Search results for: failure detection and prediction

Authors: Michaela Dušková, Kateřina Šimůnková, Martin Hill, Hana Hruškovičová, Hana Pospíšilová, Eva Králíková, Luboslav Stárka

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Smoking represents the most widespread substance dependence in the world. Several studies show the nicotine's ability to alter women hormonal homeostasis. Women smokers have higher testosterone and lower estradiol levels throughout life compared to non-smoker women. We monitored the effect of smoking discontinuation on steroid spectrum with 40 premenopausal and 60 postmenopausal women smokers. These women had been examined before they discontinued smoking and also after 6, 12, 24, and 48 weeks of abstinence. At each examination, blood was collected to determine steroid spectrum (measured by GC-MS), LH, FSH, and SHBG (measured by IRMA). Repeated measures ANOVA model was used for evaluation of the data. The study has been approved by the local Ethics Committee. Given the small number of premenopausal women who endured not to smoke, only the first 6 week period data could be analyzed. A slight increase in androgens after the smoking discontinuation occurred. In postmenopausal women, an increase in testosterone, dihydrotestosterone, dehydroepiandrosterone, and other androgens occurred, too. Nicotine replacement therapy, weight changes, and age does not play any role in the androgen level increase. The higher androgens levels correlated with failure in smoking cessation. Women smokers have higher androgen levels, which might play a role in smoking dependence development. Women successful in smoking cessation, compared to the non-successful ones, have lower androgen levels initially and also after smoking discontinuation. The question is what androgen levels women have before they start smoking.

Keywords: addiction, smoking, cessation, androgens

Procedia PDF Downloads 382

Authors: Paul Saah, Charles Mbohwa, Nelson Sizwe Madonsela

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In the context of developing countries, this study explores a crucial component of economic development by examining the reasons behind the failure of small and medium-sized enterprises (SMEs). SMEs are acknowledged as essential drivers of economic expansion, job creation, and poverty alleviation in emerging countries. This research uses South Africa as a case study to evaluate the reasons why SMEs fail in developing nations. This study explores a quantitative research methodology to investigate the complex causes of SME failures using statistical tools and reliability tests. To ensure the viability of data collection, a sample size of 400 small business owners was chosen using a non-probability selection technique. A closed-ended questionnaire was the primary technique used to obtain detailed information from the participants. Data was analysed and interpreted using computer software packages such as the Statistical Package for the Social Sciences (SPSS). According to the findings, the main reasons why SMEs fail in developing nations are a lack of strategic business planning, a lack of funding, poor management, a lack of innovation, a lack of business research and a low level of education and training. The results of this study show that SMEs can be sustainable and successful as long as they comprehend and use the suggested small business success determining variables into their daily operations. This implies that the more SMEs in developing countries implement the proposed determinant factors of small business success in their business operations the more the businesses are likely to succeed and vice versa.

Keywords: failure, developing countries, SMEs, economic development, South Africa

Procedia PDF Downloads 81

Authors: Valentina Futoryanova, Hugh Hunt

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One of the common failure modes of the diesel engine turbochargers is high cycle fatigue of the turbine wheel blades. Mistuning of the blades due to the casting process is believed to contribute to the failure mode. Laser vibrometer is used to characterize mistuning for a population of turbine wheels through the analysis of the blade response to piezo speaker induced noise. The turbine wheel design under investigation is radial and is typically used in 6-12 L diesel engine applications. Amplitudes and resonance frequencies are reviewed and summarized. The study also includes test results for a paddle wheel that represents a perfectly tuned system and acts as a reference. Mass spring model is developed for the paddle wheel and the model suitability is tested against the actual data. Randomization is applied to the stiffness matrix to model the mistuning effect in the turbine wheels. Experimental data is shown to have good agreement with the model.

Keywords: vibration, radial turbines, mistuning, turbine blades, modal analysis, periodic structures, finite element

Procedia PDF Downloads 434

Authors: Messis A., Adjebli A., Ayeche R., Talbi M., Tighilet K., Louardiane M.

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Cancers are the second cause of death worldwide. Prevalence and incidence of cancers is getting increased by aging and population growth. This study aims to predict and modeling the evolution of breast, Colorectal, Lung, Bladder and Prostate cancers over the period of 2014-2019. In this study, data were analyzed using time series analysis with double exponential smoothing method to forecast the future pattern. To describe and fit the appropriate models, Minitab statistical software version 17 was used. Between 2014 and 2019, the overall trend in the raw number of new cancer cases registered has been increasing over time; the change in observations over time has been increasing. Our forecast model is validated since we have good prediction for the period 2020 and data not available for 2021 and 2022. Time series analysis showed that the double exponential smoothing is an efficient tool to model the future data on the raw number of new cancer cases.

