Search results for: real anthropometric database

Authors: Lakmini Wijesooriya, Vicki Chalker, Jessica Day, Priyantha Perera, N. P. Sunil-Chandra

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M. pneumoniae has been identified as an etiology for exacerbation of asthma (EQA), although viruses play a major role in EOA. M. pneumoniae infection is treated empirically with macrolides, and its antibiotic sensitivity is not detected routinely. Characterization of the organism by genotyping and determination of macrolide resistance is important epidemiologically as it guides the empiric antibiotic treatment. To date, there is no such characterization of M. pneumoniae performed in Sri Lanka. The present study describes the characterization of M. pneumoniae detected from a child with EOA following a screening of 100 children with EOA. Of the hundred children with EOA, M. pneumoniae was identified only in one child by Real-Time polymerase chain reaction (PCR) test for identifying the community-acquired respiratory distress syndrome (CARDS) toxin nucleotide sequences. The M. pneumoniae identified from this patient underwent detection of macrolide resistance via conventional PCR, amplifying and sequencing the region of the 23S rDNA gene that contains single nucleotide polymorphisms that confer resistance. Genotyping of the isolate was performed via nested Multilocus Sequence Typing (MLST) in which eight (8) housekeeping genes (ppa, pgm, gyrB, gmk, glyA, atpA, arcC, and adk) were amplified via nested PCR followed by gene sequencing and analysis. As per MLST analysis, the M. pneumoniae was identified as sequence type 14 (ST14), and no mutations that confer resistance were detected. Resistance to macrolides in M. pneumoniae is an increasing problem globally. Establishing surveillance systems is the key to informing local prescriptions. In the absence of local surveillance data, antibiotics are started empirically. If the relevant microbiological samples are not obtained before antibiotic therapy, as in most occasions in children, the course of antibiotic is completed without a microbiological diagnosis. This happens more frequently in therapy for M. pneumoniae which is treated with a macrolide in most patients. Hence, it is important to understand the macrolide sensitivity of M. pneumoniae in the setting. The M. pneumoniae detected in the present study was macrolide sensitive. Further studies are needed to examine a larger dataset in Sri Lanka to determine macrolide resistance levels to inform the use of macrolides in children with EOA. The MLST type varies in different geographical settings, and it also provides a clue to the existence of macrolide resistance. The present study enhances the database of the global distribution of different genotypes of M. pneumoniae as this is the first such characterization performed with the increased number of samples to determine macrolide resistance level in Sri Lanka. M. pneumoniae detected from a child with exacerbation of asthma in Sri Lanka was characterized as ST14 by MLST and no mutations that confer resistance were detected.

Keywords: mycoplasma pneumoniae, Sri Lanka, characterization, macrolide resistance

Procedia PDF Downloads 188

Authors: Fawwaz Eniola Fajingbesi, Nur Shahida Midia, Elsheikh M. A. Elsheikh, Siti Hajar Yusoff

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Empirical analysis of lightning related phenomena in real time is extremely dangerous due to the relatively high electric discharge involved. Hence, design and optimization of efficient grounding systems depending on real time empirical methods are impeded. Using numerical methods, the dynamics of complex systems could be modeled hence solved as sets of linear and non-linear systems . In this work, the induced current distribution as lightning strike traverses the soil have been numerically modeled in a 2D axial-symmetry and solved using finite element method (FEM) in COMSOL Multiphysics 5.2 AC/DC module. Stratified and non- stratified electrode system were considered in the solved model and soil conductivity (σ) varied between 10 – 58 mS/m. The result discussed therein were the electric field distribution, current distribution and soil ionization phenomena. It can be concluded that the electric field and current distribution is influenced by the injected electric potential and the non-linearity in soil conductivity. The result from numerical calculation also agrees with previously laboratory scale empirical results.

Keywords: current distribution, grounding systems, lightning discharge, numerical model, soil conductivity, soil ionization

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Authors: Marissa Dallara, Amalia Ardeljan, Lexi Frankel, Nadia Obaed, Naureen Rashid, Omar Rashid

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Human Cytomegalovirus (HCMV) is a ubiquitous virus that remains latent in approximately 60% of individuals in developed countries. Viral load is kept at a minimum due to a robust immune response that is produced in most individuals who remain asymptomatic. HCMV has been recently implicated in cancer research because it may impose oncomodulatory effects on tumor cells of which it infects, which could have an impact on the progression of cancer. HCMV has been implicated in increased pathogenicity of certain cancers such as gliomas, but in contrast, it can also exhibit anti-tumor activity. HCMV seropositivity has been recorded in tumor cells, but this may also have implications in decreased pathogenesis of certain forms of cancer such as leukemia as well as increased pathogenesis in others. This study aimed to investigate the correlation between cytomegalovirus and the incidence of breast cancer. Methods The data used in this project was extracted from a Health Insurance Portability and Accountability Act (HIPAA) compliant national database to analyze the patients infected versus patients not infection with cytomegalovirus using ICD-10, ICD-9 codes. Permission to utilize the database was given by Holy Cross Health, Fort Lauderdale, for the purpose of academic research. Data analysis was conducted using standard statistical methods. Results The query was analyzed for dates ranging from January 2010 to December 2019, which resulted in 14,309 patients in both the infected and control groups, respectively. The two groups were matched by age range and CCI score. The incidence of breast cancer was 1.642% and 235 patients in the cytomegalovirus group compared to 4.752% and 680 patients in the control group. The difference was statistically significant by a p-value of less than 2.2x 10^-16 with an odds ratio of 0.43 (0.4 to 0.48) with a 95% confidence interval. Investigation into the effects of HCMV treatment modalities, including Valganciclovir, Cidofovir, and Foscarnet, on breast cancer in both groups was conducted, but the numbers were insufficient to yield any statistically significant correlations. Conclusion This study demonstrates a statistically significant correlation between cytomegalovirus and a reduced incidence of breast cancer. If HCMV can exert anti-tumor effects on breast cancer and inhibit growth, it could potentially be used to formulate immunotherapy that targets various types of breast cancer. Further evaluation is warranted to assess the implications of cytomegalovirus in reducing the incidence of breast cancer.

