continuous integration and deployment

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Authors: Almudena Espin Perez, Tyler Risom, Carl Pelz, Isabel English, Robert M. Angelo, Rosalie Sears, Andrew J. Gentles

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Pancreatic ductal adenocarcinoma (PDAC) is one of the most deadly malignancies. The role of the tumor microenvironment (TME) is gaining significant attention in cancer research. Despite ongoing efforts, the nature of the interactions between tumors, immune cells, and stromal cells remains poorly understood. The cell-intrinsic properties that govern cell lineage plasticity in PDAC and extrinsic influences of immune populations require technically challenging approaches due to the inherently heterogeneous nature of PDAC. Understanding the cell lineage plasticity of PDAC will improve the development of novel strategies that could be translated to the clinic. Members of the team have demonstrated that the acquisition of ductal to neuroendocrine lineage plasticity in PDAC confers therapeutic resistance and is a biomarker of poor outcomes in patients. Our approach combines computational methods for deconvolving bulk transcriptomic cancer data using CIBERSORTx and high-throughput single-cell imaging using Multiplexed Ion Beam Imaging (MIBI) to study lineage plasticity in PDAC and its relationship to the infiltrating immune system. The CIBERSORTx algorithm uses signature matrices from immune cells and stroma from sorted and single-cell data in order to 1) infer the fractions of different immune cell types and stromal cells in bulked gene expression data and 2) impute a representative transcriptome profile for each cell type. We studied a unique set of 300 genomically well-characterized primary PDAC samples with rich clinical annotation. We deconvolved the PDAC transcriptome profiles using CIBERSORTx, leveraging publicly available single-cell RNA-seq data from normal pancreatic tissue and PDAC to estimate cell type proportions in PDAC, and digitally reconstruct cell-specific transcriptional profiles from our study dataset. We built signature matrices and optimized by simulations and comparison to ground truth data. We identified cell-type-specific transcriptional programs that contribute to cancer cell lineage plasticity, especially in the ductal compartment. We also studied cell differentiation hierarchies using CytoTRACE and predict cell lineage trajectories for acinar and ductal cells that we believe are pinpointing relevant information on PDAC progression. Collaborators (Angelo lab, Stanford University) has led the development of the Multiplexed Ion Beam Imaging (MIBI) platform for spatial proteomics. We will use in the very near future MIBI from tissue microarray of 40 PDAC samples to understand the spatial relationship between cancer cell lineage plasticity and stromal cells focused on infiltrating immune cells, using the relevant markers of PDAC plasticity identified from the RNA-seq analysis.

Keywords: deconvolution, imaging, microenvironment, PDAC

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Authors: Pinzhe Zhao

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This study focuses on predicting university students' cheating tendencies using psychological traits and machine learning techniques. Academic dishonesty is a significant issue that compromises the integrity and fairness of educational institutions. While much research has been dedicated to detecting cheating behaviors after they have occurred, there is limited work on predicting such tendencies before they manifest. The aim of this research is to develop a model that can identify students who are at higher risk of engaging in academic misconduct, allowing for earlier interventions to prevent such behavior. Psychological factors are known to influence students' likelihood of cheating. Research shows that traits such as test anxiety, moral reasoning, self-efficacy, and achievement motivation are strongly linked to academic dishonesty. High levels of anxiety may lead students to cheat as a way to cope with pressure. Those with lower self-efficacy are less confident in their academic abilities, which can push them toward dishonest behaviors to secure better outcomes. Students with weaker moral judgment may also justify cheating more easily, believing it to be less wrong under certain conditions. Achievement motivation also plays a role, as students driven primarily by external rewards, such as grades, are more likely to cheat compared to those motivated by intrinsic learning goals. In this study, data on students’ psychological traits is collected through validated assessments, including scales for anxiety, moral reasoning, self-efficacy, and motivation. Additional data on academic performance, attendance, and engagement in class are also gathered to create a more comprehensive profile. Using machine learning algorithms such as Random Forest, Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks, the research builds models that can predict students’ cheating tendencies. These models are trained and evaluated using metrics like accuracy, precision, recall, and F1 scores to ensure they provide reliable predictions. The findings demonstrate that combining psychological traits with machine learning provides a powerful method for identifying students at risk of cheating. This approach allows for early detection and intervention, enabling educational institutions to take proactive steps in promoting academic integrity. The predictive model can be used to inform targeted interventions, such as counseling for students with high test anxiety or workshops aimed at strengthening moral reasoning. By addressing the underlying factors that contribute to cheating behavior, educational institutions can reduce the occurrence of academic dishonesty and foster a culture of integrity. In conclusion, this research contributes to the growing body of literature on predictive analytics in education. It offers a approach by integrating psychological assessments with machine learning to predict cheating tendencies. This method has the potential to significantly improve how academic institutions address academic dishonesty, shifting the focus from punishment after the fact to prevention before it occurs. By identifying high-risk students and providing them with the necessary support, educators can help maintain the fairness and integrity of the academic environment.

