Search results for: prediction of deterioration

Authors: Byoungjoon Yu, Jihwan Park, Sujung Sin, Junghyun Im, Minsoo Park, Sehwan Park, Seunghee Park

Abstract:

In this paper, we propose a technology to monitor the soundness of an Internet-based bridge using a non-conductive inspection function. There has been a collapse accident due to the aging of the bridge structure, and it is necessary to prepare for the deterioration of the bridge. The NDT/SHM system for maintenance of existing bridge structures requires a large number of inspection personnel and expensive inspection costs, and access of expensive and large equipment to measurement points is required. Because current drone inspection equipment can only be inspected through camera, it is difficult to inspect inside damage accurately, and the results of an internal damage evaluation are subjective, and it is difficult for non-specialists to recognize the evaluation results. Therefore, it is necessary to develop NDT/SHM techniques for maintenance of new-concept bridge structures that allow for free movement and real-time evaluation of measurement results. This work is financially supported by Korea Ministry of Land, Infrastructure, and Transport (MOLIT) as 'Smart City Master and Doctor Course Grant Program' and a grant (14SCIP-B088624-01) from Construction Technology Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.

Keywords: Structural Health Monitoring, SHM, non-contact sensing, nondestructive testing, NDT, Internet of Things, autonomous self-driving drone

Procedia PDF Downloads 267

Authors: Nataliya V. Dolgushina, Elena A. Gorodnova, Olga S. Beznoshenco, Andrey Yu Romanov, Irina V. Menzhinskaya, Lyubov V. Krechetova, Gennady T. Suchich

Abstract:

In patients with COVID-19, generalized hypercoagulability can lead to the development of severe coagulopathy. This event is accompanied by the development of a pronounced inflammatory reaction. The observational prospective study included 39 patients with mild COVID-19 and 102 patients with moderate and severe COVID-19. Patients were then stratified into groups depending on the risk of venous thromboembolism. vWF to ADAMTS-13 concentrations and activity ratios were significantly higher in patients with a high venous thromboembolism risks in patients with moderate and severe forms COVID-19.

Keywords: ADAMTS-13, COVID-19, hypercoagulation, thrombosis, von Willebrand factor

Procedia PDF Downloads 87

Authors: Myriam Ben Said, Dhekra Trabelsi, Faouzi Achouri, Marwa Ben Saad, Latifa Bousselmi, Ahmed Ghrabi

Abstract:

This present study suggests the use of biological and natural bactericide, cheap, safe to handle, natural, environmentally benign agents to enhance the conventional wastewater treatment process. In the same sense, to highlight the enhancement of wastewater photocatalytic treatability, we were used virulent bacteriophage(s) and essential oils (EOs). The pre-phago-treatment of wastewater with lytic phage(s), leads to a decrease in bacterial density and, consequently, limits the establishment of intercellular communication (QS), thus preventing biofilm formation and inhibiting the expression of other virulence factors after photocatalysis. Moreover, to increase the photocatalytic efficiency, we were added to the secondary treated wastewater 1/1000 (w/v) of EO of thyme (T. vulgaris). This EO showed in vitro an anti-biofilm activity through the inhibition of plonctonic cell mobility and their attachment on an inert surface and also the deterioration of the sessile structure. The presence of photoactivatable molecules (photosensitizes) in this type of oil allows the optimization of photocatalytic efficiency without hazards relayed to dyes and chemicals reagent. The use of ‘biological and natural tools’ in combination with usual water treatment process can be considered as a safety procedure to reduce and/or to prevent the recontamination of treated water and also to prevent the re-expression of virulent factors by pathogenic bacteria such as biofilm formation with friendly processes.

Keywords: biofilm, essential oil, optimization, phage, photocatalysis, wastewater

Procedia PDF Downloads 152

Authors: Hussain K. Chaiel

Abstract:

The development in the field of the digital signal processing (DSP) and the microelectronics technology reduces the complexity of the iterative algorithms that need large number of arithmetic operations. Virtex-Field Programmable Gate Arrays (FPGAs) are programmable silicon foundations which offer an important solution for addressing the needs of high performance DSP designer. In this work, Virtex-7 FPGA technology is used to implement an iterative algorithm to estimate the earthquake location. Simulation results show that an implementation based on block RAMB36E1 and DSP48E1 slices of Virtex-7 type reduces the number of cycles of the clock frequency. This enables the algorithm to be used for earthquake prediction.

Keywords: DSP, earthquake, FPGA, iterative algorithm

Procedia PDF Downloads 387

Authors: Chung-Ming Lo, Chung-Chien Lee

Abstract:

