Search results for: microbial detection

Authors: M. Doyeni, S. Suproniene, V. Tilvikiene

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Agricultural wastes contribute significantly to global climate change through greenhouse gas emissions if not adequately recycled and sustainably managed. A recurring agricultural waste is livestock wastes that have consistently served as feedstock for biogas systems. The objective of this study was to access the influence of digestate fertilization on soil microbial activity and greenhouse gas emissions in agricultural fields. Wheat (Triticum spp. L.) was fertilized with different types of animal wastes digestates (organic fertilizers) and mineral nitrogen (inorganic fertilizer) for three years. The 170 kg N ha⁻¹ presented in digestates were split fertilized at an application rate of 90 and 80 kg N ha⁻¹. The soil microorganism activity could be predicted significantly using the dehydrogenase activity and soil microbial biomass carbon. By combining the two different monitoring approaches, the different methods applied in this study were sensitive to enzymatic activities and organic carbon in the living component of the soil organic matter. The emissions of greenhouse gasses (carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) were monitored directly by a static chamber system. The soil and environmental variables were measured to determine their influence on greenhouse gas emissions. Emission peaks was observed in N₂O and CO₂ after the first application of fertilizers with the emissions flattening out over the cultivating season while CH₄ emission was negligible with no apparent patterns observed. Microbial biomass carbon and dehydrogenase activity were affected by the fertilized organic digestates. A significant difference was recorded between the control and the digestate treated soils for the microbial biomass carbon and dehydrogenase. Results also showed individual and cumulative emissions of CO₂, CH₄ and N₂O from the digestates were relatively low suggesting the digestate fertilization can be an efficient method for improving soil quality and reducing greenhouse gases from agricultural sources in temperate climate conditions.

Keywords: greenhouse gas emission, manure digestate, soil microbial activity, yield

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Authors: Tatsuya Takino, Izuru Nomura, Yuji Kageyama, Shin Nagata, Hiroyuki Kamata

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We propose a method of detecting the craters from the image of the lunar surface. This proposal assumes that it is applied to SLIM (Smart Lander for Investigating Moon) working group aiming at the pinpoint landing on the lunar surface and investigating scientific research. It is difficult to equip and use high-performance computers for the small space probe. So, it is necessary to use a small computer with an exclusive hardware such as FPGA. We have studied the crater detection using principal component analysis (PCA), In this paper, We implement detection algorithm into the FPGA, and the detection is performed on the data that was captured from the CMOS camera.

Keywords: crater detection, PCA, FPGA, image processing

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Authors: Guezouli Larbi, Belkacem Soundes

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The automation of the text detection process can help the human in his driving task. Its application can be very useful to help drivers to have more information about their environment by facilitating the reading of road signs such as directional signs, events, stores, etc. In this paper, a system consisting of two stages has been proposed. In the first one, we used pseudo-Zernike moments to pinpoint areas of the image that may contain text. The architecture of this part is based on three main steps, region of interest (ROI) detection, text localization, and non-text region filtering. Then, in the second step, we present a convolutional neural network architecture (On-Road Text Detection Network - ORTDN) which is considered a classification phase. The results show that the proposed framework achieved ≈ 35 fps and an mAP of ≈ 90%, thus a low computational time with competitive accuracy.

Keywords: text detection, CNN, PZM, deep learning

Procedia PDF Downloads 79

Authors: V. Ivanov, J. Chu, V. Stabnikov

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Low cost, sustainable, and environmentally friendly microbial cements, grouts, polysaccharides and bioplastics are useful in construction and geotechnical engineering. Construction-related biotechnologies are based on activity of different microorganisms: urease-producing, acidogenic, halophilic, alkaliphilic, denitrifying, iron- and sulphate-reducing bacteria, cyanobacteria, algae, microscopic fungi. The bio-related materials and processes can be used for the bioaggregation, soil biogrouting and bioclogging, biocementation, biodesaturation of water-satured soil, bioencapsulation of soft clay, biocoating, and biorepair of the concrete surface. Altogether with the most popular calcium- and urea based biocementation, there are possible and often are more effective such methods of ground improvement as calcium- and magnesium based biocementation, calcium phosphate strengthening of soil, calcium bicarbonate biocementation, and iron- or polysaccharide based bioclogging. The construction-related microbial biotechnologies have a lot of advantages over conventional construction materials and processes.

