Search results for: green extraction

Authors: Nassima Noufail, Sara Bouhali

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In this work, we develop a semi-supervised solution for the purpose of action detection in videos and propose an efficient algorithm for video segmentation. The approach is divided into video segmentation, feature extraction, and classification. In the first part, a video is segmented into clips, and we used the K-means algorithm for this segmentation; our goal is to find groups based on similarity in the video. The application of k-means clustering into all the frames is time-consuming; therefore, we started by the identification of transition frames where the scene in the video changes significantly, and then we applied K-means clustering into these transition frames. We used two image filters, the gaussian filter and the Laplacian of Gaussian. Each filter extracts a set of features from the frames. The Gaussian filter blurs the image and omits the higher frequencies, and the Laplacian of gaussian detects regions of rapid intensity changes; we then used this vector of filter responses as an input to our k-means algorithm. The output is a set of cluster centers. Each video frame pixel is then mapped to the nearest cluster center and painted with a corresponding color to form a visual map. The resulting visual map had similar pixels grouped. We then computed a cluster score indicating how clusters are near each other and plotted a signal representing frame number vs. clustering score. Our hypothesis was that the evolution of the signal would not change if semantically related events were happening in the scene. We marked the breakpoints at which the root mean square level of the signal changes significantly, and each breakpoint is an indication of the beginning of a new video segment. In the second part, for each segment from part 1, we randomly selected a 16-frame clip, then we extracted spatiotemporal features using convolutional 3D network C3D for every 16 frames using a pre-trained model. The C3D final output is a 512-feature vector dimension; hence we used principal component analysis (PCA) for dimensionality reduction. The final part is the classification. The C3D feature vectors are used as input to a multi-class linear support vector machine (SVM) for the training model, and we used a multi-classifier to detect the action. We evaluated our experiment on the UCF101 dataset, which consists of 101 human action categories, and we achieved an accuracy that outperforms the state of art by 1.2%.

Keywords: video segmentation, action detection, classification, Kmeans, C3D

Procedia PDF Downloads 71

Authors: Magdalena Smoktunowicz, Renata Wawrzyniak, Malgorzata Waleron, Krzysztof Waleron

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Pectobacterium spp. were previously classified into the Erwinia genus founded in 1917 to unite at that time all Gram-negative, fermentative, nonsporulating and peritrichous flagellated plant pathogenic bacteria. After work of Waldee (1945), on Approved Lists of Bacterial Names and bacteriology manuals in 1980, they were described either under the species named Erwinia or Pectobacterium. The Pectobacterium genus was formally described in 1998 of 265 Pectobacterium strains. Currently, there are 21 species of Pectobacterium bacteria, including Pectobacterium betavasculorum since 2003, which caused soft rot on sugar beet tubers. Based on the biochemical experiments carried out for this, it is known that these bacteria are gram-negative, catalase-positive, oxidase-negative, facultatively anaerobic, using gelatin and causing symptoms of soft rot on potato and sugar beet tubers. The mere fact of growing on sugar beet may indicate a metabolism characteristic only for this species. Metabolomics, broadly defined as the biology of the metabolic systems, which allows to make comprehensive measurements of metabolites. Metabolomics, in combination with genomics, are complementary tools for the identification of metabolites and their reactions, and thus for the reconstruction of metabolic networks. The aim of this study was to apply the GC-MS-based untargeted metabolomics to study the metabolism of P. betavasculorum in different growing conditions. The metabolomic profiles of biomass and biomass media were determined. For sample preparation the following protocol was used: extraction with 900 µl of methanol: chloroform: water mixture (10: 3: 1, v: v) were added to 900 µl of biomass from the bottom of the tube and up to 900 µl of nutrient medium from the bacterial biomass. After centrifugation (13,000 x g, 15 min, 4oC), 300µL of the obtained supernatants were concentrated by rotary vacuum and evaporated to dryness. Afterwards, two-step derivatization procedure was performed before GC-MS analyses. The obtained results were subjected to statistical calculations with the use of both uni- and multivariate tests. The obtained results were evaluated using KEGG database, to asses which metabolic pathways are activated and which genes are responsible for it, during the metabolism of given substrates contained in the growing environment. The observed metabolic changes, combined with biochemical and physiological tests, may enable pathway discovery, regulatory inference and understanding of the homeostatic abilities of P. betavasculorum.

Keywords: GC-MS chromatograpfy, metabolomics, metabolism, pectobacterium strains, pectobacterium betavasculorum

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Authors: Wei-Po Wu, Wen-Yu Liu, Wei−Ting Lin, Hen-Yu Lien

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Introduction: Improving balance function for individuals after stroke is a crucial target in physiotherapy. Aquatic therapy which challenges individual’s postural control in an unstable fluid environment may be beneficial in enhancing balance functions. The purposes of the systematic review with meta-analyses were to validate the effects of aquatic therapy in improving balance functions for individuals with strokes in contrast to conventional physiotherapy. Method: Available studies were explored from three electronic databases: PubMed, Scopus, and Web of Science. During literature search, the published date of studies was not limited. The study design of the included studies should be randomized controlled trials (RCTs) and the studies should contain at least one outcome measurement of balance function. The PEDro scale was adopted to assess the quality of included studies, while the 'Oxford Centre for Evidence-Based Medicine 2011 Levels of Evidence' was used to evaluate the level of evidence. After the data extraction, studies with same outcome measures were pooled together for meta-analysis. Result: Ten studies with 282 participants were included in analyses. The research qualities of the studies were ranged from fair to good (4 to 8 points). Levels of evidence of the included studies were graded as level 2 and 3. Finally, scores of Berg Balance Scale (BBS), Eye closed force plate center of pressure velocity (anterior-posterior, medial-lateral axis) and Timed up and Go test were pooled and analyzed separately. The pooled results shown improvement in balance function (BBS mean difference (MD): 1.39 points; 95% confidence interval (CI): 0.05-2.29; p=0.002) (Eye closed force plate center of pressure velocity (anterior-posterior axis) MD: 1.39 mm/s; 95% confidence interval (CI): 0.93-1.86; p<0.001) (Eye closed force plate center of pressure velocity (medial-lateral) MD: 1.48 mm/s; 95% confidence interval (CI): 0.15-2.82; p=0.03) and mobility (MD: 0.9 seconds; 95% CI: 0.07-1.73; p=0.03) of stroke individuals after aquatic therapy compared to conventional therapy. Although there were significant differences between two treatment groups, the differences in improvement were relatively small. Conclusion: The aquatic therapy improved general balance function and mobility in the individuals with stroke better than conventional physiotherapy.

Keywords: aquatic therapy, balance function, meta-analysis, stroke, systematic review

Procedia PDF Downloads 195

Authors: Diriba T. Balcha, Boris Kulig, Oliver Hensel, Eyassu Woldesenbet

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Enset fiber is agricultural waste and available in a surplus amount in Ethiopia. However, the hypothesized variation in properties of this fiber due to diversity of its plant source breed, fiber position within plant stem and chemical treatment duration had not proven that its application for the development of composite products is problematic. Currently, limited data are known on the functional properties of the fiber as a potential functional fiber. Thus, an effort is made in this study to narrow the knowledge gaps by characterizing it. The experimental design was conducted using Design-Expert software and the tensile test was conducted on Enset fiber from 10 breeds: Dego, Dirbo, Gishera, Itine, Siskela, Neciho, Yesherkinke, Tuzuma, Ankogena, and Kucharkia. The effects of 5% Na-OH surface treatment duration and fiber location along and across the plant pseudostem was also investigated. The test result shows that the rupture stress variation is not significant among the fibers from 10 Enset breeds. However, strain variation is significant among the fibers from 10 Enset breeds that breed Dego fiber has the highest strain before failure. Surface treated fibers showed improved rupture strength and elastic modulus per 24 hours of treatment duration. Also, the result showed that chemical treatment can deteriorate the load-bearing capacity of the fiber. The raw fiber has the higher load-bearing capacity than the treated fiber. And, it was noted that both the rupture stress and strain increase in the top to bottom gradient, whereas there is no significant variation across the stem. Elastic modulus variation both along and across the stem was insignificant. The rupture stress, elastic modulus, and strain result of Enset fiber are 360.11 ± 181.86 MPa, 12.80 ± 6.85 GPa and 0.04 ± 0.02 mm/mm, respectively. These results show that Enset fiber is comparable to other natural fibers such as abaca, banana, and sisal fibers and can be used as alternatives natural fiber for composites application. Besides, the insignificant variation of properties among breeds and across stem is essential for all breeds and all leaf sheath of the Enset fiber plant for fiber extraction. The use of short natural fiber over the long is preferable to reduce the significant variation of properties along the stem or fiber direction. In conclusion, Enset fiber application for composite product design and development is mechanically feasible.