In many countries, the lifetime prevalence of shoulder pain is up to 70%. In America, the health care system spends 7 billion per year about the healthy issues of shoulder pain. With respect to the origin, up to 70% of shoulder pain is attributed to rotator cuff lesions This study proposed a computer-aided diagnosis (CAD) system to assist radiologists classifying rotator cuff lesions with less operator dependence. Quantitative features were extracted from the shoulder ultrasound images acquired using an ALOKA alpha-6 US scanner (Hitachi-Aloka Medical, Tokyo, Japan) with linear array probe (scan width: 36mm) ranging from 5 to 13 MHz. During examination, the postures of the examined patients are standard sitting position and are followed by the regular routine. After acquisition, the shoulder US images were drawn out from the scanner and stored as 8-bit images with pixel value ranging from 0 to 255. Upon the sonographic appearance, the boundary of each lesion was delineated by a physician to indicate the specific pattern for analysis. The three lesion categories for classification were composed of 20 cases of tendon inflammation, 18 cases of calcific tendonitis, and 18 cases of supraspinatus tear. For each lesion, second-order statistics were quantified in the feature extraction. The second-order statistics were the texture features describing the correlations between adjacent pixels in a lesion. Because echogenicity patterns were expressed via grey-scale. The grey-scale co-occurrence matrixes with four angles of adjacent pixels were used. The texture metrics included the mean and standard deviation of energy, entropy, correlation, inverse different moment, inertia, cluster shade, cluster prominence, and Haralick correlation. Then, the quantitative features were combined in a multinomial logistic regression classifier to generate a prediction model of rotator cuff lesions. Multinomial logistic regression classifier is widely used in the classification of more than two categories such as the three lesion types used in this study. In the classifier, backward elimination was used to select a feature subset which is the most relevant. They were selected from the trained classifier with the lowest error rate. Leave-one-out cross-validation was used to evaluate the performance of the classifier. Each case was left out of the total cases and used to test the trained result by the remaining cases. According to the physician’s assessment, the performance of the proposed CAD system was shown by the accuracy. As a result, the proposed system achieved an accuracy of 86%. A CAD system based on the statistical texture features to interpret echogenicity values in shoulder musculoskeletal ultrasound was established to generate a prediction model for rotator cuff lesions. Clinically, it is difficult to distinguish some kinds of rotator cuff lesions, especially partial-thickness tear of rotator cuff. The shoulder orthopaedic surgeon and musculoskeletal radiologist reported greater diagnostic test accuracy than general radiologist or ultrasonographers based on the available literature. Consequently, the proposed CAD system which was developed according to the experiment of the shoulder orthopaedic surgeon can provide reliable suggestions to general radiologists or ultrasonographers. More quantitative features related to the specific patterns of different lesion types would be investigated in the further study to improve the prediction.

Keywords: shoulder ultrasound, rotator cuff lesions, texture, computer-aided diagnosis

Procedia PDF Downloads 284

Authors: Akram Khelaifia, Nesreddine Djafar Henni

Abstract:

Reinforced concrete shear walls are essential components in protecting buildings from seismic forces by providing both strength and stiffness. This study focuses on optimizing the placement of shear walls in a high seismic zone. Through nonlinear analyses conducted on an eight-story building, various scenarios of shear wall positions are investigated to evaluate their impact on seismic performance. Employing a performance-based seismic design (PBSD) approach, the study aims to meet acceptance criteria related to inter-story drift ratio and damage levels. The findings emphasize the importance of concentrating shear walls in the central area of the building during the design phase. This strategic placement proves more effective compared to peripheral distributions, resulting in reduced inter-story drift and mitigated potential damage during seismic events. Additionally, the research explores the use of shear walls that completely infill the frame, forming compound shapes like Box configurations. It is discovered that incorporating such complete shear walls significantly enhances the structure's reliability concerning inter-story drift. Conversely, the absence of complete shear walls within the frame leads to reduced stiffness and the potential deterioration of short beams.

Keywords: performance level, pushover analysis, shear wall, plastic hinge, nonlinear analyses

Procedia PDF Downloads 51

Authors: Lorenza Abbracciavento, Valerio De Biagi

Abstract:

Infrastructures are key elements to ensure the operational functionality of the transport system. The collapse of a single bridge or, equivalently, a tunnel can leads an entire motorway to be considered completely inaccessible. As a consequence, the paralysis of the communications network determines several important drawbacks for the community. Recent chronicle events have demonstrated that ensuring the functional continuity of the strategic infrastructures during and after a catastrophic event makes a significant difference in terms of life and economical losses. Moreover, it has been observed that RC structures located in mountain environments show a worst state of conservation compared to the same typology and aging structures located in temperate climates. Because of its morphology, in fact, the mountain environment is particularly exposed to severe collapse and deterioration phenomena, generally: natural hazards, e.g. rock falls, and meteorological hazards, e.g. freeze-thaw cycles or heavy snows. For these reasons, deep investigation on the characteristics of these processes becomes of fundamental importance to provide smart and sustainable solutions and make the infrastructure system more resilient. In this paper, the design of a monitoring system in mountainous environments is presented and analyzed in its parts. The method not only takes into account the peculiar climatic conditions, but it is integrated and interacts with the environment surrounding.

Keywords: structural health monitoring, resilience of bridges, mountain infrastructures, infrastructural network, maintenance

Procedia PDF Downloads 76

Authors: Kavita Bodke

Abstract:

Structural health monitoring (SHM) is experiencing growth, necessitating the development of distinct methodologies to address its expanding scope effectively. In this study, we developed automatic structure damage identification, which incorporates three unique types of a building’s structural integrity. The first pertains to the presence of fractures within the structure, the second relates to the issue of dampness within the structure, and the third involves corrosion inside the structure. This study employs image classification techniques to discern between intact and impaired structures within structural data. The aim of this research is to find automatic damage detection with the probability of each damage class being present in one image. Based on this probability, we know which class has a higher probability or is more affected than the other classes. Utilizing photographs captured by a mobile camera serves as the input for an image classification system. Image classification was employed in our study to perform multi-class and multi-label classification. The objective was to categorize structural data based on the presence of cracks, moisture, and corrosion. In the context of multi-class image classification, our study employed three distinct methodologies: Random Forest, Multilayer Perceptron, and CNN. For the task of multi-label image classification, the models employed were Rasnet, Xceptionet, and Inception.