Keywords: human cytomegalovirus, breast cancer, immunotherapy, anti-tumor

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Authors: Parmeet Kaur, Nighat Yaseen Sofi, Shakti Kumar Gupta, Veena Pandey, Rajvaedhan Azad

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Nutritional status and prevailing blood glucose level trends amongst hospitalized patients has been linked to clinical outcome. Therefore, the present study was undertaken to assess hospitalized Diabetic Eye Disease (DED) patients' anthropometric and dietary intake trends. DED patients with type 1 or 2 diabetes > 20 years were enrolled. Actual food intake was determined by weighed food record method. Mifflin St Joer predictive equation multiplied by a combined stress and activity factor of 1.3 was applied to estimate caloric needs. A questionnaire was further administered to obtain reasons of inadequate dietary intake. Results indicated validity of joint analyses of body mass index in combination with waist circumference for clinical risk prediction. Dietary data showed a significant difference (p < 0.0005) between average daily caloric and carbohydrate intake and actual daily caloric and carbohydrate needs. Mean fasting and post-prandial plasma glucose levels were 150.71 ± 72.200 mg/dL and 219.76 ± 97.365 mg/dL, respectively. Improvement in food delivery systems and nutrition educations were indicated for reducing plate waste and to enable better understanding of dietary aspects of diabetes management. A team approach of nurses, physicians and other health care providers is required besides the expertise of dietetics professional. To conclude, findings of the present study will be useful in planning nutritional care process (NCP) for optimizing glucose control as a component of quality medical nutrition therapy (MNT) in hospitalized DED patients.

Keywords: nutritional status, diabetic eye disease, nutrition care process, medical nutrition therapy

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Authors: Anida Causevic-Ramosevac, Sabina Semiz

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The aim of this study was to investigate the association of four single nucleotide polymorphisms (SNPs) - rs673548, rs693 in ApoB gene, rs1800775 in CETP gene and rs4846914 in GALNT2 gene with parameters of type 2 diabetes (T2D) and diabetic dyslipidemia in the population of Bosnia and Herzegovina (BH). Materials and methods: Our study involved 352 patients with T2D and 156 healthy subjects. Biochemical and anthropometric parameters were measured in all participants. DNA was extracted from the peripheral blood for the purpose of genetic testing. Polymorphisms in ApoB (rs673548, rs693), CETP (rs1800775) and GALNT2 (rs4846914) genes were analyzed by using Sequenom IPLEX platform. Results: Our results demonstrated significant associations for rs180075 polymorphism in CETP gene with levels of fasting insulin (p = 0.020; p = 0.027; p = 0.044), triglycerides (p = 0.046) and ALT (p = 0.031) activity in control group. In group of diabetic patients, results showed a significant association of rs673548 in ApoB gene with levels of fasting insulin (p = 0.008), HOMA-IR (p = 0.013), VLDL-C (p = 0.037) and CRP (p = 0.029) and rs693 in ApoB gene with BMI (p = 0.025), systolic blood pressure (p = 0.027), fasting insulin (p = 0.037) and HOMA-IR (p = 0.023) levels. Significant associations were also observed for rs1800775 in CETP gene with triglyceride (p = 0.023) levels and rs4846914 in GALNT2 gene with HbA1C (p = 0.013) and triglyceride (p = 0.043) levels. Conclusion: In conclusion, this is the first study that examined the impact of variations of candidate genes on a wide range of metabolic parameters in BH population. Our results suggest an association of variations of ApoB, CETP and GALNT2 genes with specific markers of T2D and dyslipidemia. Further studies would be needed in order to confirm these genetic effects in other ethnic groups as well.

Keywords: ApoB, CETP, dyslipidemia, GALNT2, type 2 diabetes

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Authors: Zhenyu Zhang, Hsi-Hsien Wei

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Highway network systems play a vital role in disaster response for disaster-damaged areas. Damaged bridges in such network systems can impede disaster response by disrupting transportation of rescue teams or humanitarian supplies. Therefore, emergency inspection and repair of bridges to quickly collect damage information of bridges and recover the functionality of highway networks is of paramount importance to disaster response. A widely used measure of a network’s capability to recover from disasters is resilience. To enhance highway network resilience, plenty of studies have developed various repair scheduling methods for the prioritization of bridge-repair tasks. These methods assume that repair activities are performed after the damage to a highway network is fully understood via inspection, although inspecting all bridges in a regional highway network may take days, leading to the significant delay in repairing bridges. In reality, emergency repair activities can be commenced as soon as the damage data of some bridges that are crucial to emergency response are obtained. Given that emergency bridge inspection and repair (EBIR) activities are executed simultaneously in the response phase, the real-time interactions between these activities can occur – the blockage of highways due to repair activities can affect inspection routes which in turn have an impact on emergency repair scheduling by providing real-time information on bridge damages. However, the impact of such interactions on the optimal emergency inspection routes (EIR) and emergency repair schedules (ERS) has not been discussed in prior studies. To overcome the aforementioned deficiencies, this study develops a routing and scheduling model for EBIR while accounting for real-time inspection-repair interactions to maximize highway network resilience. A stochastic, time-dependent integer program is proposed for the complex and real-time interacting EBIR problem given multiple inspection and repair teams at locations as set post-disaster. A hybrid genetic algorithm that integrates a heuristic approach into a traditional genetic algorithm to accelerate the evolution process is developed. Computational tests are performed using data from the 2008 Wenchuan earthquake, based on a regional highway network in Sichuan, China, consisting of 168 highway bridges on 36 highways connecting 25 cities/towns. The results show that the simultaneous implementation of bridge inspection and repair activities can significantly improve the highway network resilience. Moreover, the deployment of inspection and repair teams should match each other, and the network resilience will not be improved once the unilateral increase in inspection teams or repair teams exceeds a certain level. This study contributes to both knowledge and practice. First, the developed mathematical model makes it possible for capturing the impact of real-time inspection-repair interactions on inspection routing and repair scheduling and efficiently deriving optimal EIR and ERS on a large and complex highway network. Moreover, this study contributes to the organizational dimension of highway network resilience by providing optimal strategies for highway bridge management. With the decision support tool, disaster managers are able to identify the most critical bridges for disaster management and make decisions on proper inspection and repair strategies to improve highway network resilience.