Keywords: Agricultural waste, Chemical treatment, Fiber characteristics, Natural fiber

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Authors: Gayathri Devi V, Aravamudan Kannan, Amit Sircar

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In the inner fuel cycle system of a nuclear fusion reactor, the Hydrogen Isotopes Removal System (HIRS) plays a pivoted role. It enables the effective extraction of the hydrogen isotopes from the breeder purge gas which helps to maintain the tritium breeding ratio and sustain the fusion reaction. One of the components of HIRS, Cryogenic Molecular Sieve Bed (CMSB) columns with zeolites adsorbents are considered for the physisorption of hydrogen isotopes at 1 bar and 77 K. Even though zeolites have good thermal stability and reduced activation properties making them ideal for use in nuclear reactor applications, their modest capacity for hydrogen isotopes adsorption is a cause of concern. In order to enhance the adsorbent capacity in an informed manner, it is helpful to understand the adsorption phenomena at the quantum electronic structure level. Physicochemical modifications of the adsorbent material enhances the adsorption capacity through the incorporation of active sites. This may be accomplished through the incorporation of suitable metal ions in the zeolite framework. In this work, molecular hydrogen isotopes adsorption on the active sites of functionalized zeolites are investigated in detail using Density Functional Theory (DFT) study. This involves the utilization of hybrid Generalized Gradient Approximation (GGA) with dispersion correction to account for the exchange and correlation functional of DFT. The electronic energies, adsorption enthalpy, adsorption free energy, Highest Occupied Molecular Orbital (HOMO), Lowest Unoccupied Molecular Orbital (LUMO) energies are computed on the stable 8T zeolite clusters as well as the periodic structure functionalized with different active sites. The characteristics of the dihydrogen bond with the active metal sites and the isotopic effects are also studied in detail. Validation studies with DFT will also be presented for adsorption of hydrogen on metal ion functionalized zeolites. The ab-inito screening analysis gave insights regarding the mechanism of hydrogen interaction with the zeolites under study and also the effect of the metal ion on adsorption. This detailed study provides guidelines for selection of the appropriate metal ions that may be incorporated in the zeolites framework for effective adsorption of hydrogen isotopes in the HIRS.

Keywords: adsorption enthalpy, functionalized zeolites, hydrogen isotopes, nuclear fusion, physisorption

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Authors: M. Mutoti, J. Gumbo, A. Jideani

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Cyanobacteria or blue green algae have been part of the human diet for thousands of years. Cyanobacteria can multiply quickly in surface waters and form blooms when favorable conditions prevail, such as high temperature, intense light, high pH, and increased availability of nutrients, especially phosphorous and nitrogen, artificially released by anthropogenic activities. Consumption of edible cyanotoxins such as Spirulina may reduce risks of cataracts and age related macular degeneration. Sulfate polysaccharides exhibit antitumor, anticoagulant, anti-mutagenic, anti-inflammatory, antimicrobial, and even antiviral activity against HIV, herpes, and hepatitis. In humans, exposure to cyanotoxins can occur in various ways; however, the oral route is the most important. This is mainly through drinking water, or by eating contaminated foods; it may even involve ingesting water during recreational activities. This paper seeks to present a review on cyanobacteria/cyanotoxin contamination of water and food and implications for human health. In particular, examining the water quality used during maize seed that passes through mill grinding processes. In order to fulfil the objective, this paper starts with the theoretical framework on cyanobacteria contamination of food that will guide review of the present paper. A number of methods for decontaminating cyanotoxins in food is currently available. Therefore, physical, chemical, and biological methods for treating cyanotoxins are reviewed and compared. Furthermore, methods that are utilized for detecting and identifying cyanobacteria present in water and food were also informed in this review. This review has indicated various routes through which humans can be exposed to cyanotoxins. Accumulation of cyanotoxins, mainly microcystins, in food has raised an awareness of the importance of food as microcystins exposure route to human body. Therefore, this review demonstrates the importance of expanding research on cyanobacteria/cyanotoxin contamination of water and food for water treatment and water supply management, with focus on examining water for domestic use. This will help providing information regarding the prevention or minimization of contamination of water and food, and also reduction or removal of contamination through treatment processes and prevention of recontamination in the distribution system.

Keywords: biofilm, cyanobacteria, cyanotoxin, food contamination

Procedia PDF Downloads 154

Authors: Ali Hamza

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Accurate segmentation of breast ultrasound images is of paramount importance in enhancing the diagnostic capabilities of breast cancer detection. This study presents an approach utilizing the U-Net architecture for segmenting breast ultrasound images aimed at improving the accuracy and reliability of mass identification within the breast tissue. The proposed method encompasses a multi-stage process. Initially, preprocessing techniques are employed to refine image quality and diminish noise interference. Subsequently, the U-Net architecture, a deep learning convolutional neural network (CNN), is employed for pixel-wise segmentation of regions of interest corresponding to potential breast masses. The U-Net's distinctive architecture, characterized by a contracting and expansive pathway, enables accurate boundary delineation and detailed feature extraction. To evaluate the effectiveness of the proposed approach, an extensive dataset of breast ultrasound images is employed, encompassing diverse cases. Quantitative performance metrics such as the Dice coefficient, Jaccard index, sensitivity, specificity, and Hausdorff distance are employed to comprehensively assess the segmentation accuracy. Comparative analyses against traditional segmentation methods showcase the superiority of the U-Net architecture in capturing intricate details and accurately segmenting breast masses. The outcomes of this study emphasize the potential of the U-Net-based segmentation approach in bolstering breast ultrasound image analysis. The method's ability to reliably pinpoint mass boundaries holds promise for aiding radiologists in precise diagnosis and treatment planning. However, further validation and integration within clinical workflows are necessary to ascertain their practical clinical utility and facilitate seamless adoption by healthcare professionals. In conclusion, leveraging the U-Net architecture for breast ultrasound image segmentation showcases a robust framework that can significantly enhance diagnostic accuracy and advance the field of breast cancer detection. This approach represents a pivotal step towards empowering medical professionals with a more potent tool for early and accurate breast cancer diagnosis.

