Search results for: predict

Authors: Adebisi Adunola Demehin, Wanlaya Thamnarak, Jaruwan Chatwichien, Chatchakorn Eurtivong, Kiattawee Choowongkomon, Somsak Ruchirawat, Nopporn Thasana

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The epidermal growth factor receptor (EGFR) is a transmembrane glycoprotein involved in cellular signalling processes and, its aberrant activity is crucial in the development of many cancers such as lung cancer. Selaginellaceae are fern allies that have long been used in Chinese traditional medicine to treat various cancer types, especially lung cancer. Biflavonoids, the major secondary metabolites in Selaginellaceae, have numerous pharmacological activities, including anti-cancer and anti-inflammatory. For instance, amentoflavone induces a cytotoxic effect in the human NSCLC cell line via the inhibition of PARP-1. However, to the best of our knowledge, there are no studies on biflavonoids as EGFR inhibitors. Thus, this study aims to investigate the EGFR inhibitory activities of biflavonoids isolated from Selaginella siamensis and Selaginella bryopteris. Amentoflavone, tetrahydroamentoflavone, sciadopitysin, robustaflavone, robustaflavone-4-methylether, delicaflavone, and chrysocauloflavone were isolated from the ethyl-acetate extract of the whole plants. The structures were determined using NMR spectroscopy and mass spectrometry. In vitro study was conducted to evaluate their cytotoxicity against A549, HEPG2, and T47D human cancer cell lines using the MTT assay. In addition, a target-based assay was performed to investigate their EGFR inhibitory activity using the kinase inhibition assay. Finally, a molecular docking study was conducted to predict the binding modes of the compounds. Robustaflavone-4-methylether and delicaflavone showed the best cytotoxic activity on all the cell lines with IC50 (µM) values of 18.9 ± 2.1 and 22.7 ± 3.3 on A549, respectively. Of these biflavonoids, delicaflavone showed the most potent EGFR inhibitory activity with an 84% relative inhibition at 0.02 nM using erlotinib as a positive control. Robustaflavone-4-methylether showed a 78% inhibition at 0.15 nM. The docking scores obtained from the molecular docking study correlated with the kinase inhibition assay. Robustaflavone-4-methylether and delicaflavone had a docking score of 72.0 and 86.5, respectively. The inhibitory activity of delicaflavone seemed to be linked with the C2”=C3” and 3-O-4”’ linkage pattern. Thus, this study suggests that the structural features of these compounds could serve as a basis for developing new EGFR-TK inhibitors.

Keywords: anticancer, biflavonoids, EGFR, molecular docking, Selaginellaceae

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Authors: A. P. Pashkov

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The number of environmental factors that adversely affect children's health is growing every year; their combination in each territory is different. The contribution of socio-economic factors to the health status of the younger generation is increasing. It is the child’s body that is most sensitive to changes in environmental conditions, responding to this with a deterioration in health. Over the past years, scientists have determined the influence of environmental factors and the incidence of children. Currently, there is a tendency to study regional characteristics of the interaction of a combination of environmental factors with the child's body. The aim of the work was to identify trends in the primary non-infectious morbidity of the children of the Altai Territory as a unique region that combines territories with different levels of environmental quality indicators, as well as to assess the effect of atmospheric air, drinking water and socio-economic indicators on the incidence of children in the region. An unfavorable tendency has been revealed in the region for incidence of such nosological groups as neoplasms, including malignant ones, diseases of the endocrine system, including obesity and thyroid disease, diseases of the circulatory system, digestive diseases, diseases of the genitourinary system, congenital anomalies, and respiratory diseases. Between some groups of diseases revealed a pattern of geographical distribution during mapping and a significant correlation. Some nosologies have a relationship with socio-economic indicators for an integrated assessment: circulatory system diseases, respiratory diseases (direct connection), endocrine system diseases, eating disorders, and metabolic disorders (feedback). The analysis of associations of the incidence of children with average annual concentrations of substances that pollute the air and drinking water showed the existence of reliable correlation in areas of critical and intense degree of environmental quality. This fact confirms that the population living in contaminated areas is subject to the negative influence of environmental factors, which immediately affects the health status of children. The results obtained indicate the need for a detailed assessment of the influence of environmental factors on the incidence of children in the regional aspect, the formation of a database, and the development of automated programs that can predict the incidence in each specific territory. This will increase the effectiveness, including economic of preventive measures.

Keywords: incidence of children, regional features, socio-economic factors, environmental factors

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Authors: Giorgio Bertolazzi, Panayiotis Benos, Michele Tumminello, Claudia Coronnello

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MicroRNAs are small non-coding RNAs that post-transcriptionally regulate the expression levels of messenger RNAs. MicroRNA regulation activity depends on the recognition of binding sites located on mRNA molecules. ComiR (Combinatorial miRNA targeting) is a user friendly web tool realized to predict the targets of a set of microRNAs, starting from their expression profile. ComiR incorporates miRNA expression in a thermodynamic binding model, and it associates each gene with the probability of being a target of a set of miRNAs. ComiR algorithms were trained with the information regarding binding sites in the 3’UTR region, by using a reliable dataset containing the targets of endogenously expressed microRNA in D. melanogaster S2 cells. This dataset was obtained by comparing the results from two different experimental approaches, i.e., inhibition, and immunoprecipitation of the AGO1 protein; this protein is a component of the microRNA induced silencing complex. In this work, we tested whether including coding region binding sites in the ComiR algorithm improves the performance of the tool in predicting microRNA targets. We focused the analysis on the D. melanogaster species and updated the ComiR underlying database with the currently available releases of mRNA and microRNA sequences. As a result, we find that the ComiR algorithm trained with the information related to the coding regions is more efficient in predicting the microRNA targets, with respect to the algorithm trained with 3’utr information. On the other hand, we show that 3’utr based predictions can be seen as complementary to the coding region based predictions, which suggests that both predictions, from 3'UTR and coding regions, should be considered in a comprehensive analysis. Furthermore, we observed that the lists of targets obtained by analyzing data from one experimental approach only, that is, inhibition or immunoprecipitation of AGO1, are not reliable enough to test the performance of our microRNA target prediction algorithm. Further analysis will be conducted to investigate the effectiveness of the tool with data from other species, provided that validated datasets, as obtained from the comparison of RISC proteins inhibition and immunoprecipitation experiments, will be available for the same samples. Finally, we propose to upgrade the existing ComiR web-tool by including the coding region based trained model, available together with the 3’UTR based one.

Keywords: AGO1, coding region, Drosophila melanogaster, microRNA target prediction

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Authors: Jonathan Gong

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: Sudha T., Naveen C.