Keywords: disaster management, emergency bridge inspection and repair, highway network, resilience, uncertainty

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Authors: Aidin Foroutan, David S. Wishart, Leluo L. Guan, Carolyn Fitzsimmons

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Maximizing efficiency and growth potential of beef cattle requires not only genetic selection (i.e. residual feed intake (RFI)) but also adequate nutrition throughout all stages of growth and development. Nutrient restriction during gestation has been shown to negatively affect post-natal growth and development as well as fertility of the offspring. This, when combined with RFI may affect progeny traits. This study aims to investigate the impact of selection for divergent genetic potential for RFI and maternal nutrition during early- to mid-gestation, on bull calf traits such as fertility and muscle development using multiple ‘omics’ approaches. Comparisons were made between High-diet vs. Low-diet and between High-RFI vs. Low-RFI animals. An epigenetics experiment on semen samples identified 891 biomarkers associated with growth and development. A gene expression study on Longissimus thoracis muscle, semimembranosus muscle, liver, and testis identified 4 genes associated with muscle development and immunity of which Myocyte enhancer factor 2A [MEF2A; induces myogenesis and control muscle differentiation] was the only differentially expressed gene identified in all four tissues. An initial metabolomics experiment on serum samples using nuclear magnetic resonance (NMR) identified 4 metabolite biomarkers related to energy and protein metabolism. Once all the biomarkers are identified, bioinformatics approaches will be used to create a database covering all the ‘omics’ data collected from this project. This database will be broadened by adding other information obtained from relevant literature reviews. Association analyses with these data sets will be performed to reveal key biological pathways affected by RFI and maternal nutrition. Through these association studies between the genome and metabolome, it is expected that candidate biomarker genes and metabolites for feed efficiency, fertility, and/or muscle development are identified. If these gene/metabolite biomarkers are validated in a larger animal population, they could potentially be used in breeding programs to select superior animals. It is also expected that this work will lead to the development of an online tool that could be used to predict future traits of interest in an animal given its measurable ‘omics’ traits.

Keywords: biomarker, maternal nutrition, omics, residual feed intake

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Authors: Natalia Rudeli, Elisabeth Viles, Adrian Santilli

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Delays in the construction industry are a global phenomenon. Many construction projects experience extensive delays exceeding the initially estimated completion time. The main purpose of this study is to identify construction projects typical behaviors in order to develop a prognosis and management tool. Being able to know a construction projects schedule tendency will enable evidence-based decision-making to allow resolutions to be made before delays occur. This study presents an innovative approach that uses Cluster Analysis Method to support predictions during Earned Value Analyses. A clustering analysis was used to predict future scheduling, Earned Value Management (EVM), and Earned Schedule (ES) principal Indexes behaviors in construction projects. The analysis was made using a database with 90 different construction projects. It was validated with additional data extracted from literature and with another 15 contrasting projects. For all projects, planned and executed schedules were collected and the EVM and ES principal indexes were calculated. A complete linkage classification method was used. In this way, the cluster analysis made considers that the distance (or similarity) between two clusters must be measured by its most disparate elements, i.e. that the distance is given by the maximum span among its components. Finally, through the use of EVM and ES Indexes and Tukey and Fisher Pairwise Comparisons, the statistical dissimilarity was verified and four clusters were obtained. It can be said that construction projects show an average delay of 35% of its planned completion time. Furthermore, four typical behaviors were found and for each of the obtained clusters, the interim milestones and the necessary rhythms of construction were identified. In general, detected typical behaviors are: (1) Projects that perform a 5% of work advance in the first two tenths and maintain a constant rhythm until completion (greater than 10% for each remaining tenth), being able to finish on the initially estimated time. (2) Projects that start with an adequate construction rate but suffer minor delays culminating with a total delay of almost 27% of the planned time. (3) Projects which start with a performance below the planned rate and end up with an average delay of 64%, and (4) projects that begin with a poor performance, suffer great delays and end up with an average delay of a 120% of the planned completion time. The obtained clusters compose a tool to identify the behavior of new construction projects by comparing their current work performance to the validated database, thus allowing the correction of initial estimations towards more accurate completion schedules.