Keywords: mage segmentation, U-Net, deep learning, breast cancer detection, diagnostic accuracy, mass identification, convolutional neural network

Procedia PDF Downloads 79

Authors: Yu-Min Chang, Kuo-Sheng Tsai, Hung-Te Tsai, Chia-Hsin Li

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This paper presents the early-warning lights classification management system for industrial parks promoted by the Taiwan Environmental Protection Administration (EPA) since 2011, including the definition of each early-warning light, objectives, action program and accomplishments. All of the 151 industrial parks in Taiwan were classified into four early-warning lights, including red, orange, yellow and green, for carrying out respective pollution management according to the monitoring data of soil and groundwater quality, regulatory compliance, and regulatory listing of control site or remediation site. The Taiwan EPA set up a priority list for high potential polluted industrial parks and investigated their soil and groundwater qualities based on the results of the light classification and pollution potential assessment. In 2011-2013, there were 44 industrial parks selected and carried out different investigation, such as the early warning groundwater well networks establishment and pollution investigation/verification for the red and orange-light industrial parks and the environmental background survey for the yellow-light industrial parks. Among them, 22 industrial parks were newly or continuously confirmed that the concentrations of pollutants exceeded those in soil or groundwater pollution control standards. Thus, the further investigation, groundwater use restriction, listing of pollution control site or remediation site, and pollutant isolation measures were implemented by the local environmental protection and industry competent authorities; the early warning lights of those industrial parks were proposed to adjust up to orange or red-light. Up to the present, the preliminary positive effect of the soil and groundwater quality management system for industrial parks has been noticed in several aspects, such as environmental background information collection, early warning of pollution risk, pollution investigation and control, information integration and application, and inter-agency collaboration. Finally, the work and goal of self-initiated quality management of industrial parks will be carried out on the basis of the inter-agency collaboration by the classified lights system of early warning and management as well as the regular announcement of the status of each industrial park.

Keywords: industrial park, soil and groundwater quality management, early-warning lights classification, SOP for reporting and treatment of monitored abnormal events

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Authors: Mustafa Rezazada, Takuya Maruyama

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Cars have a strong integration with the human being since its introduction, and this interaction is more evident in the urban context. Therefore, shifting city residents from driving private vehicles to public transits has been a big challenge. Accordingly, carsharing as an innovative, environmentally friendly transport alternative had a significant contribution to this transition so far. It helped to reduce the numbers of household car ownership, declining demand for on-street parking, dropping the numbers of kilometers traveled by car, and affects the future of mobility by decreasing the Green House Gases (GHS) emissions’ and the numbers of new cars to be purchased otherwise. However, majorities of carsharing researches were conducted in highly developed cities, and less attention has been paid to the cities of developing countries. This study is conducted in the Capital of Afghanistan, Kabul to investigate the current transport pattern, user behavior, and to examine the possibility of introducing the carsharing system. This study established a new survey method called Onboard Carsharing Survey OCS. In this survey, the carpooling passengers aboard are interviewed following the Onboard Transit Survey OTS guideline with a few refinements. The survey focuses on respondents’ daily travel behavior and hypothetical stated choice of carsharing opportunities. Moreover, it followed by an aggregate analysis at the end. The survey results indicate the following: two-thirds of the respondents 62% have been carpooling every day since 5 years or more, more than half of the respondents are not satisfied with current modes, besides other attributes the Traffic Congestion, Environment and Insufficient Public Transport were ranked the most critical in daily transportation by survey participants. Moreover, 68.24% of the respondent chose Carsharing over carpooling under different choice game scenarios. Overall, the findings in this research show that Kabul City is a potential underground for the introduction of Carsharing in the future. Taken together, insufficient public transit, dissatisfaction with current modes, and their stated interest will affect the future of carsharing positively in Kabul City. The modal choice in this study is limited to carpooling and carsharing; more choice sets, including bus, cycling, and walking, will have to be added to evaluate further.

Keywords: carsharing, developing countries, Kabul Afghanistan, onboard carsharing survey, transportation, urban planning

Procedia PDF Downloads 132

Authors: Sanjay Rathee, Arti Kashyap

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Extraction of useful information from large datasets is one of the most important research problems. Association rule mining is one of the best methods for this purpose. Finding possible associations between items in large transaction based datasets (finding frequent patterns) is most important part of the association rule mining. There exist many algorithms to find frequent patterns but Apriori algorithm always remains a preferred choice due to its ease of implementation and natural tendency to be parallelized. Many single-machine based Apriori variants exist but massive amount of data available these days is above capacity of a single machine. Therefore, to meet the demands of this ever-growing huge data, there is a need of multiple machines based Apriori algorithm. For these types of distributed applications, MapReduce is a popular fault-tolerant framework. Hadoop is one of the best open-source software frameworks with MapReduce approach for distributed storage and distributed processing of huge datasets using clusters built from commodity hardware. However, heavy disk I/O operation at each iteration of a highly iterative algorithm like Apriori makes Hadoop inefficient. A number of MapReduce-based platforms are being developed for parallel computing in recent years. Among them, two platforms, namely, Spark and Flink have attracted a lot of attention because of their inbuilt support to distributed computations. Earlier we proposed a reduced- Apriori algorithm on Spark platform which outperforms parallel Apriori, one because of use of Spark and secondly because of the improvement we proposed in standard Apriori. Therefore, this work is a natural sequel of our work and targets on implementing, testing and benchmarking Apriori and Reduced-Apriori and our new algorithm ReducedAll-Apriori on Apache Flink and compares it with Spark implementation. Flink, a streaming dataflow engine, overcomes disk I/O bottlenecks in MapReduce, providing an ideal platform for distributed Apriori. Flink's pipelining based structure allows starting a next iteration as soon as partial results of earlier iteration are available. Therefore, there is no need to wait for all reducers result to start a next iteration. We conduct in-depth experiments to gain insight into the effectiveness, efficiency and scalability of the Apriori and RA-Apriori algorithm on Flink.

Keywords: apriori, apache flink, Mapreduce, spark, Hadoop, R-Apriori, frequent itemset mining

Procedia PDF Downloads 290

Authors: Matteo Nicoli, Gianvito Colucci, Valeria Di Cosmo, Daniele Lerede, Laura Savoldi

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The transport sector is currently responsible for approximately 1/3 of greenhouse gas emissions in Europe. In the wider context of achieving carbon neutrality of the global energy system, different alternatives are available to decarbonizethe transport sector. In particular, while electricity is already the most consumed energy commodity in rail transport, battery electric vehicles are one of the zero-emissions options on the market for road transportation. On the other hand, hydrogen-based fuel cell vehicles are available for road and non-road vehicles. The European Commission is strongly pushing toward the integration of hydrogen in the energy systems of European countries and its widespread adoption as an energy vector to achieve the Green Deal targets. Furthermore, the Italian government is defining hydrogen-related objectives with the publication of a dedicated Hydrogen Strategy. The adoption of energy system optimization models to study the possible penetration of alternative zero-emitting transport technologies gives the opportunity to perform an overall analysis of the effects that the development of innovative technologies has on the entire energy system and on the supply-side, devoted to the production of energy carriers such as hydrogen and electricity. Using an open-source modeling framework such as TEMOA, this work aims to compare the role of hydrogen and electric vehicles in the decarbonization of the transport sector. The analysis investigates the advantages and disadvantages of adopting the two options, from the economic point of view (costs associated with the two options) and the environmental one (looking at the emissions reduction perspectives). Moreover, an analysis on the profitability of the investments in hydrogen and electric vehicles will be performed. The study investigates the evolution of energy consumption and greenhouse gas emissions in different transportation modes (road, rail, navigation, and aviation) by detailed analysis of the full range of vehicles included in the techno-economic database used in the TEMOA model instance adopted for this work. The transparency of the analysis is guaranteed by the accessibility of the TEMOA models, based on an open-access source code and databases.