Keywords: SHM, CNN, deep learning, multi-class classification, multi-label classification

Procedia PDF Downloads 35

Authors: Kwok W. Mui, Ling T. Wong, Chin T. Cheung, Ho C. Yu

Abstract:

Based on an indoor environmental quality (IEQ) index established by previous work that indicates the overall IEQ acceptance from the prospect of an occupant in residential buildings in terms of four IEQ factors - thermal comfort, indoor air quality, visual and aural comforts, this study develops a user-friendly IEQ calculator for iOS and Android users to calculate the occupant acceptance and compare the relative performance of IEQ in apartments. The calculator allows the prediction of the best IEQ scenario on a quantitative scale. Any indoor environments under the specific IEQ conditions can be benchmarked against the predicted IEQ acceptance range. This calculator can also suggest how to achieve the best IEQ acceptance among a group of residents.

Keywords: calculator, indoor environmental quality (IEQ), residential buildings, 5-star benchmarks

Procedia PDF Downloads 473

Authors: F. Z. Benabid, F. Zouai, A. Douibi, D. Benachour

Abstract:

These Fluorinated polymers and polyacrylics have known a wide use in the field of historical monuments. PVDF provides a great easiness to processing, a good UV resistance and good chemical inertia. Although the quality of physical characteristics of the PMMA and its low price with a respect to PVDF, its deterioration against UV radiations limits its use as protector agent for the stones. On the other hand, PVDF/PMMA blend is a compromise of a great development in the field of architectural restoration, since it is the best method in term of quality and price to make new polymeric materials having enhanced properties. Films of different compositions based on the two polymers within an adequate solvent (DMF) were obtained to perform an exposition to artificial ageing and to the salted fog, a spectroscopic analysis (FTIR and UV) and optical analysis (refractive index). Based on its great interest in the field of building, a variety of standard tests has been elaborated for the first time at the central laboratory of ENAP (Souk-Ahras) in order to evaluate our blend performance. The obtained results have allowed observing the behavior of the different compositions of the blend under various tests. The addition of PVDF to PMMA enhances the properties of this last to know the exhibition to the natural and artificial ageing and to the saline fog. On the other hand, PMMA enhances the optical properties of the blend. Finally, 70/30 composition of the blend is in concordance with results of previous works and it is the adequate proportion for an eventual application.

Keywords: blend, PVDF, PMMA, preservation, historic monuments

Procedia PDF Downloads 308

Authors: Ana P. R. N. Barroso, Maurício N. Kleinberg, Frederico R. Silva, Rodrigo F. Guimarães, Marcelo M. V. Parente, Walney S. Araújo

Abstract:

The fight against accelerated wear of the equipment used in the oil and gas sector is a challenge for minimizing maintenance costs. Corrosion being one of the main agents of equipment deterioration, we seek alternative materials that exhibit improved corrosion resistance at low cost of production. This study aims to evaluate the corrosion behavior of experimental alloys containing 15% and 17% of chromium (Cr) and 5% of molybdenum (Mo) in comparison with an AISI 444 commercial alloy. Microstructural analyzes were performed on samples of the alloys before and after the electrochemical tests. Two samples of each solubilized alloy were also taken for analysis of the corrosion behavior by testing potentiodynamic polarization (PP) and Electrochemical Impedance Spectroscopy (EIS) with immersion time of 24 hours in electrolytic solution with acidic character. The graphics obtained through electrochemical tests of PP and EIS indicated that among the experimental alloys, the alloy with higher chromium content (17%) had a higher corrosion resistance, confirming the beneficial effect of adding chromium. When comparing the experimental alloys with the AISI 444 commercial alloy, it is observed that the AISI 444 commercial alloy showed superior corrosion resistance to that of the experimental alloys for both assays, PP and EIS. The microstructural analyzes performed after the PP and EIS tests confirmed the results previously described. These results suggest that the addition of these levels of molybdenum did not favor the electrochemical behavior of experimental ferritic alloys for the electrolytic medium studied.

Keywords: corrosion, molybdenum, electrochemical tests, experimental alloys

Procedia PDF Downloads 572

Authors: Zhongzhi Hu, Jie Shen, Jiqiang Wang

Abstract:

A high precision aeroengine model is needed when developing the engine control system. Compared with other main components, the axial compressor is the most challenging component to simulate. In this paper, a compressor map optimizing tool based on the introduction of a modifiable β function is developed for FWorks (FADEC Works). Three parameters (d density, f fitting coefficient, k₀ slope of the line β=0) are introduced to the β function to make it modifiable. The comparison of the traditional β function and the modifiable β function is carried out for a certain type of compressor. The interpolation errors show that both methods meet the modeling requirements, while the modifiable β function can predict compressor performance more accurately for some areas of the compressor map where the users are interested in.