Keywords: academic dishonesty, cheating prediction, intervention strategies, machine learning, psychological traits, academic integrity

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Authors: Jae Won Lee, Kyung Hyun Min, Hong Jae Lee, Sang Cheon Lee

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To date, imaging techniques have attracted much attention in medicine because the detection of diseases at an early stage provides greater opportunities for successful treatment. Consequently, over the past few decades, diverse imaging modalities including magnetic resonance (MR), positron emission tomography, computed tomography, and ultrasound (US) have been developed and applied widely in the field of clinical diagnosis. However, each of the above-mentioned imaging modalities possesses unique strengths and intrinsic weaknesses, which limit their abilities to provide accurate information. Therefore, multimodal imaging systems may be a solution that can provide improved diagnostic performance. Among the current medical imaging modalities, US is a widely available real-time imaging modality. It has many advantages including safety, low cost and easy access for patients. However, its low spatial resolution precludes accurate discrimination of diseased region such as cancer sites. In contrast, MR has no tissue-penetrating limit and can provide images possessing exquisite soft tissue contrast and high spatial resolution. However, it cannot offer real-time images and needs a comparatively long imaging time. The characteristics of these imaging modalities may be considered complementary, and the modalities have been frequently combined for the clinical diagnostic process. Biominerals such as calcium carbonate (CaCO3) and calcium phosphate (CaP) exhibit pH-dependent dissolution behavior. They demonstrate pH-controlled drug release due to the dissolution of minerals in acidic pH conditions. In particular, the application of this mineralization technique to a US contrast agent has been reported recently. The CaCO3 mineral reacts with acids and decomposes to generate calcium dioxide (CO2) gas in an acidic environment. These gas-generating mineralized nanoparticles generated CO2 bubbles in the acidic environment of the tumor, thereby allowing for strong echogenic US imaging of tumor tissues. On the basis of this previous work, it was hypothesized that the loading of MR contrast agents into the CaCO3 mineralized nanoparticles may be a novel strategy in designing a contrast agent for dual imaging. Herein, CaCO3 mineralized nanoparticles that were capable of generating CO2 bubbles to trigger the release of entrapped MR contrast agents in response to tumoral acidic pH were developed for the purposes of US and MR dual-modality imaging of tumors. Gd2O3 nanoparticles were selected as an MR contrast agent. A key strategy employed in this study was to prepare Gd2O3 nanoparticle-loaded mineralized nanoparticles (Gd2O3-MNPs) using block copolymer-templated CaCO3 mineralization in the presence of calcium cations (Ca2+), carbonate anions (CO32-) and positively charged Gd2O3 nanoparticles. The CaCO3 core was considered suitable because it may effectively shield Gd2O3 nanoparticles from water molecules in the blood (pH 7.4) before decomposing to generate CO2 gas, triggering the release of Gd2O3 nanoparticles in tumor tissues (pH 6.4~7.4). The kinetics of CaCO3 dissolution and CO2 generation from the Gd2O3-MNPs were examined as a function of pH and pH-dependent in vitro magnetic relaxation; additionally, the echogenic properties were estimated to demonstrate the potential of the particles for the tumor-specific US and MR imaging.

Keywords: calcium carbonate, mineralization, ultrasound imaging, magnetic resonance imaging

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Authors: Qian Chen, Lin Xu, Ping Ju, Zhuoran Li, Yiping Yu, Yuqing Jin

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The operation of power grid is becoming more and more complex and difficult due to its rapid development towards high voltage, long distance, and large capacity. For instance, many large-scale wind farms have connected to power grid, where their fluctuation and randomness is very likely to affect the stability and safety of the grid. Fortunately, many new-type equipments based on power electronics have been applied to power grid, such as UPFC (Unified Power Flow Controller), TCSC (Thyristor Controlled Series Compensation), STATCOM (Static Synchronous Compensator) and so on, which can help to deal with the problem above. Compared with traditional equipment such as generator, new-type controllable devices, represented by the FACTS (Flexible AC Transmission System), have more accurate control ability and respond faster. But they are too expensive to use widely. Therefore, on the basis of the comparison and analysis of the controlling characteristics between traditional control equipment and new-type controllable equipment in both time and space scale, a coordinated optimizing control method within mutil-time-space frame is proposed in this paper to bring both kinds of advantages into play, which can better both control ability and economical efficiency. Firstly, the coordination of different space sizes of grid is studied focused on the fluctuation caused by large-scale wind farms connected to power grid. With generator, FSC (Fixed Series Compensation) and TCSC, the coordination method on two-layer regional power grid vs. its sub grid is studied in detail. The coordination control model is built, the corresponding scheme is promoted, and the conclusion is verified by simulation. By analysis, interface power flow can be controlled by generator and the specific line power flow between two-layer regions can be adjusted by FSC and TCSC. The smaller the interface power flow adjusted by generator, the bigger the control margin of TCSC, instead, the total consumption of generator is much higher. Secondly, the coordination of different time sizes is studied to further the amount of the total consumption of generator and the control margin of TCSC, where the minimum control cost can be acquired. The coordination method on two-layer ultra short-term correction vs. AGC (Automatic Generation Control) is studied with generator, FSC and TCSC. The optimal control model is founded, genetic algorithm is selected to solve the problem, and the conclusion is verified by simulation. Finally, the aforementioned method within multi-time-space scale is analyzed with practical cases, and simulated on PSASP (Power System Analysis Software Package) platform. The correctness and effectiveness are verified by the simulation result. Moreover, this coordinated optimizing control method can contribute to the decrease of control cost and will provide reference to the following studies in this field.

Keywords: FACTS, multi-space-time frame, optimal control, TCSC

Procedia PDF Downloads 265