Search results for: plant classification

Authors: Tine Cencič, Marko Hočevar, Brane Širok

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An experimental investigation has been made to detect cavitation in pump–storage hydro power plant prototype suffering from cavitation in pump mode. Vibrations and acoustic emission on the housing of turbine bearing and pressure fluctuations in the draft tube were measured and the corresponding signals have been recorded and analyzed. The analysis was based on the analysis of high-frequency content of measured variables. The pump-storage hydro power plant prototype has been operated at various input loads and Thoma numbers. Several estimators of cavitation were evaluated according to coefficient of determination between Thoma number and cavitation estimators. The best results were achieved with a compound discharge coefficient cavitation estimator. Cavitation estimators were evaluated in several intervals of frequencies. Also, a prediction of cavitation erosion was made in order to choose the appropriate maintenance and repair periods.

Keywords: cavitation erosion, turbine, cavitation measurement, fluid dynamics

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Authors: Ali Fadhel Ahmed, Tuqa Abdulkareem Hameed

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Moringa oleifera (leaves and seeds) ethanolic and aqueous extracts were tested for antibacterial activity. The effect of plant extracts on three types of bacterial species: Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae, was investigated. Using the agar well diffusion method, ethanolic extracts of Moringa oleifera demonstrated a significant antibacterial effect on the forty tested bacterial strains. Seed-induced inhibition zones (ethanolic extracts)were ranged from16 to 24 mm in diameter against S. aureus, respectively, whileE. coli and K. pneumonia had no effect. Gram-positive and Gram-negative bacteria were not affected by alcoholic and aqueous plant leaf extracts. The purpose of this present study was to look at the cytotoxic effects of M.Oleifera plant (alcoholic extracts).

Keywords: moringa oleifera, escherichia coli, klebsiella pneumoniae, staphylococcus aureus

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Authors: Behic Mert

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This study reports a facile approach to structure soft solids from microfluidizer lycopene-rich plant based structure and oil. First carotenoid-rich plant material (pumpkin was used in this study) processed with high-pressure microfluidizer to release lycopene molecules, then an emulsion was formed by mixing processed plant material and oil. While, in emulsion state lipid soluble carotenoid molecules were allowed to dissolve in the oil phase, the fiber material of plant material provided the network which was required for emulsion stabilization. Additional hydrocolloids (gelatin, xhantan, and pectin) up to 0.5% were also used to reinforce the emulsion stability and their impact on final product properties were evaluated via rheological, textural and oxidation studies. Finally, water was removed from emulsion phase by drying in a tray dryer at 40°C for 36 hours, and subsequent shearing resulted in soft solid (ole gel) structures. The microstructure of these systems was revealed by cryo-scanning electron microscopy. Effect of hydrocolloids on total lycopene and surface lycopene contents were also evaluated. The surface lycopene was lowest in gelatin containing oleo gels and highest in pectin-containing oleo gels. This study outlines the novel emulsion-based structuring method that can be used to encapsulate lycopene without the need of separate extraction of them.

Keywords: lycopene, encapsulation, fiber, oleo gel

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Authors: Sidra Naeem, Ayesha Naeem, Sahar Rahim, Nadia Nawaz Qadri

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Citrus greening is a bacterial disease that causes considerable damage to citrus fruits worldwide. Efficient method for this disease detection must be carried out to minimize the production loss. This paper presents a pattern recognition system that comprises three stages for the detection of citrus greening from Orange leaves: segmentation, feature extraction and classification. Image segmentation is accomplished by adaptive thresholding. The feature extraction stage comprises of three visual descriptors i.e. shape, color and texture. From shape feature we have used asymmetry index, from color feature we have used histogram of Cb component from YCbCr domain and from texture feature we have used local binary pattern. Classification was done using support vector machines and k nearest neighbors. The best performances of the system is Accuracy = 88.02% and AUROC = 90.1% was achieved by automatic segmented images. Our experiments validate that: (1). Segmentation is an imperative preprocessing step for computer assisted diagnosis of citrus greening, and (2). The combination of shape, color and texture features form a complementary set towards the identification of citrus greening disease.