Keywords: beta function, compressor map, interpolation error, map optimization tool

Procedia PDF Downloads 265

Authors: Rafik Balti, Nourhene Zayoud, Mohamed Ben Mansour, Abdellah Arhaliass, Anthony Masse

Abstract:

Application of edible films and coatings with natural active compounds for enhancing storage stability of food products is a promising active packaging approach. Shrimp are generally known as valuable seafood products around the world because of their delicacy and good nutritional. However, shrimp is highly vulnerable to quality deterioration associated with biochemical, microbiological or physical changes during postmortem storage, which results in the limited shelf life of the product. Chitosan is considered as a functional packaging component for maintaining the quality and increasing the shelf life of perishable foods. The present study was conducted to evaluate edible coating of crab chitosan containing variable levels of ethanolic extract of Spirulina on microbiological (mesophilic aerobic, psychrotrophic, lactic acid bacteria, and enterobacteriacea), chemical (pH, TVB-N, TMA-N, PV, TBARS) and sensory (odor, color, texture, taste, and overall acceptance) properties of shrimp during refrigerated storage. Also, textural and color characteristics of coated shrimp were performed. According to the obtained results, crab chitosan in combination with Spirulina extract was very effective in order to extend the shelf life of shrimp during storage in refrigerated condition.

Keywords: food packaging, chitosan, spirulina extract, white shrimp, shelf life

Procedia PDF Downloads 208

Authors: Subhankar Karmakar, Madhan Kumar Vasudevan, Manivannan Muniyandi

Abstract:

Thermal Grill Illusion (TGI) elicits a strong and often painful sensation of burn when interlacing warm and cold stimuli that are individually non-painful, excites thermoreceptors beneath the skin. Among several theories of TGI, the “disinhibition” theory is the most widely accepted in the literature. According to this theory, TGI is the result of the disinhibition or unmasking of the pain-sensitive HPC (Heat-Pinch-Cold) nerve fibers due to the inhibition of cold-sensitive nerve fibers that are responsible for masking HPC nerve fibers. Although researchers focused on understanding TGI throughexperiments and models, none of them investigated the prediction of TGI pain intensity through a computational model. Furthermore, the comparison of psychophysically perceived TGI intensity with neurophysiological models has not yet been studied. The prediction of pain intensity through a computational model of TGI can help inoptimizing thermal displays and understanding pathological conditions related to temperature perception. The current studyfocuses on developing a computational model to predict the intensity of TGI pain and experimentally observe the perceived TGI pain. The computational model is developed based on the disinhibition theory and by utilizing the existing popular models of warm and cold receptors in the skin. The model aims to predict the neuronal activity of the HPC nerve fibers. With a temperature-controlled thermal grill setup, fifteen participants (ten males and five females) were presented with five temperature differences between warm and cold grills (each repeated three times). All the participants rated the perceived TGI pain sensation on a scale of one to ten. For the range of temperature differences, the experimentally observed perceived intensity of TGI is compared with the neuronal activity of pain-sensitive HPC nerve fibers. The simulation results show a monotonically increasing relationship between the temperature differences and the neuronal activity of the HPC nerve fibers. Moreover, a similar monotonically increasing relationship is experimentally observed between temperature differences and the perceived TGI intensity. This shows the potential comparison of TGI pain intensity observed through the experimental study with the neuronal activity predicted through the model. The proposed model intends to bridge the theoretical understanding of the TGI and the experimental results obtained through psychophysics. Further studies in pain perception are needed to develop a more accurate version of the current model.

Keywords: thermal grill Illusion, computational modelling, simulation, psychophysics, haptics

Procedia PDF Downloads 171

Authors: L. Paulino Ferreira, M. Gomes Neto, M. Duarte, S. Serra

Abstract:

Objectives: COVID-19 pandemic has caused older adults to experience a degree of social isolation and loneliness that is unprecedented. Our aim is to review state of the art regarding the consequences of social isolation due to COVID-19 in elderly people. Methods: The authors conducted a search on Medscape and PubMed with the keywords mentioned below, and the most relevant articles were selected. Results: Social isolation leads many elderlies to experience loneliness, anxiety, depression, alcohol abuse, and feelings of abandonment with a perception of being a burden on society. Thus, social isolation has increased the risk for suicide in older people. It is also noteworthy that the exacerbation of psychiatric disorders (such as depression, anxiety, and post-traumatic stress disorder) without correct treatment and follow-up also increases suicide risk. Loneliness is also associated with accelerated cognitive deterioration and dementia. Besides that, during social isolation, it could be more difficult for older people to get medication as well as proper health care. It is also noticed an increase in the risk of falls, poor nutrition, and lack of exercise. All this contributes to weakening elderlies’ immune systems leading to a higher risk of developing infections, cardiovascular events, and cancer, increasing hospitalization and morbimortality. Conclusion: Social isolation in the elderly has a significant impact on physical and mental health, as well as morbimortality and hospitalizations due to non-COVID causes. Nevertheless, further studies will be needed to assess the real dimension of the effects of social isolation due to COVID-19.

