Search results for: intelligent fire detection

Authors: Rana Praween Kumar

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Preterm-low birth weight delivery is a major cause of infant morbidity and mortality in developing countries and has been linked to poor periodontal health during pregnancy. Gingivitis and chronic periodontitis are highly prevalent chronic inflammatory oral diseases. The detection and diagnosis of these common diseases is a fundamentally important component of oral health care. This study is intended to investigate predisposing and enabling factors as determinants of oral health indicators in pregnancy as well as the association between periodontal problems during pregnancy with age and socio economic status of the individual. A community –based prospective cohort study will be conducted in Jamshedpur, Jharkhand, India among pregnant women using completed interviews and a full mouth oral clinical examination using the CPITN (Community Periodontal Index of Treatment Need) and OHI-S (Simplified Oral Hygiene) indices with adequate sample size and informed consent to the patient following proper inclusion and exclusion criteria. Multiple logistic regression analyses will be used to identify independent determinants of periodontal problems and use of dental services during pregnancy. Analysis of covariance (ANCOVA) will be used to investigate the relationship between periodontal problems with the age and socioeconomic status. The result will help in proper monitoring of periodontal health during pregnancy encouraging the delivery of healthy child and the maintenance of proper health of the mother.

Keywords: infant, periodontal problems, pregnancy, pre-term-low birth weight delivery

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Authors: Shengnan Chen, Shuhua Wang

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Successful production of hydrocarbon from unconventional tight oil reserves has changed the energy landscape in North America. The oil contained within these reservoirs typically will not flow to the wellbore at economic rates without assistance from advanced horizontal well and multi-stage hydraulic fracturing. Efficient and economic development of these reserves is a priority of society, government, and industry, especially under the current low oil prices. Meanwhile, society needs technological and process innovations to enhance oil recovery while concurrently reducing environmental impacts. Recently, big data analysis and artificial intelligence become very popular, developing data-driven insights for better designs and decisions in various engineering disciplines. However, the application of data mining in petroleum engineering is still in its infancy. The objective of this research aims to apply intelligent data analysis and data-driven models to exploit unconventional oil reserves both efficiently and economically. More specifically, a comprehensive database including the reservoir geological data, reservoir geophysical data, well completion data and production data for thousands of wells is firstly established to discover the valuable insights and knowledge related to tight oil reserves development. Several data analysis methods are introduced to analysis such a huge dataset. For example, K-means clustering is used to partition all observations into clusters; principle component analysis is applied to emphasize the variation and bring out strong patterns in the dataset, making the big data easy to explore and visualize; exploratory factor analysis (EFA) is used to identify the complex interrelationships between well completion data and well production data. Different data mining techniques, such as artificial neural network, fuzzy logic, and machine learning technique are then summarized, and appropriate ones are selected to analyze the database based on the prediction accuracy, model robustness, and reproducibility. Advanced knowledge and patterned are finally recognized and integrated into a modified self-adaptive differential evolution optimization workflow to enhance the oil recovery and maximize the net present value (NPV) of the unconventional oil resources. This research will advance the knowledge in the development of unconventional oil reserves and bridge the gap between the big data and performance optimizations in these formations. The newly developed data-driven optimization workflow is a powerful approach to guide field operation, which leads to better designs, higher oil recovery and economic return of future wells in the unconventional oil reserves.

Keywords: big data, artificial intelligence, enhance oil recovery, unconventional oil reserves

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Authors: Mohammad Bakhshandeh

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Structural Health Monitoring (SHM) is a crucial and necessary practice that plays a vital role in ensuring the safety and integrity of critical structures, and in particular, bridges. The continuous monitoring of bridges for signs of damage or degradation through Bridge Health Monitoring (BHM) enables early detection of potential problems, allowing for prompt corrective action to be taken before significant damage occurs. Although all monitoring techniques aim to provide accurate and decisive information regarding the remaining useful life, safety, integrity, and serviceability of bridges, understanding the development and propagation of damage is vital for maintaining uninterrupted bridge operation. Over the years, extensive research has been conducted on BHM methods, and experts in the field have increasingly adopted new methodologies. In this article, we provide a comprehensive exploration of the various BHM approaches, including sensor-based, non-destructive testing (NDT), model-based, and artificial intelligence (AI)-based methods. We also discuss the challenges associated with BHM, including sensor placement and data acquisition, data analysis and interpretation, cost and complexity, and environmental effects, through an extensive review of relevant literature and research studies. Additionally, we examine potential solutions to these challenges and propose future research ideas to address critical gaps in BHM.

Keywords: structural health monitoring (SHM), bridge health monitoring (BHM), sensor-based methods, machine-learning algorithms, and model-based techniques, sensor placement, data acquisition, data analysis

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Authors: Huawei Bai, Jianguo Yao, Fellow, IEEE

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Atrial fibrillation (AF) is the most common form of heart arrhythmia and is closely associated with mortality and morbidity in heart failure, stroke, and coronary artery disease. The development of single spot optical sensors enables widespread photoplethysmography (PPG) screening, especially for AF, since it represents a more convenient and noninvasive approach. To our knowledge, most existing studies based on public and unbalanced datasets can barely handle the multiple noises sources in the real world and, also, lack interpretability. In this paper, we construct a large- scale PPG dataset using measurements collected from PPG wrist- watch devices worn by volunteers and propose an attention-based two-stream heterogeneous neural network (TSHNN). The first stream is a hybrid neural network consisting of a three-layer one-dimensional convolutional neural network (1D-CNN) and two-layer attention- based bidirectional long short-term memory (Bi-LSTM) network to learn representations from temporally sampled signals. The second stream extracts latent representations from the PPG time-frequency spectrogram using a five-layer CNN. The outputs from both streams are fed into a fusion layer for the outcome. Visualization of the attention weights learned demonstrates the effectiveness of the attention mechanism against noise. The experimental results show that the TSHNN outperforms all the competitive baseline approaches and with 98.09% accuracy, achieves state-of-the-art performance.