Keywords: battery electric vehicles, decarbonization, energy system optimization models, fuel cell vehicles, hydrogen, open-source modeling, TEMOA, transport

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Authors: I. V. Palamarchuk, N. V. Zaichko

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Background and Aims: Galectin-3 is a marker of subclinical cardiac injury and is elevated in individuals with type 2 diabetes mellitus; while hydrogen sulfide (H₂S), metabolite of sulfur-containing amino acids, is considered having antifibrogenic effects. This study was designed to investigate whether metformin and its combination with NaHS can influence plasma galectin-3 and cystathionine-γ-lyase/hydrogen sulfide (CSE/H₂S) system in diabetic rat’s heart. Methods: 32 healthy male rats (180-250 g) were divided into 4 groups. To induct diabetes, rats (group 2-4) were injected with streptozotocin (STZ, 40 mg/kg/i.p., 0.1 M citrate buffer (pH 4.5). Rats from 3d (STZ+Metf) and 4th (STZ+Metf+NaHS) groups were given metformin (500 mg/kg/day) orally, and rats from 4th (STZ+Metf+NaHS) group were injected sodium hydrosulfide (NaHS, 3 mg/kg/i.p.) once per day starting from 3 to 28 day after streptozotocin injection. Rats of first group (control) were administered the equivalent volumes of 0.9% NaCl. Plasma galectin-3 was measured by ELISA. Rats’ hearts were sampled for determination of H2S by reaction with N,N-Dimethyl-p-phenylenediamine. Determination of CSE gene expression was performed in real time using PCR in the presence of SYBR Green I, using DT-Light detecting amplifier ('DNA-technology', Russia). Results: Induction of streptozotocin diabetes (STZ-diabetes, group 2) was followed by low myocardial H2S concentration and CSE expression (by 35%, p < 0.05 and 60.5%, p < 0.001 respectively, than that in controls), while plasma galectin-3 in this group was significantly higher than in controls (by 3.8 times, p < 0.05). Administration of metformin (group 3) resulted in significantly higher H₂S concentration (by 28.5%, p < 0.05), whereas CSE expression was only by 6% more than that in STZ-diabetes, as well as plasma galectin-3 was only by 14.8% lower in comparison with untreated diabetic rats. The inhibition of H₂S generation and CSE activity by diabetes was greatly attenuated in STZ+Metf+NaHS group. The combination of metformin with NaHS significantly stimulated H₂S production (by 48%, p < 0.05 and 15%, p < 0.05 more than STZ-diabetes and STZ+Metf respectively) and CSE gene expression (by 64.8%, p < 0.05 compared to STZ-diabetes and by 55.4%,p < 0.05 compared to STZ+Metf). Besides, plasma galectin-3 in rats receiving metformin and NaHS was significantly lower by 42%, p < 0.05 and 32.5%, p < 0.05 compared to STZ-diabetes and STZ+Metf groups respectively. Conclusions: To summarize, dysfunction of CSE/H2S system and galectin-3 stimulation was found in streptozotocin-induced diabetic rats. Metformin and its combination with exogenous H2S effectively prevented the development of metabolic changes induced by diabetes. These findings suggest that CSE/H₂S system can be integrated into pathogenesis of diabetic complications through modulation of pro-inflammatory and pro-fibrogenic mediator galectin-3.

Keywords: cystathionine-γ-lyase, diabetic heart, galectin-3, hydrogen sulfide, metformin, sodium hydrosulfide

Procedia PDF Downloads 219

Authors: Alija Uzunovic, Sasa Pilipovic, Aida Sapcanin, Zahida Ademovic, Berina Pilipović

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Capsaicin is a naturally occurring alkaloid extracted from capsicum fruit extracts of different of Capsicum species. It has been employed topically to treat many diseases such as rheumatoid arthritis, osteoarthritis, cancer pain and nerve pain in diabetes. The high degree of pre-systemic metabolism of intragastrical capsaicin and the short half-life of capsaicin by intravenous administration made topical application of capsaicin advantageous. In this study, we have evaluated differences in the dissolution characteristics of capsaicin patch 11 mg (purchased from market) at different dissolution rotation speed. The proposed patch area is 308 cm2 (22 cm x 14 cm; it contains 36 µg of capsaicin per square centimeter of adhesive). USP Apparatus 5 (Paddle Over Disc) is used for transdermal patch testing. The dissolution study was conducted using USP apparatus 5 (n=6), ERWEKA DT800 dissolution tester (paddle-type) with addition of a disc. The fabricated patch of 308 cm2 is to be cut into 9 cm2 was placed against a disc (delivery side up) retained with the stainless-steel screen and exposed to 500 mL of phosphate buffer solution pH 7.4. All dissolution studies were carried out at 32 ± 0.5 °C and different rotation speed (50± 5; 100± 5 and 150± 5 rpm). 5 ml aliquots of samples were withdrawn at various time intervals (1, 4, 8 and 12 hours) and replaced with 5 ml of dissolution medium. Withdrawn were appropriately diluted and analyzed by reversed-phase liquid chromatography (RP-LC). A Reversed Phase Liquid Chromatography (RP-LC) method has been developed, optimized and validated for the separation and quantitation of capsaicin in a transdermal patch. The method uses a ProntoSIL 120-3-C18 AQ 125 x 4,0 mm (3 μm) column maintained at 600C. The mobile phase consisted of acetonitrile: water (50:50 v/v), the flow rate of 0.9 mL/min, the injection volume 10 μL and the detection wavelength 222 nm. The used RP-LC method is simple, sensitive and accurate and can be applied for fast (total chromatographic run time was 4.0 minutes) and simultaneous analysis of capsaicin and dihydrocapsaicin in a transdermal patch. According to the results obtained in this study, we can conclude that the relative difference of dissolution rate of capsaicin after 12 hours was elevated by increase of dissolution rotation speed (100 rpm vs 50 rpm: 84.9± 11.3% and 150 rpm vs 100 rpm: 39.8± 8.3%). Although several apparatus and procedures (USP apparatus 5, 6, 7 and a paddle over extraction cell method) have been used to study in vitro release characteristics of transdermal patches, USP Apparatus 5 (Paddle Over Disc) could be considered as a discriminatory test. would be able to point out the differences in the dissolution rate of capsaicin at different rotation speed.

Keywords: capsaicin, in vitro, patch, RP-LC, transdermal

Procedia PDF Downloads 221

Authors: Md. Khayrul Bashar, Md. Saiful Islam, Kimiko Yamashita, Yano Midori

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Despite having some progress in human authentication, conventional biometrics (e.g., facial features, fingerprints, retinal scans, gait, voice patterns) are not robust against falsification because they are neither confidential nor secret to an individual. As a non-invasive tool, electrocardiogram (ECG) has recently shown a great potential in human recognition due to its unique rhythms characterizing the variability of human heart structures (chest geometry, sizes, and positions). Moreover, ECG has a real-time vitality characteristic that signifies the live signs, which ensure legitimate individual to be identified. However, the detection accuracy of the current ECG-based methods is not sufficient due to a high variability of the individual’s heartbeats at a different instance of time. These variations may occur due to muscle flexure, the change of mental or emotional states, and the change of sensor positions or long-term baseline shift during the recording of ECG signal. In this study, a new method is proposed for human identification, which is based on the extraction of the local roughness of ECG heartbeat signals. First ECG signal is preprocessed using a second order band-pass Butterworth filter having cut-off frequencies of 0.00025 and 0.04. A number of local binary patterns are then extracted by applying a moving neighborhood window along the ECG signal. At each instant of the ECG signal, the pattern is formed by comparing the ECG intensities at neighboring time points with the central intensity in the moving window. Then, binary weights are multiplied with the pattern to come up with the local roughness description of the signal. Finally, histograms are constructed that describe the heartbeat signals of individual subjects in the database. One advantage of the proposed feature is that it does not depend on the accuracy of detecting QRS complex, unlike the conventional methods. Supervised recognition methods are then designed using minimum distance to mean and Bayesian classifiers to identify authentic human subjects. An experiment with sixty (60) ECG signals from sixty adult subjects from National Metrology Institute of Germany (NMIG) - PTB database, showed that the proposed new method is promising compared to a conventional interval and amplitude feature-based method.