Keywords: social isolation, COVID-19, elderly, mental health

Procedia PDF Downloads 93

Authors: Khamdan Cahyari, Pratikno Hidayat

Abstract:

Indonesia has produced more than 1.8 million ton pineapple fruit in 2013 of which turned into waste due to industrial processing, deterioration and low qualities. It was estimated that this waste accounted for more than 40 percent of harvested fruits. In addition, pineapple leaves were one of biomass waste from pineapple farming land, which contributed even higher percentages. Most of the waste was only dumped into landfill area without proper pretreatment causing severe environmental problem. This research was meant to valorize the pineapple waste for producing renewable energy source of biogas through mesophilic (30℃) anaerobic digestion process. Especially, it was aimed to investigate effect of substrate concentration of pineapple fruit waste i.e. peel, core as well as effect of microwave pretreatment of pineapple leaves waste. The concentration of substrate was set at value 12, 24 and 36 g VS/liter culture whereas 800-Watt microwave pretreatment conducted at 2 and 5 minutes. It was noticed that optimum biogas production obtained at concentration 24 g VS/l with biogas yield 0.649 liter/g VS (45%v CH4) whereas microwave pretreatment at 2 minutes duration performed better compare to 5 minutes due to shorter exposure of microwave heat. This results suggested that valorization of pineapple waste could be carried out through biogas production at the aforementioned process condition. Application of this method is able to both reduce the environmental problem of the waste and produce renewable energy source of biogas to fulfill local energy demand of pineapple farming areas.

Keywords: pineapple waste, substrate concentration, microwave pretreatment, biogas, anaerobic digestion

Procedia PDF Downloads 580

Authors: Alessandro Giardino

Abstract:

The Iranian Revolution has been the object of numberless historical and semi-fictional accounts, often providing a monolithic perspective on the events, due to the westerner positioning of those recounting them. Against this tradition, two contemporary French-Iranian novels "Disoriental" (2016) by Negar Djavadi and "Marx and The Doll" (2017) by Maryam Madjidi have offered readers a female-oriented and interestingly layered representation of the Iranian Revolution, hence addressing the responsibilities and misconceptions of Western countries. Furthermore, these two women writers have shed light on the disenchantment of the Iranian intellectual class vis-à-vis the foundation of the Islamic Republic, by particularly focusing on the deterioration of women’s rights, as well as the repression of political, ethnical, religious and sexual minorities. By a psycholinguistic and semasiological analysis of the two novels by Djavadi and Madjidi, this essay will focus on alternative accounts of the revolution in order to reflect upon the role of intersectional literature to the understanding of history. More specifically, as both women, refugees, and bi-cultural writers, Djavadi and Madjidi unearthed moments and figures of the revolution which had disappeared from the prevalent narrative. In doing so, however, these two writers resorted to entirely opposite styles of writing that, it will be argued, stem from different types of female resistance. In defining these two approaches as a "narrative resistance" and a "photographic resistance," the essay will elucidate the dependence of these writers’ language on generational and psychological factors, but it will also stir a reflection on their different communicative strategies.

Keywords: Iranian revolution, French-Iranian, intersectionality, literature, women writers

Procedia PDF Downloads 155

Authors: Eun-Taek Sin, Hyun-Ju Jang, Chang Geun Song, Yong-Sik Han

Abstract:

Due to the climate change, the coastal urban area repeatedly suffers from the loss of property and life by flooding. There are three main causes of inland submergence. First, when heavy rain with high intensity occurs, the water quantity in inland cannot be drained into rivers by increase in impervious surface of the land development and defect of the pump, storm sewer. Second, river inundation occurs then water surface level surpasses the top of levee. Finally, Coastal inundation occurs due to rising sea water. However, previous studies ignored the complex mechanism of flooding, and showed discrepancy and inadequacy due to linear summation of each analysis result. In this study, inland flooding and river inundation were analyzed together by HDM-2D model. Petrov-Galerkin stabilizing method and flux-blocking algorithm were applied to simulate the inland flooding. In addition, sink/source terms with exponentially growth rate attribute were added to the shallow water equations to include the inland flooding analysis module. The applications of developed model gave satisfactory results, and provided accurate prediction in comprehensive flooding analysis. The applications of developed model gave satisfactory results, and provided accurate prediction in comprehensive flooding analysis. To consider the coastal surge, another module was developed by adding seawater to the existing Inland Flooding-River Inundation binding module for comprehensive flooding analysis. Based on the combined modules, the Coastal Inundation – Inland & River Flow Interface was simulated by inputting the flow rate and depth data in artificial flume. Accordingly, it was able to analyze the flood patterns of coastal cities over time. This study is expected to help identify the complex causes of flooding in coastal areas where complex flooding occurs, and assist in analyzing damage to coastal cities. Acknowledgements—This research was supported by a grant ‘Development of the Evaluation Technology for Complex Causes of Inundation Vulnerability and the Response Plans in Coastal Urban Areas for Adaptation to Climate Change’ [MPSS-NH-2015-77] from the Natural Hazard Mitigation Research Group, Ministry of Public Safety and Security of Korea.