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Dengue is a serious public health issue that causes significant annual economic and welfare burdens on nations. However, enhanced optimization techniques and quantitative modeling approaches can predict the incidence of dengue. By advocating for a data-driven approach, public health officials can make informed decisions, thereby improving the overall effectiveness of sudden disease outbreak control efforts. The National Oceanic and Atmospheric Administration and the Centers for Disease Control and Prevention are two of the U.S. Federal Government agencies from which this study uses environmental data. Based on environmental data that describe changes in temperature, precipitation, vegetation, and other factors known to affect dengue incidence, many predictive models are constructed that use different machine learning methods to estimate weekly dengue cases. The first step involves preparing the data, which includes handling outliers and missing values to make sure the data is prepared for subsequent processing and the creation of an accurate forecasting model. In the second phase, multiple feature selection procedures are applied using various machine learning models and optimization techniques. During the third phase of the research, machine learning models like the Huber Regressor, Support Vector Machine, Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR) are compared with several optimization techniques for feature selection, such as Harmony Search and Genetic Algorithm. In the fourth stage, the model's performance is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) as assistance. Selecting an optimization strategy with the least number of errors, lowest price, biggest productivity, or maximum potential results is the goal. In a variety of industries, including engineering, science, management, mathematics, finance, and medicine, optimization is widely employed. An effective optimization method based on harmony search and an integrated genetic algorithm is introduced for input feature selection, and it shows an important improvement in the model's predictive accuracy. The predictive models with Huber Regressor as the foundation perform the best for optimization and also prediction.

Keywords: deep learning model, dengue fever, prediction, optimization

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Authors: Morteza Esfandiari, Shabnam Jabari, Heather MacGrath, David Coleman

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Flood is a severe issue in different places in the world as well as the city of Fredericton, New Brunswick, Canada. The downtown area of Fredericton is close to the Saint John River, which is susceptible to flood around May every year. Recently, the frequency of flooding seems to be increased, especially after the fact that the downtown area and surrounding urban/agricultural lands got flooded in two consecutive years in 2018 and 2019. In order to have an explicit vision of flood span and damage to affected areas, it is necessary to use either flood inundation modelling or satellite data. Due to contingent availability and weather dependency of optical satellites, and limited existing data for the high cost of hydrodynamic models, it is not always feasible to rely on these sources of data to generate quality flood maps after or during the catastrophe. Height Above the Nearest Drainage (HAND), a state-of-the-art topo-hydrological index, normalizes the height of a basin based on the relative elevation along with the stream network and specifies the gravitational or the relative drainage potential of an area. HAND is a relative height difference between the stream network and each cell on a Digital Terrain Model (DTM). The stream layer is provided through a multi-step, time-consuming process which does not always result in an optimal representation of the river centerline depending on the topographic complexity of that region. HAND is used in numerous case studies with quite acceptable and sometimes unexpected results because of natural and human-made features on the surface of the earth. Some of these features might cause a disturbance in the generated model, and consequently, the model might not be able to predict the flow simulation accurately. We propose to include a previously existing stream layer generated by the province of New Brunswick and benefit from culvert maps to improve the water flow simulation and accordingly the accuracy of HAND model. By considering these parameters in our processing, we were able to increase the accuracy of the model from nearly 74% to almost 92%. The improved model can be used for generating highly accurate flood maps, which is necessary for future urban planning and flood damage estimation without any need for satellite imagery or hydrodynamic computations.

Keywords: HAND, DTM, rapid floodplain, simplified conceptual models

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Authors: Calvin Ralph, Edward Archer, Alistair McIlhagger

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3D woven textile composites continue to emerge as an advanced material for structural applications and composite manufacture due to their bespoke nature, through thickness reinforcement and near net shape capabilities. When 3D woven preforms are produced, they are in their optimal physical state. As 3D weaving is a dry preforming technology it relies on compression of the preform to achieve the desired composite thickness, fibre volume fraction (Vf) and consolidation. This compression of the preform during manufacture results in changes to its thickness and architecture which can often lead to under-performance or changes of the 3D woven composite. Unlike traditional 2D fabrics, the bespoke nature and variability of 3D woven architectures makes it difficult to know exactly how each 3D preform will behave during processing. Therefore, the focus of this study is to investigate the effect of compression on differing 3D woven architectures in terms of structure, crimp or fibre waviness and thickness as well as analysing the accuracy of available software to predict how 3D woven preforms behave under compression. To achieve this, 3D preforms are modelled and compression simulated in Wisetex with varying architectures of binder style, pick density, thickness and tow size. These architectures have then been woven with samples dry compression tested to determine the compressibility of the preforms under various pressures. Additional preform samples were manufactured using Resin Transfer Moulding (RTM) with varying compressive force. Composite samples were cross sectioned, polished and analysed using microscopy to investigate changes in architecture and crimp. Data from dry fabric compression and composite samples were then compared alongside the Wisetex models to determine accuracy of the prediction and identify architecture parameters that can affect the preform compressibility and stability. Results indicate that binder style/pick density, tow size and thickness have a significant effect on compressibility of 3D woven preforms with lower pick density allowing for greater compression and distortion of the architecture. It was further highlighted that binder style combined with pressure had a significant effect on changes to preform architecture where orthogonal binders experienced highest level of deformation, but highest overall stability, with compression while layer to layer indicated a reduction in fibre crimp of the binder. In general, simulations showed a relative comparison to experimental results; however, deviation is evident due to assumptions present within the modelled results.

Keywords: 3D woven composites, compression, preforms, textile composites

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Authors: Jillian M. Hagel, Joseph E. Tucker, Peter J. Facchini

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With the aim of establishing a holistic approach for the treatment of central nervous system (CNS) disorders, we are pursuing a drug development program rapidly progressing through discovery and characterization phases. The drug candidates identified in this program are referred to as neuroplastogens owing to their ability to mediate neuroplasticity, which can be beneficial to patients suffering from anxiety, depression, or posttraumatic stress disorder. These and other related neuropsychiatric conditions are associated with the onset of neuronal atrophy, which is defined as a reduction in the number and/or productivity of neurons. The stimulation of neuroplasticity results in an increase in the connectivity between neurons and promotes the restoration of healthy brain function. We have synthesized a substantial catalogue of proprietary indolethylamine derivatives based on the general structures of serotonin (5-hydroxytryptamine) and psychedelic molecules such as N,N-dimethyltryptamine (DMT) and psilocin (4-hydroxy-DMT) that function as neuroplastogens. A primary objective in our screening protocol is the identification of derivatives associated with a significant reduction in hallucination, which will allow administration of the drug at a dose that induces neuroplasticity and triggers other efficacious outcomes in the treatment of targeted CNS disorders but which does not cause a psychedelic response in the patient. Both neuroplasticity and hallucination are associated with engagement of the 5HT2A receptor, requiring drug candidates differentially coupled to these two outcomes at a molecular level. We use novel and proprietary artificial intelligence algorithms to predict the mode of binding to the 5HT2A receptor, which has been shown to correlate with the hallucinogenic response. Hallucination is tested using the mouse head-twitch response model, whereas mouse marble-burying and sucrose preference assays are used to evaluate anxiolytic and anti-depressive potential. Neuroplasticity is assays using dendritic outgrowth assays and cell-based ELISA analysis. Pharmacokinetics and additional receptor-binding analyses also contribute the selection of lead candidates. A summary of the program is presented.