Keywords: cancer, time series, prediction, double exponential smoothing

Procedia PDF Downloads 92

Authors: S. B. Mayil Vealan, C. Sekar

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Bisphenol A (BIS A) and 4 Nitrophenol (4N) are the most prevalent environmental endocrine-disrupting chemicals which mimic hormones and have a direct relationship to the development and growth of animal and human reproductive systems. Moreover, intensive exposure to the compound is related to prostate and breast cancer, infertility, obesity, and diabetes. Hence, accurate and reliable determination techniques are crucial for preventing human exposure to these harmful chemicals. Lanthanum Copper Oxide (La₂Cu₂O₅) nanoparticles were synthesized and investigated through various techniques such as scanning electron microscopy, high-resolution transmission electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, and electrochemical impedance spectroscopy. Cyclic voltammetry and square wave voltammetry techniques are employed to evaluate the electrochemical behavior of as-synthesized samples toward the electrochemical detection of Bisphenol A and 4-Nitrophenol. Under the optimal conditions, the oxidation current increased linearly with increasing the concentration of BIS A and 4-N in the range of 0.01 to 600 μM with a detection limit of 2.44 nM and 3.8 nM. These are the lowest limits of detection and the widest linear ranges in the literature for this determination. The method was applied to the simultaneous determination of BIS A and 4-N in real samples (food packing materials and river water) with excellent recovery values ranging from 95% to 99%. Better stability, sensitivity, selectivity and reproducibility, fast response, and ease of preparation made the sensor well-suitable for the simultaneous determination of bisphenol and 4 Nitrophenol. To the best of our knowledge, this is the first report in which La₂Cu₂O₅ nano particles were used as efficient electron mediators for the fabrication of endocrine disruptor (BIS A and 4N) chemical sensors.

Keywords: endocrine disruptors, electrochemical sensor, Food contacting materials, lanthanum cuprates, nanomaterials

Procedia PDF Downloads 89

Authors: Youn-Sung Kim, Hye-Mi Kim, Sang-Hoon Seo, Jaden Cha

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This International standard IEC 61508 sets out a generic approach for all safety lifecycle activities for systems comprised of electrical/electronic/programmable electronic (E/E/PE) elements that are used to perform safety functions. The control unit in stage control system is safety related facilities to control state and speed for stage system running, and it performs safety-critical function by stage control system. The controller unit is part of safety loops corresponding to the IEC 61508 and classified as logic part in the safety loop. In this paper, we analyze using FMEDA (Failure Mode Effect and Diagnostic Analysis) to verification for fault tolerance methods and functional safety of control unit. Moreover, we determined SIL (Safety Integrity Level) for control unit according to the safety requirements defined in IEC 61508-2 based on an analyzed functional safety.

Keywords: safety function, failure mode effect, IEC 61508-2, diagnostic analysis, stage control system

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Authors: Ehsan Alishahi, Chuang Deng

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Commercial applications of bulk metallic glasses (BMGs) were restricted due to the sudden brittle failure mode which was the main drawback in these new class of materials. Therefore, crystalline-amorphous (C-A) composites were introduced as a toughening strategy in BMGs. In spite of numerous researches in the area of metallic C-A composites, the fundamental structure-property relation in these composites that are not exactly known yet. In this study, it is aimed to investigate the fundamental properties of crystalline-amorphous interface in a model system of Cu/CuZr by using molecular dynamics simulations. Several parameters including interface energy and mechanical properties were investigated by means of atomic models and employing Embedded Atom Method (EAM) potential function. It is found that the crystalline-amorphous interfacial energy weakly depends on the orientation of the crystalline layer, which is in stark contrast to that in a regular crystalline grain boundary. Additionally, the results showed that the interface controls the yielding of the crystalline-amorphous composites during uniaxial tension either by serving as sources for dislocation nucleation in the crystalline layer or triggering local shear transformation zones in amorphous layer. The critical resolved shear stress required to nucleate the first dislocation is also found to strongly depend on the crystalline orientation. Furthermore, it is found that the interaction between dislocations and shear localization at the crystalline-amorphous interface oriented in different directions can lead to a change in the deformation mode. For instance, while the dislocation and shear banding are aligned to each other in {0 0 1} interface plane, the misorientation angle between these failure mechanisms causing more homogeneous deformation in {1 1 0} and {1 1 1} crystalline-amorphous interfaces. These results should help clarify the failure mechanism of crystalline-amorphous composites under various loading conditions.

Keywords: crystalline-amorphous, composites, orientation, plasticity

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Authors: Rajender Dahiya

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Investigation outcomes of major process incidents have been consistent for decades and validate that the causes and consequences are often identical. The debate remains as we continue to experience similar process incidents even with enormous development of new tools, technologies, industry standards, codes, regulations, and learning processes? The objective of this paper is to investigate the most common causes of major industrial incidents and reveal industry challenges and best practices to prevent such incidents. The author, in his current role, performs audits and inspections of a variety of high-hazard industries in North America, including petroleum refineries, chemicals, petrochemicals, manufacturing, etc. In this paper, he shares real life scenarios, examples, and case studies from high hazards operating facilities including key challenges and best practices. This case study will provide a clear understanding of the importance of near miss incident investigation. The incident was a Safe operating limit excursion. The case describes the deficiencies in management programs, the competency of employees, and the culture of the corporation that includes hazard identification and risk assessment, maintaining the integrity of safety-critical equipment, operating discipline, learning from process safety near misses, process safety competency, process safety culture, audits, and performance measurement. Failure to identify the hazards and manage the risks of highly hazardous materials and processes is one of the primary root-causes of an incident, and failure to learn from past incidents is the leading cause of the recurrence of incidents. Several investigations of major incidents discovered that each showed several warning signs before occurring, and most importantly, all were preventable. The author will discuss why preventable incidents were not prevented and review the mutual causes of learning failures from past major incidents. The leading causes of past incidents are summarized below. Management failure to identify the hazard and/or mitigate the risk of hazardous processes or materials. This process starts early in the project stage and continues throughout the life cycle of the facility. For example, a poorly done hazard study such as HAZID, PHA, or LOPA is one of the leading causes of the failure. If this step is performed correctly, then the next potential cause is. Management failure to maintain the integrity of safety critical systems and equipment. In most of the incidents, mechanical integrity of the critical equipment was not maintained, safety barriers were either bypassed, disabled, or not maintained. The third major cause is Management failure to learn and/or apply learning from the past incidents. There were several precursors before those incidents. These precursors were either ignored altogether or not taken seriously. This paper will conclude by sharing how a well-implemented operating management system, good process safety culture, and competent leaders and staff contributed to managing the risks to prevent major incidents.