Keywords: flooding analysis, river inundation, inland flooding, 2D hydrodynamic model

Procedia PDF Downloads 361

Authors: Naveen Kumar

Abstract:

Microplastics, tiny plastic particles less than 5 mm in size formed by the disposal and breakdown of industrial and consumer products, have become a primary environmental concern due to their ubiquitous presence and application in the environment and their potential to cause harm to the ecosystem, wildlife and human health. In this, we study the ability of the fungus Rasamsonia emersonii IMI 393752 to degrade the rigid microplastics of Coke bottles. Microplastics were extracted from Coke bottles and incubated with Rasamsonia emersonii in Sabouraud dextrose agar media. Microplastics were pre-sterilized without altering the chemistry of microplastic. Preliminary analysis was performed by observing radial growth assessment of microplastic-containing media enriched with fungi vs. control. The assay confirmed no impedance or change in the fungi's growth pattern and rate by introducing microplastics. The degradation of the microplastics was monitored over time using microscopy and FTIR, and biodegradation/deterioration on the plastic surface was observed. Furthermore, the liquid assay was performed. HPLC and GCMS will be conducted to check the biodegradation and presence of enzyme release by fungi to counteract the presence of microplastics. These findings have important implications for managing plastic waste, as they suggest that fungi such as Rasamsonia emersonii can potentially degrade microplastics safely and effectively. However, further research to optimise the conditions for microplastic degradation by Rasamsonia emersonii and to develop strategies for scaling up the process for industrial applications will be beneficial.

Keywords: bioremediation, mycoremediation, plastic degradtion, polyethylene terephthalate

Procedia PDF Downloads 95

Authors: Kwok W. Mui, Ling T. Wong, F. Xiao, Chin T. Cheung, Ho C. Yu

Abstract:

This study addresses a concept of the Sustainable Building Environmental Model (SBEM) developed to optimize energy consumption in air conditioning and ventilation (ACV) systems without any deterioration of indoor environmental quality (IEQ). The SBEM incorporates two main components: an adaptive comfort temperature control module (ACT) and a new carbon dioxide demand control module (nDCV). These two modules take an innovative approach to maintain satisfaction of the Indoor Environmental Quality (IEQ) with optimum energy consumption, they provide a rational basis of effective control. A total of 2133 sets of measurement data of indoor air temperature (Ta), relative humidity (Rh) and carbon dioxide concentration (CO2) were conducted in some Hong Kong offices to investigate the potential of integrating the SBEM. A simulation was used to evaluate the dynamic performance of the energy and air conditioning system with the integration of the SBEM in an air-conditioned building. It allows us make a clear picture of the control strategies and performed any pre-tuned of controllers before utilized in real systems. With the integration of SBEM, it was able to save up to 12.3% in simulation and 15% in field measurement of overall electricity consumption, and maintain the average carbon dioxide concentration within 1000ppm and occupant dissatisfaction in 20%.

Keywords: sustainable building environmental model (SBEM), adaptive comfort temperature (ACT), new demand control ventilation (nDCV), energy saving

Procedia PDF Downloads 634

Authors: Md. Abud Darda

Abstract:

Different options for natural gas production in wide geographic areas may be described through diffusion of innovation models. This type of modeling approach provides an indirect estimate of an ultimately recoverable resource, URR, capture the quantitative effects of observed strategic interventions, and allow ex-ante assessments of future scenarios over time. In order to ensure a sustainable energy policy, it is important to forecast the availability of this natural resource. Considering a finite life cycle, in this paper we try to investigate the natural gas production of Myanmar and Algeria, two important natural gas provider in the world energy market. A number of homogeneous and heterogeneous diffusion models, with convenient extensions, have been used. Models validation has also been performed in terms of prediction capability.

Keywords: diffusion models, energy forecast, natural gas, nonlinear production

Procedia PDF Downloads 225

Authors: Mona Heydari, Ehsan Motamedian, Seyed Abbas Shojaosadati

Abstract:

Prediction of perturbations after genetic manipulation (especially gene knockout) is one of the important challenges in systems biology. In this paper, a new algorithm is introduced that integrates microarray data into the metabolic model. The algorithm was used to study the change in the cell phenotype after knockout of Gss gene in Escherichia coli BW25113. Algorithm implementation indicated that gene deletion resulted in more activation of the metabolic network. Growth yield was more and less regulating gene were identified for mutant in comparison with the wild-type strain.

Keywords: metabolic network, gene knockout, flux balance analysis, microarray data, integration

Procedia PDF Downloads 577

Authors: Hernan D. Mejia, Gilberto B. Gaitan, Mauricio A. Franco

Abstract:

420 steel is normally used in the manufacture of dental and surgical instrumentation, as well as parts in the chemical, pharmaceutical, and food industries, among others, where they must withstand heavy loads and often be in contact with corrosive environments, which leads to wear and deterioration of these steels in relatively short times. In the case of medical applications, the instruments made of this steel also suffer wear and corrosion during the repetitive sterilization processes due to the relatively low achievable hardness of just 50 HRC and its hardly acceptable resistance to corrosion. In order to improve the wear resistance of 420 steel, TiAlN coatings were deposited, increasing the aluminum content in the alloy by varying the power applied to the aluminum target of 900, 1100, and 1300 W. Evaluations using XRD, Micro Raman, XPS, AFM, SEM, and TEM showed a columnar growth crystal structure with an average thickness of 2 microns and consisting of the TiN and TiAlN phases, whose roughness and grain size decrease with a higher Al content. The AlN phase also appears in the sample deposited at 1300W. The hardness, determined by nanoindentation, initially increases with the aluminum content from 9.7 GPa to 17.1 GPa, but then decreases to 15.4 GPa for the sample with the highest aluminum content due to the appearance of hexagonal AlN and a decrease of harder TiN and TiAlN phases. It was observed that the wear coefficient had a contrary behavior, which took values of 2.7; 1.7 and 6.6x10⁻⁶ mm³/N.m, respectively. All the coated samples significantly improved the wear resistance of the uncoated 420 steel.