Keywords: human identification, ECG biometrics, local roughness patterns, supervised classification

Procedia PDF Downloads 400

Authors: S. Ozeri, D. Shmilovitz

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Micro‐Turbine Generators (MTG) are small size power plants that consist of a high speed, gas turbine driving an electrical generator. MTGs may be fueled by either natural gas or kerosene and may also use sustainable and recycled green fuels such as biomass, landfill or digester gas. The typical ratings of MTGs start from 20 kW up to 200 kW. The primary use of MTGs is for backup for sensitive load sites such as hospitals, and they are also considered a feasible power source for Distributed Generation (DG) providing on-site generation in proximity to remote loads. The MTGs have the compressor, the turbine, and the electrical generator mounted on a single shaft. For this reason, the electrical energy is generated at high frequency and is incompatible with the power grid. Therefore, MTGs must contain, in addition, a power conditioning unit to generate an AC voltage at the grid frequency. Presently, this power conditioning unit consists of a rectifier followed by a DC/AC inverter, both rated at the full MTG’s power. The losses of the power conditioning unit account to some 3-5%. Moreover, the full-power processing stage is a bulky and costly piece of equipment that also lowers the overall system reliability. In this study, we propose a new type of power conditioning stage in which only a small fraction of the power is processed. A low power converter is used only to program the rotor current (i.e. the excitation current which is substantially lower). Thus, the MTG's output voltage is shaped to the desired amplitude and frequency by proper programming of the excitation current. The control is realized by causing the rotor current to track the electrical frequency (which is related to the shaft frequency) with a difference that is exactly equal to the line frequency. Since the phasor of the rotation speed and the phasor of the rotor magnetic field are multiplied, the spectrum of the MTG generator voltage contains the sum and the difference components. The desired difference component is at the line frequency (50/60 Hz), whereas the unwanted sum component is at about twice the electrical frequency of the stator. The unwanted high frequency component can be filtered out by a low-pass filter leaving only the low-frequency output. This approach allows elimination of the large power conditioning unit incorporated in conventional MTGs. Instead, a much smaller and cheaper fractional power stage can be used. The proposed technology is also applicable to other high rotation generator sets such as aircraft power units.

Keywords: gas turbine, inverter, power multiplier, distributed generation

Procedia PDF Downloads 235

Authors: Babak Sadeghi, Rony Snyders, Marie-Paule.Delplancke-Ogletree

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Nitrogen fixation (NF) is one of the crucial industrial processes. Many attempts have been made in order to artificially fix nitrogen, and among them, the Haber-Bosch’s (H-B) process is widely used. However, it presents two major drawbacks: huge fossil feedstock consumption and noticeable greenhouse gases emission. It is, therefore, necessary to develop alternatives. Plasma technology, as an inherent “green” technology, is considered to have a great potential for reducing the environmental impacts and improving the energy efficiency of the NF process. In this work, we have studied the catalyst assisted microwave plasma for NF application. Heterogeneous catalysts of MoO₃, with various loads 0, 5, 10, 20, and 30 wt%, supported on γ-alumina were prepared by conventional wet impregnation. Crystallinity, surface area, pore size, and microstructure were obtained by X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) adsorption isotherm, Scanning electron microscopy (SEM), and Transmission electron microscopy (TEM). The XRD patterns of calcined alumina confirm the γ- phase. Characteristic picks of MoO₃ could not be observed for low loads (< 20 wt%), likely indicating a high dispersion of metal oxide over the support. The specific surface area along with pores size are decreasing with increasing calcination temperature and MoO₃ loading. The MoO₃ loading does not modify the microstructure. TEM and SEM results for loading inferior to 20 wt% are coherent with a monolayer of MoO₃ on the support as proposed elsewhere. For loading of 20 wt% and more, TEM and Electron diffraction (ED) show nanocrystalline ₃-D MoO₃ particles. The catalytic performances of these catalysts were investigated in the post-discharge of a microwave plasma for NOx formation from N₂/O₂ mixtures. The plasma is sustained by a surface wave launched in a quartz tube via a surfaguide supplied by a 2.45 GHz microwave generator in pulse mode. In-situ identification and quantification of the products were carried out by Fourier-transform infrared spectroscopy (FTIR) in the post-discharge region. FTIR analysis of the exhausted gas reveal NO and NO₂ bands in presence of catalyst while only NO band were assigned without catalyst. On the other hand, in presence of catalyst, a 10% increase of NOₓ formation and of 20% increase in energy efficiency are observed.

Keywords: γ-Al2O₃-MoO₃, µ-waveplasma, N2 fixation, Plasma-catalysis, Plasma diagnostic

Procedia PDF Downloads 169

Authors: Aaron Huber, Manoj Karwa

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Floods, heat waves, drought, wildfires, tornadoes and other environmental disasters are a snapshot of looming national problems that can create increasing demands on the national grid. With nearly 500,000 school buses on the road and the environmental protection agency (EPA) providing nearly $1B for electric school buses, there is a solution for this national issue. Bidirectional batteries in electric school buses enable a future proof solution to sustain the power grid during adverse environmental conditions and other periods of high demand. School buses have larger batteries than standard electric vehicles. When they are not transporting students, these buses can spend peak solar hours parked and plugged into bi-directional direct current fast chargers (DCFC). A partnership with Highland Electric, Proterra and Rhombus enabled over 7 MWh of energy servicing Massachusetts and Vermont grids. The buses were part of a vehicle to grid (V2G) program with National Grid and Green Mountain Power that can charge an average American home for one month with a single bus. V2G infrastructure enables school systems to future proof their charging strategies, strengthen their local grids and can create additional revenue streams with their EV fleets. A bidirectional ecosystem with Highland, Proterra and Rhombus can enable grid resiliency or the ability to withstand power outages caused by excessive demands, natural disasters or rogue nation's attacks with no loss of service. A fleet of school buses is a standalone resilient asset that can be accessed across a city to keep its citizens safe without having any toxic fumes. Nearly 95% of all school buses across USA are powered by diesel internal combustion engines. Diesel exhaust has been classified as a human carcinogen, and it can lead to and exacerbate respiratory conditions. Bidirectional school buses and chargers enable energy justice by providing backup power in case of emergencies or high demand for marginalized communities and aim to make energy more accessible, affordable, clean, and democratically managed.

Keywords: V2G, vehicle to grid, electric buses, eBuses, DC fast chargers, DCFC

Procedia PDF Downloads 73

Authors: Jonathan Gong

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

Procedia PDF Downloads 125

Authors: Amlan Kumar Das, Avinash Marwal, Vikram Pareek

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Liposome plays an important role in medical and pharmaceutical science as e.g. nano scale drug carriers. Liposomes are vesicles of varying size consisting of a spherical lipid bilayer and an aqueous inner compartment. Magnet-driven liposome used for the targeted delivery of drugs to organs and tissues1. These liposome preparations contain encapsulated drug components and finely dispersed magnetic particles. Liposomes are vesicles of varying size consisting of a spherical lipid bilayer and an aqueous inner compartment that are generated in vitro. These are useful in terms of biocompatibility, biodegradability, and low toxicity, and can control biodistribution by changing the size, lipid composition, and physical characteristics2. Furthermore, liposomes can entrap both hydrophobic and hydrophilic drugs and are able to continuously release the entrapped substrate, thus being useful drug carriers. Magnetic liposomes (MLs) are phospholipid vesicles that encapsulate magneticor paramagnetic nanoparticles. They are applied as contrast agents for magnetic resonance imaging (MRI)3. The biological synthesis of nanoparticles using plant extracts plays an important role in the field of nanotechnology4. Green-synthesized magnetite nanoparticles-protein hybrid has been produced by treating Iron (III)/Iron(II) chloride with the leaf extract of Dhatura Inoxia. The phytochemicals present in the leaf extracts act as a reducing as well stabilizing agents preventing agglomeration, which include flavonoids, phenolic compounds, cardiac glycosides, proteins and sugars. The magnetite nanoparticles-protein hybrid has been trapped inside the aqueous core of the liposome prepared by reversed phase evaporation (REV) method using oleic and linoleic acid which has been shown to be driven under magnetic field confirming the formation magnetic liposome (ML). Chemical characterization of stealth magnetic liposome has been performed by breaking the liposome and release of magnetic nanoparticles. The presence iron has been confirmed by colour complex formation with KSCN and UV-Vis study using spectrophotometer Cary 60, Agilent. This magnet driven liposome using nanoparticles-protein hybrid can be a smart vesicles for the targeted drug delivery.