Keywords: incident investigation, risk management, loss prevention, process safety, accident prevention

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Authors: D. W. Mohammed, J. Bowen, S. N. Kukureka

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Successful design and fabrication of flexible devices for electrode components requires a low sheet resistance, high optical transmittance, high mechanical reliability. Indium tin oxide (ITO) film is currently the predominant transparent conductive oxide (TCO) film in potential applications such as flexible organic light- emitting diodes, flat-panel displays, solar cells, and thin film transistors (TFTs). However ITO films are too brittle and their resistivity is rather high in some cases compared with ITO/Ag/ ITO, and they cannot completely meet flexible optoelectronic device requirements. Therefore, in this work the mechanical properties of ITO /Ag/ITO multilayer film that deposited on Polyethylene terephthalate (PET) compared with the single layered ITO sample were investigated using bending fatigue, twisting fatigue and thermal cycling experiments. The electrical resistance was monitored during the application of mechanical and thermal loads to see the pattern of relationship between the load and the electrical continuity as a consequent of failure. Scanning electron microscopy and atomic force microscopy were used to provide surface characterization of the mechanically-tested samples. The effective embedment of the Ag layer between upper and lower ITO films led to metallic conductivity and superior flexibility to the single ITO electrode, due to the high failure strain of the ductile Ag layer. These results indicate that flexible ITO/Ag/ITO multilayer electrodes are a promising candidate for use as transparent conductor in flexible displays. They provided significantly reduced sheet resistance compared to ITO, and improved bending and twisting properties both as a function of radius, angle and thermal cycling.

Keywords: ITO/Ag/ITO multilayer, failure strain, mechanical properties, PET

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Authors: Ch. Sridevi, S. P. Shailender Kumar, B. Gurudayal, A. Chalapathi Rao, K. Koteswara Rao, P. Srinivasulu

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System reliability and system availability evaluation plays a crucial role in ensuring the seamless operation of complex satellite data reception system with consistent performance for longer periods. This paper presents a novel approach for the same using a case study on one of the antenna systems at satellite data reception ground station in India. The methodology involves analyzing system's components, their failure rates, system's architecture, generation of logical reliability block diagram model and estimating the reliability of the system using the component level mean time between failures considering exponential distribution to derive a baseline estimate of the system's reliability. The model is then validated with collected system level field failure data from the operational satellite data reception systems that includes failure occurred, failure time, criticality of the failure and repair times by using statistical techniques like median rank, regression and Weibull analysis to extract meaningful insights regarding failure patterns and practical reliability of the system and to assess the accuracy of the developed reliability model. The study mainly focused on identification of critical units within the system, which are prone to failures and have a significant impact on overall performance and brought out a reliability model of the identified critical unit. This model takes into account the interdependencies among system components and their impact on overall system reliability and provides valuable insights into the performance of the system to understand the Improvement or degradation of the system over a period of time and will be the vital input to arrive at the optimized design for future development. It also provides a plug and play framework to understand the effect on performance of the system in case of any up gradations or new designs of the unit. It helps in effective planning and formulating contingency plans to address potential system failures, ensuring the continuity of operations. Furthermore, to instill confidence in system users, the duration for which the system can operate continuously with the desired level of 3 sigma reliability was estimated that turned out to be a vital input to maintenance plan. System availability and station availability was also assessed by considering scenarios of clash and non-clash to determine the overall system performance and potential bottlenecks. Overall, this paper establishes a comprehensive methodology for reliability and availability analysis of complex satellite data reception systems. The results derived from this approach facilitate effective planning contingency measures, and provide users with confidence in system performance and enables decision-makers to make informed choices about system maintenance, upgrades and replacements. It also aids in identifying critical units and assessing system availability in various scenarios and helps in minimizing downtime and optimizing resource allocation.

Keywords: exponential distribution, reliability modeling, reliability block diagram, satellite data reception system, system availability, weibull analysis

Procedia PDF Downloads 85

Authors: Tianle Yang

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As a common mental disorder, depression is a leading cause of various diseases worldwide. Early detection and treatment of depression can dramatically promote remission and prevent relapse. However, conventional ways of depression diagnosis require considerable human effort and cause economic burden, while still being prone to misdiagnosis. On the other hand, recent studies report that physical characteristics are major contributors to the diagnosis of depression, which inspires us to mine the internal relationship by neural networks instead of relying on clinical experiences. In this paper, neural networks are constructed to predict depression from physical characteristics. Two initialization methods are examined - Xaiver and Kaiming initialization. Experimental results show that a 3-layers neural network with Kaiming initialization achieves 83% accuracy.

Keywords: depression, neural network, Xavier initialization, Kaiming initialization

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Authors: Ionel D. Craiu, Mihai Nedelcu

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Thin-walled cylinders are often used by the offshore industry as columns of floating installations. Based on observed strains, the inverse Finite Element Method (iFEM) may rebuild the deformation of structures. Structural Health Monitoring uses this approach extensively. However, the number of in-situ strain gauges is what determines how accurate it is, and for shell structures with complicated deformation, this number can easily become too high for practical use. Any thin-walled beam member's complicated deformation can be modeled by the Generalized Beam Theory (GBT) as a linear combination of pre-specified cross-section deformation modes. GBT uses bar finite elements as opposed to shell finite elements. This paper proposes an iFEM/GBT formulation for the shape sensing of thin-walled cylinders based on these benefits. This method significantly reduces the number of strain gauges compared to using the traditional inverse-shell finite elements. Using numerical simulations, dent damage detection is achieved by comparing the strain distributions of the undamaged and damaged members. The effect of noise on strain measurements is also investigated.