Keywords: hard coatings, magnetron sputtering, TiAlN coatings, surgical instruments, wear resistance

Procedia PDF Downloads 122

Authors: Ridha Zgolli, Ahmed Belhaj, Maroua Ennouri

Abstract:

This study presents a new method to predict cavitation area that may be eroded. It is based on the post-treatment of URANS simulations in cavitant flows. The most RANS calculations with incompressible consideration are based on cavitation model using mixture fluid with density (ρm) calculated as a function of liquid density (ρliq), vapour or gas density (ρvap) and vapour or gas volume fraction α (ρm = αρvap + (1-α) ρliq). The calculations are performed on hydrofoil geometries and compared with experimental works concerning flows characteristics (size of pocket, pressure, velocity). We present here the used cavitation model and the approach followed to evaluate the value of α fixing the shape of pocket around wall before collapsing.

Keywords: flows, CFD, cavitation, erosion

Procedia PDF Downloads 337

Authors: Mohammad Ghahramani, Fahimeh Saei Manesh

Abstract:

Winning a soccer game is based on thorough and deep analysis of the ongoing match. On the other hand, giant gambling companies are in vital need of such analysis to reduce their loss against their customers. In this research work, we perform deep, real-time analysis on every soccer match around the world that distinguishes our work from others by focusing on particular seasons, teams and partial analytics. Our contributions are presented in the platform called “Analyst Masters.” First, we introduce various sources of information available for soccer analysis for teams around the world that helped us record live statistical data and information from more than 50,000 soccer matches a year. Our second and main contribution is to introduce our proposed in-play performance evaluation. The third contribution is developing new features from stable soccer matches. The statistics of soccer matches and their odds before and in-play are considered in the image format versus time including the halftime. Local Binary patterns, (LBP) is then employed to extract features from the image. Our analyses reveal incredibly interesting features and rules if a soccer match has reached enough stability. For example, our “8-minute rule” implies if 'Team A' scores a goal and can maintain the result for at least 8 minutes then the match would end in their favor in a stable match. We could also make accurate predictions before the match of scoring less/more than 2.5 goals. We benefit from the Gradient Boosting Trees, GBT, to extract highly related features. Once the features are selected from this pool of data, the Decision trees decide if the match is stable. A stable match is then passed to a post-processing stage to check its properties such as betters’ and punters’ behavior and its statistical data to issue the prediction. The proposed method was trained using 140,000 soccer matches and tested on more than 100,000 samples achieving 98% accuracy to select stable matches. Our database from 240,000 matches shows that one can get over 20% betting profit per month using Analyst Masters. Such consistent profit outperforms human experts and shows the inefficiency of the betting market. Top soccer tipsters achieve 50% accuracy and 8% monthly profit in average only on regional matches. Both our collected database of more than 240,000 soccer matches from 2012 and our algorithm would greatly benefit coaches and punters to get accurate analysis.

Keywords: soccer, analytics, machine learning, database

Procedia PDF Downloads 238

Authors: Fathi Abid, Bilel Kaffel

Abstract:

The olive tree, the olive harvest during winter season and the production of olive oil better known by professionals under the name of the crushing operation have interested institutional traders such as olive-oil offices and private companies such as food industry refining and extracting pomace olive oil as well as export-import public and private companies specializing in olive oil. The major problem facing producers of olive oil each winter campaign, contrary to what is expected, it is not whether the harvest will be good or not but whether the sale price will allow them to cover production costs and achieve a reasonable margin of profit or not. These questions are entirely legitimate if we judge by the importance of the issue and the heavy complexity of the uncertainty and competition made tougher by a high level of indebtedness and the experience and expertise of speculators and producers whose objectives are sometimes conflicting. The aim of this paper is to study the formation mechanism of olive oil prices in order to learn about speculators’ behavior and expectations in the market, how they contribute by their industry knowledge and their financial alliances and the size the financial challenge that may be involved for them to build private information hoses globally to take advantage. The methodology used in this paper is based on two stages, in the first stage we study econometrically the formation mechanisms of olive oil price in order to understand the market participant behavior by implementing ARMA, SARMA, GARCH and stochastic diffusion processes models, the second stage is devoted to prediction purposes, we use a combined wavelet- ANN approach. Our main findings indicate that olive oil market participants interact with each other in a way that they promote stylized facts formation. The unstable participant’s behaviors create the volatility clustering, non-linearity dependent and cyclicity phenomena. By imitating each other in some periods of the campaign, different participants contribute to the fat tails observed in the olive oil price distribution. The best prediction model for the olive oil price is based on a back propagation artificial neural network approach with input information based on wavelet decomposition and recent past history.