Keywords: cluster analysis, construction management, earned value, schedule

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Authors: Mohamed I. Mahrous, Mohamed G. Ashmawy

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Hybrid genetic algorithm (HGA) is proposed in this paper to determine the economic scheduling of electric power generation over a fixed time period under various system and operational constraints. The proposed technique can outperform conventional genetic algorithms (CGAs) in the sense that HGA make it possible to improve both the quality of the solution and reduce the computing expenses. In contrast, any carefully designed GA is only able to balance the exploration and the exploitation of the search effort, which means that an increase in the accuracy of a solution can only occure at the sacrifice of convergent speed, and vice visa. It is unlikely that both of them can be improved simultaneously. The proposed hybrid scheme is developed in such a way that a simple GA is acting as a base level search, which makes a quick decision to direct the search towards the optimal region, and a local search method (pattern search technique) is next employed to do the fine tuning. The aim of the strategy is to achieve the cost reduction within a reasonable computing time. The effectiveness of the proposed hybrid technique is verified on two real public electricity supply systems with 13 and 40 generator units respectively. The simulation results obtained with the HGA for the two real systems are very encouraging with regard to the computational expenses and the cost reduction of power generation.

Keywords: genetic algorithms, economic dispatch, pattern search

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Authors: Ergun Sakalar, Kubra Bilgic

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Faster detection and characterization of pathogens are the basis of the evoid from foodborne pathogens. Salmonella spp., Shigella spp. and Listeria monocytogenes are common foodborne bacteria that are among the most life-threatining. It is important to rapid and accurate detection of these pathogens to prevent food poisoning and outbreaks or to manage food chains. The present work promise to develop a sensitive, species specific and reliable PCR based detection system for simultaneous detection of Salmonella spp., Shigella spp. and Listeria monocytogenes. For this purpose, three genes were picked out, ompC for Salmonella spp., ipaH for Shigella spp. and hlyA for L. monocytogenes. After short pre-enrichment of milk was passed through a vacuum filter and bacterial DNA was exracted using commercially available kit GIDAGEN®(Turkey, İstanbul). Detection of amplicons was verified by examination of the melting temperature (Tm) that are 72° C, 78° C, 82° C for Salmonella spp., Shigella spp. and L. monocytogenes, respectively. The method specificity was checked against a group of bacteria strains, and also carried out sensitivity test resulting in under 10² CFU mL⁻¹ of milk for each bacteria strain. Our results show that the flourescence based triplex qPCR method can be used routinely to detect Salmonella spp., Shigella spp. and L. monocytogenes during the milk processing procedures in order to reduce cost, time of analysis and the risk of foodborne disease outbreaks.

Keywords: evagreen, food-born bacteria, pathogen detection, real-time pcr

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Authors: Jameelah Omolara Yaqub

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One of the main reasons for the existence of central bank is that it is believed that central banks have some influence on private sector decisions which will enable the Central Bank to achieve some of its objectives especially that of stable price and economic growth. By the assumption of the New Keynesian theory that prices are fully flexible in the short run, the central bank can temporarily influence real interest rate and, therefore, have an effect on real output in addition to nominal prices. There is, therefore, the need for the Central Bank to monitor, respond to, and influence private sector decisions appropriately. This thus shows that the Central Bank and the private sector will both affect and be affected by each other implying considerable interdependence between the sectors. The interdependence may be simultaneous or not depending on the level of information, readily available and how sensitive prices are to agents’ expectations about the future. The aim of this paper is, therefore, to determine whether the interdependence between asset prices and monetary policy are simultaneous or not and how important is this relationship. Studies on the effects of monetary policy have largely used VAR models to identify the interdependence but most have found small effects of interaction. Some earlier studies have ignored the possibility of simultaneous interdependence while those that have allowed for simultaneous interdependence used data from developed economies only. This study, therefore, extends the literature by using data from a developing economy where information might not be readily available to influence agents’ expectation. In this study, the direction of relationship among variables of interest will be tested by carrying out the Granger causality test. Thereafter, the interaction between asset prices and monetary policy in Nigeria will be tested. Asset prices will be represented by the NSE index as well as real estate prices while monetary policy will be represented by money supply and the MPR respectively. The VAR model will be used to analyse the relationship between the variables in order to take account of potential simultaneity of interdependence. The study will cover the period between 1980 and 2014 due to data availability. It is believed that the outcome of the research will guide monetary policymakers especially the CBN to effectively influence the private sector decisions and thereby achieve its objectives of price stability and economic growth.

Keywords: asset prices, granger causality, monetary policy rate, Nigeria

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Authors: Konstanca Nikolajevic, Nicolas Belanger, David Duvivier, Rabie Ben Atitallah, Abdelhakim Artiba

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Finding the optimal 3D path of an aerial vehicle under flight mechanics constraints is a major challenge, especially when the algorithm has to produce real-time results in flight. Kinematics models and Pythagorian Hodograph curves have been widely used in mobile robotics to solve this problematic. The level of difficulty is mainly driven by the number of constraints to be saturated at the same time while minimizing the total length of the path. In this paper, we suggest a pragmatic algorithm capable of saturating at the same time most of dimensioning helicopter 3D trajectories’ constraints like: curvature, curvature derivative, torsion, torsion derivative, climb angle, climb angle derivative, positions. The trajectories generation algorithm is able to generate versatile complex 3D motion primitives feasible by a helicopter with parameterization of the curvature and the climb angle. An upper ”motion primitives’ concatenation” algorithm is presented based. In this article we introduce a new way of designing three-dimensional trajectories based on what we call the ”Dubins gliding symmetry conjecture”. This extremely performing algorithm will be soon integrated to a real-time decisional system dealing with inflight safety issues.

Keywords: robotics, aerial robots, motion primitives, helicopter

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Authors: Souad Romdhane, Lotfi Belkacem

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When referring to actuarial analysis of lifetime, only models accounting for observable risk factors have been developed. Within this context, Cox proportional hazards model (CPH model) is commonly used to assess the effects of observable covariates as gender, age, smoking habits, on the hazard rates. These covariates may fail to fully account for the true lifetime interval. This may be due to the existence of another random variable (frailty) that is still being ignored. The aim of this paper is to examine the shared frailty issue in the Cox proportional hazard model by including two different parametric forms of frailty into the hazard function. Four estimated methods are used to fit them. The performance of the parameter estimates is assessed and compared between the classical Cox model and these frailty models through a real-life data set from the Quebec Pension Plan and then using a more general simulation study. This performance is investigated in terms of the bias of point estimates and their empirical standard errors in both fixed and random effect parts. Both the simulation and the real dataset studies showed differences between classical Cox model and shared frailty model.