Keywords: citrus greening, pattern recognition, feature extraction, classification

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Authors: K. Zhumadilov, P. Kazymbet, A. Ivannikov, M. Bakhtin, A. Akylbekov, K. Kadyrzhanov, A. Morzabayev, M. Hoshi

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The aim of the study is to evaluate the possible excess of dose received by uranium processing plant workers. The possible excess of dose of workers was evaluated with comparison with population pool (Stepnogorsk) and control pool (Astana city). The measured teeth samples were extracted according to medical indications. In total, twenty-seven tooth enamel samples were analyzed from the residents of Stepnogorsk city (180 km from Astana city, Kazakhstan). About 6 tooth samples were collected from the workers of uranium processing plant. The results of tooth enamel dose estimation show us small influence of working conditions to workers, the maximum excess dose is less than 100 mGy. This is pilot study of EPR dose estimation and for a final conclusion additional sample is required.

Keywords: EPR dose, workers, uranium mines, tooth samples

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Authors: Ercan Ceyhan, Mehmet Ali Avci

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This research was made during the 2015 growing periods in the trial filed of ‘Research Station for Department of Field Crops, Agricultural Faculty, Selcuk University’ according to ‘Randomized Blocks Design’ with 3 replications. Research material was the following pea lines; PS16, PS18, PS21, PS23, PS24, PS25, PS36, PS47, PS49, PS51, PS54, PS58, PS67, PS69, PS71, PS73, PS83, PS84, PS87 and PSKY and three cultivars and other 2 commercial varieties named as Bolero, Rondo and Ultrello. Some agronomical characteristics such as plant height (cm) number of pod per plant number of seed per pod number of seed per plant 100 seed weight (g) and seed yield (kg ha-1) were determined. Results of the research implicated that the new developed lines were superior compared with the control (commercial) varieties by means of most of the characteristics. Nevertheless, similar researches should be continued in different locations and years.

Keywords: agricultural characters, pea, Pisum sativum, seed yield

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Authors: Renzo Castillo, George Tsatsaronis

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High-temperature ceramic membranes for air separation represents an important option to reduce the significant efficiency drops incurred in state-of-the-art cryogenic air separation for high tonnage oxygen production required in oxyfuel power stations. This study is focused on the thermodynamic analysis of two power plant model designs: the state-of-the-art supercritical 600ᵒC hard coal plant (reference power plant Nordrhein-Westfalen) and the membrane-based oxyfuel concept implemented in this reference plant. In the latter case, the oxygen is separated through a mixed-conducting hollow fiber perovskite membrane unit in the three-end operation mode, which has been simulated under vacuum conditions on the permeate side and at high-pressure conditions on the feed side. The thermodynamic performance of each plant concept is assessed by conventional exergetic analysis, which determines location, magnitude and sources of efficiency losses, and advanced exergetic analysis, where endogenous/exogenous and avoidable/unavoidable parts of exergy destruction are calculated at the component and full process level. These calculations identify thermodynamic interdependencies among components and reveal the real potential for efficiency improvements. The endogenous and exogenous exergy destruction portions are calculated by the decomposition method, a recently developed straightforward methodology, which is suitable for complex power stations with a large number of process components. Lastly, an improvement priority ranking for relevant components, as well as suggested changes in process layouts are presented for both power stations.

Keywords: exergy, carbon capture and storage, ceramic membranes, perovskite, oxyfuel combustion

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Authors: S. Nandagopalan, N. Pradeep

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The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, Bayesian, echocardiographic image, feature vector

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Authors: Obulesh Avuku, Satwik Sunnam, Sri Charan Mohan Janthuka, Keerthi Yalamaddi

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In the United States alone, there are over 1.5 million spine fractures per year, resulting in about 17,730 spinal cord injuries. The cervical spine is where fractures in the spine most frequently occur. The prevalence of spinal fractures in the elderly has increased, and in this population, fractures may be harder to see on imaging because of coexisting degenerative illness and osteoporosis. Nowadays, computed tomography (CT) is almost completely used instead of radiography for the imaging diagnosis of adult spine fractures (x-rays). To stop neurologic degeneration and paralysis following trauma, it is vital to trace any vertebral fractures at the earliest. Many approaches have been proposed for the classification of the cervical spine [2d models]. We are here in this paper trying to break the bounds and use the vision transformers, a State-Of-The-Art- Model in image classification, by making minimal changes possible to the architecture of ViT and making it 3D-enabled architecture and this is evaluated using a weighted multi-label logarithmic loss. We have taken this problem statement from a previously held Kaggle competition, i.e., RSNA 2022 Cervical Spine Fracture Detection.