Keywords: ground improvement, biocementation, biogrouting, microorganisms

Procedia PDF Downloads 225

Authors: Anshu Siwach, Qianlai Zhuang, Ratul Baishya

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Context: This study focuses on the influence of moss cover and seasonality on soil microbial biomass and enzymatic activity in different Central Himalayan temperate forest types. Soil microbial biomass and enzymes are key indicators of microbial communities in soil and provide information on soil properties, microbial status, and organic matter dynamics. The activity of microorganisms in the soil varies depending on the vegetation type and environmental conditions. Therefore, this study aims to assess the effects of moss cover, seasons, and different forest types on soil microbial biomass carbon (SMBC), soil microbial biomass nitrogen (SMBN), and soil enzymatic activity in the Central Himalayas, Uttarakhand, India. Research Aim: The aim of this study is to evaluate the levels of SMBC, SMBN, and soil enzymatic activity in different temperate forest types under the influence of two ground covers (soil with and without moss cover) during the rainy and winter seasons. Question Addressed: This study addresses the following questions: 1. How does the presence of moss cover and seasonality affect soil microbial biomass and enzymatic activity? 2. What is the influence of different forest types on SMBC, SMBN, and enzymatic activity? Methodology: Soil samples were collected from different forest types during the rainy and winter seasons. The study utilizes the chloroform-fumigation extraction method to determine SMBC and SMBN. Standard methodologies are followed to measure enzymatic activities, including dehydrogenase, acid phosphatase, aryl sulfatase, β-glucosidase, phenol oxidase, and urease. Findings: The study reveals significant variations in SMBC, SMBN, and enzymatic activity under different ground covers, within the rainy and winter seasons, and among the forest types. Moss cover positively influences SMBC and enzymatic activity during the rainy season, while soil without moss cover shows higher values during the winter season. Quercus-dominated forests, as well as Cupressus torulosa forests, exhibit higher levels of SMBC and enzymatic activity, while Pinus roxburghii forests show lower levels. Theoretical Importance: The findings highlight the importance of considering mosses in forest management plans to improve soil microbial diversity, enzymatic activity, soil quality, and health. Additionally, this research contributes to understanding the role of lower plants, such as mosses, in influencing ecosystem dynamics. Conclusion: The study concludes that moss cover during the rainy season significantly influences soil microbial biomass and enzymatic activity. Quercus and Cupressus torulosa dominated forests demonstrate higher levels of SMBC and enzymatic activity, indicating the importance of these forest types in sustaining soil microbial diversity and soil health. Including mosses in forest management plans can improve soil quality and overall ecosystem dynamics.

Keywords: moss cover, seasons, soil enzymes, soil microbial biomass, temperate forest types

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Authors: Emine G. Cansu Ergun

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Conjugated system based sensors for selective detection of metal ions have been taking attention during last two decades. Fluorescent sensors are the promising candidates for ion detection due to their high selectivity towards metal ions, and rapid response times. Detection of mercury in an environmenet is important since mercury is a toxic element for human. Beyond the maximum allowable limit, mercury may cause serious problems in human health by spreading into the atmosphere, water and the food chain. In this study, a quinoxaline and 3,4-ethylenedioxy thiophene based donor-acceptor-donor type conjugated molecule used as a fluorescent sensor for detecting the mercury ion in aqueous medium. Among other various cations, existence of mercury resulted in a full quenching of the fluorescence signal. Then, a paper based sensor is constructed and used for mercury detection. As a result it is concluded that the offering sensor is a good candidate for selective mercury detection in aqueous media both in solution and paper based forms.

Keywords: Conjugated molecules , fluorescence quenching, metal ion detection , sensors

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Authors: Eshta Ranyal, Kamal Jain, Vikrant Ranyal

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Potholes are a severe threat to road safety and a major contributing factor towards road distress. In the Indian context, they are a major road hazard. Timely detection of potholes and subsequent repair can prevent the roads from deteriorating. To facilitate the roadway authorities in the timely detection and repair of potholes, we propose a pothole detection methodology using convolutional neural networks. The YOLOv3 model is used as it is fast and accurate in comparison to other state-of-the-art models. You only look once v3 (YOLOv3) is a state-of-the-art, real-time object detection system that features multi-scale detection. A mean average precision(mAP) of 73% was obtained on a training dataset of 200 images. The dataset was then increased to 500 images, resulting in an increase in mAP. We further calculated the depth of the potholes using stereoscopic vision by reconstruction of 3D potholes. This enables calculating pothole volume, its extent, which can then be used to evaluate the pothole severity as low, moderate, high.