Keywords: neuroplastogen, non-hallucinogenic, drug development, anxiety, depression, PTSD, indolethylamine derivatives, psychedelic-inspired, 5-HT2A receptor, computational chemistry, head-twitch response behavioural model, neurite outgrowth assay

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Authors: Kurt Maxwell Kusterer, Sylvain Mignot, Annick Vignes

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To our best knowledge YouTube is the largest video hosting platform in the world. A high number of creators, viewers, subscribers and commentators act in this specific eco-system which generates huge sums of money. Views, subscribers, and comments help to increase the popularity of content creators. The most popular creators are sponsored by brands and participate in marketing campaigns. For a few of them, this becomes a financially rewarding profession. This is made possible through the YouTube Partner Program, which shares revenue among creators based on their popularity. We believe that the role of comments in increasing the popularity is to be emphasized. In what follows, YouTube is considered as a bilateral network between the videos and the commentators. Analyzing a detailed data set focused on French YouTubers, we consider each comment as a link between a commentator and a video. Our research question asks what are the predominant features of a video which give it the highest probability to be commented on. Following on from this question, how can we use these features to predict the action of the agent in commenting one video instead of another, considering the characteristics of the commentators, videos, topics, channels, and recommendations. We expect to see that the videos of more popular channels generate higher viewer engagement and thus are more frequently commented. The interest lies in discovering features which have not classically been considered as markers for popularity on the platform. A quick view of our data set shows that 96% of the commentators comment only once on a certain video. Thus, we study a non-weighted bipartite network between commentators and videos built on the sub-sample of 96% of unique comments. A link exists between two nodes when a commentator makes a comment on a video. We run an Exponential Random Graph Model (ERGM) approach to evaluate which characteristics influence the probability of commenting a video. The creation of a link will be explained in terms of common video features, such as duration, quality, number of likes, number of views, etc. Our data is relevant for the period of 2020-2021 and focuses on the French YouTube environment. From this set of 391 588 videos, we extract the channels which can be monetized according to YouTube regulations (channels with at least 1000 subscribers and more than 4000 hours of viewing time during the last twelve months).In the end, we have a data set of 128 462 videos which consist of 4093 channels. Based on these videos, we have a data set of 1 032 771 unique commentators, with a mean of 2 comments per a commentator, a minimum of 1 comment each, and a maximum of 584 comments.

Keywords: YouTube, social networks, economics, consumer behaviour

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Authors: Marios Papaevripidou, Yvoni Pavlou, Zacharias Zacharia

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Embodied cognition and additional (touch) sensory channel theories indicate that physical manipulation is crucial to learning since it provides, among others, touch sensory input, which is needed for constructing knowledge. Given these theories, the use of Physical Manipulatives (PM) becomes a prerequisite for learning. On the other hand, empirical research on Virtual Manipulatives (VM) (e.g., simulations) learning has provided evidence showing that the use of PM, and thus haptic sensory input, is not always a prerequisite for learning. In order to investigate which means of experimentation, PM or VM, are required for enhancing student science learning at the kindergarten level, an empirical study was conducted that sought to investigate the impact of haptic feedback on the conceptual understanding of pre-school students (n=44, age mean=5,7) in three science domains: beam balance (D1), sinking/floating (D2) and springs (D3). The participants were equally divided in two groups according to the type of manipulatives used (PM: presence of haptic feedback, VM: absence of haptic feedback) during a semi-structured interview for each of the domains. All interviews followed the Predict-Observe-Explain (POE) strategy and consisted of three phases: initial evaluation, experimentation, final evaluation. The data collected through the interviews were analyzed qualitatively (open-coding for identifying students’ ideas in each domain) and quantitatively (use of non-parametric tests). Findings revealed that the haptic feedback enabled students to distinguish heavier to lighter objects when held in hands during experimentation. In D1 the haptic feedback did not differentiate PM and VM students' conceptual understanding of the function of the beam as a mean to compare the mass of objects. In D2 the haptic feedback appeared to have a negative impact on PM students’ learning. Feeling the weight of an object strengthen PM students’ misconception that heavier objects always sink, whereas the scientifically correct idea that the material of an object determines its sinking/floating behavior in the water was found to be significantly higher among the VM students than the PM ones. In D3 the PM students outperformed significantly the VM students with regard to the idea that the heavier an object is the more the spring will expand, indicating that the haptic input experienced by the PM students served as an advantage to their learning. These findings point to the fact that PMs, and thus touch sensory input, might not always be a requirement for science learning and that VMs could be considered, under certain circumstances, as a viable means for experimentation.

Keywords: haptic feedback, physical and virtual manipulatives, pre-school science learning, science experimentation

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Authors: Leonard Simoni, Ilir Alimehmeti, Ervina Shirka, Endri Hasimi, Ndricim Kallashi, Verona Beka, Suerta Kabili, Artan Goda

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Background: Diabetes Mellitus type 2, small vessel calibre, stented length of vessel, complex lesion morphology, and prior bypass surgery have resulted risk factors for In-Stent Restenosis (ISR). However, there are some contradictory results about body mass index (BMI) as a risk factor for ISR. Purpose: We want to identify clinical, lesional and procedural factors that can predict clinical ISR in our patients. Methods: Were enrolled 759 patients who underwent first-time elective PCI with Bare Metal Stents (BMS) from September 2011 to December 2013 in our Department of Cardiology and followed them for at least 1.5 years with a median of 862 days (2 years and 4 months). Only the patients re-admitted with ischemic heart disease underwent control coronary angiography but no routine angiographic control was performed. Patients were categorized in ISR and non-ISR groups and compared between them. Multivariate analysis - Binary Logistic Regression: Forward Conditional Method was used to identify independent predictive risk factors. P was considered statistically significant when <0.05. Results: ISR compared to non-ISR individuals had a significantly lower BMI (25.7±3.3 vs. 26.9±3.7, p=0.004), higher risk anatomy (LM + 3-vessel CAD) (23% vs. 14%, p=0.03), higher number of stents/person used (2.1±1.1 vs. 1.75±0.96, p=0.004), greater length of stents/person used (39.3±21.6 vs. 33.3±18.5, p=0.01), and a lower use of clopidogrel and ASA (together) (95% vs. 99%, p=0.012). They also had a higher, although not statistically significant, prevalence of Diabetes Mellitus (42% vs. 32%, p=0.072) and a greater number of treated vessels (1.36±0.5 vs. 1.26±0.5, p=0.08). In the multivariate analysis, Diabetes Mellitus type 2 and multiple stents used were independent predictors risk factors for In-Stent Restenosis, OR 1.66 [1.03-2.68], p=0.039, and OR 1.44 [1.16-1.78,] p=0.001, respectively. On the other side higher BMI and use of clopidogrel and ASA together resulted protective factors OR 0.88 [0.81-0.95], p=0.001 and OR 0.2 [0.06-0.72] p=0.013, respectively. Conclusion: Diabetes Mellitus and multiple stents are strong predictive risk factors, whereas the use of clopidogrel and ASA together are protective factors for clinical In-Stent Restenosis. Paradoxically High BMI is a protective factor for In-stent Restenosis, probably related to a larger diameter of vessels and consequently a larger diameter of stents implanted in these patients. Further studies are needed to clarify this finding.