Keywords: PPG wearables, atrial fibrillation, feature fusion, attention mechanism, hyber network

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Authors: Zegrar Ahmed, Ghabi Mohamed

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The degradation of arid and semi-arid ecosystems in Algeria has become a palpable fact that only hinders progress and rural development. In these exceptionally fragile environments, the decline of vegetation is done according to an alarming increase and wind erosion dominates. The ecosystem is subjected to a long hot dry season and low annual average rainfall. The urgency of the fight against desertification is imposed by the very nature of the process that tends to self-accelerate, resulting when human intervention is not forthcoming the irreversibility situations, preventing any possibility of restoration state of these zones. These phenomena have led to different degradation processes, such as the destruction of vegetation, soil erosion, and deterioration of the physical environment. In this study, the work is mainly based on the criteria for classification and identification of physical parameters for spatial analysis and multi-sources to determine the vulnerability of major steppe formations and their impact on desertification. we used Landsat data with two different dates March 2010 and November 2014 in order to determine the changes in land cover, sand moving and land degradation for the diagnosis of the desertification Phenomenon. The application, through specific processes, including the supervised classification was used to characterize the main steppe formations. An analysis of the vulnerability of plant communities was conducted to assign weights and identify areas most susceptible to desertification. Vegetation indices are used to characterize the steppe formations to determine changes in land use.

Keywords: remote sensing, SIG, ecosystem, degradation, desertification

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Authors: Saad Chahba, Rabia Sehab, Ahmad Akrad, Cristina Morel

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Nowadays, the electric machines used in urban electric vehicles are, in most cases, three-phase electric machines with or without a magnet in the rotor. Permanent Magnet Synchronous Machine (PMSM) and Induction Machine (IM) are the main components of drive trains of electric and hybrid vehicles. These machines have very good performance in healthy operation mode, but they are not redundant to ensure safety in faulty operation mode. Faced with the continued growth in the demand for electric vehicles in the automotive market, improving the reliability of electric vehicles is necessary over the lifecycle of the electric vehicle. Multiphase electric machines respond well to this constraint because, on the one hand, they have better robustness in the event of a breakdown (opening of a phase, opening of an arm of the power stage, intern-turn short circuit) and, on the other hand, better power density. In this work, a diagnosis approach using a neural network for an open circuit fault or more of a five-phase induction machine is developed. Validation on the simulator of the vehicle drivetrain, at reduced power, is carried out, creating one and more open circuit stator phases showing the efficiency and the reliability of the new approach to detect and to locate on-line one or more open phases of a five-induction machine.

Keywords: electric vehicle drivetrain, multiphase drives, induction machine, control, open circuit (OC) fault diagnosis, artificial neural network

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Authors: Dorothy Kagazi

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The African elephant (Loxodonta africana) remains one of the most crop-damaging species around Kibale National Park, western Uganda. Elephant crop raiding deprives communities of food and incomes, consequently impacting livelihoods, attitude, and support for conservation. It also attracts an aggressive reaction from local communities including the retaliatory killing of a species that is already endangered and listed under Appendix I of the Convention on Endangered Species of Flora and Fauna (CITES). In order to mitigate against elephant crop raiding and minimize conflict, a number of interventions were devised by the government of Uganda such as physical guarding, scare-shooting, excavation of trenches, growing of unpalatable crops and fire lighting all of which have over the years been implemented around the park. These generated varying degrees of effectiveness but largely never solved the problem of elephants crossing into communities to destroy food and shelter which had a negative effect onto sustainable tourism of the communities who often resorted to killing these animals and hence contributing the falling numbers of these animals. It was until government discovered that there are far more effective ways of deterring these animals from crossing to communities that it commissioned a study to deploy the African honeybee (Apis mellifera scutellata) as a deterrent against elephant crop raiding and income enhancement for local people around the park. These efforts led to a number of projects around Kibale National Park where communities were facilitated to keep bees for human-elephant conflict mitigation and rural income enhancement through the sale of honey. These projects have registered tremendous success in reducing crop damage, enhance rural incomes, influence positive attitude change and ultimately secure community support for elephant and park conservation which is a clear manifestation of sustainable tourism development in the area. To address the issue of sustainability, the project was aligned with four major objectives that contributed to the overall goal of maintaining the areas around the parks and the national park itself in such a manner that it remains viable over an infinite period. Among these included determining deterrence effects of bees against elephant crop raiding, assessing the contribution of beekeeping towards rural income enhancement, determining the impact of community involvement of park conservation and management among others. The project deployed 500 improved hives by placing them at specific and previously identified and mapped out elephant crossing points along the park boundary. A control site was established without any intervention to facilitate comparison of findings and data was collected on elephant raiding frequency, patterns, honey harvested, and community attitude towards the park. A socio-economic assessment was also undertaken to ascertain the contribution of beekeeping to incomes and attitude change. In conclusion, human-wildlife conflicts have disturbed conservation and sustainable tourism development efforts. Such success stories like the beekeeping strategy should hence be extensively discussed and widely shared as a conservation technique for sustainable tourism.