Keywords: CNN, pothole detection, pothole severity, YOLO, stereovision

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Authors: Tao Feng, Wei-Wei Zhang, Chang-Ming Ding

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Cross-site scripting (XSS) is one of the most popular WEB Attacking methods at present, and also one of the most risky web attacks. Because of the population of JavaScript, the scene of the cross site scripting attack is also gradually expanded. However, since the web application developers tend to only focus on functional testing and lack the awareness of the XSS, which has made the on-line web projects exist many XSS vulnerabilities. In this paper, different various techniques of XSS attack are analyzed, and a method automatically to detect it is proposed. It is easy to check the results of vulnerability detection when running it as a plug-in.

Keywords: XSS, no target attack platform, automatic detection，XSS detection

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Authors: Azna Zuberi

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Biofilm is a sessile bacterial accretion in which bacteria adapts different physiological and morphological behavior from planktonic form. It is the root cause of about 80% microbial infections in human. Among them, E. coli biofilms are most prevalent in medical devices associated nosocomial infections. The objective of this study was to inhibit biofilm formation by targeting LuxS gene, involved in quorum sensing using CRISPRi. luxS is a synthase, involved in the synthesis of Autoinducer-2(AI-2), which in turn guides the initial stage of biofilm formation. To implement CRISPRi system, we have synthesized complementary sgRNA to target gene sequence and co-expressed with dCas9. Suppression of luxS was confirmed through qRT-PCR. The effect of luxS gene on biofilm inhibition was studied through crystal violet assay, XTT reduction assay and scanning electron microscopy. We conclude that CRISPRi system could be a potential strategy to inhibit bacterial biofilm through mechanism base approach.

Keywords: biofilm, CRISPRi, luxS, microbial

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Authors: Umar Albalawi, Sang C. Suh, Jinoh Kim

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As internet continues to expand its usage with an enormous number of applications, cyber-threats have significantly increased accordingly. Thus, accurate detection of malicious traffic in a timely manner is a critical concern in today’s Internet for security. One approach for intrusion detection is to use Machine Learning (ML) techniques. Several methods based on ML algorithms have been introduced over the past years, but they are largely limited in terms of detection accuracy and/or time and space complexity to run. In this work, we present a novel method for intrusion detection that incorporates a set of supervised learning algorithms. The proposed technique provides high accuracy and outperforms existing techniques that simply utilizes a single learning method. In addition, our technique relies on partial flow information (rather than full information) for detection, and thus, it is light-weight and desirable for online operations with the property of early identification. With the mid-Atlantic CCDC intrusion dataset publicly available, we show that our proposed technique yields a high degree of detection rate over 99% with a very low false alarm rate (0.4%).

Keywords: intrusion detection, supervised learning, traffic classification, computer networks

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Authors: Muhammad Ammar, Talha Ali, Abdul Basit, Bakhtawar Rajput, Zobia Sohail

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Music note detection is an area of study for the past few years and has its own influence in music file generation from sheet music. We proposed a method to detect music notes on sheet music using basic thresholding and blob detection. Subsequently, we created a notes dictionary using a semi-supervised learning approach. After notes detection, for each test image, the new symbols are added to the dictionary. This makes the notes detection semi-automatic. The experiments are done on images from a dataset and also on the captured images. The developed approach showed almost 100% accuracy on the dataset images, whereas varying results have been seen on captured images.

Keywords: music note, sheet music, optical music recognition, blob detection, thresholding, dictionary generation

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Authors: Hwan-Jun Choi, Sung-Bok Choi, Hyoung-Kyu Song

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Recently, among the MIMO-OFDM detection techniques, a lot of papers suggested V-BLAST scheme which can achieve high data rate. Therefore, the signal detection of MIMOOFDM system is important issue. In this paper, efficient iterative VBLAST detection technique is proposed in wireless communication system. The proposed scheme adjusts the number of candidate symbol and iterative scheme based on channel state. According to the simulation result, the proposed scheme has better BER performance than conventional schemes and similar BER performance of the QRD-M with iterative scheme. Moreover complexity of proposed scheme has 50.6 % less than complexity of QRD-M detection with iterative scheme. Therefore the proposed detection scheme can be efficiently used in wireless communication.