Keywords: damage detection, generalized beam theory, inverse finite element method, shape sensing

Procedia PDF Downloads 115

Authors: J. Martin Leal-Graciano, Juan J. Pérez-Gavilán, A. Reyes-Salazar, J. H. Castorena, J. L. Rivera-Salas

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The experimental results about the global behavior of twelve 1:2 scaled reinforced concrete frame subject to in-plane lateral load are presented. The main objective was to generate experimental evidence about the use of steel bars within mortar bed-joints as shear reinforcement in infill walls. Similar to the Canadian and New Zealand standards, the Mexican code includes specifications for this type of reinforcement. However, these specifications were obtained through experimental studies of load-bearing walls, mainly confined walls. Little information is found in the existing literature about the effects of joint reinforcement on the seismic behavior of infill masonry walls. Consequently, the Mexican code establishes the same equations to estimate the contribution of joint reinforcement for both confined walls and infill walls. A confined masonry construction and a reinforced concrete frame infilled with masonry walls have similar appearances. However, substantial differences exist between these two construction systems, which are mainly related to the sequence of construction and to how these structures support vertical and lateral loads. To achieve the objective established, ten reinforced concrete frames with masonry infill walls were built and tested in pairs, having both specimens in the pair identical characteristics except that one of them included joint reinforcement. The variables between pairs were the type of units, the size of the columns of the frame and the aspect ratio of the wall. All cases included tie-columns and tie-beams on the perimeter of the wall to anchor the joint reinforcement. Also, two bare frame with identical characteristic to the infilled frames were tested. The purpose was to investigate the effects of the infill wall on the behavior of the system to in-plane lateral load. In addition, the experimental results were compared with the prediction of the Mexican code. All the specimens were tested in cantilever under reversible cyclic lateral load. To simulate gravity load, constant vertical load was applied on the top of the columns. The results indicate that the contribution of the joint reinforcement to lateral strength depends on the size of the columns of the frame. Larger size columns produce a failure mode that is predominantly a sliding mode. Sliding inhibits the production of new inclined cracks, which are necessary to activate (deform) the joint reinforcement. Regarding the effects of joint reinforcement in the performance of confined masonry walls, many facts were confirmed for infill walls: this type of reinforcement increases the lateral strength of the wall, produces a more distributed cracking and reduces the width of the cracks. Moreover, it reduces the ductility demand of the system at maximum strength. The prediction of the lateral strength provided by the Mexican code is property in some cases; however, the effect of the size of the columns on the contribution of joint reinforcement needs to be better understood.

Keywords: experimental study, Infill wall, Infilled frame, masonry wall

Procedia PDF Downloads 78

Authors: Piyarat Parklug, Busaba Supawattanaobodee, Penpat Pinyowattanasilp

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The radioactive iodine thyroid uptake (RAIU) has been widely used to differentiate the cause of thyrotoxicosis and treatment. Twenty-four hours RAIU is routinely used to calculate the dose of radioactive iodine (RAI) therapy; however, 2 days protocol is required. This study aims to evaluate the modification of Penpat Pinyowattanasilp equation application by the exclusion of outlier data, 3 hours RAIU less than 20% and more than 80%, to improve prediction of 24-hour uptake. The equation is predicted 24 hours RAIU (P24RAIU) = 32.5+0.702 (3 hours RAIU). Then calculating separation first and second therapeutic doses in Graves’ disease patients. Methods; This study was a retrospective study at Faculty of Medicine Vajira Hospital in Bangkok, Thailand. Inclusion were Graves’ disease patients who visited RAI clinic between January 2014-March 2019. We divided subjects into 2 groups according to first and second therapeutic doses. Results; Our study had a total of 151 patients. The study was done in 115 patients with first RAI dose and 36 patients with second RAI dose. The P24RAIU are highly correlated with actual 24-hour RAIU in first and second therapeutic doses (r = 0.913, 95% CI = 0.876 to 0.939 and r = 0.806, 95% CI = 0.649 to 0.897). Bland-Altman plot shows that mean differences between predictive and actual 24 hours RAI in the first dose and second dose were 2.14% (95%CI 0.83-3.46) and 1.37% (95%CI -1.41-4.14). The mean first actual and predictive therapeutic doses are 8.33 ± 4.93 and 7.38 ± 3.43 milliCuries (mCi) respectively. The mean second actual and predictive therapeutic doses are 6.51 ± 3.96 and 6.01 ± 3.11 mCi respectively. The predictive therapeutic doses are highly correlated with the actual dose in first and second therapeutic doses (r = 0.907, 95% CI = 0.868 to 0.935 and r = 0.953, 95% CI = 0.909 to 0.976). Bland-Altman plot shows that mean difference between predictive and actual P24RAIU in the first dose and second dose were less than 1 mCi (-0.94 and -0.5 mCi). This modification equation application is simply used in clinical practice especially patient with 3 hours RAIU in range of 20-80% in a Thai population. Before use, this equation for other population should be tested for the correlation.