Keywords: bees, communities, conservation, elephants

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Authors: Pattarachaya Preechakasedkit, Kota Osada, Koji Suzuki, Daniel Citterio, Orawon Chailapakul

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A paper-based lateral flow device for screening thyroid stimulating hormone (TSH) is reported. A sandwich immunoassay was performed using two mouse monoclonal TSH antibodies (anti-hTSH 5403 and 5404) as immobilized and labeled antibodies for capturing TSH samples. Test (anti-hTSH 5403) and control (goat anti-Mouse IgG) lines were fabricated on nitrocellulose membrane (NCM) using ballpoint pen printed with a speed of 3 cm/s and thickness setting of 1. The novel gold nanoparticles europium complex (AuNPs@Eu) was used as fluorescence label compared to conventional AuNPs label. The results obtained with this device can be visually assessed by the naked eyes and under UV hand lamps, and quantitative analysis can be performed using the ImageJ program. The limit of detection (LOD) under UV hand lamps (0.1 µIU/mL) provided 50-fold greater sensitivity than AuNPs (5 µIU/mL), which is suitable for both hypothyroidism and hyperthyroidism screening within 30 min. A linear relationship between the red intensity and the logarithmic concentrations of TSH was observed with a good correlation (R²=0.992). Furthermore, the device can be effectively applied for screening TSH in the spiked human serum with recovery range of 96.80-104.45% and RSD of 2.18-3.63%. Therefore, the developed device is an alternative method for TSH screening which provides a lot of advantages including low cost, short time analysis, ease of use, disposability, portability, and on-site measurement.

Keywords: thyroid stimulating hormone, paper-based lateral flow, hypothyroidism, hyperthyroidism

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Authors: M. Devaki, K. B. Jayanthi

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The identification of water bodies from satellite images has recently received a great deal of attention. Different methods have been developed to distinguish water bodies from various satellite images that vary in terms of time and space. Urban water identification issues body manifests in numerous applications with a great deal of certainty. There has been a sharp rise in the usage of satellite images to map natural resources, including urban water bodies and forests, during the past several years. This is because water and forest resources depend on each other so heavily that ongoing monitoring of both is essential to their sustainable management. The relevant elements from satellite pictures have been chosen using a variety of techniques, including machine learning. Then, a convolution neural network (CNN) architecture is created that can identify a superpixel as either one of two classes, one that includes water or doesn't from input data in a complex metropolitan scene. The deep learning technique, CNN, has advanced tremendously in a variety of visual-related tasks. CNN can improve classification performance by reducing the spectral-spatial regularities of the input data and extracting deep features hierarchically from raw pictures. Calculate the water body using the satellite image's resolution. Experimental results demonstrate that the suggested method outperformed conventional approaches in terms of water extraction accuracy from remote-sensing images, with an average overall accuracy of 97%.

Keywords: water body, Deep learning, satellite images, convolution neural network

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Authors: Riaan Kleyn

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Mass wine production companies across the globe are provided with grapes from winegrowers that predominantly utilize mechanical harvesting machines to harvest wine grapes. Mechanical harvesting accelerates the rate at which grapes are harvested, allowing grapes to be delivered faster to meet the demands of wine cellars. The disadvantage of the mechanical harvesting method is the inclusion of material-other-than-grapes (MOG) in the harvested wine grape loads arriving at the cellar which degrades the quality of wine that can be produced. Currently, wine cellars do not have a method to determine the amount of MOG present within wine grape loads. This paper seeks to find an optimal computer vision method capable of detecting the amount of MOG within a wine grape load. A MOG detection method will encourage winegrowers to deliver MOG-free wine grape loads to avoid penalties which will indirectly enhance the quality of the wine to be produced. Traditional image segmentation methods were compared to deep learning segmentation methods based on images of wine grape loads that were captured at a wine cellar. The Mask R-CNN model with a ResNet-50 convolutional neural network backbone emerged as the optimal method for this study to determine the amount of MOG in an image of a wine grape load. Furthermore, a statistical analysis was conducted to determine how the MOG on the surface of a grape load relates to the mass of MOG within the corresponding grape load.

Keywords: computer vision, wine grapes, machine learning, machine harvested grapes

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Authors: Raana Babadi Fathipour

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Street foods hold a significant place in the tapestry of socioeconomic and cultural norms, beloved across the globe. Serving as a convenient and affordable option for city dwellers seeking nourishment, these culinary delights also serve as a vital source of income for vendors, particularly women. Additionally, street food acts as a mirror reflecting traditional local customs and practices, an element that draws tourists to experience the authenticity of a culture firsthand. Despite its many virtues, concerns have emerged regarding the microbiological safety of street food worldwide. Often prepared and sold in subpar conditions without proper oversight or regulation, street food has become synonymous with potential health risks. The presence of elevated levels of fecal indicator bacteria and various pathogens in these unregulated delicacies further perpetuates anxieties surrounding their consumption. This analysis delves into the intricate microbiological intricacies inherent in street food, shedding light on the pertinent safety concerns and prevalent pathogens. Additionally, it elaborates on the worldwide standing of this vital economic endeavor. Moreover, it advocates for the adoption of molecular detection techniques over conventional culture-based methods to gain a more comprehensive grasp of the true microbial risks posed by street cuisine. Acknowledgment marks the initial step towards resolving any given issue.

Keywords: foodborne pathogens, microbiological safety, street food, viruses

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Authors: Messaoud Eljamai, Sami Hidouri

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Massive multiple-input multiple-output (MIMO) is an emerging technology for new cellular networks such as 5G systems. Its principle is to use many antennas per cell in order to maximize the network's spectral efficiency. Inter-cellular interference remains a fundamental problem. The use of massive MIMO will not derogate from the rule. It improves performances only when the number of antennas is significantly greater than the number of users. This, considerably, limits the networks spectral efficiency. In this paper, a coordinated detector for an uplink massive MIMO system is proposed in order to mitigate the inter-cellular interference. The proposed scheme combines the coordinated multipoint technique with an interference-cancelling algorithm. It requires the serving cell to send their received symbols, after processing, decision and error detection, to the interfered cells via a backhaul link. Each interfered cell is capable of eliminating intercellular interferences by generating and subtracting the user’s contribution from the received signal. The resulting signal is more reliable than the original received signal. This allows the uplink massive MIMO system to improve their performances dramatically. Simulation results show that the proposed detector improves system spectral efficiency compared to classical linear detectors.