Keywords: MIMO-OFDM, V-BLAST, QR-decomposition, QRDM, DFE, iterative scheme, channel condition

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Authors: Surajbhan Sevda

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Metal containing wastewater is generated in large quintiles due to rapid industrialization. Generally, the metal present in wastewater is not biodegradable and can be accumulated in living animals, humans and plant tissue, causing disorder and diseases. The conventional metal recovery methods include chemical, physical and biological methods, but these are chemical and energy intensive. The recent development in microbial fuel cell (MFC) technology provides a new approach for metal recovery; this technology offers a flexible platform for both reduction and oxidation reaction oriented process. The use of MFCs will be a new platform for more efficient and low energy approach for metal recovery from the wastewater. So far metal recover was extensively studied using chemical, physical and biological methods. The MFCs present a new and efficient approach for removing and recovering metals from different wastewater, suggesting the use of different electrode for metal recovery can be a new efficient and effective approach.

Keywords: metal recovery, microbial fuel cell, wastewater, bioelectricity

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Authors: N. Noori, P. Taherkhani, A. Akhondzadeh Basti, H. Gandomi, M. Alimohammadi

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Research on natural antimicrobial agents, especially of plant origin, highly noticed in recent years and evaluation of antimicrobial effects of native plants such as Bunium persicum Boiss. is especially important. In the present study, sensory characteristics and microbiological properties of Gouda cheese affected by different concentrations of Bunium persicum Boiss. essential oil were investigated. Extraction of the essential oil was performed by hydro distillation. The oil was analyzed by GC using flame ionization (FID) and GC/ MS for detection. The antimicrobial effects were determined against various microbial groups (aerobic mesophilic bacteria, enterococci, mesophilic lactobacilli, enterobacteriaceae, lactococcus and yeasts). Microbial groups were counted during ripening period using plate count on specific culture media. Organoleptic evaluation including teture, flavor, odor, color and total acceptability were determined at the end of aging. According to results, the essential oil yield was 4/1 % ( W/ W). Twenty- six compounds were identified in the oil that concluded 99.7 % of the total oil. The major components of Bunium persicum Boiss. essential oil were γ- terpinene- 7- al (26.9 %) and cuminaldehyde (23.3 %). Generally, the increase of Black Cumin essential oil concentration led to reduction in microbial counts in different groups. The maximum antimicrobial effect was seen in yeast that reduced by 2 log compared to the control group at EO concentration of 4µl/ ml at day 90.The minimum reduction was observed in enterobacteriaceae that showed only 0.75 log decreese compared to the control at the same concentration of EO. Addition of EO improved organoleptic properties of Gouda cheese especially in the case of flavor and odor characteristic. However, no significant differences were observed in texture and color between treatment and control groups. Bunium persicum Boiss. essential oil could be used as preservative material and flavoring agent in some kinds of food such as cheese and also could be provided consumers health.

Keywords: Bunium persicum Boiss. essential oil, Microbiological properties, sensory evaluation, gouda cheese

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Authors: Majed Sanan, Mohammad Rammal, Wassim Rammal