Keywords: life insurance-pension plan, survival analysis, risk factors, cox proportional hazards model, multivariate failure-time data, shared frailty, simulations study

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Authors: Fatema Akram, Mohammad G. Rasul, M. Masud K. Khan, M. Sharif I. I. Amir

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Australia is a country of some 7,700 million square kilometres with a population of about 22.6 million. At present water security is a major challenge for Australia. In some areas the use of water resources is approaching and in some parts it is exceeding the limits of sustainability. A focal point of proposed national water conservation programs is the recycling of both urban storm-water and treated wastewater. But till now it is not widely practiced in Australia, and particularly storm-water is neglected. In Australia, only 4% of storm-water and rainwater is recycled, whereas less than 1% of reclaimed wastewater is reused within urban areas. Therefore, accurately monitoring, assessing and predicting the availability, quality and use of this precious resource are required for better management. As storm-water is usually of better quality than untreated sewage or industrial discharge, it has better public acceptance for recycling and reuse, particularly for non-potable use such as irrigation, watering lawns, gardens, etc. Existing storm-water recycling practice is far behind of research and no robust technologies developed for this purpose. Therefore, there is a clear need for using modern technologies for assessing feasibility of storm-water harvesting and reuse. Numerical modelling has, in recent times, become a popular tool for doing this job. It includes complex hydrological and hydraulic processes of the study area. The hydrologic model computes storm-water quantity to design the system components, and the hydraulic model helps to route the flow through storm-water infrastructures. Nowadays water quality module is incorporated with these models. Integration of Geographic Information System (GIS) with these models provides extra advantage of managing spatial information. However for the overall management of a storm-water harvesting project, Decision Support System (DSS) plays an important role incorporating database with model and GIS for the proper management of temporal information. Additionally DSS includes evaluation tools and Graphical user interface. This research aims to critically review and discuss all the aspects of storm-water harvesting and reuse such as available guidelines of storm-water harvesting and reuse, public acceptance of water reuse, the scopes and recommendation for future studies. In addition to these, this paper identifies, understand and address the importance of modern technologies capable of proper management of storm-water harvesting and reuse.

Keywords: storm-water management, storm-water harvesting and reuse, numerical modelling, geographic information system, decision support system, database

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Authors: Satien Janpla, Kunyanuth Kularbphettong

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This paper aims to present the development of a web‑based system to serve the need of selling OTOP products in Samut Songkhram, Thailand. This system was designed to promote and sell OTOP products on website. We describe the design approaches and functional components of this system. The system was developed by PHP and JavaScript and MySQL database System. To evaluate the system performance, questionnaires were used to measure user satisfaction with system usability by specialists and users. The results were satisfactory as followed: Means for specialists and users were 4.05 and 3.97, and standard deviation for specialists and users were 0.563 and 0.644 respectively. Further analysis showed that the quality of One Tambon One Product (OTOP) Website was also at a good level as well.

Keywords: web-based system, OTOP, product, website

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Authors: René A. Sørensen, Søren Skovsen, Peter Christiansen, Henrik Karstoft

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Autonomous agricultural machines act in stochastic surroundings and therefore, must be able to perceive the surroundings in real time. This perception can be achieved using image sensors combined with advanced machine learning, in particular Deep Learning. Deep convolutional neural networks excel in labeling and perceiving color images and since the cost of high-quality RGB-cameras is low, the hardware cost of good perception depends heavily on memory and computation power. This paper investigates the possibility of designing lightweight convolutional neural networks for semantic segmentation (pixel wise classification) with reduced hardware requirements, to allow for embedded usage in autonomous agricultural machines. Using compression techniques, a lightweight convolutional neural network is designed to perform real-time semantic segmentation on an embedded platform. The network is trained on two large datasets, ImageNet and Pascal Context, to recognize up to 400 individual classes. The 400 classes are remapped into agricultural superclasses (e.g. human, animal, sky, road, field, shelterbelt and obstacle) and the ability to provide accurate real-time perception of agricultural surroundings is studied. The network is applied to the case of autonomous grass mowing using the NVIDIA Tegra X1 embedded platform. Feeding case-specific images to the network results in a fully segmented map of the superclasses in the image. As the network is still being designed and optimized, only a qualitative analysis of the method is complete at the abstract submission deadline. Proceeding this deadline, the finalized design is quantitatively evaluated on 20 annotated grass mowing images. Lightweight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show competitive performance with regards to accuracy and speed. It is feasible to provide cost-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.