Keywords: body mass index, diabetes mellitus, in-stent restenosis, percutaneous coronary intervention

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Authors: Soumita Banerjee

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The State of West Bengal is mostly watered by innumerable rivers, and they are different in nature in both the northern and the southern part of the state. The southern part of West Bengal is mainly drained with the river Bhagirathi-Hooghly, and its major distributaries and tributaries have divided this major river basin into many subparts like the Ichamati-Bidyadhari, Pagla-Bansloi, Mayurakshi-Babla, Ajay, Damodar, Kangsabati Sub-basin to name a few. These rivers basically drain the Districts of Bankura, Burdwan, Hooghly, Nadia and Purulia, Birbhum, Midnapore, Murshidabad, North 24-Parganas, Kolkata, Howrah and South 24-Parganas. West Bengal has a huge number of flood-prone blocks in the southern part of the state of West Bengal, the responsible factors for flood situation are the shape and size of the catchment area, its steep gradient starting from plateau to flat terrain, the river bank erosion and its siltation, tidal condition especially in the lower Ganga Basin and very low maintenance of the embankments which are mostly used as communication links. Along with these factors, DVC (Damodar Valley Corporation) plays an important role in the generation (with the release of water) and controlling the flood situation. This year the whole Gangetic West Bengal is being flooded due to high intensity and long duration rainfall, and the release of water from the Durgapur Barrage As most of the rivers are interstate in nature at times floods also take place with release of water from the dams of the neighbouring states like Jharkhand. Other than Embankments, there is no such structural measures for combatting flood in West Bengal. This paper tries to analyse the reasons behind the flood situation this year especially with the help of climatic data collected from the Indian Metrological Department, flood related data from the Irrigation and Waterways Department, West Bengal and GPM (General Precipitation Measurement) data for rainfall analysis. Based on the threshold value derived from the calculation of the past available flood data, it is possible to predict the flood events which may occur in the near future and with the help of social media it can be spread out within a very short span of time to aware the mass. On a larger or a governmental scale, heightening the settlements situated on the either banks of the river can yield a better result than building up embankments.

Keywords: dam failure, embankments, flood, rainfall

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Authors: Jithin S. Kumar, Ramesh Kannan Kandasami

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Geomaterials are generally porous in nature due to the presence of discrete particles and interconnected voids. The porosity present in these geomaterials play a critical role in many engineering applications such as CO2 sequestration, well bore strengthening, enhanced oil and hydrocarbon recovery, hydraulic fracturing, and subsurface waste storage. These applications involves solid-fluid interactions, which govern the changes in the porosity which in turn affect the permeability and stiffness of the medium. Injecting fluid into the geomaterials results in permeation which exhibits small or negligible deformation of the soil skeleton followed by cavity expansion/ fingering/ fracturing (different forms of instabilities) due to the large deformation especially when the flow rate is greater than the ability of the medium to permeate the fluid. The complexity of this problem increases as the geomaterial behaves like a solid and fluid under certain conditions. Thus it is important to understand this multiphysics problem where in addition to the permeation, the elastic-plastic deformation of the soil skeleton plays a vital role during fluid injection. The phenomenon of permeation and cavity expansion in porous medium has been studied independently through extensive experimental and analytical/ numerical models. The analytical models generally use Darcy's/ diffusion equations to capture the fluid flow during permeation while elastic-plastic (Mohr-Coulomb and Modified Cam-Clay) models were used to predict the solid deformations. Hitherto, the research generally focused on modelling cavity expansion without considering the effect of injected fluid coming into the medium. Very few studies have considered the effect of injected fluid on the deformation of soil skeleton. However, the porosity changes during the fluid injection and coupled elastic-plastic deformation are not clearly understood. In this study, the phenomenon of permeation and instabilities such as cavity and finger/ fracture formation will be quantified extensively by performing experiments using a novel experimental setup in addition to utilizing image processing techniques. This experimental study will describe the fluid flow and soil deformation characteristics under different boundary conditions. Further, a well refined coupled semi-analytical model will be developed to capture the physics involved in quantifying the deformation behaviour of geomaterial during fluid injection.

Keywords: solid-fluid interaction, permeation, poroelasticity, plasticity, continuum model

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Authors: Jeffrey L. Hurst, Sarah M. Coyne

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With the growth of technology and media, teens are increasingly exposed to media such as pornography and engaging in risky media use such as sexting. Parental media mediation strategies including controlling or autonomy supporting strategies can be an important protective factor against risky media uses. The purpose of this study is to examine how parental media mediation around media, influence adolescents’ behaviors including frequency of pornography use and sexting. We also examine the effects of parental media mediation on adolescents disclosing pornography use to parents and the amount of secrets that adolescents keep about pornography use. We hypothesize that controlling media mediation will result in more sexting, more frequency pornography use, more secrets about pornography and less disclosure to parents. We also predict that autonomy supportive media mediation will show the opposite pattern. Data for this study came from a nationally representative research project, Project M.E.D.I.A. Participants included 783 adolescents. 49% of the participants were male, and the mean age for boys was 15.44 years (SD= 3.34) and for girls was 15.3 years (SD=2.93). Parental media mediation was assessed using an eight-item measure with subscales of controlling and autonomy supporting media mediation. Participants were also asked if they have ever viewed pornography. If they answered yes, they were asked about the frequency of pornography use as well as if they have ever kept secrets from their parents about it and if they had ever disclosed their pornography use to their parents. The data analysis strategy for this study was a multiple group path analysis. Frequency of pornography use, sexting, secrets from parents and disclosure to parents were predicted by controlling and autonomy supporting parental media mediation, frequency of parents warning against pornography use, income and ethnicity. Groups were distinguished by boys and girls, allowing for sex differences. After running the model in MPLUS, we found partial support for our hypotheses. Autonomy supportive media mediation resulted in less sexting for boys (β= -.15, p < .05) and girls ( β= -.13, p < .05). Autonomy supportive media mediation also predicted keeping fewer secrets for girls (β=-.27, p < .01) but had no effect for boys. Controlling media mediation predicted more disclosure about pornography to parents for boys (β=.16, p < .05) and less disclosure to parents about pornography for girls (β=-.14, p < .05). Frequency of pornography was not predicted by any of the predictors in the model. Autonomy supportive media mediation was a very strong predictor of less sexting for both boys and girls. Parents should approach media mediation with this supportive and understanding mindset. Parental autonomy support allows adolescents to explore and develop their own moral beliefs without feeling guilt or shame from their parents. This need to have autonomy is also shown by girls disclosing less pornography use to their parents when parents are really controlling about media use. Interestingly, boys disclosed more to their parents when their parents were controlling. Further research is needed on why this is. Further research should also look at the effects that disclosing pornography use to parents has on future pornography use.