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Today, security is a major concern. Intrusion Detection, Prevention Systems and Honeypot can be used to moderate attacks. Many researchers have proposed to use many IDSs ((Intrusion Detection System) time to time. Some of these IDS’s combine their features of two or more IDSs which are called Hybrid Intrusion Detection Systems. Most of the researchers combine the features of Signature based detection methodology and Anomaly based detection methodology. For a signature based IDS, if an attacker attacks slowly and in organized way, the attack may go undetected through the IDS, as signatures include factors based on duration of the events but the actions of attacker do not match. Sometimes, for an unknown attack there is no signature updated or an attacker attack in the mean time when the database is updating. Thus, signature-based IDS fail to detect unknown attacks. Anomaly based IDS suffer from many false-positive readings. So there is a need to hybridize those IDS which can overcome the shortcomings of each other. In this paper we propose a new approach to IDS (Intrusion Detection System) which is more efficient than the traditional IDS (Intrusion Detection System). The IDS is based on Honeypot Technology and Anomaly based Detection Methodology. We have designed Architecture for the IDS in a packet tracer and then implemented it in real time. We have discussed experimental results performed: both the Honeypot and Anomaly based IDS have some shortcomings but if we hybridized these two technologies, the newly proposed Hybrid Intrusion Detection System (HIDS) is capable enough to overcome these shortcomings with much enhanced performance. In this paper, we present a modified Hybrid Intrusion Detection System (HIDS) that combines the positive features of two different detection methodologies - Honeypot methodology and anomaly based intrusion detection methodology. In the experiment, we ran both the Intrusion Detection System individually first and then together and recorded the data from time to time. From the data we can conclude that the resulting IDS are much better in detecting intrusions from the existing IDSs.

Keywords: security, intrusion detection, intrusion prevention, honeypot, anomaly-based detection, signature-based detection, cloud computing, kfsensor

Procedia PDF Downloads 372

Authors: Olivia A. Kjorlien, Maryam Asghari, Farshid Alizadeh-Shabdiz

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The goal of this paper is to investigate the impact of mosaic augmentation on the performance of object detection solutions. To carry out the study, YOLOv4 and YOLOv4-Tiny models have been selected, which are popular, advanced object detection models. These models are also representatives of two classes of complex and simple models. The study also has been carried out on two categories of objects, simple and complex. For this study, YOLOv4 and YOLOv4 Tiny are trained with and without mosaic augmentation for two sets of objects. While mosaic augmentation improves the performance of simple object detection, it deteriorates the performance of complex object detection, specifically having the largest negative impact on the false positive rate in a complex object detection case.

Keywords: accuracy, false positives, mosaic augmentation, object detection, YOLOV4, YOLOV4-Tiny

Procedia PDF Downloads 122

Authors: Yu Pu, Chien-Ping Hsiao, Chien-Chang Huang, Chieh-Lun Cheng

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Free radicals can accelerate aging on human skin by causing lipid oxidation, protein denaturation, and even DNA mutation. Substances with the ability of anti-free radicals can be used as functional components in cosmetic products. Research are attracted to develop new anti-free radical components for cosmetic application. This study was aimed to evaluate the microbial metabolites on free radical scavenging ability. Two microorganisms, PU-01 and PU-02, were isolated from soil of hot spring environment and grew in LB agar at 50°C for 24 h. The suspension was collected by centrifugation at 4800 g for 3 min, The anti-free radical activity was determined by DPPH (1,1-diphenyl-2-picrylhydrazyl) scavenging assay. The result showed that the growth medium of PU-01 presented a higher DPPH scavenging effect than that of PU-02. This study presented potential anti-free radical components from microbial metabolites that might be applied in anti-aging cosmetics.

Keywords: anti-ageing, anti-free radical, biotechnology, microorganism

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Authors: Anton Stadler, Thorsten Ike

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In this paper, we present a video based smoke detection algorithm based on TVL1 optical flow estimation. The main part of the algorithm is an accumulating system for motion angles and upward motion speed of the flow field. We optimized the usage of TVL1 flow estimation for the detection of smoke with very low smoke density. Therefore, we use adapted flow parameters and estimate the flow field on difference images. We show in theory and in evaluation that this improves the performance of smoke detection significantly. We evaluate the smoke algorithm using videos with different smoke densities and different backgrounds. We show that smoke detection is very reliable in varying scenarios. Further we verify that our algorithm is very robust towards crowded scenes disturbance videos.

Keywords: low density, optical flow, upward smoke motion, video based smoke detection

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Authors: Tomokazu Shirai, Akihiko Kondo

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Technologies of metabolic engineering or synthetic biology are essential for effective microbial bioproduction. It is especially important to develop an in silico tool for designing a metabolic pathway producing an unnatural and valuable chemical such as fossil materials of fuel or plastics. We here demonstrated two in silico tools for designing novel metabolic pathways: BioProV and HyMeP. Furthermore, we succeeded in creating an artificial metabolic pathway by enzyme engineering.