Keywords: massive MIMO, COMP, interference canceling algorithm, spectral efficiency

Procedia PDF Downloads 146

Authors: Mihai-Alexandru Florea, Veronica Drumea, Roxana Nita, Cerasela Gird, Laura Olariu

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The aim of this study was to investigate the residues of pesticide found in tea water infusions. A multi-residues method to determine 147 pesticides has been developed using the QuEChERS (Quick, Easy, Cheap, Effective, Rugged, Safe) procedure and dispersive solid phase extraction (d-SPE) for the cleanup the pesticides from complex matrices such as plants and tea. Sample preparation was carefully optimized for the efficient removal of coextracted matrix components by testing more solvent systems. Determination of pesticides was performed using GC-MS/MS (100 of pesticides) and LC-MS/MS (47 of pesticides). The selected reaction monitoring (SRM) mode was chosen to achieve low detection limits and high compounds selectivity and sensitivity. Overall performance was evaluated and validated according to DG-SANTE Guidelines. To assess the pesticide residue transfer rate (qualitative) from dried tea in infusions the samples (tea) were spiked with a mixture of pesticides at the maximum residues level accepted for teas and herbal infusions. In order to investigate the release of the pesticides in tea preparations, the medicinal plants were prepared in four ways by variation of water temperature and the infusion time. The pesticides from infusions were extracted using two methods: QuEChERS versus solid-phase extraction (SPE). More that 90 % of the pesticides studied was identified in infusion.

Keywords: tea, solid-phase extraction (SPE), selected reaction monitoring (SRM), QuEChERS

Procedia PDF Downloads 212

Authors: Rajkumar Kolangarakandy

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Feature extraction and selection is the primary part of any mammogram classification algorithms. The choice of feature, attribute or measurements have an important influence in any classification system. Discrete Wavelet Transformation (DWT) coefficients are one of the prominent features for representing images in frequency domain. The features obtained after the decomposition of the mammogram images using wavelet transformations have higher dimension. Even though the features are higher in dimension, they were highly correlated and redundant in nature. The dimensionality reduction techniques play an important role in selecting the optimum number of features from the higher dimension data, which are highly correlated. PCA is a mathematical tool that reduces the dimensionality of the data while retaining most of the variation in the dataset. In this paper, a multilevel classification of mammogram images using reduced discrete wavelet transformation coefficients and lazy classifiers is proposed. The classification is accomplished in two different levels. In the first level, mammogram ROIs extracted from the dataset is classified as normal and abnormal types. In the second level, all the abnormal mammogram ROIs is classified into benign and malignant too. A further classification is also accomplished based on the variation in structure and intensity distribution of the images in the dataset. The Lazy classifiers called Kstar, IBL and LWL are used for classification. The classification results obtained with the reduced feature set is highly promising and the result is also compared with the performance obtained without dimension reduction.

Keywords: PCA, wavelet transformation, lazy classifiers, Kstar, IBL, LWL

Procedia PDF Downloads 333

Authors: Mokoena Oratilwe Penwell, Seeletse Solly Matshonisa

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The demand for quality products is a vital factor determining the success of a producing company, and the reality of this demand influences customer satisfaction. In Sefako Makgatho Health Sciences University (SMU), concerns over the quality of food being sold have been raised by mostly students and staff who are primary consumers of food being sold by the cafeteria. Suspicions of food poisoning and the occurrence of diarrhea-related to food from the cafeteria, amongst others, have been raised. However, minimal measures have been taken to resolve the issue of food quality. New service providers have been appointed, and still, the same trends are being observed, the quality of food seems to depreciate continuously. This paper uses multi-attribute failure mode analysis (MAFMA) for failure detection and minimization on the machines used for food production by SMU catering company before being sold to both staff, and students so as to improve production plant reliability, and performance. Analytical Hierarchy Process (AHP) will be used for the severity ranking of the weight criterions and development of the hierarchical structure for the cafeteria company. Amongst other potential issues detected, maintenance of the machines and equipment used for food preparations was of concern. Also, the staff lacked sufficient hospitality skills, supervision, and management in the cafeteria needed greater attention to mitigate some of the failures occurring in the food production plant.

Keywords: MAFMA, food quality, maintenance, supervision

Procedia PDF Downloads 133

Authors: Iryna Teslenko

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According to the provisions of the current criminal procedural legislation of Ukraine, the issue of admissibility of evidence is closely related to both the right to a fair trial and the presumption of innocence. The general rule is that evidence obtained improperly or illegally cannot be taken into account in a court case. Therefore, the evidence base of the prosecution, collected at the stage of the pre-trial investigation, compliance with the requirements of the law during the collection of evidence, is of crucial importance for the criminal process, the violation of which entails the recognition of the relevant evidence as inadmissible, which can nullify all the efforts of the pre-trial investigation body and the prosecution. Therefore, the issue of admissibility of evidence in criminal proceedings is fundamentally important and decisive for the entire process. Research on this issue began in December 2021. At that time, there was still no clear understanding of what needed to be conveyed to the scientific community. In February 2022, the lives of all citizens of Ukraine have totally changed. A war broke out in the country. At a time when the entire world community is on the path of humanizing society, respecting the rights and freedoms of man and citizen, a military conflict has arisen in the middle of Europe - one country attacked another, war crimes are being committed. The world still cannot believe it, but it is happening here and now, people are dying, infrastructure is being destroyed, war crimes are being committed, contrary to the signed and ratified international conventions, and contrary to all the acquisitions and development of world law. At this time, the life of the world has divided into before and after February 24, 2022, the world cannot be the same as it was before, and the approach to solving legal issues in the criminal process, in particular, issues of proving the commission of crimes and the involvement of certain persons in their commission. An international criminal has appeared in the humane European world, who disregards all norms of law and morality, and does not adhere to any principles. Until now, the practice of the European Court of Human Rights and domestic courts of Ukraine treated with certain formalism, such a property of evidence in criminal proceedings as the admissibility of evidence. Currently, we have information that the Office of the Prosecutor of the International Criminal Court in The Hague has started an investigation into war crimes in Ukraine and is documenting them. In our opinion, the world cannot allow formalism in bringing a war criminal to justice. There is a war going on in Ukraine, the cities are under round-the-clock missile fire from the aggressor country, which makes it impossible to carry out certain investigative actions. If due to formal deficiencies, the collected evidence is declared inadmissible, it may lead to the fact that the guilty people will not be punished. And this, in turn, sends a message to other terrorists in the world about the impunity of their actions, the system of deterring criminals from committing criminal offenses (crimes) will collapse due to the understanding of the inevitability of punishment, and this will affect the entire world security and European security in particular. Therefore, we believe that the world cannot allow chaos in the issue of general security, there should be a transformation of the approach in general to such a property of evidence in the criminal process as admissibility in order to ensure the inevitability of the punishment of criminals. We believe that the scientific and legal community should not allow criminals to avoid responsibility. The evil that is destroying Ukraine should be punished. We must all together prove that legal norms are not just words written on paper but rules of behavior of all members of society, their non-observance leads to mandatory responsibility. Everybody who commits crimes will be punished, which is inevitable, and this principle is the guarantor of world security in the future.