Keywords: cervical spine, spinal fractures, osteoporosis, computed tomography, 2d-models, ViT, multi-label logarithmic loss, Kaggle, public score, private score

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Authors: Kazeem Adesina Dauda, Waheed Babatunde Yahya

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To design a tree that maximizes within-node homogeneity, there is a need for a homogeneity measure that is appropriate for event history data with multiple risks. We consider the use of Deviance and Modified Cox-Snell residuals as a measure of impurity in Classification Regression Tree (CART) and compare our results with the results of Fiona (2008) in which homogeneity measures were based on Martingale Residual. Data structure approach was used to validate the performance of our proposed techniques via simulation and real life data. The results of univariate competing risk revealed that: using Deviance and Cox-Snell residuals as a response in within node homogeneity classification tree perform better than using other residuals irrespective of performance techniques. Bone marrow transplant data and double-blinded randomized clinical trial, conducted in other to compare two treatments for patients with prostate cancer were used to demonstrate the efficiency of our proposed method vis-à-vis the existing ones. Results from empirical studies of the bone marrow transplant data showed that the proposed model with Cox-Snell residual (Deviance=16.6498) performs better than both the Martingale residual (deviance=160.3592) and Deviance residual (Deviance=556.8822) in both event of interest and competing risks. Additionally, results from prostate cancer also reveal the performance of proposed model over the existing one in both causes, interestingly, Cox-Snell residual (MSE=0.01783563) outfit both the Martingale residual (MSE=0.1853148) and Deviance residual (MSE=0.8043366). Moreover, these results validate those obtained from the Monte-Carlo studies.

Keywords: within-node homogeneity, Martingale residual, modified Cox-Snell residual, classification and regression tree

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Authors: Kumalo F. I., Malimabe M. A., Gumede T. P., Mosoabisane M. F. T.

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This study investigates the fabrication and characterization of bio-nanocomposites consisting of poly (butylene succinate) (PBS) and poly (ԑ-caprolactone) (PCL), reinforced with cellulose extracted from Eucomis autumnalis, a medicinal plant. Bio-nanocomposite films were prepared using the solvent casting method, with cellulose content ranging from 1 to 3 wt%. Comprehensive analysis was conducted using FTIR, SEM, TEM, DSC, TGA, and XRD, to assess morphological, thermal, and structural properties. The results indicated significant improvements in the thermal stability and morphological properties with increasing cellulose content, showcasing the potential of these materials for tissue engineering applications. The use of cellulose extracted from a medicinal plant highlight the potential for sustainable and biocompatible materials in biomedical applications.

Keywords: Bionanocomposites, poly(butylene succinate), poly(caprolactone), eucomis autumnalis, medicinal plant

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Authors: Kishen Prathivadi

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The Sewer Authority Mid-Coastside (SAM) is a Joint Powers Authority formed in 1976 and provides secondary wastewater treatment to an average flow of 2.0 million gallons per day. SAM owns and operates a Wastewater Treatment Plant (WWTP) and a sanitary sewage collection system that collects sewage from its three member agencies: the City of Half Moon Bay, the Granada Community Services District and Montara Water and Sanitary District. The Sewer Authority Mid-Coastside (SAM) partnered with Pacific Gas & Electric, and its contractor GEL America, to review and replace all inefficient lighting fixtures and bulbs at the SAM treatment plant and administrative office. The project focused on replacing old and inefficient lighting fixtures and bulbs, reducing annual operating and maintenance costs, and reducing SAM’s carbon footprint. The project resulted in a 55% overall energy reduction, higher light quality and acuity, and a total operational savings of $495,000 over ten years.

Keywords: energy savings, LED, lighting, electrical

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Authors: Syamala Krishnannair

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A multivariate statistical process monitoring methodology using dissimilarity scale-based singular spectrum analysis (SSA) is proposed for the detection and diagnosis of process faults in the base metal flotation plant. Process faults are detected based on the multi-level decomposition of process signals by SSA using the dissimilarity structure of the process data and the subsequent monitoring of the multiscale signals using the unified monitoring index which combines T² with SPE. Contribution plots are used to identify the root causes of the process faults. The overall results indicated that the proposed technique outperformed the conventional multivariate techniques in the detection and diagnosis of the process faults in the flotation plant.