Keywords: equation, Graves’disease, prediction, 24-hour uptake

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Authors: Agustyas Tjiptaningrum, Intanri Kurniati, Fadilah Fadilah, Linda Erlina, Tiwuk Susantiningsih

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Coronavirus disease-19 (COVID-19) is still one of the health problems. It can be a severe condition that is caused by a cytokine storm. In a cytokine storm, several proinflammatory cytokines are released massively. It destroys epithelial cells, and subsequently, it can cause death. The anti-inflammatory agent can be used to decrease the number of severe Covid-19 conditions. Melaleuca cajuputi is a plant that has antiviral, antibiotic, antioxidant, and anti-inflammatory activities. This study was carried out to analyze the prediction mechanism of the M. cajuputi extract from Lampung, Indonesia, as an anti-inflammatory agent for COVID-19. This study constructed a database of protein host target that was involved in the inflammation process of COVID-19 using data retrieval from GeneCards with the keyword “SARS-CoV2”, “inflammation,” “cytokine storm,” and “acute respiratory distress syndrome.” Subsequent protein-protein interaction was generated by using Cytoscape version 3.9.1. It can predict the significant target protein. Then the analysis of the Gene Ontology (GO) and KEGG pathways was conducted to generate the genes and components that play a role in COVID-19. The result of this study was 30 nodes representing significant proteins, namely NF-κβ, IL-6, IL-6R, IL-2RA, IL-2, IFN2, C3, TRAF6, IFNAR1, and DOX58. From the KEGG pathway, we obtained the result that NF-κβ has a role in the production of proinflammatory cytokines, which play a role in the COVID-19 cytokine storm. It is an important factor for macrophage transcription; therefore, it will induce inflammatory gene expression that encodes proinflammatory cytokines such as IL-6, TNF-α, and IL-1β. In conclusion, the blocking of NF-κβ is the prediction mechanism of the M. cajuputi extract as an anti-inflammation agent for COVID-19.

Keywords: antiinflammation, COVID-19, cytokine storm, NF-κβ, M. cajuputi

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Authors: Kasra Majbouri Yazdi, Adel Majbouri Yazdi, Saeid Khodayi, Jingyu Hou, Wanlei Zhou, Saeed Saedy

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Fake news and false information are big challenges of all types of media, especially social media. There is a lot of false information, fake likes, views and duplicated accounts as big social networks such as Facebook and Twitter admitted. Most information appearing on social media is doubtful and in some cases misleading. They need to be detected as soon as possible to avoid a negative impact on society. The dimensions of the fake news datasets are growing rapidly, so to obtain a better result of detecting false information with less computation time and complexity, the dimensions need to be reduced. One of the best techniques of reducing data size is using feature selection method. The aim of this technique is to choose a feature subset from the original set to improve the classification performance. In this paper, a feature selection method is proposed with the integration of K-means clustering and Support Vector Machine (SVM) approaches which work in four steps. First, the similarities between all features are calculated. Then, features are divided into several clusters. Next, the final feature set is selected from all clusters, and finally, fake news is classified based on the final feature subset using the SVM method. The proposed method was evaluated by comparing its performance with other state-of-the-art methods on several specific benchmark datasets and the outcome showed a better classification of false information for our work. The detection performance was improved in two aspects. On the one hand, the detection runtime process decreased, and on the other hand, the classification accuracy increased because of the elimination of redundant features and the reduction of datasets dimensions.

Keywords: clustering, fake news detection, feature selection, machine learning, social media, support vector machine

Procedia PDF Downloads 179

Authors: Farbod Rohani, Elyar Ghafoori, Matin Saeedkondori

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Impulse noise is electromagnetic emission which generated by many house hold appliances that are attached to the electrical network. The main difficulty of impulsive noise (IN) elimination process from communication channels is to distinguish it from the transmitted signal and more importantly choosing the proper threshold bandwidth in order to eliminate the signal. Because of wide band property of impulsive noise, we present a novel method for setting the detection threshold, by taking advantage of the fact that impulsive noise bandwidth is usually wider than that of typical communication channels and specifically OFDM channel. After IN detection procedure, we apply simple windowing mechanisms to eliminate them from the communication channel.

Keywords: impulsive noise, OFDM channel, threshold detecting, windowing mechanisms

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Authors: M. R. Ghasemi, R. Ghiasi, H. Varaee

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Surrogate model has received increasing attention for use in detecting damage of structures based on vibration modal parameters. However, uncertainties existing in the measured vibration data may lead to false or unreliable output result from such model. In this study, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The kriging technique allows one to genuinely quantify the surrogate error, therefore it is chosen as metamodeling technique. Enhanced version of ideal gas molecular movement (EIGMM) algorithm is used as main algorithm for model updating. The developed approach is applied to detect simulated damage in numerical models of 72-bar space truss and 120-bar dome truss. The simulation results show the proposed method can perform well in probability-based damage detection of structures with less computational effort compared to direct finite element model.

Keywords: probability-based damage detection (PBDD), Kriging, surrogate modeling, uncertainty quantification, artificial intelligence, enhanced ideal gas molecular movement (EIGMM)

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Authors: Y. J. Chen, T. M. Yue, Z. N. Guo

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This paper reports the effects of laser energy on the characteristics of bubbles generated in the weld zone and the formation of new chemical bonds at the Polyethylene Terephthalate (PET)/Ti joint interface in laser joining of PET to Ti. The samples were produced by using different laser energies ranging from 1.5 J – 6 J in steps of 1.5 J, while all other joining parameters remained unchanged. The types of chemical bonding at the joint interface were analysed by the x-ray photoelectron spectroscopy (XPS) depth-profiling method. The results show that the characteristics of the bubbles and the thickness of the chemically bonded interface, which contains the laser generated bonds of Ti–C and Ti–O, increase markedly with increasing laser energy input. The tensile failure load of the joint depends on the combined effect of the amount and distribution of the bubbles formed and the chemical bonding intensity of the joint interface.