Keywords: nanoparticles-protein hybrid, magnetic liposome, medical, pharmaceutical science

Procedia PDF Downloads 245

Authors: Alicia Yanadira Nava Fuentes, Ilse Cervantes Camacho, Amadeo José Argüelles Cruz, Ana María Balboa Verduzco

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Augmented reality promises being present in future driving, with its immersive technology let to show directions and maps to identify important places indicating with graphic elements when the car driver requires the information. On the other side, driving is considered a multitasking activity and, for some people, a complex activity where different situations commonly occur that require the immediate attention of the car driver to make decisions that contribute to avoid accidents; therefore, the main aim of the project is the instrumentation of a platform with biometric sensors that allows evaluating the performance in driving vehicles with the influence of augmented reality devices to detect the level of attention in drivers, since it is important to know the effect that it produces. In this study, the physiological sensors EPOC X (EEG), ECG06 PRO and EMG Myoware are joined in the driving test platform with a Logitech G29 steering wheel and the simulation software City Car Driving in which the level of traffic can be controlled, as well as the number of pedestrians that exist within the simulation obtaining a driver interaction in real mode and through a MSP430 microcontroller achieves the acquisition of data for storage. The sensors bring a continuous analog signal in time that needs signal conditioning, at this point, a signal amplifier is incorporated due to the acquired signals having a sensitive range of 1.25 mm/mV, also filtering that consists in eliminating the frequency bands of the signal in order to be interpretative and without noise to convert it from an analog signal into a digital signal to analyze the physiological signals of the drivers, these values are stored in a database. Based on this compilation, we work on the extraction of signal features and implement K-NN (k-nearest neighbor) classification methods and decision trees (unsupervised learning) that enable the study of data for the identification of patterns and determine by classification methods different effects of augmented reality on drivers. The expected results of this project include are a test platform instrumented with biometric sensors for data acquisition during driving and a database with the required variables to determine the effect caused by augmented reality on people in simulated driving.

Keywords: augmented reality, driving, physiological signals, test platform

Procedia PDF Downloads 134

Authors: Anderson Rocha-Buelvas, Jaqueline Mena Huertas, Edith Burbano Rosero, Arsenio Hidalgo Troya, Mauricio Casas Cruz

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COVID-19 is a disruptive pandemic for the public health and economic system of whole countries, including Colombia. Nariño Department is the southwest of the country and draws attention to being on the border with Ecuador, constantly facing demographic transition affecting infections between countries. In Nariño, the early routine diagnosis of SARS-CoV-2, which can be handled at BSL-2, has affected the transmission dynamics of COVID-19. However, new emerging and re-emerging viruses with biological flexibility classified as a Risk Group 3 agent can take advantage of epidemiological opportunities, generating the need to increase clinical diagnosis, mainly in border regions between countries. The overall objective of this project was to assure the quality of the analytical process in the diagnosis of high biological risk pathogens in Nariño by building a laboratory that includes biosafety level (BSL)-2 and (BSL)-3 containment zones. The delimitation of zones was carried out according to the Verification Tool of the National Health Institute of Colombia and following the standard requirements for the competence of testing and calibration laboratories of the International Organization for Standardization. This is achieved by harmonization of methods and equipment for effective and durable diagnostics of the large-scale spread of highly pathogenic microorganisms, employing negative-pressure containment systems and UV Systems in accordance with a finely controlled electrical system and PCR systems as new diagnostic tools. That increases laboratory capacity. Protection in BSL-3 zones will separate the handling of potentially infectious aerosols within the laboratory from the community and the environment. It will also allow the handling and inactivation of samples with suspected pathogens and the extraction of molecular material from them, allowing research with pathogens with high risks, such as SARS-CoV-2, Influenza, and syncytial virus, and malaria, among others. The diagnosis of these pathogens will be articulated across the spectrum of basic, applied, and translational research that could receive about 60 daily samples. It is expected that this project will be articulated with the health policies of neighboring countries to increase research capacity.

Keywords: medical laboratory science, SARS-CoV-2, public health surveillance, Colombia

Procedia PDF Downloads 85

Authors: Bao Chen, Frederic Kuznik, Matthieu Horgnies, Kevyn Johannes, Vincent Morin, Edouard Gengembre

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The contradiction between energy consumption, environment protection, and social development is increasingly intensified during recent decade years. At the same time, as avoiding fossil-fuels-thirsty, people turn their view on the renewable green energy, such as solar energy, wind power, hydropower, etc. However, due to the unavoidable mismatch on geography and time for production and consumption, energy storage seems to be one of the most reasonable solutions to enlarge the use of renewable energies. Thermal energy storage (TES), a branch of energy storage solution, mainly concerns the capture, storage and consumption of thermal energy for later use in different scales (individual house, apartment, district, and city). In TES research field, sensible heat and latent heat storage have been widely studied and presented at an advanced stage of development. Compared with them, thermochemical energy storage is still at initial phase but provides a relatively higher theoretical energy density and a long shelf life without heat dissipation during storage. Among thermochemical energy storage materials, inorganic pure or composite compounds like micro-porous silica gel, SrBr₂ hydrate and MgSO₄-Zeolithe have been reported as promising to be integrated into thermal energy storage systems. However, the cost of these materials, one of main obstacles, may hinder the wide use of energy storage systems in real application scales (individual house, apartment, district and even city). New studies on ettringite show promising application for thermal energy storage since its high energy density and large resource from cementitious materials. Ettringite, or calcium trisulfoaluminate hydrate, of which chemical formula is 3CaO∙Al₂O₃∙3CaSO₄∙32H₂O, or C₆AS̅₃H₃₂ as known in cement chemistry notation, is one of the most important members of AFt group. As a common compound in hydrated cements, ettringite has been widely studied for its performances in construction but barely known as a thermochemical material. For this study, we summarize available data about the structure and properties of ettringite and its metastable phase (meta-ettringite), including the processes of hydration, thermal conversion and carbonation durability for thermal energy storage.

Keywords: building materials, ettringite, meta-ettringite, thermal energy storage

Procedia PDF Downloads 209

Authors: T. Sarat Chandra, M. Maneesh Kumar, S. N. Mudliar, V. S. Chauhan, S. Mukherji, R. Sarada

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The life cycle assessment (LCA) of biodiesel production from freshwater microalgae Scenedesmus dimorphus cultivated in open raceway pond is performed. Various scenarios for biodiesel production were simulated using primary and secondary data. The parameters varied in the modelled scenarios were related to biomass productivity, mode of culture mixing and type of energy source. The process steps included algae cultivation in open raceway ponds, harvesting by chemical flocculation, dewatering by mechanical drying option (MDO) followed by extraction, reaction and purification. Anaerobic digestion of defatted algal biomass (DAB) for biogas generation is considered as a co-product allocation and the energy derived from DAB was thereby used in the upstream of the process. The scenarios were analysed for energy demand, emissions and environmental impacts within the boundary conditions grounded on "cradle to gate" inventory. Across all the Scenarios, cultivation via raceway pond was observed to be energy intensive process. The mode of culture mixing and biomass productivity determined the energy requirements of the cultivation step. Emissions to Freshwater were found to be maximum contributing to 93-97% of total emissions in all the scenarios. Global warming potential (GWP) was the found to be major environmental impact accounting to about 99% of total environmental impacts in all the modelled scenarios. It was noticed that overall emissions and impacts were directly related to energy demand and an inverse relationship was observed with biomass productivity. The geographic location of an energy source affected the environmental impact of a given process. The integration of defatted algal remnants derived electricity with the cultivation system resulted in a 2% reduction in overall energy demand. Direct biogas generation from microalgae post harvesting is also analysed. Energy surplus was observed after using part of the energy in upstream for biomass production. Results suggest biogas production from microalgae post harvesting as an environmentally viable and sustainable option compared to biodiesel production.