Keywords: autonomous agricultural machines, deep learning, safety, visual perception

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Authors: Vinícius L. L. Morrone, David Douek, Helder M. F. Pereira, Bernadete L. M. Grandolpho

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This work aimed to identify the relationships between the level of consumer environmental awareness and their search for sustainable properties, as well as to understand the main sustainability structures considered by these consumers during the decision process. Additionally, the paper looked up to the influence environmental awareness and financial status have over the disposition of buyers to pay more for sustainable properties. To achieve these objectives, 318 questionnaires were answered electronically, after being sent to the Green Building Brazil email basis, as to other Real Estate developers client basis. From all the questionnaires answered, 71 were discarded, leaving a total amount of 247 admitted questionnaires to be analyzed. The responses were evaluated based on the theory of consumer decision making, especially on the influence factors of this process. The data were processed using a PLS model, using the R software. The results have shown that the level of consumer environmental awareness effectively affects the consumer’s will of acquiring a sustainable property or, at least, a property with some environmental friendly structures. The consumer’s environmental awareness also positively impacts the importance consumers give to individual environmental friendly structures. Also, as a consumer value to those individual structures raises, it is also observed a raise in his will to buy a sustainable property. Additionally, the impact of consumer’s environmental awareness and financial status over the willingness to pay more for a property with those attributes. The results indicate that there was no relationship between consumers' environmental awareness and their willingness to pay more for a sustainable property. On the other hand, the financial status and the family income of the consumers showed a positive relation with the willingness to pay more for a sustainable property. This indicates that consumers with better financial conditions, which according to the analysis do not necessarily have a greater environmental awareness, are those who are willing to pay more for a sustainable property. Thus, this study indicates that, even if the environmental awareness impact positively the demand for sustainable structures and properties, this impact is not price reflected, due to the price elasticity of the consumption, especially for a category of lower income consumers. This paper adds to the literature in the way it projects some guidelines to the consumer’s decision process in the Real Estate market in emerging economies, as well as it presents some drivers to pricing decisions.

Keywords: consumer behavior, environmental awareness, real estate pricing, sustainable buildings

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Authors: Rabaa Tlili Yaakoubi, Hind Nakouri, Olivier Blanpain

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The current tools for real time management of sewer systems are based on two software tools: the software of weather forecast and the software of hydraulic simulation. The use of the first ones is an important cause of imprecision and uncertainty, the use of the second requires temporal important steps of decision because of their need in times of calculation. This way of proceeding fact that the obtained results are generally different from those waited. The major idea of the CARDIO project is to change the basic paradigm by approaching the problem by the "automatic" face rather than by that "hydrology". The objective is to make possible the realization of a large number of simulations at very short times (a few seconds) allowing to take place weather forecasts by using directly the real time meditative pluviometric data. The aim is to reach a system where the decision-making is realized from reliable data and where the correction of the error is permanent. A first model of control laws was realized and tested with different return-period rainfalls. The gains obtained in rejecting volume vary from 40 to 100%. The development of a new algorithm was then used to optimize calculation time and thus to overcome the subsequent combinatorial problem in our first approach. Finally, this new algorithm was tested with 16- year-rainfall series. The obtained gains are 60% of total volume rejected to the natural environment and of 80 % in the number of discharges.

Keywords: RTC, paradigm, optimization, automation

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Authors: Sofiane Bououden, Ilyes Boulkaibet

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In this paper, a robust fault-tolerant predictive control (FTPC) strategy is proposed for systems with linear parameter varying (LPV) models and input constraints subject to sensor faults. Generally, virtual observers are used for improving the observation precision and reduce the impacts of sensor faults and uncertainties in the system. However, this type of observer lacks certain system measurements which substantially reduce its accuracy. To deal with this issue, a real observer is then designed based on the virtual observer, and consequently a real observer-based robust predictive control is designed for polytopic LPV systems. Moreover, the proposed observer can entirely assure that all system states and sensor faults are estimated. As a result, and based on both observers, a robust fault-tolerant predictive control is then established via the Lyapunov method where sufficient conditions are proposed, for stability analysis and control purposes, in linear matrix inequalities (LMIs) form. Finally, simulation results are given to show the effectiveness of the proposed approach.

Keywords: linear parameter varying systems, fault-tolerant predictive control, observer-based control, sensor faults, input constraints, linear matrix inequalities

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Authors: Nayomi Ranathunga, Priyantha Perera, Sumal Nandasena, Nalini Sathiakumar, Anuradhini Kasthuriratne, Rajitha Wikremasinghe

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Solid fuel combustion is an important source of indoor air pollution (IAP) in developing countries that has adverse health impacts particularly in children. This study was conducted to determine the effect of IAP due to solid fuel combustion on physical growth of children under five in a Sri Lankan setting. A prospective study was conducted in a mixed population comprising urban and semi urban residents. The study included 240 children under 5 who were permanent residents of the area. Physical growth was assessed by measuring anthropometric indices based on the World Health Organization (WHO) guidelines and standards. Exposure levels were defined according to the main type of fuel used for cooking at home: children residing in households using biomass fuel or kerosene as the main type of fuel for cooking were classified as the “high exposure” group and children resident in households using liquefied petroleum gas (LPG) or electricity for cooking were classified as the “low exposure” group. Sixty percent of the children were classified as from the “high” exposure group and 40% of the children were classified as from the “low” exposure group; 54% of the children were male. At baseline, the prevalence of wasting was 17.1% and the prevalence of stunting was 10.4%; the mean z-score for weight for height was - 0.85, weight for age was - 0.46 and height for age was -0.38. At baseline, children from the “high” exposure group had a significantly lower mean weight for height z-score (p=0.02) and a mean height for age z-score (p=0.001) as compared to children from the “low” exposure group after adjusting for confounding factors such as father’s education, mother’s education and family income. Poor maternal education was significantly associated with lower height for age z-scores (p=0.04) after adjusting for exposure status. IAP due to combustion of biomass fuel leads to chronic malnutrition.