Keywords: laser direct joining, Ti/PET interface, laser energy, XPS depth profiling, chemical bond, tensile failure load

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Authors: Zain Ul Hassan, Muhammad Zain Ul Abadin, Muhammad Zubair Khan

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Unmanned aerial vehicles (UAVs) have now become of predominant importance for various operations, and an immense amount of work is going on in this specific category. The structural stability and life of these UAVs is key factor that should be considered while deploying them to different intelligent operations as their failure leads to loss of sensitive real-time data and cost. This paper presents an applied research on the development of a structural health monitoring system for a UAV designed and fabricated by deploying modular approach. Firstly, a modular UAV has been designed which allows to dismantle and to reassemble the components of the UAV without effecting the whole assembly of UAV. This novel approach makes the vehicle very sustainable and decreases its maintenance cost to a significant value by making possible to replace only the part leading to failure. Then the SHM for the designed architecture of the UAV had been specified as a combination of wings integrated with strain gauges, on-board data logger, bridge circuitry and the ground station. For the research purpose sensors have only been attached to the wings being the most load bearing part and as per analysis was done on ANSYS. On the basis of analysis of the load time spectrum obtained by the data logger during flight, fatigue life of the respective component has been predicted using fracture mechanics techniques of Rain Flow Method and Miner’s Rule. Thus allowing us to monitor the health of a specified component time to time aiding to avoid any failure.

Keywords: fracture mechanics, rain flow method, structural health monitoring system, unmanned aerial vehicle

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Authors: Antonio Monteiro, Jorge Azevedo, Severiano Silva, Alfredo Teixeira

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The objective of this study was to compare the carcass and in vivo real-time ultrasound measurements (RTU) and their capacity to predict the composition of Serrana goats up to 40% of maturity. Twenty one females (11.1 ± 3.97 kg) and Twenty one males (15.6 ± 5.38 kg) were utilized to made in vivo measurements with a 5 MHz probe (ALOKA 500V scanner) at the 9th-10th, 10th-11th thoracic vertebrae (uT910 and uT1011, respectively), at the 1st- 2nd, 3rd-4th, and 4th-5th lumbar vertebrae (uL12, ul34 and uL45, respectively) and also at the 3rd-4th sternebrae (EEST). It was recorded the images of RTU measurements of Longissimus thoracis et lumborum muscle (LTL) depth (EM), width (LM), perimeter (PM), area (AM) and subcutaneous fat thickness (SFD) above the LTL, as well as the depth of tissues of the sternum (EEST) between the 3rd-4th sternebrae. All RTU images were analyzed using the ImageJ software. After slaughter, the carcasses were stored at 4 ºC for 24 h. After this period the carcasses were divided and the left half was entirely dissected into muscle, dissected fat (subcutaneous fat plus intermuscular fat) and bone. Prior to the dissection measurements equivalent to those obtained in vivo with RTU were recorded. Using the Statistica 5, correlation and regression analyses were performed. The prediction of carcass composition was achieved by stepwise regression procedure, with live weight and RTU measurements with and without transformation of variables to the same dimension. The RTU and carcass measurements, except for SFD measurements, showed high correlation (r > 0.60, P < 0.001). The RTU measurements and the live weight, showed ability to predict carcass composition on muscle (R2 = 0.99, P < 0.001), subcutaneous fat (R2 = 0.41, P < 0.001), intermuscular fat (R2 = 0.84, P < 0.001), dissected fat (R2 = 0.71, P < 0.001) and bone (R2 = 0.94, P < 0.001). The transformation of variables allowed a slight increase of precision, but with the increase in the number of variables, with the exception of subcutaneous fat prediction. In vivo measurements by RTU can be applied to predict kid goat carcass composition, from 5 measurements of RTU and the live weight.

Keywords: carcass, goats, real time, ultrasound

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Authors: Natasha Mandal, Rakesh Singh Moirangthem

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The present work reports a theoretical model to develop a plasmonic biosensor for the detection of glucose concentrations in human blood and urine as the abnormality of glucose label is the major cause of diabetes which becomes a life-threatening disease worldwide. This study is based on the surface plasmon resonance (SPR) sensor applications which is a well-established, highly sensitive, label-free, rapid optical sensing tool. Here we have introduced a sandwich assay of two dielectric spacer layers of MgF2 and BaTiO3which gives better performance compared to commonly used SiO2 and TiO2 dielectric spacers due to their low dielectric loss and higher refractive index. The sensitivity of our proposed sensor was found as 3242 nm/RIU approximately, with an excellent linear response of 0.958, which is higher than the conventional single-layer Au SPR sensor. Further, the sensitivity enhancement is also optimized by coating a few layers of two-dimensional (2D) nanomaterials (e.g., Graphene, h-BN, MXene, MoS2, WS2, etc.) on the sensor chip. Hence, our proposed SPR sensor has the potential for the detection of glucose concentration in blood and urine with enhanced sensitivity and high affinity and could be utilized as a reliable platform for the optical biosensing application in the field of medical diagnosis.