Keywords: biomass productivity, energy demand, energy source, Lifecycle Assessment (LCA), microalgae, open raceway pond

Procedia PDF Downloads 284

Authors: Ahmed Auda, Manjree Agarwala, Giles Hardya, Yonglin Rena

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Botrytis cinerea is a major pest for many plants. It can attack a wide range of plant parts. It can attack buds, flowers, and leaves, stems, and fruit. However, B. cinerea can be mixed with other diseases that cause the same damage. There are many species of botrytis and more than one different strains of each. Botrytis might infect the foliage of nursery stock stored through winter in damp conditions. There are no known resistant plants. Botrytis must have nutrients or food source before it infests the plant. Nutrients leaking from wounded plant parts or dying tissue like old flower petals give the required nutrients. From this food, the fungus becomes more attackers and invades healthy tissue. Dark to light brown rot forms in the ill tissue. High humidity conditions support the growth of this fungus. However, we suppose that selection pressure can act on the morphological and neurophysiologic filter properties of the receiver and on both the biochemical and the physiological regulation of the signal. Communication is implied when signal and receiver evolves toward more and more specific matching, culminating. In other hand, receivers respond to portions of a body odor bouquet which is released to the environment not as an (intentional) signal but as an unavoidable consequence of metabolic activity or tissue damage. Each year Botrytis species can cause considerable economic losses to plant crops. Even with the application of strict quarantine and control measures, these fungi can still find their way into crops and cause the imposition of onerous restrictions on exports. Blueberry fruit mould caused by a fungal infection usually results in major losses during post-harvest storage. Therefore, the management of infection in early stages of disease development is necessary to minimize losses. The overall purpose of this study will develop sensitive, cheap, quick and robust diagnostic techniques for the detection of B. cinerea in blueberry. The specific aim was designed to investigate the performance of volatile organic compounds (VOCs) in the detection and discrimination of blueberry fruits infected by fungal pathogens with an emphasis on Botrytis in the early storage stage of post-harvest.

Keywords: botrytis cinerea, blueberry, GC/MS, VOCs

Procedia PDF Downloads 237

Authors: Connor Dunlop, Bassim Abbassi, Richard G. Zytner

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Life cycle assessment (LCA) is a powerful tool established by the International Organization for Standardization (ISO) that can be used to assess the environmental impacts of a product or process from cradle to grave. Many studies utilize the LCA methodology within the site remediation field to compare various decontamination methods, including bioremediation, soil vapor extraction or excavation, and off-site disposal. However, with the authors' best knowledge, limited information is available in the literature on a sustainability tool that could be used to help with the selection of the optimal remediation technology. This tool, based on the LCA methodology, would consider site conditions like environmental, economic, and social impacts. Accordingly, this project was undertaken to develop a tool to assist with the selection of optimal sustainable technology. Developing a proper tool requires a large amount of data. As such, data was collected from previous LCA studies looking at site remediation technologies. This step identified knowledge gaps or limitations within project data. Next, utilizing the data obtained from the literature review and other organizations, an extensive LCA study is being completed following the ISO 14040 requirements. Initial technologies being compared include bioremediation, excavation with off-site disposal, and a no-remediation option for a generic gasoline-contaminated site. To complete the LCA study, the modelling software SimaPro is being utilized. A sensitivity analysis of the LCA results will also be incorporated to evaluate the impact on the overall results. Finally, the economic and social impacts associated with each option will then be reviewed to understand how they fluctuate at different sites. All the results will then be summarized, and an interactive tool using Excel will be developed to help select the best sustainable site remediation technology. Preliminary LCA results show improved sustainability for the decontamination of a gasoline-contaminated site for each technology compared to the no-remediation option. Sensitivity analyses are now being completed on on-site parameters to determine how the environmental impacts fluctuate at other contaminated gasoline locations as the parameters vary, including soil type and transportation distances. Additionally, the social improvements and overall economic costs associated with each technology are being reviewed. Utilizing these results, the sustainability tool created to assist in the selection of the overall best option will be refined.

Keywords: life cycle assessment, site remediation, sustainability tool, contaminated sites

Procedia PDF Downloads 54

Authors: Aashutosh Garg, Ankur Rajpal, A. A. Kazmi

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The efficiency of an on-site wastewater treatment system named Johkasou was evaluated based on its pollutant removal efficiency over 10 months. This system was installed at IIT Roorkee and had a capacity of treating 7 m3/d of sewage water, sufficient for a group of 30-50 people. This system was fed with actual wastewater through an equalization tank to eliminate the fluctuations throughout the day. Methanol and ammonium chloride was added into this equalization tank to increase the Chemical Oxygen Demand (COD) and ammonia content of the influent. The outlet from Johkasou is sent to a tertiary unit consisting of a Pressure Sand Filter and an Activated Carbon Filter for further treatment. Samples were collected on alternate days from Monday to Friday and the following parameters were evaluated: Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), Total Suspended Solids (TSS), and Total Nitrogen (TN). The Average removal efficiency for Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), Total Suspended Solids (TSS), and Total Nitrogen (TN) was observed as 89.6, 97.7, 96, and 80% respectively. The cost of treating the wastewater comes out to be Rs 23/m3 which includes electricity, cleaning and maintenance, chemical, and desludging costs. Tests for the coliforms were also performed and it was observed that the removal efficiency for total and fecal coliforms was 100%. The sludge generation rate is approximately 20% of the BOD removal and it needed to be removed twice a year. It also showed a very good response against the hydraulic shock load. We performed vacation stress analysis on the system to evaluate the performance of the system when there is no influent for 8 consecutive days. From the result of stress analysis, we concluded that system needs a recovery time of about 48 hours to stabilize. After about 2 days, the system returns again to original conditions and all the parameters in the effluent become within the limits of National Green Tribunal (NGT) standards. We also performed another stress analysis to save the electricity in which we turned the main aeration blower off for 2 to 12 hrs a day and the results showed that we can turn the blower off for about 4-6 hrs a day and this will help in reducing the electricity costs by about 25%. It was concluded that the Johkasou system can remove a sufficient amount of all the physiochemical parameters tested to satisfy the prescribed limit set as per Indian Standard.