Keywords: children, growth, indoor air pollution, solid fuel

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Authors: Mohammad Abdulaziz Algrnas, Emma Mulliner

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Housing projects have been established in Saudi Arabia, by both government and private construction companies, to meet the increasing demand from Saudi inhabitants across the country. However, the real estate market supply does not meet consumer preference requirements. Preferences normally differ depending on the consumer’s situation, such as the household’s sociological characteristics (age, household size and composition), resources (income, wealth, information and experience), tastes and priorities. Collecting information about consumer attitudes, preferences and perceptions is important for the real estate market in order to better understand housing demand and to ensure that this is met by appropriate supply. The aim of this paper is to identify consumer preferences for housing in Saudi Arabia. A quantitative closed-ended questionnaire was conducted with housing consumers in Saudi Arabia in order to gain insight into consumer needs, current household situation, preferences for a number of investigated housing attributes and consumers’ perceptions around the current housing problem. 752 survey responses were obtained and analysed in order to describe preferences for housing attributes and make comparisons between groups. Factor analysis was also conducted to identify and reduce the attributes. The results indicate a difference in preference according to the gender of the respondents and depending on their region of residence.

Keywords: housing attributes, Saudi Arabia, consumer preferences, housing preferences

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Authors: Christoph Ostermair

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We deal with the effect of correlation between prospects on human decision making under uncertainty as proposed by the comparatively new and promising model of “salience theory of choice under risk”. In this regard, we show that the theory entails the prediction that the inconsistency of choices, known as the Allais paradox, should not be an issue in the context of “real-life decision making”, which typically corresponds to situations of subjective uncertainty. The Allais paradox, probably the best-known anomaly regarding expected utility theory, would then essentially have no practical relevance. If, however, empiricism contradicts this prediction, salience theory might suffer a serious setback. Explanations of the model for variable human choice behavior are mostly the result of a particular mechanism that does not come to play under perfect correlation. Hence, if it turns out that correlation between prospects – as typically found in real-world applications – does not influence human decision making in the expected way, this might to a large extent cost the theory its explanatory power. The empirical literature regarding the Allais paradox under subjective uncertainty is so far rather moderate. Beyond that, the results are hard to maintain as an argument, as the presentation formats commonly employed, supposably have generated so-called event-splitting effects, thereby distorting subjects’ choice behavior. In our own incentivized experimental study, we control for such effects by means of two different choice settings. We find significant event-splitting effects in both settings, thereby supporting the suspicion that the so far existing empirical results related to Allais paradoxes under subjective uncertainty may not be able to answer the question at hand. Nevertheless, we find that the basic tendency behind the Allais paradox, which is a particular switch of the preference relation due to a modified common consequence, shared by two prospects, is still existent both under an event-splitting and a coalesced presentation format. Yet, the modal choice pattern is in line with the prediction of salience theory. As a consequence, the effect of correlation, as proposed by the model, might - if anything - only weaken the systematic choice pattern behind the Allais paradox.

Keywords: Allais paradox, common consequence effect, models of decision making under risk and uncertainty, salience theory

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Authors: Tetsuro Saeki, Yuichi Kato, Shoutarou Mizuno

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STRIM (Statistical Test Rule Induction Method) has been proposed as a method to effectively induct if-then rules from the decision table which is considered as a sample set obtained from the population of interest. Its usefulness has been confirmed by simulation experiments specifying rules in advance, and by comparison with conventional methods. However, scope for future development remains before STRIM can be applied to the analysis of real-world data sets. The first requirement is to determine the size of the dataset needed for inducting true rules, since finding statistically significant rules is the core of the method. The second is to examine the capacity of rule induction from datasets with contaminated attribute values created by missing data and noise, since real-world datasets usually contain such contaminated data. This paper examines the first problem theoretically, in connection with the rule length. The second problem is then examined in a simulation experiment, utilizing the critical size of dataset derived from the first step. The experimental results show that STRIM is highly robust in the analysis of datasets with contaminated attribute values, and hence is applicable to realworld data.

Keywords: rule induction, decision table, missing data, noise

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Authors: Ayadi Aya, Ghorbel Oussama, M. Obeid Abdulfattah, Abid Mohamed

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Nowadays, many wireless sensor networks have been distributed in the real world to collect valuable raw sensed data. The challenge is to extract high-level knowledge from this huge amount of data. However, the identification of outliers can lead to the discovery of useful and meaningful knowledge. In the field of wireless sensor networks, an outlier is defined as a measurement that deviates from the normal behavior of sensed data. Many detection techniques of outliers in WSNs have been extensively studied in the past decade and have focused on classic based algorithms. These techniques identify outlier in the real transaction dataset. This survey aims at providing a structured and comprehensive overview of the existing researches on classification based outlier detection techniques as applicable to WSNs. Thus, we have identified key hypotheses, which are used by these approaches to differentiate between normal and outlier behavior. In addition, this paper tries to provide an easier and a succinct understanding of the classification based techniques. Furthermore, we identified the advantages and disadvantages of different classification based techniques and we presented a comparative guide with useful paradigms for promoting outliers detection research in various WSN applications and suggested further opportunities for future research.

Keywords: bayesian networks, classification-based approaches, KPCA, neural networks, one-class SVM, outlier detection, wireless sensor networks

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Authors: Marcio Freire Cruz, Naoaki Ono, Shigehiko Kanaya, Carlos Arthur Mattos Teixeira Cavalcante

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The systems that record patient care information, known as Electronic Medical Record (EMR) and those that monitor vital signs of patients, such as heart rate, body temperature, and blood pressure have been extremely valuable for the effectiveness of the patient’s treatment. Several kinds of research have been using data from EMRs and vital signs of patients to predict illnesses. Among them, we highlight those that intend to predict, classify, or, at least identify patterns, of sepsis illness in patients under vital signs monitoring. Sepsis is an organic dysfunction caused by a dysregulated patient's response to an infection that affects millions of people worldwide. Early detection of sepsis is expected to provide a significant improvement in its treatment. Preceding works usually combined medical, statistical, mathematical and computational models to develop detection methods for early prediction, getting higher accuracies, and using the smallest number of variables. Among other techniques, we could find researches using survival analysis, specialist systems, machine learning and deep learning that reached great results. In our research, patients are modeled as points moving each hour in an n-dimensional space where n is the number of vital signs (variables). These points can reach a sepsis target point after some time. For now, the sepsis target point was calculated using the median of all patients’ variables on the sepsis onset. From these points, we calculate for each hour the position vector, the first derivative (velocity vector) and the second derivative (acceleration vector) of the variables to evaluate their behavior. And we construct a prediction model based on a Long Short-Term Memory (LSTM) Network, including these derivatives as explanatory variables. The accuracy of the prediction 6 hours before the time of sepsis, considering only the vital signs reached 83.24% and by including the vectors position, speed, and acceleration, we obtained 94.96%. The data are being collected from Medical Information Mart for Intensive Care (MIMIC) Database, a public database that contains vital signs, laboratory test results, observations, notes, and so on, from more than 60.000 patients.