Keywords: biosensor, surface plasmon resonance, dielectric spacer, 2D nanomaterials

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Authors: I. Alhajri, G. Zahedi, R. Alazmi, A. Akbari

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In this paper, an Artificial Neural Network (ANN) was developed to predict Asphaltene Precipitation (AP) during the injection of carbon dioxide into crude oil reservoirs. In this study, the experimental data from six different oil fields were collected. Seventy percent of the data was used to develop the ANN model, and different ANN architectures were examined. A network with the Trainlm training algorithm was found to be the best network to estimate the AP. To check the validity of the proposed model, the model was used to predict the AP for the thirty percent of the data that was unevaluated. The Mean Square Error (MSE) of the prediction was 0.0018, which confirms the excellent prediction capability of the proposed model. In the second part of this study, the ANN model predictions were compared with modified Hirschberg model predictions. The ANN was found to provide more accurate estimates compared to the modified Hirschberg model. Finally, the proposed model was employed to examine the effect of different operating parameters during gas injection on the AP. It was found that the AP is mostly sensitive to the reservoir temperature. Furthermore, the carbon dioxide concentration in liquid phase increases the AP.

Keywords: artificial neural network, asphaltene, CO2 injection, Hirschberg model, oil reservoirs

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Authors: Maria Nejjari, Michel Cloutier, Guylaine Talbot, Martin Lanthier

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The fluorescence in situ hybridization (FISH) is a staining technique that allows the identification, detection and quantification of microorganisms without prior cultivation by means of epifluorescence and confocal laser scanning microscopy (CLSM). Oligonucleotide probes have been used to detect bacteria and archaea that colonize the cattle and swine digestive systems. These bacterial strains have been obtained from fecal samples issued from cattle manure and swine slurry. The collection of these samples has been done at 3 different pit’s levels A, B and C with same height. Two collection depth levels have been taken in consideration, one collection level just under the pit’s surface and the second one at the bottom of the pit. Cells were fixed and FISH was performed using oligonucleotides of 15 to 25 nucleotides of length associated with a fluorescent molecule Cy3 or Cy5. The double hybridization using Cy3 probe targeting bacteria (Cy3-EUB338-I) along with a Cy5 probe targeting Archaea (Gy5-ARCH915) gave a better signal. The CLSM images show that there are more bacteria than archaea in swine slurry. However, the choice of fluorescent probes is critical for getting the double hybridization and a unique signature for each microorganism. FISH technique is an easy way to detect pathogens like E. coli O157, Listeria, Salmonella that easily contaminate water streams, agricultural soils and, consequently, food products and endanger human health.

Keywords: archaea, bacteria, detection, FISH, fluorescence

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Authors: Rehan Deshmukh, Sunil Bhand, Utpal Roy

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We report the label-free electrochemical detection of Escherichia coli O157:H7 (ATCC 43895) in potable water using a DNA probe as a sensing molecule targeting the open reading frame marker. Indium tin oxide (ITO) surface was modified with organosilane and, glutaraldehyde was applied as a linker to fabricate the DNA sensor chip. Non-Faradic electrochemical impedance spectroscopy (EIS) behavior was investigated at each step of sensor fabrication using cyclic voltammetry, impedance, phase, relative permittivity, capacitance, and admittance. Atomic force microscopy (AFM) and scanning electron microscopy (SEM) revealed significant changes in surface topographies of DNA sensor chip fabrication. The decrease in the percentage of pinholes from 2.05 (Bare ITO) to 1.46 (after DNA hybridization) suggested the capacitive behavior of the DNA sensor chip. The results of non-Faradic EIS studies of DNA sensor chip showed a systematic declining trend of the capacitance as well as the relative permittivity upon DNA hybridization. DNA sensor chip exhibited linearity in 0.5 to 25 pg/10mL for E. coli O157:H7 (ATCC 43895). The limit of detection (LOD) at 95% confidence estimated by logistic regression was 0.1 pg DNA/10mL of E. coli O157:H7 (equivalent to 13.67 CFU/10mL) with a p-value of 0.0237. Moreover, the fabricated DNA sensor chip used for detection of E. coli O157:H7 showed no significant cross-reactivity with closely and distantly related bacteria such as Escherichia coli MTCC 3221, Escherichia coli O78:H11 MTCC 723 and Bacillus subtilis MTCC 736. Consequently, the results obtained in our study demonstrated the possible application of developed DNA sensor chips for E. coli O157:H7 ATCC 43895 in real water samples as well.

Keywords: capacitance, DNA sensor, Escherichia coli O157:H7, open reading frame marker

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Authors: Mekha Mathew, Varun P Gopi

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Wireless Capsule Endoscopy (WCE) has become a great device in Gastrointestinal (GI) tract diagnosis, which can examine the entire GI tract, especially the small intestine without invasiveness and sedation. Bleeding in the digestive tract is a symptom of a disease rather than a disease itself. Hence the detection of bleeding is important in diagnosing many diseases. In this paper we proposes a novel method for distinguishing bleeding regions from normal regions based on Contourlet transform and Local Binary Pattern (LBP). Experiments show that this method provides a high accuracy rate of 96.38% in CIE XYZ colour space for k-Nearest Neighbour (k-NN) classifier.

Keywords: Wireless Capsule Endoscopy, local binary pattern, k-NN classifier, contourlet transform

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Authors: Ezil Sam Leni, Shalen S.