Keywords: on-site treatment, domestic wastewater, Johkasou, nutrient removal, pathogens removal

Procedia PDF Downloads 108

Authors: Siti Aisya Gany, Swee Ching Tan, Sook Yee Gan

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Diminished antioxidant defense or increased production of reactive oxygen species in the biological system can result in oxidative stress which may lead to various neurodegenerative diseases including Alzheimer’s disease (AD). Microglial activation also contributes to the progression of AD by producing several pro-inflammatory cytokines, nitric oxide (NO), and prostaglandin E2 (PGE2). Oxidative stress and inflammation have been reported to be possible pathophysiological mechanisms underlying AD. In addition, the cholinergic hypothesis postulates that memory impairment in patient with AD is also associated with the deficit of cholinergic function in the brain. Although a number of drugs have been approved for the treatment of AD, most of these synthetic drugs have diverse side effects and yield relatively modest benefits. Marine algae have great potential in pharmaceutical and biomedical applications as they are valuable sources of bioactive properties such as anti-coagulation, anti-microbial, anti-oxidative, anti-cancer and anti-inflammatory. Hence, this study aimed to provide an overview of the properties of Malaysian seaweeds (Padina australis, Sargassum polycystum and Caulerpa racemosa) in inhibiting oxidative stress, neuroinflammation and cholinesterase enzymes. All tested samples significantly exhibit potent DPPH and moderate Superoxide anion radical scavenging ability (P<0.05). Hexane and methanol extracts of S. polycystum exhibited the most potent radical scavenging ability with IC50 values of 0.1572 ± 0.004 mg/ml and 0.8493 ± 0.02 for DPPH and ABTS assays, respectively. Hexane extract of C. racemosa gave the strongest superoxide radical inhibitory effect (IC50 of 0.3862± 0.01 mg/ml). Most seaweed extracts significantly inhibited the production of cytokine (IL-6, IL-1 β, TNFα) and NO in a concentration-dependent manner without causing significant cytotoxicity to the lipopolysaccharide (LPS)-stimulated microglia cells (P<0.05). All extracts suppressed cytokine and NO level by more than 80% at the concentration of 0.4mg/ml. In addition, C. racemosa and S. polycystum also showed anti-acetylcholinesterase activities with the IC50 values ranging from 0.086-0.115 mg/ml. Moreover, C. racemosa and P. australis were also found to be active against butyrylcholinesterase with IC50 values ranging from 0.118-0.287 mg/ml.

Keywords: anti-cholinesterase, anti-oxidative, neuroinflammation, seaweeds

Procedia PDF Downloads 660

Authors: M. Amparo Núñez-Andrés, Felipe Buill, Albert Prades

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More and more frequently, geomatics techniques such as terrestrial laser scanning or digital photogrammetry, either terrestrial or from drones, are being used to obtain digital terrain models (DTM) used for the monitoring of geological phenomena that cause natural disasters, such as landslides, rockfalls, debris-flow. One of the main multitemporal analyses developed from these models is the quantification of volume changes in the slopes and hillsides, either caused by erosion, fall, or land movement in the source area or sedimentation in the deposition zone. To carry out this task, it is necessary to filter the point clouds of all those elements that do not belong to the slopes. Among these elements, vegetation stands out as it is the one we find with the greatest presence and its constant change, both seasonal and daily, as it is affected by factors such as wind. One of the best-known indexes to detect vegetation on the image is the NVDI (Normalized Difference Vegetation Index), which is obtained from the combination of the infrared and red channels. Therefore it is necessary to have a multispectral camera. These cameras are generally of lower resolution than conventional RGB cameras, while their cost is much higher. Therefore we have to look for alternative indices based on RGB. In this communication, we present the results obtained in Georisk project (PID2019‐103974RB‐I00/MCIN/AEI/10.13039/501100011033) by using the GLI (Green Leaf Index) and ExG (Excessive Greenness), as well as the change to the Hue-Saturation-Value (HSV) color space being the H coordinate the one that gives us the most information for vegetation filtering. These filters are applied both to the images, creating binary masks to be used when applying the SfM algorithms, and to the point cloud obtained directly by the photogrammetric process without any previous filter or the one obtained by TLS (Terrestrial Laser Scanning). In this last case, we have also tried to work with a Riegl VZ400i sensor that allows the reception, as in the aerial LiDAR, of several returns of the signal. Information to be used for the classification on the point cloud. After applying all the techniques in different locations, the results show that the color-based filters allow correct filtering in those areas where the presence of shadows is not excessive and there is a contrast between the color of the slope lithology and the vegetation. As we have advanced in the case of using the HSV color space, it is the H coordinate that responds best for this filtering. Finally, the use of the various returns of the TLS signal allows filtering with some limitations.

Keywords: RGB index, TLS, photogrammetry, multispectral camera, point cloud

Procedia PDF Downloads 146

Authors: N. N. Pandey, Raghvendra Singh, Biju S. Kamlam, Bipin K. Vishwakarma, Preetam Kala

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The rainbow trout (Oncorhynchus mykiss) is an exotic salmonid fish, well known for its fast growth, tremendous ability to thrive in diverse conditions, delicious flesh and hard fighting nature in Europe and other countries. Rainbow trout farming has a great potential for its contribution to the mainstream economy of Himalayan states in India and other temperate countries. These characteristics establish them as one of the most widely introduced and cultured fish across the globe, and its farming is also prominent in the cold water regions of India. Nevertheless, genetic fatigue, slow growth, early maturity, and low productivity are limiting the expansion of trout production. Moreover, farms adjacent to natural streams or other water sources are subject to escape of domesticated rainbow trout into the wild, which is a serious environmental concern as the escaped fish is subject to contaminate and disrupt the receiving ecosystem. A decline in production traits due to early maturity prolongs the culture duration and affects the profit margin of rainbow trout farms in India. A viable strategy that could overcome these farming constraints in large scale operation is the production of triploid fish that are sterile and more heterozygous. For better triploidy induction rate (TR), heat shock at 28°C for 10 minutes and pressure shock 9500 psi pressure for 5 minutes is applied to green eggs with 90-100% of triploidy success and 72-80% survival upto swim-up fry stage. There is 20% better growth in aquaculture with triploids rainbow trout over diploids. As compared to wild diploid fish, larger sized and fitter triploid rainbow trout in natural waters attract to trout anglers, and support the development of recreational fisheries by state fisheries departments without the risk of contaminating existing gene pools and disrupting local fish diversity. Overall, enhancement of productivity in rainbow trout farms and trout production in coldwater regions, development of lucrative trout angling and better ecological management is feasible with triploid rainbow trout.

Keywords: rainbow trout, triploids fish, heat shock, pressure shock, trout angling

Procedia PDF Downloads 122

Authors: Ali Pourkazemi

Abstract:

The transient radar method (TRM) is one of the non-destructive methods that was introduced by authors a few years ago. The transient radar method can be classified as a wave-based non destructive testing (NDT) method that can be used in a wide frequency range. Nevertheless, it requires a narrow band, ranging from a few GHz to a few THz, depending on the application. As a time-of-flight and real-time method, TRM can measure the electromagnetic properties of the sample under test not only quickly and accurately, but also blindly. This means that it requires no prior knowledge of the sample under test. For multi-layer structures, TRM is not only able to detect changes related to any parameter within the multi-layer structure but can also measure the electromagnetic properties of each layer and its thickness individually. Although the temperature, humidity, and general environmental conditions may affect the sample under test, they do not affect the accuracy of the Blind TRM algorithm. In this paper, the electromagnetic properties as well as the thickness of the individual building insulation materials - as a single-layer structure - are measured experimentally. Finally, the correlation between the reflection coefficients and some other technical parameters such as sound insulation, thermal resistance, thermal conductivity, compressive strength, and density is investigated. The sample to be studied is 30 cm x 50 cm and the thickness of the samples varies from a few millimeters to 6 centimeters. This experiment is performed with both biostatic and differential hardware at 10 GHz. Since it is a narrow-band system, high-speed computation for analysis, free-space application, and real-time sensor, it has a wide range of potential applications, e.g., in the construction industry, rubber industry, piping industry, wind energy industry, automotive industry, biotechnology, food industry, pharmaceuticals, etc. Detection of metallic, plastic pipes wires, etc. through or behind the walls are specific applications for the construction industry.

Keywords: transient radar method, blind electromagnetic geometrical parameter extraction technique, ultrafast nondestructive multilayer dielectric structure characterization, electronic measurement systems, illumination, data acquisition performance, submillimeter depth resolution, time-dependent reflected electromagnetic signal blind analysis method, EM signal blind analysis method, time domain reflectometer, microwave, milimeter wave frequencies

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