Keywords: fault detection, fault diagnosis, process monitoring, dissimilarity scale

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Authors: Zayneb Kthiri, Walid Hamada

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The application of organic fertilizers is generally known to be useful to sustain soil fertility and plant growth, especially in poor soils, with less than 1% of organic matter, as it is very common in our Tunisian fields. Therefore, we focused on evaluating the effect of a new released liquid organic fertilizer named Solorga (with 5% of organic matter) compared to a reference product (Espartan: Kimitec, Spain) on tomato plant growth and physiology. Both fertilizers, derived from plant decomposition, were applied at an early stage in hydroponic system and under greenhouse. In hydroponic system, after 14 days of their application by root feeding, a significant difference was observed between treatments. Indeed, Solorga improved shoots and roots length, as well as the biomass respectively, by 45%, 27%, and 27.8% increase rate, while compared to control plants. However, Espartan induced less the measured parameters while compared to untreated control. Moreover, Solorga significantly increased the chlorophyll content by 42% compared to control and by 32% compared to Espartan. In the greenhouse, after 20 days of treatments, the results showed a significant effect of both fertilizers on SPAD index and the number of flowers blossom. Solorga increased the amount of chlorophyll present in the leaf by 7% compared to Espartan as well as the plant height under greenhouse. Moreover, the number of flowers blossom increased by 15% in plants treated with Solorga while compared to Espartan. Whereas, there is no notable difference between both organic fertilizers on the fruits blossom and the number of fruits per blossom. In conclusion, even though there is a difference in the organic matter between both fertilizers, Solorga improved better the plant growth in controlled conditions in hydroponic system while compared to Espartan. Altogether the obtained results are encouraging for the use of Solorga as a soil enriching source of organic matter to help plants to boost their growth and help them to overcome abiotic stresses linked to soil fertility.

Keywords: tomato, plant growth, organic fertilizer, hydroponic system, greenhouse

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Authors: Ji Wen Sun, Nan Ping Shen, Yi Bei Wu

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This study used Nursing Interventions Classification (NIC-6th) to identify the interventions performed in a pediatric cardiology unit, and then to analysis its frequency, time and difficulty, so as to give a brief review on what our nurses have done. The research team selected a 35 beds pediatric cardiology unit, and drawn all the nursing interventions in the nursing record from our hospital information system (HIS) from 1 October 2015 to 30 November 2015, using NIC-6th to do the matching and then counting their frequencies. Then giving each intervention its own time and difficulty code according to NIC-6th. The results showed that nurses in pediatric cardiology unit performed totally 43 interventions from 5394 statements, and most of them were in RN(basic) education level needed and less than 15 minutes time needed. There still had some interventions just needed by a nursing assistant but done by nurses, which should call for nurse managers to think about the suitable staffing. Thus, counting the summary of the product of frequency, time and difficulty for each intervention of each nurse can know one's performance. Acknowledgement Clinical Management Optimization Project of Shanghai Shen Kang Hospital Development Center (SHDC2014615); Hundred-Talent Program of Construction of Nursing Plateau Discipline (hlgy16073qnhb).

Keywords: nursing interventions, nursing interventions classification, nursing record, pediatric cardiology

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Authors: Jacqueline T. Madaure, Phatu W. Mashela

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Interactive effects of cucurbitacin-containing phytonematicides and biocontrol agents on growth and nematode suppression on tomato (Solanum lycopersicum) had not been documented. The objective of this study was to determine the interactive effects of Nemafric-BL phytonematicide, Trichoderma harzianum and Steinernema feltiae on growth of tomato plants and suppression of root-knot (Meloidogyne species) nematodes. A 2x2x2 trial was conducted using tomato cv. ‘HTX’ on a field infested with Meloidogyne species. The treatments were applied at commercial rates. At 56 days after treatments, interactions were significant (P ≤ 0.05) for selected plant variables, without significant interactions on nematode variables. In conclusion, results of the current study did not support the combination of the test products for nematode suppression, except that some combinations improved plant growth.