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Transport service monitoring and upkeep are essential components of smart city initiatives. The main risks to the relevant departments and authorities are the ever-increasing vehicular traffic and the conditions of the roads. In India, the economy is greatly impacted by the road transport sector. In 2021, the Ministry of Road Transport and Highways Transport, Government of India, produced a report with statistical data on traffic accidents. The data included the number of fatalities, injuries, and other pertinent criteria. This study proposes a distributed infrastructure for the monitoring, detection, and reporting of potholes to the appropriate authorities. In a distributed environment, the nodes are the edge devices, and local edge servers, and global servers. The edge devices receive the initial model to be employed from the global server. The YOLOv8 model for pothole detection is used in the edge devices. The edge devices run the pothole detection model, gather the pothole images on their path, and send the updates to the nearby edge server. The local edge server selects the clients for its aggregation process, aggregates the model updates and sends the updates to the global server. The global server collects the updates from the local edge servers, performs aggregation and derives the updated model. The updated model has the information about the potholes received from the local edge servers and notifies the updates to the local edge servers and concerned authorities for monitoring and maintenance of road conditions. The entire process is implemented in FedCV distributed environment with the implementation using the client-server model and aggregation entities. After choosing the clients for its aggregation process, the local edge server gathers the model updates and transmits them to the global server. After gathering the updates from the regional edge servers, the global server aggregates them and creates the updated model. Performance indicators and the experimentation environment are assessed, discussed, and presented. Accelerometer data may be taken into consideration for improved performance in the future development of this study, in addition to the images captured from the transportation routes.

Keywords: federated Learning, pothole detection, distributed framework, federated averaging

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Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

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Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

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Authors: Divyanshoo Singh, Hemant Kumar Singh, Kumar Nilankar

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Major engineering constructions and foundations (e.g., dams, tunnels, bridges, underground caverns, etc.) in and around the Himalayan region of Uttarakhand are not only confined within hard and crystalline rocks but also stretched within weak and anisotropic rocks. While constructing within such anisotropic rocks, engineers more often encounter geotechnical complications such as structural instability, slope failure, and excessive deformation. These severities/complexities arise mainly due to inherent anisotropy such as layering/foliations, preferred mineral orientations, and geo-mechanical anisotropy present within rocks and vary when measured in different directions. Of all the inherent anisotropy present within the rocks, major geotechnical complexities mainly arise due to the inappropriate orientation of weak planes (bedding/foliation). Thus, Orientations of such weak planes highly affect the fracture patterns, failure mechanism, and strength of rocks. This has led to an improved understanding of the physico-mechanical behavior of anisotropic rocks with different orientations of weak planes. Therefore, in this study, block samples of phyllite belonging to the Chandpur Group of Lesser Himalaya were collected from the Srinagar area of Uttarakhand, India, to investigate the effect of foliation angles on physico-mechanical properties of the rock. Further, collected block samples were core drilled of diameter 50 mm at different foliation angles, β (angle between foliation plane and drilling direction), i.e., 0⁰, 30⁰, 60⁰, and 90⁰, respectively. Before the test, drilled core samples were oven-dried at 110⁰C to achieve uniformity. Physical and mechanical properties such as Seismic wave velocity, density, uniaxial compressive strength (UCS), point load strength (PLS), and Brazilian tensile strength (BTS) test were carried out on prepared core specimens. The results indicate that seismic wave velocities (P-wave and S-wave) decrease with increasing β angle. As the β angle increases, the number of foliation planes that the wave needs to pass through increases and thus causes the dissipation of wave energy with increasing β. Maximum strength for UCS, PLS, and BTS was found to be at β angle of 90⁰. However, minimum strength for UCS and BTS was found to be at β angle of 30⁰, which differs from PLS, where minimum strength was found at 0⁰ β angle. Furthermore, failure modes also correspond to the strength of the rock, showing along foliation and non-central failure as characteristics of low strength values, while multiple fractures and central failure as characteristics of high strength values. Thus, this study will provide a better understanding of the anisotropic features of phyllite for the purpose of major engineering construction and foundations within the Himalayan Region.

Keywords: anisotropic rocks, foliation angle, Physico-mechanical properties, phyllite, Himalayan region

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Authors: Elsayed A. M. Shokr, A. Alhazemi, T. Naser, Talal A. Zuhair, Adel A. Zuhair, Ahmed N. Alshamary, Thamer A. Alanazi, Hosam A. Alanazi

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The main threats to human health from heavy metals are associated with exposure to Pb, Cd, Cu, Mo, Zn, Ni, Mn Co and Cr. is mainly via intake of drinking water being the most important source in most populations. These metals have been extensively studied and their effects on human health regularly reviewed by international bodies such as the WHO. Heavy metals have been used by humans for thousands of years. Although several adverse health effects of heavy metals have been known for a long time, exposure to heavy metals continues, and is even increasing in some parts of the world, in particular in less developed countries, though emissions have declined in most developed countries over the last 100 years. A strong relationship between contaminated drinking water with heavy metals from some of the stations of water shopping in Hail, KSA and chronic diseases such as renal failure, liver cirrhosis, and chronic anemia has been identified in this study. These diseases are apparently related to contaminant drinking water with heavy metals such as Pb, Cd, Cu, Mo, Zn, Ni, Mn Co and Cr. Renal failure is related to contaminate drinking water with lead and cadmium, liver cirrhosis to copper and molybdenum, and chronic anemia to copper and cadmium. Recent data indicate that adverse health effects of cadmium exposure may occur at lower exposure levels than previously anticipated, primarily in the form of kidney damage but possibly also bone effects and fractures. The general population is primarily exposed to mercury via drinking water being a major source of methyl mercury exposure, and dental amalgam. During the last century lead, cadmium, zinc, iron and arsenic is mainly via intake of drinking water being the most important source in most populations. Long-term exposure to lead, cadmium, zinc, iron and arsenic in drinking-water is mainly related to primarily in the form of kidney damage. Studies of these diseases suggest that abnormal incidence in specific areas is related to toxic materials in the groundwater and thereby led to the contamination of drinking water in these areas.

Keywords: heavy metals, liver functions, kidney functions and chronic renal failure, hail, renal, water

Procedia PDF Downloads 323