Keywords: media, moral development, parental mediation, pornography, sexting

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Authors: S. R. Dehghani, G. F. Naterer, Y. S. Muzychka

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Wave-impact sea spray creates many droplets which form a spray cloud traveling over marine objects same as marine vessels and offshore structures. In cold climates such as Arctic reigns, sea spray icing, which is ice accretion on cold substrates, is strongly dependent on the wave-impact sea spray. The rate of cooling of droplets affects the process of icing that can yield to dry or wet ice accretion. Trajectories of droplets determine the potential places for ice accretion. Combining two models of trajectories and heat transfer for droplets can predict the risk of ice accretion reasonably. The majority of the cooling of droplets is because of droplet evaporations. In this study, a combined model using trajectory and heat transfer evaluate the situation of a cloud of spray from the generation to impingement. The model uses some known geometry and initial information from the previous case studies. The 3D model is solved numerically using a standard numerical scheme. Droplets are generated in various size ranges from 7 mm to 0.07 mm which is a suggested range for sea spray icing. The initial temperature of droplets is considered to be the sea water temperature. Wind velocities are assumed same as that of the field observations. Evaluations are conducted using some important heading angles and wind velocities. The characteristic of size-velocity dependence is used to establish a relation between initial sizes and velocities of droplets. Time intervals are chosen properly to maintain a stable and fast numerical solution. A statistical process is conducted to evaluate the probability of expected occurrences. The medium size droplets can reach the highest heights. Very small and very large droplets are limited to lower heights. Results show that higher initial velocities create the most expanded cloud of spray. Wind velocities affect the extent of the spray cloud. The rate of droplet cooling at the start of spray formation is higher than the rest of the process. This is because of higher relative velocities and also higher temperature differences. The amount of water delivery and overall temperature for some sample surfaces over a marine vessel are calculated. Comparing results and some field observations show that the model works accurately. This model is suggested as a primary model for ice accretion on marine vessels.

Keywords: evaporation, sea spray, marine icing, numerical solution, trajectory

Procedia PDF Downloads 197

Authors: Pavana Basavakumar, Devadas Bhat

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Diabetes is a disease which often goes undiagnosed until its secondary effects are noticed. Early detection of the disease is necessary to avoid serious consequences which could lead to the death of the patient. Conventional invasive tests for screening of diabetes are mostly painful, time consuming and expensive. There’s also a risk of infection involved, therefore it is very essential to develop non-invasive methods to screen and estimate the level of blood glucose. Extensive research is going on with this perspective, involving various techniques that explore optical, electrical, chemical and thermal properties of the human body that directly or indirectly depend on the blood glucose concentration. Thus, non-invasive blood glucose monitoring has grown into a vast field of research. In this project, an attempt was made to device a prototype for screening of diabetes by measuring electrical impedance of the skin and building a model to predict a patient’s condition based on the measured impedance. The prototype developed, passes a negligible amount of constant current (0.5mA) across a subject’s index finger through tetra polar silver electrodes and measures output voltage across a wide range of frequencies (10 KHz – 4 MHz). The measured voltage is proportional to the impedance of the skin. The impedance was acquired in real-time for further analysis. Study was conducted on over 75 subjects with permission from the institutional ethics committee, along with impedance, subject’s blood glucose values were also noted, using conventional method. Nonlinear regression analysis was performed on the features extracted from the impedance data to obtain a model that predicts blood glucose values for a given set of features. When the predicted data was depicted on Clarke’s Error Grid, only 58% of the values predicted were clinically acceptable. Since the objective of the project was to screen diabetes and not actual estimation of blood glucose, the data was classified into three classes ‘NORMAL FASTING’,’NORMAL POSTPRANDIAL’ and ‘HIGH’ using linear Support Vector Machine (SVM). Classification accuracy obtained was 91.4%. The developed prototype was economical, fast and pain free. Thus, it can be used for mass screening of diabetes.

Keywords: Clarke’s error grid, electrical impedance of skin, linear SVM, nonlinear regression, non-invasive blood glucose monitoring, screening device for diabetes

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Authors: Zan Chen, D. Kwok

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The SkillsFuture Singapore’s mission to develop a responsive and forward-looking Training and Adult Education (TAE) and workforce development system is undergirded by how successful TAE providers are in their business performance and strategies that strengthen their operational efficiency and processes. Therefore, understanding the factors that drive the business performance of TAE providers is critical to the success of SkillsFuture Singapore’s initiatives. This study aims to investigate how business strategy, work autonomy, work intensity and professional development support impact the business performance of private TAE providers. Specifically, the three research questions are: (1) Are there significant relationships between the above-mentioned four factors and TAE providers’ business performance?; (2) Are there significant differences on the four factors between low and high TAE providers’ business performance groups?; and (3) To what extent and in what manner do the four factors predict TAE providers’ business performance? This was part of the first national study on organizations and professionals working in the Training and Adult Education (TAE) sector. Data from 265 private TAE providers where respondents were Chief Executive Officers representatives from the Senior Management were analyzed. The results showed that business strategy (the extent that the organization leads the way in terms of developing new products and services; uses up-to-date learning technologies; customizes its products and services to the client’s needs), work autonomy (the extent that the staff personally have an influence on how hard they work; deciding what tasks they are to do; deciding how they are to do the tasks, and deciding the quality standards to which they work) and professional development support (both monetary and non-monetary support and incentives) had positive and significant relationships with business performance. However, no significant relationship is found between work intensity and business performance. A business strategy, work autonomy and professional development support were significantly higher in the high business performance group compared to the low-performance group among the TAE providers. Results of hierarchical regression analyses controlling for the size of the TAE providers showed significant impacts of business strategy, work autonomy and professional development support on TAE providers’ business performance. Overall, the model accounted for 27% of the variance in TAE providers’ business performance. This study provides policymakers with insights into improving existing policies, designing new initiatives and implementing targeting interventions to support TAE providers. The findings also have implications on how the TAE providers could better formulate their organizational strategies and business models. Finally, limitations of study, along with directions for future research will be discussed in the paper.