Keywords: admissibility of evidence, criminal process, war, Ukraine

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Authors: Abu Salim Mustafa

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Water plays a major role in maintaining life on earth, but it can also serve as a matrix for pathogenic organisms, posing substantial health threats to humans. Although, outbreaks of diseases attributable to drinking water may not be common in industrialized countries, they still occur and can lead to serious acute, chronic, or sometimes fatal health consequences. The analysis of drinking water samples from different regions of Kuwait was performed in this study for bacterial and viral contaminations. Drinking tap water samples were collected from 15 different locations of the six Kuwait governorates. All samples were analyzed by confocal microscopy for the presence of bacteria. The samples were cultured in vitro to detect cultivable organisms. DNA was isolated from the cultured organisms and the identity of the bacteria was determined by sequencing the bacterial 16S rRNA genes, followed by BLAST analysis in the database of NCBI, USA. RNA was extracted from water samples and analyzed by real-time PCR for the detection of viruses with potential health risks, i.e. Astrovirus, Enterovirus, Norovirus, Rotavirus, and Hepatitis A. Confocal microscopy showed the presence of bacteria in some water samples. The 16S rRNA gene sequencing of culture grown organisms, followed by BLAST analysis, identified the presence of several non-pathogenic bacterial species. However, one sample had Acinetobacter baumannii, which often causes opportunistic infections in immunocompromised people, but none of the studied viruses could be detected in the drinking water samples analyzed. The results indicate that drinking water samples analyzed from various locations in Kuwait are relatively safe for drinking and do not contain many harmful pathogens.

Keywords: drinking water, microbial contaminant, 16S rDNA, Kuwait

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Authors: Lucia Salvioni, Pietro Giorgio Lovaglio, Valerio Leoni, Miriam Colombo, Luisa Fiandra

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The involvement of plasmatic surfactant protein-D (SP-D) in pulmonary diseases has been long investigated, and over the last two years, more interest has been directed to determine its role as a marker of COVID-19. In this direction, several studies aimed to correlate pulmonary surfactant proteins with the clinical manifestations of the virus indicated SP-D as a prognostic biomarker of COVID-19 pneumonia severity. The present work has performed a retrospective study on a relatively large cohort of patients of Hospital Pio XI of Desio (Lombardia, Italy) with the aim to assess differences in the hematic SP-D concentrations among COVID-19 patients and healthy donors and the role of SP-D as a prognostic marker of severity and/or of mortality risk. The obtained results showed a significant difference in the mean of log SP-D levels between COVID-19 patients and healthy donors, so as between dead and survived patients. SP-D values were significantly higher for both hospitalized COVID-19 and dead patients, with threshold values of 150 and 250 ng/mL, respectively. SP-D levels at admission and increasing differences among follow-up and admission values resulted in the strongest significant risk factors of mortality. Therefore, this study demonstrated the role of SP-D as a predictive marker of SARS-CoV-2 infection and its outcome. A significant correlation of SP-D with patient mortality indicated that it is also a prognostic factor in terms of mortality, and its early detection should be considered to design adequate preventive treatments for COVID-19 patients.

Keywords: SARS-CoV-2 infection, COVID-19, surfactant protein-D (SP-D), mortality, biomarker

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Authors: Arzyana Sunkar, Yanto Santosa, Siti Badriyah Rushayati

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Indonesia has experienced annual forest fires that have rapidly destroyed and degraded its forests. Fires in the peat swamp forests of Riau Province, have set the stage for problems to worsen, this being the ecosystem most prone to fires (which are also the most difficult, to extinguish). Despite various efforts to curb deforestation, and forest degradation processes, severe forest fires are still occurring. To find an effective solution, the basic causes of the problems must be identified. It is therefore critical to have an in-depth understanding of the underlying causal factors that have contributed to deforestation and forest degradation as a whole, in order to attain reductions in their rates. An assessment of the drivers of deforestation and forest degradation was carried out, in order to design and implement measures that could slow these destructive processes. Research was conducted in Giam Siak Kecil–Bukit Batu Biosphere Reserve (GSKBB BR), in the Riau Province of Sumatera, Indonesia. A biosphere reserve was selected as the study site because such reserves aim to reconcile conservation with sustainable development. A biosphere reserve should promote a range of local human activities, together with development values that are in line spatially and economically with the area conservation values, through use of a zoning system. Moreover, GSKBB BR is an area with vast peatlands, and is experiencing forest fires annually. Various factors were analysed to assess the drivers of deforestation and forest degradation in GSKBB BR; data were collected from focus group discussions with stakeholders, key informant interviews with key stakeholders, field observation and a literature review. Landsat satellite imagery was used to map forest-cover changes for various periods. Analysis of landsat images, taken during the period 2010-2014, revealed that within the non-protected area of core zone, there was a trend towards decreasing peat swamp forest areas, increasing land clearance, and increasing areas of community oil-palm and rubber plantations. Fire was used for land clearing and most of the forest fires occurred in the most populous area (the transition area). The study found a relationship between the deforested/ degraded areas, and certain distance variables, i.e. distance from roads, villages and the borders between the core area and the buffer zone. The further the distance from the core area of the reserve, the higher was the degree of deforestation and forest degradation. Research findings suggested that agricultural expansion may be the direct cause of deforestation and forest degradation in the reserve, whereas socio-economic factors were the underlying driver of forest cover changes; such factors consisting of a combination of socio-cultural, infrastructural, technological, institutional (policy and governance), demographic (population pressure) and economic (market demand) considerations. These findings indicated that local factors/problems were the critical causes of deforestation and degradation in GSKBB BR. This research therefore concluded that reductions in deforestation and forest degradation in GSKBB BR could be achieved through ‘local actor’-tailored approaches such as community empowerment