Keywords: bioinformatics, metabolic engineering, synthetic biology, genome scale model

Procedia PDF Downloads 332

Authors: Fnu Asina, Ivana Brzonova, Keith Voeller, Yun Ji, Alena Kubatova, Evguenii Kozliak

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Lignin, a heterogeneous three-dimensional biopolymer, is one of the building blocks of lignocellulosic biomass. Due to its limited chemical reactivity, lignin is currently processed as a low-value by-product in pulp and paper mills. Among various industrial lignins, Kraft lignin represents a major source of by-products generated during the widely employed pulping process across the pulp and paper industry. Therefore, valorization of Kraft lignin holds great potential as this would provide a readily available source of aromatic compounds for various industrial applications. Microbial degradation is well known for using both highly specific ligninolytic enzymes secreted by microorganisms and mild operating conditions compared with conventional chemical approaches. In this study, the degradation of Indulin AT lignin was assessed by comparing the effects of Basidiomycetous fungi (Coriolus versicolour and Trametes gallica) and Actinobacteria (Mycobacterium sp. and Streptomyces sp.) to two commercial laccases, T. versicolour ( ≥ 10 U/mg) and C. versicolour ( ≥ 0.3 U/mg). After 54 days of cultivation, the extent of microbial degradation was significantly higher than that of commercial laccases, reaching a maximum of 38 wt% degradation for C. versicolour treated samples. Lignin degradation was further confirmed by thermal carbon analysis with a five-step temperature protocol. Compared with commercial laccases, a significant decrease in char formation at 850ºC was observed among all microbial-degraded lignins with a corresponding carbon percentage increase from 200ºC to 500ºC. To complement the carbon analysis result, chemical characterization of the degraded products at different stages of the delignification by microorganisms and commercial laccases was performed by Pyrolysis-GC-MS.

Keywords: lignin, microbial degradation, pyrolysis-GC-MS, thermal carbon analysis

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Authors: Nisha Gaur, K.Narasimhulu, Pydi Setty Yelamarthy

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Persistent organic pollutants (POPs) have become a great concern due to their toxicity, transformation and bioaccumulation property. Therefore, this review highlights the types, sources, classification health hazards and mobility of organochlorine pesticides, industrial chemicals and their by-products. Moreover, with the signing of Aarhus and Stockholm convention on POPs there is an increased demand to identify and characterise such chemicals from industries and environment which are toxic in nature or to existing biota. Due to long life, persistent nature they enter into body through food and transfer to all tropic levels of ecological unit. In addition, POPs are lipophilic in nature and accumulate in lipid-containing tissues and organs which further indicates the adverse symptoms after the threshold limit. Though, several potential enzymes are reported from various categories of microorganism and their interaction with POPs may break down the complex compounds either through biodegradation, biostimulation or bioaugmentation process, however technological advancement and human activities have also indicated to explore the possibilities for the role of genetically modified organisms and metagenomics and metabolomics. Though many studies have been done to develop low cost, effective and reliable method for detection, determination and removal of ultra-trace concentration of persistent organic pollutants (POPs) but due to insufficient knowledge and non-feasibility of technique, the safe management of POPs is still a global challenge.

Keywords: persistent organic pollutants, bioaccumulation, biostimulation, microbial remediation

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Authors: Kuang-Hsiung Tan, Chih-Chan Hu, Chien-Wu Lan, Shih-Sung Lin, Te-Jen Chang

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An active islanding detection method using disturbance signal injection with intelligent controller is proposed in this study. First, a DC\AC power inverter is emulated in the distributed generator (DG) system to implement the tracking control of active power, reactive power outputs and the islanding detection. The proposed active islanding detection method is based on injecting a disturbance signal into the power inverter system through the d-axis current which leads to a frequency deviation at the terminal of the RLC load when the utility power is disconnected. Moreover, in order to improve the transient and steady-state responses of the active power and reactive power outputs of the power inverter, and to further improve the performance of the islanding detection method, two probabilistic fuzzy neural networks (PFNN) are adopted to replace the traditional proportional-integral (PI) controllers for the tracking control and the islanding detection. Furthermore, the network structure and the online learning algorithm of the PFNN are introduced in detail. Finally, the feasibility and effectiveness of the tracking control and the proposed active islanding detection method are verified with experimental results.