Keywords: adult education, business performance, business strategy, training, work autonomy

Procedia PDF Downloads 177

Authors: Vijay Kumar, Jaspreet Singh, Manoj Wadhwa

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Piezo-resistive pressure sensors were one of the first developed micromechanical system (MEMS) devices and still display a significant growth prompted by the advancements in micromachining techniques and material technology. In MEMS based piezo-resistive pressure sensors, temperature can be considered as the main environmental condition which affects the system performance. The study of the thermal behavior of these sensors is essential to define the parameters that cause the output characteristics to drift. In this work, a study on the effects of temperature and doping concentration in a boron implanted piezoresistor for a silicon-based pressure sensor is discussed. We have optimized the temperature coefficient of resistance (TCR) and temperature coefficient of sensitivity (TCS) values to determine the effect of temperature drift on the sensor performance. To be more precise, in order to reduce the temperature drift, a high doping concentration is needed. And it is well known that the Wheatstone bridge in a pressure sensor is supplied with a constant voltage or a constant current input supply. With a constant voltage supply, the thermal drift can be compensated along with an external compensation circuit, whereas the thermal drift in the constant current supply can be directly compensated by the bridge itself. But it would be beneficial to also compensate the temperature coefficient of piezoresistors so as to further reduce the temperature drift. So, with a current supply, the TCS is dependent on both the TCπ and TCR. As TCπ is a negative quantity and TCR is a positive quantity, it is possible to choose an appropriate doping concentration at which both of them cancel each other. An exact cancellation of TCR and TCπ values is not readily attainable; therefore, an adjustable approach is generally used in practical applications. Thus, one goal of this work has been to better understand the origin of temperature drift in pressure sensor devices so that the temperature effects can be minimized or eliminated. This paper describes the optimum doping levels for the piezoresistors where the TCS of the pressure transducers will be zero due to the cancellation of TCR and TCπ values. Also, the fabrication and characterization of the pressure sensor are carried out. The optimized TCR value obtained for the fabricated die is 2300 ± 100ppm/ᵒC, for which the piezoresistors are implanted at a doping concentration of 5E13 ions/cm³ and the TCS value of -2100ppm/ᵒC is achieved. Therefore, the desired TCR and TCS value is achieved, which are approximately equal to each other, so the thermal effects are considerably reduced. Finally, we have calculated the effect of temperature and doping concentration on the output characteristics of the sensor. This study allows us to predict the sensor behavior against temperature and to minimize this effect by optimizing the doping concentration.

Keywords: piezo-resistive, pressure sensor, doping concentration, TCR, TCS

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Authors: Janaina Pereira, Barbara Gonzalez, Valentina Chitas, Teresa Molina

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Intro: The positive impact of opioid maintenance programs on the health of heroin addicts, and on public health in general, has been widely recognized, namely on the prevalence reduction of infectious diseases as HIV, and on the social reintegration of this population. Nevertheless, a part of patients in these programs cannot remain heroin abstinent, or has relapses, during the treatment. Method: Thus, this cross-sectional research aims at analyzing the relation between a set of psychological and psychosocial variables, which have been associated with the onset of heroin use, and assess if they are also associated with absence of abstinence in participants in an opioid maintenance program. A total of 62 patients, aged between 26 and 58 years old (M= 40.87, DP= 7.39) with a time in opioid maintenance program between 1 and 10 years (M= 5.42, DP= 3.05), 77.4% male and 22.6% female, participated in this research. To assess the criterion variable (heroin use) we used the mean value of positive results in urine tests during the participation in the program, weighted according to the number of months in program. The predictor variables were the coping strategies, the dispositional sensation seeking, and the existence of Posttraumatic stress disorder (PTSD). Results: The results showed that only 33.87% of the patients were totally abstinent of heroin use since the beginning of the program, and the absence of abstinence, as the number of positive heroin tests, was primarily predicted by less proactive coping, and secondarily by a higher level of sensation seeking. 16.13% of the sample fulfilled diagnosis criteria for PTSD, and 67.74 % had at least one traumatic experience throughout their lives. The total of PTSD symptoms had a positive correlation with the number of physical health problems, and with the lack of professional occupation. These results have several implications for the clinical practice in this field, and we suggest the promotion of proactive coping strategies should integrate these opioid maintenance programs, as they represent the tendency to face future events as challenges and opportunities, being positively related to positive results on several fields. The early identification of PTSD in the participants, before entering the opioid maintenance programs, would be important as it is related to negative features that hinder social reintegration, Finally, to identify individuals with a sensation seeking profile would be relevant, not only because they face a higher risk of relapse, but also because the therapeutical approaches should not ignore this dispositional feature in the alternatives they propose to the patients.

Keywords: opioid maintenance programs, proactive coping, PTSD, sensation seeking

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Authors: Abdreheman Seid Abdella

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This study examined instructors willingness, self-efficacy beliefs, attitudes and knowledge about provisions of instructional accommodations for students with disabilities in universities. Major concepts used in this study operationally defined and some models of disability were reviewed. Questionnaires were distributed to a total of 181 instructors from four universities and quantitative data was generated. Then to analyze the data, appropriate methods of data analysis were employed. The result indicated that on average instructors had positive willingness, strong self-efficacy beliefs and positive attitudes towards providing instructional accommodations. In addition, the result showed that the majority of participants had moderate level of knowledge about provision of instructional accommodations. Concerning the relationship between instructors background variables and dependent variables, the result revealed that location of university and awareness raising training about Inclusive Education showed statistically significant relationship with all dependent variables (willingness, self-efficacy beliefs, attitudes and knowledge). On the other hand, gender and college/faculty did not show a statistically significant relationship. In addition, it was found that among the inter-correlation of dependent variables, the correlation between attitudes and willingness to provide accommodations was the strongest. Furthermore, using multiple linear regression analysis, this study also indicated that predictor variables like self-efficacy beliefs, attitudes, knowledge and teaching methodology training made statistically significant contribution to predicting the criterion willingness. Predictor variables like willingness and attitudes made statistically significant contribution to predicting self-efficacy beliefs. Predictor variables like willingness, Special Needs Education course and self-efficacy beliefs made statistically significant contribution to predict attitudes. Predictor variables like Special Needs Education courses, the location of university and willingness made statistically significant contribution to predicting knowledge. Finally, using exploratory factor analysis, this study showed that there were four components or factors each that represent the underlying constructs of willingness and self-efficacy beliefs to provide instructional accommodations items, five components for attitudes towards providing accommodations items and three components represent the underlying constructs for knowledge about provisions of instructional accommodations items. Based on the findings, recommendations were made for improving the situation of instructional accommodations in Ethiopian universities.