Keywords: Actor-led solution, community empowerment, drivers of deforestation and forest degradation, Giam Siak Kecil – Bukit Batu Biosphere Reserve

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Authors: Mateusz Paszko, Ksenia Siadkowska

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Due to the low-altitude and relatively low-speed flight, helicopters are easy targets for actual combat assets e.g. infrared-guided missiles. Current techniques aim to increase the combat effectiveness of the military helicopters. Protection of the helicopter in flight from early detection, tracking and finally destruction can be realized in many ways. One of them is cooling hot exhaust gasses, emitting from the engines to the atmosphere in special heat exchangers. Nowadays, this process is realized in ejective coolers, where strong heat and momentum exchange between hot exhaust gases and cold air ejected from atmosphere takes place. Flow effects of air, exhaust gases; mixture of those two and the heat transfer between cold air and hot exhaust gases are given by differential equations of: Mass transportation–flow continuity, ejection of cold air through expanding exhaust gasses, conservation of momentum, energy and physical relationship equations. Calculation of those processes in ejective cooler by means of classic mathematical analysis is extremely hard or even impossible. Because of this, it is necessary to apply the numeric approach with modern, numeric computer programs. The paper discussed the general usability of the Computational Fluid Dynamics (CFD) in a process of projecting the ejective exhaust gases cooler cooperating with helicopter turbine engine. In this work, the CFD calculations have been performed for ejective-based cooler cooperating with the PA W3 helicopter’s engines.

Keywords: aviation, CFD analysis, ejective-cooler, helicopter techniques

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Authors: Mateusz Paszko, Mirosław Wendeker, Adam Majczak

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Every material body that temperature is higher than 0K (absolute zero) emits infrared radiation to the surroundings. Infrared radiation is highly meaningful in military aviation, especially in military applications of helicopters. Helicopters, in comparison to other aircraft, have much lower flight speeds and maneuverability, which makes them easy targets for actual combat assets like infrared-guided missiles. When designing new helicopter types, especially for combat applications, it is essential to pay enormous attention to infrared emissions of the solid parts composing the helicopter’s structure, as well as to exhaust gases egressing from the engine’s exhaust system. Due to their high temperature, exhaust gases, egressed to the surroundings are a major factor in infrared radiation emission and, in consequence, detectability of a helicopter performing air combat operations. Protection of the helicopter in flight from early detection, tracking and finally destruction can be realized in many ways. This paper presents the analysis of possibilities to decrease the infrared radiation level that is emitted to the environment by helicopter in flight, by cooling exhaust in special ejection-based coolers. The paper also presents the concept 3D model and results of numeric analysis of ejective-based cooler cooperation with PA-10W turbine engine. Numeric analysis presented promising results in decreasing the infrared emission level by PA W-3 helicopter in flight.

Keywords: exhaust cooler, helicopter propulsion, infrared radiation, stealth

Procedia PDF Downloads 344

Authors: Itti Bist, Snehasis Bhakta, Di Jiang, Tia E. Keyes, Aaron Martin, Robert J. Forster, James F. Rusling

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DNA damage from metabolites of lipophilic drugs and pollutants, generated by enzymes, represents a major toxicity pathway in humans. These metabolites can react with DNA to form either 8-oxo-7,8-dihydro-2-deoxyguanosine (8-oxodG), which is the oxidative product of DNA or covalent DNA adducts, both of which are genotoxic and hence considered important biomarkers to detect cancer in humans. Therefore, detecting reactions of metabolites with DNA is an effective approach for the safety assessment of new chemicals and drugs. Here we describe a novel electrochemiluminescent (ECL) sensor array which can detect DNA oxidation and chemical DNA damage in a single array, facilitating a more accurate diagnostic tool for genotoxicity screening. Layer-by-layer assembly of DNA and enzyme are assembled on the pyrolytic graphite array which is housed in a microfluidic device for sequential detection of two type of the DNA damages. Multiple enzyme reactions are run on test compounds using the array, generating toxic metabolites in situ. These metabolites react with DNA in the films to cause DNA oxidation and chemical DNA damage which are detected by ECL generating osmium compound and ruthenium polymer, respectively. The method is further validated by the formation of 8-oxodG and DNA adduct using similar films of DNA/enzyme on magnetic bead biocolloid reactors, hydrolyzing the DNA, and analyzing by liquid chromatography-mass spectrometry (LC-MS). Hence, this combined DNA/enzyme array/LC-MS approach can efficiently explore metabolic genotoxic pathways for drugs and environmental chemicals.