Keywords: olive oil price, stylized facts, ARMA model, SARMA model, GARCH model, combined wavelet-artificial neural network, continuous-time stochastic volatility mode

Procedia PDF Downloads 338

Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

Procedia PDF Downloads 36

Authors: Elisa Coraggio, Dawei Han, Weiru Liu, Theo Tryfonas

Abstract:

Water is one of the most important resources for human society. The world is currently undergoing a wave of urban growth, and pollution problems are of a great impact. Monitoring water quality is a key task for the future of the environment and human species. In recent times, researchers, using Smart Cities technologies are trying to mitigate the problems generated by the population growth in urban areas. The availability of huge amounts of data collected by a pervasive urban IoT can increase the transparency of decision making. Several services have already been implemented in Smart Cities, but more and more services will be involved in the future. Water quality monitoring can successfully be implemented in the urban IoT. The combination of water quality sensors, cloud computing, smart city infrastructure, and IoT technology can lead to a bright future for environmental monitoring. In the past decades, lots of effort has been put on monitoring and predicting water quality using traditional approaches based on manual collection and laboratory-based analysis, which are slow and laborious. The present study proposes a methodology for implementing a water quality prediction model using artificial intelligence techniques and comparing the results obtained with different algorithms. Furthermore, a 3D numerical model will be created using the software D-Water Quality, and simulation results will be used as a training dataset for the artificial intelligence algorithm. This study derives the methodology and demonstrates its implementation based on information and data collected at the floating harbour in the city of Bristol (UK). The city of Bristol is blessed with the Bristol-Is-Open infrastructure that includes Wi-Fi network and virtual machines. It was also named the UK ’s smartest city in 2017.In recent times, researchers, using Smart Cities technologies are trying to mitigate the problems generated by the population growth in urban areas. The availability of huge amounts of data collected by a pervasive urban IoT can increase the transparency of decision making. Several services have already been implemented in Smart Cities, but more and more services will be involved in the future. Water quality monitoring can successfully be implemented in the urban IoT. The combination of water quality sensors, cloud computing, smart city infrastructure, and IoT technology can lead to a bright future for the environment monitoring. In the past decades, lots of effort has been put on monitoring and predicting water quality using traditional approaches based on manual collection and laboratory-based analysis, which are slow and laborious. The present study proposes a new methodology for implementing a water quality prediction model using artificial intelligence techniques and comparing the results obtained with different algorithms. Furthermore, a 3D numerical model will be created using the software D-Water Quality, and simulation results will be used as a training dataset for the Artificial Intelligence algorithm. This study derives the methodology and demonstrate its implementation based on information and data collected at the floating harbour in the city of Bristol (UK). The city of Bristol is blessed with the Bristol-Is-Open infrastructure that includes Wi-Fi network and virtual machines. It was also named the UK ’s smartest city in 2017.

Keywords: artificial intelligence, hydroinformatics, numerical modelling, smart cities, water quality

Procedia PDF Downloads 185

Authors: George Eduful, Ebenezer A. Jackson, Kingsford A. Atanga

Abstract:

This paper investigates the impacts of cable length and capacity of transformer on ferroresonant overvoltage in cable-fed transformers. The study was conducted by simulation using the EMTP RV. Results show that ferroresonance can cause dangerous overvoltages ranging from 2 to 5 per unit. These overvoltages impose stress on insulations of transformers and cables and subsequently result in system failures. Undertaking Basic Multiple Regression Analysis (BMR) on the results obtained, a statistical model was obtained in terms of cable length and transformer capacity. The model is useful for ferroresonant prediction and control in cable-fed transformers.

Keywords: ferroresonance, cable-fed transformers, EMTP RV, regression analysis

Procedia PDF Downloads 531

Authors: Summer Hassan, David Crossman

Abstract:

Myocardial fibrosis (MF) has been loosely defined as the process occurring in the pathological remodeling of the myocardium due to excessive production and deposition of extracellular matrix (ECM) proteins, including collagen. This reduces tissue compliance and accelerates progression to heart failure, as well as affecting the electrical properties of the myocytes resulting in arrhythmias. Microscopic interrogation of MF is key to understanding the molecular orchestrators of disease. It is well-established that recruitment and stimulation of myofibroblasts result in Collagen deposition and the resulting expansion in the ECM. Many types of Collagens have been identified and implicated in scarring of tissue. In a series of experiments conducted at our lab, we aim to elucidate the role collagen VI plays in the development of myocardial fibrosis and its direct impact on myocardial function. This was investigated through an animal experiment in Rats with Collagen VI knockout diseased and healthy animals as well as Collagen VI wild diseased and healthy rats. Echocardiogram assessments of these rats ensued at four-time points, followed by microscopic interrogation of the myocardium aiming to correlate the role collagen VI plays in myocardial function. Our results demonstrate a deterioration in cardiac function as represented by the ejection fraction in the knockout healthy and diseased rats. This elucidates a potential protective role that collagen-VI plays following a myocardial insult. Current work is dedicated to the microscopic characterisation of the fibrotic process in all rat groups, with the results to follow.

Keywords: heart failure, myocardial fibrosis, collagen, echocardiogram, confocal microscopy

Procedia PDF Downloads 81