Keywords: willingness, self-efficacy belief, attitude, knowledge

Procedia PDF Downloads 246

Authors: Zoran Veršić, Josip Galić, Marin Binički, Lucija Stepinac

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With the current increase in CO₂ emissions and global warming, the sustainability of both existing and new solutions must be assessed on a wide scale. As the implementation of closed cavity facades (CCF) is on the rise, a variety of factors must be included in the analysis of new types of CCF. This paper aims to cover the relevant factors included in the sustainability assessment of new types of CCF. Several mathematical models are being used to describe the physical behavior of CCF. Depending on the type of CCF, they cover the main factors which affect the durability of the façade: thermal behavior of various elements in the façade, stress, and deflection of the glass panels, pressure inside a cavity, exchange rate, and the moisture buildup in the cavity. CCF itself represents a complex system in which all mentioned factors must be considered mutually. Still, the façade is only an envelope of a more complex system, the building. Choice of the façade dictates the heat loss and the heat gain, thermal comfort of inner space, natural lighting, and ventilation. Annual consumption of energy for heating, cooling, lighting, and maintenance costs will present the operational advantages or disadvantages of the chosen façade system in both the economic and environmental aspects. Still, the only operational viewpoint is not all-inclusive. As the building codes constantly demand higher energy efficiency as well as transfer to renewable energy sources, the ratio of embodied and lifetime operational energy footprint of buildings is changing. With the drop in operational energy CO₂ emissions, embodied energy emissions present a larger and larger share in the lifecycle emissions of the building. Taken all into account, the sustainability assessment of a façade, as well as other major building elements, should include all mentioned factors during the lifecycle of an element. The challenge of such an approach is a timescale. Depending on the climatic conditions on the building site, the expected lifetime of CCF can exceed 25 years. In such a time span, some of the factors can be estimated more precisely than others. The ones depending on the socio-economic conditions are more likely to be harder to predict than the natural ones like the climatic load. This work recognizes and summarizes the relevant factors needed for the assessment of new types of CCF, considering the entire lifetime of a façade element and economic and environmental aspects.

Keywords: assessment, closed cavity façade, life cycle, sustainability

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Authors: Hany Elsaid Fawaz Abdallah

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This study presents a new artificial neural network(ANN) model to predict the Nusselt Number and pressure drop for the turbulent flow in a triangular corrugated plate heat exchanger for forced air and turbulent water flow. An experimental investigation was performed to create a new dataset for the Nusselt Number and pressure drop values in the following range of dimensionless parameters: The plate corrugation angles (from 0° to 60°), the Reynolds number (from 10000 to 40000), pitch to height ratio (from 1 to 4), and Prandtl number (from 0.7 to 200). Based on the ANN performance graph, the three-layer structure with {12-8-6} hidden neurons has been chosen. The training procedure includes back-propagation with the biases and weight adjustment, the evaluation of the loss function for the training and validation dataset and feed-forward propagation of the input parameters. The linear function was used at the output layer as the activation function, while for the hidden layers, the rectified linear unit activation function was utilized. In order to accelerate the ANN training, the loss function minimization may be achieved by the adaptive moment estimation algorithm (ADAM). The ‘‘MinMax’’ normalization approach was utilized to avoid the increase in the training time due to drastic differences in the loss function gradients with respect to the values of weights. Since the test dataset is not being used for the ANN training, a cross-validation technique is applied to the ANN network using the new data. Such procedure was repeated until loss function convergence was achieved or for 4000 epochs with a batch size of 200 points. The program code was written in Python 3.0 using open-source ANN libraries such as Scikit learn, TensorFlow and Keras libraries. The mean average percent error values of 9.4% for the Nusselt number and 8.2% for pressure drop for the ANN model have been achieved. Therefore, higher accuracy compared to the generalized correlations was achieved. The performance validation of the obtained model was based on a comparison of predicted data with the experimental results yielding excellent accuracy.

Keywords: artificial neural networks, corrugated channel, heat transfer enhancement, Nusselt number, pressure drop, generalized correlations

Procedia PDF Downloads 56

Authors: Siqing Guan, Fumiyo Oshima, Eiji Shimizu, Nozomi Tomita, Toru Takahashi, Hiroaki Kumano

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Risk factors for social anxiety in autism spectrum conditions involve executive attention, emotion regulation, and thought regulation as processes of cognitive dysregulation. Social camouflaging behaviors as strategies used to mask and/or compensate for autism characteristics during social interactions in autism spectrum conditions have also been emphasized. However, the role of cognitive dysregulation and social camouflaging related to social anxiety in autism spectrum conditions has not been clarified. Whether these factors are specific to social anxiety in autism spectrum conditions or common to social anxiety independent of autism spectrum conditions needs to be clarified. Here, we explored risk factors specific to social anxiety in autism spectrum conditions and general risk factors for social anxiety independent of autism spectrum conditions. From the Japanese participants in early adulthood (age=18~39) of the online survey in Japan, those who exceeded the Japanese version Autism-Spectrum Quotient cutoff (33 points or more )were divided into the autism spectrum conditions group (ASC; N=255, mean age=32.08, SD age=5.16)and those who did not exceed the cutoff were divided into the non-autism spectrum conditions group (Non-ASC; N=255, mean age=31.70, SD age=5.09). Using the Japanese versions of the Social Phobia Scale, the Social Interaction Anxiety Scale, and the Short Fear of Negative Evaluation Scale, a composite score for social anxiety was calculated using a method of principal. We also measured emotional control difficulties using the Difficulties in Emotion Regulation Scale, executive attention using the Effortful Control Scale for Adults, rumination using the Rumination-Reflection Questionnaire, and worry using the Penn State Worry Questionnaire. This study was passed through the review of the Ethics Committee. No conflicts of interest. Multiple regression analysis with forced entry method was used to predict social anxiety in the ASC and non-ASC groups separately, based on executive attention, emotion dysregulation, worry, rumination, and social camouflage. In the ASC group, emotion dysregulation (β=.277, p<.001), worry (β=.162, p<.05), assimilation (β=.308, p<.001) and masking (β=.275, p<.001) were significant predictors of social anxiety (F (7,247) = 45.791, p <.001, R2=.565). In the non-ASC groups,emotion dysregulation (β=.171, p<.05), worry (β=.344，p <.001), assimilation (β=.366,p <.001) and executive attention (β=-.132,p <.05) were significant predictors of social anxiety (F (7,207) =47.333, p <.001, R2=.615).The findings suggest that masking was shown to be a risk factor for social anxiety specific to autism spectrum conditions, while emotion dysregulation, worry, and assimilation were shown to be common risk factors for social anxiety, regardless of autism spectrum conditions. In addition, executive attention is a risk factor for social anxiety without autism spectrum conditions.