Keywords: biosensor, electrochemiluminescence, DNA damage, microfluidic array

Procedia PDF Downloads 365

Authors: Husain Rajib, K. S. Kishor, D. G. Jewel

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Background: Uncorrected refractive error is a common cause of preventable visual impairment in pediatric age group which can be lead to blindness but early detection of visual impairment can reduce the problem that will have good effective in education and more involve in social activities. Glasses are the cheapest and commonest form of correction of refractive errors. To achieve this, patient must exhibit good compliance to spectacle wear. Patient’s attitude and perception of glasses and eye health could affect compliance. Material and method: A Prospective community based cross sectional study was designed in order to evaluate the knowledge, attitude and practices about refractive errors and eye health amongst the primary school going children. Result: Among 140 respondents, 72 were males and 68 were females. We found 50 children were myopic and out of them 26 were male and 24 were female, 27 children were hyperopic and out of them 14 were male and 13 were female. About 63 children were astigmatic and out of them 32 were male and 31 were female. The level of knowledge, attitude was satisfactory. The attitude of the students, teachers and parents was cooperative which helps to do cycloplegic refraction. Practice was not satisfactory due to social stigma and information gap. Conclusion: Knowledge of refractive error and acceptance of glasses for the correction of uncorrected refractive error. Public awareness program such as vision screening program, eye camp, and teachers training program are more beneficial for wearing and prescribing spectacle.

Keywords: refractive error, stigma, knowledge, attitude, practice

Procedia PDF Downloads 259

Authors: Charles Mazhazhate

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This study interrogates and analyses the intricate kin- and- kith network patterns of corruption and mismanagement of resources prevalent in public sector supply chains bedeviling the developing economies of Sub-Saharan Africa with particular reference to Zimbabwe. This is forcing governments to resort to harsh fiscal policies that see their citizens paying high taxes against a backdrop of incomes below the poverty datum line, and this negatively affects their quality of life. The corporate world is also affected by the various tax-regime instituted. Mismanagement of resources and corrupt practices are rampant in state-owned enterprises to the extent that institutional policies, procedures, and practices are often flouted for the benefit of a clique of individuals. This interwoven in kith and kin blood human relations in organizations where appointments to critical positions are based on ascribed status. People no longer place value in their systems to make them work thereby violating corporate governance principles. Greediness and ‘unholy friendship connections’ are instrumental in fueling the employment of people who know each other from their discrete backgrounds. Such employments or socio-metric unions are meant to protect those at the top by giving them intelligent information through spying on what other subordinates are doing inside and outside the organization. This practice has led to the underperforming of organizations as those employees with connections and their upper echelons favorites connive to abuse resources for their own benefit. Even if culprits are known, no draconian measures are employed as a deterrence measure. Public value along public sector supply chains is lost. The study used a descriptive case study research design on fifty organizations in Zimbabwe mainly state-owned enterprises. Both qualitative and quantitative instrumentations were used. Both Snowball and random sampling techniques were used. The study found out that in all the fifty SOEs, there were employees in key positions related to top management, with tentacles feeding into the law enforcement agents, judiciary, security systems, and the executive. Such employees in public seem not to know each other with but would be involved in dirty scams and then share the proceeds with top people behind the scenes. The study also established that the same employees do not have the necessary competencies, qualifications, abilities, and capabilities to be in those positions. This culture is now strong that it is difficult to bust. The study recommends recruitment of all employees through an independent employment bureau to ensure strategic fit.

Keywords: corruption, state owned enterprises, strategic fit, public sector supply chains, efficiency

Procedia PDF Downloads 160

Authors: Yih-Chien Chen, Yue-Xuan Du, Min-Zhe Weng

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Carbon monoxide is a substance produced by the incomplete combustion. It is toxic even at concentrations of less than 100ppm. Since it is colorless and odorless, it is difficult to detect. CO sensors have been developed using a variety of physical mechanisms, including semiconductor oxides, solid electrolytes, and organic semiconductors. Many works have focused on using semiconducting sensors composed of sensitive layers such as ZnO, TiO₂, and NiO with high sensitivity for gases. However, these sensors working at high temperatures increased their power consumption. On the other hand, the dielectric resonator (DR) is attractive for gas detection due to its large surface area and sensitivity for external environments. Materials that are to be employed in sensing devices must have a high-quality factor. Numerous researches into the fergusonite-type structure and related ceramic systems have explored. Extensive research into RENbO₄ ceramics has explored their potential application in resonators, filters, and antennas in modern communication systems, which are operated at microwave frequencies. Nd(Ti₀.₅W₀.₅)O₄ ceramics were synthesized herein using the conventional mixed-oxide method. The Nd(Ti₀.₅W₀.₅)O₄ ceramics were prepared using the conventional solid-state method. Dielectric constants (εᵣ) of 15.4-19.4 and quality factor (Q×f) of 3,600-11,100 GHz were obtained at sintering temperatures in the range 1425-1525°C for 4 h. The dielectric properties of the Nd(Ti₀.₅W₀.₅)O₄ ceramics at microwave frequencies were found to vary with the sintering temperature. For a further understanding of these microwave dielectric properties, they were analyzed by densification, X-ray diffraction (XRD), and by making microstructural observations.

Keywords: dielectric constant, dielectric resonators, sensors, quality factor

Procedia PDF Downloads 259

Authors: Tsegahun Manyazewal, Francesco Marinucci, Getachew Belay, Abraham Tesfaye, Gonfa Ayana, Amaha Kebede, Tsegahun Manyazewal, Francesco Marinucci, Getachew Belay, Abraham Tesfaye, Gonfa Ayana, Amaha Kebede, Yewondwossen Tadesse, Susan Lehman, Zelalem Temesgen

Abstract:

In-service health trainings in Sub-Saharan Africa are mostly content-centered with higher disconnection with the real practice in the facility. This study intended to evaluate in-service training approach aimed to strengthen health care human resources. A combined web-based and face-to-face training was designed and piloted in Ethiopia with the diagnosis of tuberculosis. During the first part, which lasted 43 days, trainees accessed web-based material and read without leaving their work; while the second part comprised a one-day hands-on evaluation. Trainee’s competency was measured using multiple-choice questions, written-assignments, exercises and hands-on evaluation. Of 108 participants invited, 81 (75%) attended the course and 71 (88%) of them successfully completed. Of those completed, 73 (90%) scored a grade from A to C. The approach was effective to transfer knowledge and turn it into practical skills. In-service health training should transform from a passive one-time-event to a continuous behavioral change of participants and improvements on their actual work.