Keywords: cucumis africanus, cucurbitacin b, ethnobotanicals, entomopathogenic nematodes, natural enemies, plant extracts

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Authors: Oluyemi O. Awolusi, Abimbola M. Enitan, Sheena Kumari, Faizal Bux

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Nitrification is essential to biological processes designed to remove ammonia and/or total nitrogen. It removes the excess nitrogenous compound in wastewater which could be very toxic to the aquatic fauna or cause a serious imbalance of such aquatic ecosystem. Efficient nitrification is linked to an in-depth knowledge of the structure and dynamics of the nitrifying community structure within the wastewater treatment systems. In this study, molecular technique was employed for characterizing the microbial structure of activated sludge [ammonia oxidizing bacteria (AOB) and nitrite oxidizing bacteria (NOB)] in a municipal wastewater treatment with intention of linking it to the plant efficiency. PCR-based phylogenetic analysis was also carried out for. The average operating and environmental parameters, as well as specific nitrification rate of a plant, was investigated during the study. During the investigation, the average temperature was 23±1.5oC. Other operational parameters such as mixed liquor suspended solids and chemical oxygen demand inversely correlated with ammonia removal. The dissolved oxygen level in the plant was constantly lower than the optimum (between 0.24 and 1.267 mg/l) during this study. The plant was treating wastewater with the influent ammonia concentration of 31.69 and 24.47 mg/l. The influent flow rates (ML/day) was 96.81 during the period. The dominant nitrifiers include: Nitrosomonas spp. Nitrobacter spp. and Nitrospira spp. The AOB had a correlation with nitrification efficiency and temperature. This study shows that the specific ammonia oxidizing rate and the specific nitrate formation rates can serve as a good indicator of the plant overall nitrification performance.

Keywords: Ammonia monooxygenase α-subunit gene, amoA, ammonia-oxidizing bacteria, AOB, nitrite-oxidizing bacteria, NOB, specific nitrification rate

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Authors: Abebe Mulugojam Negash, Yongbin Yu, Ekong Favour, Bekalu Nigus Dawit, Molla Woretaw Teshome, Aynalem Birtukan Yirga

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Breast cancer remains a significant health concern, necessitating advancements in diagnostic methodologies. Addressing this, our paper confronts the notable challenges in breast cancer classification, particularly the imbalance in datasets and the constraints in the accuracy and interpretability of prevailing deep learning approaches. We proposed an attention-based residual neural network (ResNet), which effectively combines the robust features of ResNet with an advanced attention mechanism. Enhanced through strategic data augmentation and positive weight adjustments, this approach specifically targets the issue of data imbalance. The proposed model is tested on the BreakHis dataset and achieved accuracies of 99.00%, 99.04%, 98.67%, and 98.08% in different magnifications (40X, 100X, 200X, and 400X), respectively. We evaluated the performance by using different evaluation metrics such as precision, recall, and F1-Score and made comparisons with other state-of-the-art methods. Our experiments demonstrate that the proposed model outperforms existing approaches, achieving higher accuracy in breast cancer classification.

Keywords: residual neural network, attention mechanism, positive weight, data augmentation

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Authors: S. Mokhtar, R. Ibrahim, K. Abdan, A. Rashidi

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The population in the world are growing in very fast rate. It is expected that urban growth and development would create serious questions of food production and processing, transport, and consumption. Future smart green city policies are emerging to support new ways of visualizing, organizing and managing the city and its flows towards developing more sustainable cities in ensuring food security while maintaining its biodiversity. This is a survey paper analyzing the feasibility of developing a smart vertical farming system for climbing food plant to meet the need of food consumption in urban cities with an alternative green material. This paper documents our investigation on specific requirement for farming high valued climbing type food plant suitable for vertical farming, development of appropriate biocomposite material composition, and design recommendations for developing a new smart vertical farming system inside urban buildings. Results include determination of suitable specific climbing food plant species and material manufacturing processes for reinforcing natural fiber for biocomposite material. The results are expected to become recommendations for developing alternative structural materials for climbing food plant later on towards the development of the future smart vertical farming system. This paper contributes to supporting urban farming in cities and promotes green materials for preserving the environment. Hence supporting efforts in food security agenda especially for developing nations.