Keywords: distributed generators, probabilistic fuzzy neural network, islanding detection, non-detection zone

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Authors: Carlos Alberto Riveros, Edwin Fabián García, Javier Enrique Rivero

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The measured data obtained from sensors in continuous monitoring of civil structures are mainly used for modal identification and damage detection. Therefore when modal identification analysis is carried out the quality in the identification of the modes will highly influence the damage detection results. It is also widely recognized that the usefulness of the measured data used for modal identification and damage detection is significantly influenced by the number and locations of sensors. The objective of this study is the numerical implementation of two widely known optimum sensor placement methods in beam-like structures

Keywords: optimum sensor placement, structural damage detection, modal identification, beam-like structures.

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Authors: Jie Yang, Jian-Min Meng

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A GPU-based floating object detection scheme is presented in this paper which is designed for floating mine detection tasks. This system uses contrast and motion information to eliminate as many false positives as possible while avoiding false negatives. The GPU computation platform is deployed to allow detecting objects in real-time. From the experimental results, it is shown that with certain configuration, the GPU-based scheme can speed up the computation up to one thousand times compared to the CPU-based scheme.

Keywords: object detection, GPU, motion estimation, parallel processing

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Authors: Mustafa Salman, Nurcan Cetinkaya, Zehra Selcuk, Bugra Genc

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The objectives of the present study were to estimate the microbial-N flow to the small intestine and to predict the digestible organic matter intake (DOMI) in grazing Karayaka sheep based on urinary excretion of purine derivatives (xanthine, hypoxanthine, uric acid, and allantoin) by the use of spot urine sampling under field conditions. In the trial, 10 Karayaka sheep from 2 to 3 years of age were used. The animals were grazed in a pasture for ten months and fed with concentrate and vetch plus oat hay for the other two months (January and February) indoors. Highly significant linear and cubic relationships (P<0.001) were found among months for purine derivatives index, purine derivatives excretion, purine derivatives absorption, microbial-N and DOMI. Through urine sampling and the determination of levels of excreted urinary PD and Purine Derivatives / Creatinine ratio (PDC index), microbial-N values were estimated and they indicated that the protein nutrition of the sheep was insufficient. In conclusion, the prediction of protein nutrition of sheep under the field conditions may be possible with the use of spot urine sampling, urinary excreted PD and PDC index. The mean purine derivative levels in spot urine samples from sheep were highest in June, July and October. Protein nutrition of pastured sheep may be affected by weather changes, including rainfall. Spot urine sampling may useful in modeling the feed consumption of pasturing sheep. However, further studies are required under different field conditions with different breeds of sheep to develop spot urine sampling as a model.

Keywords: Karayaka sheep, spot sampling, urinary purine derivatives, PDC index, microbial-N, feed intake

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Authors: Siddig H. Hamad, Salah M. Al-Eid, Fahad M. Al-Jassas

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Composite samples of minced chicken were vacuum-packaged and pressure treated at 300, 400, 450 and 500 MPa in a Stansted 'FOOD-LAB' model S-FL-850-9-W high hydrostatic pressure research apparatus (Stansted Fluid Power Ltd., Stansted, UK). Treated and untreated samples were then stored at 3°C, and microbial content as well as some chemical and physical properties monitored. The microbial load of the untreated samples reached the spoilage level of 107 cfu/g in about one week, resulting in bad smell and dark brown color. The pressure treatments reduced total bacterial counts by about 1.8 to 3.2 log10 cycles and reduced counts of Enterobacteriaceae and Salmonella to non-detectable levels. The color of meat was slightly affected, but pH, moisture content and the oxidation products of lipids were not substantially changed. The treatment killed mainly gram negative bacteria but also caused sub-lethal injury to part of the population resulting in prolonged lag phase. The population not killed by the 350 to 450 MPa treatments grew relatively slowly during storage, and its loads reached spoilage level in 4 to 6 weeks, while the load of the population treated at 500 MPa did not reach this level till the end of a storage period of 9 weeks.