Keywords: Ethiopia, health care, Mycobacterium tuberculosis, training

Procedia PDF Downloads 502

Authors: Kun Chen, Yan Wang, Yangyu Zhao, Shufang Li, Lian Chen, Xiaoyue Guo, Jue Zhang, Jing Fang

Abstract:

The coupling strength between uterine electromyography (UEMG) and Fetal heart rate (FHR) signals during peripartum reflects the fetal biophysical activities. Therefore, UEMG-FHR coupling characterization is instructive in assessing placenta function. This study introduced a physiological marker named elevated frequency of UEMG-FHR coupling (E-UFC) and explored its predictive value for pregnancies complicated by pre-eclampsia and small for gestational age (SGA). Placental insufficiency patients (n=12) and healthy volunteers (n=24) were recruited and participated. UEMG and FHR were recorded non-invasively by a trans-abdominal device in women at term with singleton pregnancy (32-37 weeks) from 10:00 pm to 8:00 am. The product of the wavelet coherence and the wavelet cross-spectral power between UEMG and FHR was used to weight these two effects in order to quantify the degree of the UEMG-FHR coupling. E-UFC was exacted from the resultant spectrogram by calculating the mean value of the high-coherence (r > 0.5) frequency band. Results showed the high-coherence between UEMG and FHR was observed in the frequency band (1/512-1/16Hz). In addition, E-UFC in placental insufficiency patients was weaker compared to healthy controls (p < 0.001) at group level. These findings suggested the proposed approach could be used to quantitatively characterize the fetal biophysical activities, which is beneficial for early detection of placental insufficiency and reduces the occurrence of adverse pregnancy.

Keywords: uterine electromyography, fetal heart rate, coupling analysis, wavelet analysis

Procedia PDF Downloads 200

Authors: Edward K. Boahen, Brunel E. Bouya-Moko, Changda Wang

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The major threat to online social network (OSN) users is account compromisation. Spammers now spread malicious messages by exploiting the trust relationship established between account owners and their friends. The challenge in detecting a compromised account by service providers is validating the trusted relationship established between the account owners, their friends, and the spammers. Another challenge is the increase in required human interaction with the feature selection. Research available on supervised learning (machine learning) has limitations with the feature selection and accounts that cannot be profiled, like application programming interface (API). Therefore, this paper discusses the various behaviours of the OSN users and the current approaches in detecting a compromised OSN account, emphasizing its limitations and challenges. We propose a deep learning approach that addresses and resolve the constraints faced by the previous schemes. We detailed our proposed optimized nonsymmetric deep auto-encoder (OPT_NDAE) for unsupervised feature learning, which reduces the required human interaction levels in the selection and extraction of features. We evaluated our proposed classifier using the NSL-KDD and KDDCUP'99 datasets in a graphical user interface enabled Weka application. The results obtained indicate that our proposed approach outperformed most of the traditional schemes in OSN compromised account detection with an accuracy rate of 99.86%.

Keywords: computer security, network security, online social network, account compromisation

Procedia PDF Downloads 115

Authors: Y. L. Hor, H. S. Chu, V. P. Bui

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Composite material is widely used to replace conventional material, especially in the aerospace industry to reduce the weight of the devices. It is formed by combining reinforced materials together via adhesive bonding to produce a bulk material with alternated macroscopic properties. In bulk composites, degradation may occur in microscopic scale, which is in each individual reinforced fiber layer or especially in its matrix layer such as delamination, inclusion, disbond, void, cracks, and porosity. In this paper, we focus on the detection of defect in matrix layer which the adhesion between the composite plies is in contact but coupled through a weak bond. In fact, the adhesive defects are tested through various nondestructive methods. Among them, pulsed phase thermography (PPT) has shown some advantages providing improved sensitivity, large-area coverage, and high-speed testing. The aim of this work is to develop an efficient numerical model to study the application of PPT to the nondestructive inspection of weak bonding in composite material. The resulting thermal evolution field is comprised of internal reflections between the interfaces of defects and the specimen, and the important key-features of the defects presented in the material can be obtained from the investigation of the thermal evolution of the field distribution. Computational simulation of such inspections has allowed the improvement of the techniques to apply in various inspections, such as materials with high thermal conductivity and more complex structures.

Keywords: pulsed phase thermography, weak bond, composite, CFRP, computational modelling, optimization

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Authors: Hendradi Hardhienata, Tony Sumaryada, Sri Setyaningsih

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Current progress in the field of nonlinear optics has enabled precise surface characterization in semiconductor materials. Nonlinear optical techniques are favorable due to their nondestructive measurement and ability to work in nonvacuum and ambient conditions. The advance of the bond hyperpolarizability models opens a wide range of nanoscale surface investigation including the possibility to detect molecular orientation at the surface of silicon and zincblende semiconductors, investigation of electric field induced second harmonic fields at the semiconductor interface, detection of surface impurities, and very recently, study surface defects such as twin boundary in wurtzite semiconductors. In this work, we show using nonlinear optical techniques, e.g. nonlinear bond models how arbitrary polarization of the incoming electric field in Rotational Anisotropy Spectroscopy experiments can provide more information regarding the origin of the nonlinear sources in zincblende and wurtzite semiconductor structure. In addition, using hyperpolarizability consideration, we describe how the nonlinear susceptibility tensor describing SHG can be well modelled using only few parameter because of the symmetry of the bonds. We also show how the third harmonic intensity feature shows considerable changes when the incoming field polarization angle is changed from s-polarized to p-polarized. We also propose a method how to investigate surface reconstruction and defects in wurtzite and zincblende structure at the nanoscale level.

Keywords: surface characterization, bond model, rotational anisotropy spectroscopy, effective hyperpolarizability

Procedia PDF Downloads 155