Keywords: biocomposite, natural reinforce fiber, smart farming, vertical farming

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Authors: Bliss Singhal

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Machine learning (ML) is a branch of Artificial Intelligence (AI) where computers analyze data and find patterns in the data. The study focuses on the detection of metastatic cancer using ML. Metastatic cancer is the stage where cancer has spread to other parts of the body and is the cause of approximately 90% of cancer-related deaths. Normally, pathologists spend hours each day to manually classifying whether tumors are benign or malignant. This tedious task contributes to mislabeling metastasis being over 60% of the time and emphasizes the importance of being aware of human error and other inefficiencies. ML is a good candidate to improve the correct identification of metastatic cancer, saving thousands of lives and can also improve the speed and efficiency of the process, thereby taking fewer resources and time. So far, the deep learning methodology of AI has been used in research to detect cancer. This study is a novel approach to determining the potential of using preprocessing algorithms combined with classification algorithms in detecting metastatic cancer. The study used two preprocessing algorithms: principal component analysis (PCA) and the genetic algorithm, to reduce the dimensionality of the dataset and then used three classification algorithms: logistic regression, decision tree classifier, and k-nearest neighbors to detect metastatic cancer in the pathology scans. The highest accuracy of 71.14% was produced by the ML pipeline comprising of PCA, the genetic algorithm, and the k-nearest neighbor algorithm, suggesting that preprocessing and classification algorithms have great potential for detecting metastatic cancer.

Keywords: breast cancer, principal component analysis, genetic algorithm, k-nearest neighbors, decision tree classifier, logistic regression

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Authors: Mouli Banga, Chaya Ravindra

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Wound classification is a crucial step in wound diagnosis. An effective classifier can aid wound specialists in identifying wound types with reduced financial and time investments, facilitating the determination of optimal treatment procedures. This study presents a deep neural network-based classifier that leverages wound images and their corresponding locations to categorize wounds into various classes, such as diabetic, pressure, surgical, and venous ulcers. By incorporating a developed body map, the process of tagging wound locations is significantly enhanced, providing healthcare specialists with a more efficient tool for wound analysis. We conducted a comparative analysis between two prominent convolutional neural network models, ResNet50 and MobileNetV2, utilizing a dataset of 730 images. Our findings reveal that the RestNet50 outperforms MovileNetV2, achieving an accuracy of approximately 90%, compared to MobileNetV2’s 83%. This disparity highlights the superior capability of ResNet50 in the context of this dataset. The results underscore the potential of integrating deep learning with spatial data to improve the precision and efficiency of wound diagnosis, ultimately contributing to better patient outcomes and reducing healthcare costs.

Keywords: wound classification, MobileNetV2, ResNet50, multimodel

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Authors: Valerija Dunkić, Nada Bezić

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Salvia is a genus of the well-known medicinal plant of Lamiaceae family and growing wild throughout the world. This abstract reports the comparation of the essential oils from leaves and flowers composition of Salvia pratensis L. from mountain Velebit, Croatia. Water distilled essential oils from aerial parts of investigation plant have been analysed by GC and GC/MS using VF-5ms capillary column. Fifty-three constituents, representing 99.4% of the leaf oil composition; 51 constituents, representing 86.8% of the flower oil composition. Essential oil yield varied from 0.9% to 1.3% in the leaf and flower parts of the plant. The flower essential oil was characterized by a high concentration of E-caryophyllene (21.9%) and germacrene D (10.2%). Major constituents of the leaf oil were linalool (17.7%), linalool acetate (15.3%) and limonene (9.8%). The comparative results clearly indicated that the leaf and flower oil compositions of S. pratensis were quite different in terms of major components content. The present study gives additional knowledge about secondary metabolites contents on the genus Salvia.

Keywords: essential oil, leaf, flower, Salvia pratensis L.