Keywords: dynamic analysis, long short-term memory, prediction, sepsis

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Authors: Ju Hyun Won, Seok Hong Min, Tae Kwon Ha

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Phase equilibria of AZ91D Mg alloys for nonflammable use, containing Ca and Y, were carried out by using FactSage® and FTLite database, which revealed that solid solution treatment, could be performed at temperatures from 400 to 450 °C. Solid solution treatment of AZ91D Mg alloy without Ca and Y was successfully conducted at 420 °C and supersaturated microstructure with all beta phase resolved into matrix was obtained. In the case of AZ91D Mg alloy with some Ca and Y, however, a little amount of intermetallic particles were observed after solid solution treatment. After solid solution treatment, each alloy was annealed at temperatures of 180 and 200 °C for time intervals from 1 min to 48 hrs and hardness of each condition was measured by micro-Vickers method. Peak aging conditions were deduced as at the temperature of 200 °C for 10 hrs.

Keywords: Mg alloy, AZ91D, nonflammable alloy, phase equilibrium, peak aging

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Authors: Zahid Ullah, Atlas Khan

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This abstract focuses on the advancements in mathematical modeling and optimization techniques that play a crucial role in enhancing the efficiency, reliability, and performance of these systems. In this era of rapidly evolving technology, mathematical modeling and optimization offer powerful tools to tackle the complex challenges faced by control, signal processing, and energy systems. This abstract presents the latest research and developments in mathematical methodologies, encompassing areas such as control theory, system identification, signal processing algorithms, and energy optimization. The abstract highlights the interdisciplinary nature of mathematical modeling and optimization, showcasing their applications in a wide range of domains, including power systems, communication networks, industrial automation, and renewable energy. It explores key mathematical techniques, such as linear and nonlinear programming, convex optimization, stochastic modeling, and numerical algorithms, that enable the design, analysis, and optimization of complex control and signal processing systems. Furthermore, the abstract emphasizes the importance of addressing real-world challenges in control, signal processing, and energy systems through innovative mathematical approaches. It discusses the integration of mathematical models with data-driven approaches, machine learning, and artificial intelligence to enhance system performance, adaptability, and decision-making capabilities. The abstract also underscores the significance of bridging the gap between theoretical advancements and practical applications. It recognizes the need for practical implementation of mathematical models and optimization algorithms in real-world systems, considering factors such as scalability, computational efficiency, and robustness. In summary, this abstract showcases the advancements in mathematical modeling and optimization techniques for control, signal processing, and energy systems. It highlights the interdisciplinary nature of these techniques, their applications across various domains, and their potential to address real-world challenges. The abstract emphasizes the importance of practical implementation and integration with emerging technologies to drive innovation and improve the performance of control, signal processing, and energy.

Keywords: mathematical modeling, optimization, control systems, signal processing, energy systems, interdisciplinary applications, system identification, numerical algorithms

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Authors: Zhan Haiyang, Zhang Lei, Ning Juan

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This paper presents a configuration software solution in Linux, which is used for the control of space environment simulator. After introducing the structure and basic principle, it is said that the developing of QT software frame and the dynamic data exchanging between PLC and computer. The OPC driver in Linux is also developed. This driver realizes many-to-many communication between hardware devices and SCADA software. Moreover, an algorithm named “Scan PRI” is put forward. This algorithm is much more optimizable and efficient compared with "Scan in sequence" in Windows. This software has been used in practical project. It has a good control effect and can achieve the expected goal.

Keywords: Linux OS, configuration software, OPC Server driver, MYSQL database

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Authors: Yeonjeong Lee, Kyoung-jae Kim, Youngtae Kim

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This study proposes a novel recommender system that uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user’s preference. The proposed model consists of two steps. In the first step, this study uses logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. Then, this study combines the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. In the second step, this study uses the market basket analysis to extract association rules for co-purchased products. Finally, the system selects customers who have high likelihood to purchase products in each product group and recommends proper products from same or different product groups to them through above two steps. We test the usability of the proposed system by using prototype and real-world transaction and profile data. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The results also show that the proposed system may be useful in real-world online shopping store.

Keywords: product recommender system, ensemble technique, association rules, decision tree, artificial neural networks

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Authors: Temidayo James Aransiola, Vania Ceccato, Marcelo Justus

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This paper investigates the effect of absolute deprivation (proxy unemployment) and relative deprivation (proxy income inequality) on homicide levels in Brazil. A database from the Brazilian Information System about Mortality and Census of the year 2000 and 2010 was used to estimate negative binomial models of homicide levels controlling for socioeconomic, demographic and geographic factors. Findings show that unemployment and income inequality affect homicides levels and that the effect of the former is more pronounced compared to the latter. Moreover, the combination of income inequality and unemployment exacerbates the overall effect of deprivation on homicide levels.

Keywords: deprivation, inequality, interaction, unemployment, violence

Procedia PDF Downloads 146