Keywords: chicken, cold storage, microbial spoilage, high hydrostatic pressure

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Authors: Prachi Abhishek Dave, Manisha Singh

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Purpose: The purpose is to study the microbiological profile, prognostic factors, and outcomes of surgery for microbial keratitis (MK) associated endophthalmitis. Methods: It is a retrospective analysis of 39 MK associated endophthalmitis cases which underwent combined PKP with VR surgery. Results: The majority (84.6%) of patients had a history of previous ocular surgery, cataract surgery being the most common (51.2%). Six patients had a history of trauma and 8 patients had corneal graft infection. The culture positivity rate was 92.3%. Organisms isolated were Streptococcal sp (20.5%), Pseudomonas (15.3%), Staphylococcal sp (12.8%) and Fungus (35.8%). Final visual acuities ranged from NPL to 20/120. Poor anatomic success was seen in 15(38.46%) eyes (9-phthisis, 6-eviscerated). Fungal cases had a poor success rate (P-0.02). Conclusion: MK associated endophthalmitis is a sight threatening ocular condition. Fungal etiology carries a poorer prognosis. Timely surgical intervention can achieve favourable anatomical and functional results.

Keywords: endophthalmitis, microbial keratitis, penetrating keratoplasty, vitreo retinal surgery

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Authors: Vedant Subhash

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This paper discusses the physics of the detection of thermal neutrons using thin-film coated semiconductor detectors. The thermal neutron detection efficiency as a function of film thickness is calculated for the front and back irradiation detector devices coated with ¹⁰B, ⁶LiF, and pure Li thin films. The detection efficiency for back irradiation devices is 4.15% that is slightly higher than that for front irradiation detectors, 4.0% for ¹⁰B films of thickness 2.4μm. The theoretically calculated thermal neutron detection efficiency using ¹⁰B film thickness of 1.1 μm for the back irradiation device is 3.0367%, which has an offset of 0.0367% from the experimental value of 3.0%. The detection efficiency values are compared and proved consistent with the given calculations.

Keywords: detection efficiency, neutron detection, semiconductor detectors, thermal neutrons

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Authors: Salah Mohammed Al-Eid, Siddig Hussein Hamad, Fahad Mohammed Aljassas

Abstract:

The effects of high hydrostatic pressure (HHP) treatments on microbial contamination, chemical and physical properties of fresh dates (Rutab stage) were studied. Khalas, Barhi and Hilali cultivars were treated at 200, 250, 300 and 350 MPa using HHP research apparatus. The objective of such treatments was to preserve fresh dates without adversely affecting its properties. Treating fresh dates at 300 MPa for 5 minutes at 40°C reduced microbial contamination in about 2.5 log cycles. Applying 250 MPa was enough to control Rutab contamination with molds, yeasts, and coliforms. Both treatments were enough to reduce Rutab microbial contamination to acceptable levels. HHP caused no significant effect on Rutab chemical properties (moisture, sugars, protein, pectin and acidity). However, a slight decrease in moisture contents due to HHP was observed. Rutab lightness (L*) significantly decreased due to the application of HHP. Only Rutab treated at 300 MPs gave lower redness (a*) values compared with an untreated sample. The effect of 300 MPa on increasing yellowness (b*) was observed for Barhi and Hilali but decreasing for Khalas. The hardness of all Rutab cultivars significantly decreased as a result of HHP application. In fact, the pressure applied at 300 MPa had an adverse effect on texture, which may limit its suitability for use in Rutab preservation.

Keywords: high hydrostatic pressure, fresh dates (Rutab), microbial contamination, color, texture

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Authors: Manuel Alves, Angelica Reis, Armindo Lobo, Valdemar Leiras

Abstract:

In industry 4.0, it is common to have a lot of sensor data. In this deluge of data, hints of possible problems are difficult to spot. The digital twin concept aims to help answer this problem, but it is mainly used as a monitoring tool to handle the visualisation of data. Failure detection is of paramount importance in any industry, and it consumes a lot of resources. Any improvement in this regard is of tangible value to the organisation. The aim of this paper is to add the ability to forecast test failures, curtailing detection times. To achieve this, several anomaly detection algorithms were compared with a symbolic regression approach. To this end, Isolation Forest, One-Class SVM and an auto-encoder have been explored. For the symbolic regression PySR library was used. The first results show that this approach is valid and can be added to the tools available in this context as a low resource anomaly detection method since, after training, the only requirement is the calculation of a polynomial, a useful feature in the digital twin context.

Keywords: anomaly detection, digital twin, industry 4.0, symbolic regression

Procedia PDF Downloads 113