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Authors: Komal Rathore, Aydin Sunol, Gita Iranipour, Luke Mulford

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Advanced wastewater treatment plants have complex biological kinetics, time variant influent flow rates and long processing times. Due to these factors, the modeling and operational control of advanced wastewater treatment plants become complicated. However, development of a robust model for advanced wastewater treatment plants has become necessary in order to increase the efficiency of the plants, reduce energy costs and meet the discharge limits set by the government. A dynamic model was designed using the Envirosim (Canada) platform software called BioWin for several wastewater treatment plants in Hillsborough County, Florida. Proper control strategies for various parameters such as mixed liquor suspended solids, recycle activated sludge and waste activated sludge were developed for models to match the plant performance. The models were tuned using both the influent and effluent data from the plant and their laboratories. The plant SCADA was used to predict the influent wastewater rates and concentration profiles as a function of time. The kinetic parameters were tuned based on sensitivity analysis and trial and error methods. The dynamic models were validated by using experimental data for influent and effluent parameters. The dissolved oxygen measurements were taken to validate the model by coupling them with Computational Fluid Dynamics (CFD) models. The Biowin models were able to exactly mimic the plant performance and predict effluent behavior for extended periods. The models are useful for plant engineers and operators as they can take decisions beforehand by predicting the plant performance with the use of BioWin models. One of the important findings from the model was the effects of recycle and wastage ratios on the mixed liquor suspended solids. The model was also useful in determining the significant kinetic parameters for biological wastewater treatment systems.

Keywords: BioWin, kinetic modeling, flowsheet simulation, dynamic modeling

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Authors: Khalid Al Mutairi, Mashhor Mansor, Magdy El-Bana, Saud L. Al-Rowaily, Asyraf Mansor

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The present study was carried out in 20 islands of Farasan Archipelago in Saudi Arabia to describe the biogeography patterns of plants. A total of 191 species belonging to 129 genera and 53 families were identified. Following island biogeography theory, total plant species richness and their ecological groups were positively influenced by island size, number of habitats,elevation and were not affected by isolation. The high level of nestedness, the strong effect of area on total plant species richness and ecological groups, and the similarity of vegetation composition on the islands has several implications for conservation. In conclusion the large and richest islands in Farasan Archipelago such as Farasan Alkbir would conserve higher diversity than several smaller islands. This island also includes rare habitats like coral rocks and rare species. The invasion of the unique habitats such as wadi channels and water catchments in this island by the exotic tree Prosopis juliflora should be managed to conserve the native biodiversity. The protection of such critical habitats is very important on the other large island (e.g. Zufaf), due to their limited distribution in the country.

Keywords: island biogeography, conservation, farasan archipelago, saudi arabia, plant diversity

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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The widespread use of machine learning applications in production is significantly accelerated by improved computing power and increasing data availability. Predictive quality enables the assurance of product quality by using machine learning models as a basis for decisions on test results. The use of real Bosch production data based on geometric gauge blocks from machining, mating data from assembly and hydraulic measurement data from final testing of directional valves is a promising approach to classifying the quality characteristics of workpieces.

Keywords: machine learning, classification, predictive quality, hydraulics, supervised learning

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Authors: Yuntao Liu, Lei Wang, Haoran Xia

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Machine learning has shown extensive applications in the development of classification models for autism spectrum disorder (ASD) using neural image data. This paper proposes a fusion multi-modal classification network based on a graph neural network. First, the brain is segmented into 116 regions of interest using a medical segmentation template (AAL, Anatomical Automatic Labeling). The image features of sMRI and the signal features of fMRI are extracted, which build the node and edge embedding representations of the brain map. Then, we construct a dynamically updated brain map neural network and propose a method based on a dynamic brain map adjacency matrix update mechanism and learnable graph to further improve the accuracy of autism diagnosis and recognition results. Based on the Autism Brain Imaging Data Exchange I dataset(ABIDE I), we reached a prediction accuracy of 74% between ASD and TD subjects. Besides, to study the biomarkers that can help doctors analyze diseases and interpretability, we used the features by extracting the top five maximum and minimum ROI weights. This work provides a meaningful way for brain disorder identification.

Keywords: autism spectrum disorder, brain map, supervised machine learning, graph network, multimodal data, model interpretability

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Authors: Weiwei Cui, Dejian Lin, Leigang Zhang, Yao Wang, Zheng Sun, Lianfeng Li

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Built-in test (BIT) is an important technology in testability field, and it is widely used in state monitoring and fault diagnosis. With the improvement of modern equipment performance and complexity, the scope of BIT becomes larger, and it leads to the emergence of false alarm problem. The false alarm makes the health assessment unstable, and it reduces the effectiveness of BIT. The conventional false alarm suppression methods such as repeated test and majority voting cannot meet the requirement for a complicated system, and the intelligence algorithms such as artificial neural networks (ANN) are widely studied and used. However, false alarm has a very low frequency and small sample, yet a method based on ANN requires a large size of training sample. To recognize the false alarm, we propose a method based on multi-classification support vector machine (SVM) in this paper. Firstly, we divide the state of a system into three states: healthy, false-alarm, and faulty. Then we use multi-classification with '1 vs 1' policy to train and recognize the state of a system. Finally, an example of fault injection system is taken to verify the effectiveness of the proposed method by comparing ANN. The result shows that the method is reasonable and effective.

Keywords: false alarm, fault diagnosis, SVM, k-means, BIT

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Authors: David Hasurungan

Abstract:

This paper objective is to assess the power distribution reliability improvement by retrofitting obsolete vacuum contactor. The case study in Badak Liquefied Natural Gas (LNG) plant is presented in this paper. To support plant operational, Badak LNG is equipped with 4.16 kV switchgear for supplying the storage and loading facilities, utilities facilities, and train facilities. However, there is a problem in two switch gears of sixteen switch gears. The problem is the obsolescence issue in its vacuum contactor. Not only that, but the same switchgear also has suffered from electrical fault due to contact fingering misalignment. In order to improve the reliability in switchgear, the vacuum contactor retrofit project is done. The retrofit will introduce new vacuum contactor design. The comparison between existing design and the new design is presented in this paper. Meanwhile, The reliability assessment and calculation are performed using software Reliasoft 7.

Keywords: reliability, obsolescence, retrofit, vacuum contactor

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Authors: Zdenek Silar, Martin Dobrovolny

Abstract:

This article deals with the obstacle detection on a railway crossing (clearance detection). Detection is based on the optical flow estimation and classification of the flow vectors by K-means clustering algorithm. For classification of passing vehicles is used optical flow direction determination. The optical flow estimation is based on a modified Lucas-Kanade method.

Keywords: background estimation, direction of optical flow, K-means clustering, objects detection, railway crossing monitoring, velocity vectors

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Authors: C. G. Sawant, C. S. Patil

Abstract:

The investigation was conducted during January 2016 on Farmer’s field at Nandur Madhyameshwar, Tq. Niphad, Dist. Nashik (Maharashtra: India) on bio-efficacy of newer insecticides against Plutella xylostella L. infesting cabbage. The cabbage crop (var. Saint) was raised according to package of practices except for plant protection measures. Six newer insecticides along with two conventional insecticides and one synthetic pyrethroid were applied twice at 30 and 55 days after transplanting. Insecticidal solutions were diluted in water (375-500 L ha-1) and applied using knapsack sprayer (16L) with hollow cone nozzle. Treatments included indoxacarb @ 40 g a.i.ha-1, spinosad @ 17.5 g a.i.ha-1, flubendiamide @18.24 g a.i. ha-1, diafenthiuron @ 300 g a. i. ha-1, emamectin benzoate @ 10 g a. i. ha-1, chlorantraniliprole @ 10 g a. i. ha-1, quinalphos @ 250 g a. i. ha-1, triazophos @ 500 g a. i. ha-1, bifenthrin @ 50 g a.i. ha-1 and untreated control. The larvae were counted on head and outside the head. Observations were recorded one day before spray (Precount) and 1,3,7,14 days after spray. Results revealed that all the insecticidal treatments were significantly superior over untreated control by recording lower larval count. Among the insecticidal treatments, significantly lowest number of larvae of diamondback moth was recorded in chlorantraniliprole @ 10 g a.i.ha-1 (1.00 larvae plant-1) followed by spinosad @ 17.5 g a.i. ha-1 (1.45 larvae plant-1 and flubendiamide 18.24 g a.i. ha-1(1.53 larvae plant-1). The efficacy of insecticides reflected on yield of marketable cabbage heads by recording 242.27 qt ha-1 (1:33.38) in the treatment of chlorantraniliprole @ 10 g a.i.ha-1. It was followed by spinosad @ 17.5 g a.i. ha-1 with 236.91 qt ha-1 (1:24.92) and flubendiamide 18.24 g a.i. ha-1 with 228.49 qt ha-1 (1:30.43).

Keywords: bio-efficacy, cabbage, chlorantraniliprole, Plutella xylostella L.

Procedia PDF Downloads 145