Keywords: autism spectrum, cognitive control, social anxiety, social camouflaging

Procedia PDF Downloads 186

Authors: L. Gómez-Limia, I. Franco, T. Blanco, S. Martínez

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European eels (Anguilla anguilla) belong to Anguilliformes order and Anguillidae family. They are generally classified as warm-water fish. Eels have a great commercial value in Europe and Asian countries. Eels can reach high weights, although their commercial size is relatively low in some countries. The capture of larger eels would facilitate the recovery of the species, as well as having a greater number of either glass eels or elvers for aquaculture. In the last years, the demand and the price of eels have increased significantly. However, European eel is considered critically endangered by the International Union for the Conservation of Nature (IUCN) Red List. The biochemical composition of fishes is an important aspect of quality and affects the nutritional value and consumption quality of fish. In addition, knowing this composition can help predict an individual’s condition for their recovery. Fish is known to be important source of protein rich in essential amino acids. However, there is very little information about changes in amino acids composition of European eels with increase in size. The aim of this study was to evaluate the effect of two different weight categories on the amino acids content in muscle tissue of wild European eels. European eels were caught in River Ulla (Galicia, NW Spain), during winter. The eels were slaughtered in ice water immersion. Then, they were purchased and transferred to the laboratory. The eels were subdivided into two groups, according to the weight. The samples were kept frozen (-20 °C) until their analysis. Frozen eels were defrosted and the white muscle between the head and the anal hole. was extracted, in order to obtain amino acids composition. Thirty eels for each group were used. Liquid chromatography was used for separation and quantification of amino a cids. The results conclude that the eels are rich in glutamic acid, leucine, lysine, threonine, valine, isoleucine and phenylalanine. The analysis showed that there are significant differences (p < 0.05) among the eels with different sizes. Histidine, threonine, lysine, hydroxyproline, serine, glycine, arginine, alanine and proline were higher in small eels. European eels muscle presents between 45 and 46% of essential amino acids in the total amino acids. European eels have a well-balanced and high quality protein source in the respect of E/NE ratio. However, eels with higher weight showed a better ratio of essential and non-essential amino acid.

Keywords: European eels, amino acids, HPLC, body size

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Authors: Francis C. Emejeamara, Alison S. Tomlin, James Gooding

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Due to the highly turbulent nature of urban air flows, and by virtue of the fact that turbines are likely to be located within the roughness sublayer of the urban boundary layer, proposed urban wind installations are faced with major challenges compared to rural installations. The challenge of operating within turbulent winds can however, be counteracted by the development of suitable gust tracking solutions. In order to assess the cost effectiveness of such controls, a detailed understanding of the urban wind resource, including its turbulent characteristics, is required. Estimating the ambient turbulence and total kinetic energy available at different control response times is essential in evaluating the potential performance of wind systems within the urban environment should effective control solutions be employed. However, high resolution wind measurements within the urban roughness sub-layer are uncommon, and detailed CFD modelling approaches are too computationally expensive to apply routinely on a city wide scale. This paper therefore presents an alternative semi-empirical methodology for estimating the excess energy content (EEC) present in the complex and gusty urban wind. An analytical methodology for predicting the total wind energy available at a potential turbine site is proposed by assessing the relationship between turbulence intensities and EEC, for different control response times. The semi-empirical model is then incorporated with an analytical methodology that was initially developed to predict mean wind speeds at various heights within the built environment based on detailed mapping of its aerodynamic characteristics. Based on the current methodology, additional estimates of turbulence intensities and EEC allow a more complete assessment of the available wind resource. The methodology is applied to 4 UK cities with results showing the potential of mapping turbulence intensities and the total wind energy available at different heights within each city. Considering the effect of ambient turbulence and choice of wind system, the wind resource over neighbourhood regions (of 250 m uniform resolution) and building rooftops within the 4 cities were assessed with results highlighting the promise of mapping potential turbine sites within each city.

Keywords: excess energy content, small-scale wind, turbulence intensity, urban wind energy, wind resource assessment

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Authors: Amr Mohamadien, Ali Imanpour, Sylvester Agbo, Nader Yoosef-Ghodsi, Samer Adeeb

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The fracture toughness resistance curve (e.g., J-R curve and crack tip opening displacement (CTOD) or δ-R curve) is important in facilitating strain-based design and integrity assessment of oil and gas pipelines. This paper aims to present laboratory experimental data to characterize the fracture behavior of pipeline steel. The influential parameters associated with the fracture of API 5L X52 pipeline steel, including different initial crack sizes, were experimentally investigated for a single notch edge bend (SENB). A total of 9 small-scale specimens with different crack length to specimen depth ratios were conducted and tested using single edge notch bending (SENB). ASTM E1820 and BS7448 provide testing procedures to construct the fracture resistance curve (Load-CTOD, CTOD-R, or J-R) from test results. However, these procedures are limited by standard specimens’ dimensions, displacement gauges, and calibration curves. To overcome these limitations, this paper presents the use of small-scale specimens and a 3D-digital image correlation (DIC) system to extract the parameters required for fracture toughness estimation. Fracture resistance curve parameters in terms of crack mouth open displacement (CMOD), crack tip opening displacement (CTOD), and crack growth length (∆a) were carried out from test results by utilizing the DIC system, and an improved regression fitting resistance function (CTOD Vs. crack growth), or (J-integral Vs. crack growth) that is dependent on a variety of initial crack sizes was constructed and presented. The obtained results were compared to the available results of the classical physical measurement techniques, and acceptable matchings were observed. Moreover, a case study was implemented to estimate the maximum strain value that initiates the stable crack growth. This might be of interest to developing more accurate strain-based damage models. The results of laboratory testing in this study offer a valuable database to develop and validate damage models that are able to predict crack propagation of pipeline steel, accounting for the influential parameters associated with fracture toughness.

Keywords: fracture toughness, crack propagation in pipeline steels, CTOD-R, strain-based damage model

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Authors: Abdullah Almilaibary, Jeremy Jolley, Mark Hayter

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Background: Despite recent progress in enhancing the accessibility of HIV-related health services worldwide, opportunities to diagnose patients are often missed due to genuine barriers at different levels. The aim of the study is to explore the factors that affect the utilization of HIV testing services by young people aged 17-25 in Saudi Arabia. Methods: A non-experimental descriptive cross-sectional design was used to predict factors that influenced HIV testing among Umm- Al Qura University students aged 17-25 years. A newly developed self-completed online questionnaire was used and the study sample was drawn using a convenience sampling technique. The questionnaire consisted of 52 items divided into three scales: 12 items for HIV/AIDS-related knowledge, 3 items for risk perception, and 37 items for attitudes toward HIV testing. Five experts in the field of HIV/AIDS validated the contents of the questionnaire and agreed that the items included were related to the construct being measured. The reliability of the questionnaire was also assessed using a test/re-test strategy with 27 participants recruited from the population under study. The reliability assessment revealed that the questionnaire was consistent as Cronbach’s Alpha was 0.80 for HIV/ADS knowledge, 0.88 for risk perception and 0.78 for attitudes towards HIV testing. The data were collected between 14th of July and 14th of October 2014. Results: 394 participants completed the questionnaires: 116 (29.4%) male and 278 (70%) female. 50.5% of the participants were aged 20 to 22 years, 34.8% were 17-19 years and 14.7% were aged between 23-25 years; about 93% of the participants were single. Only 20 (6%) participants had previously been tested for HIV. The main reasons for not being tested for HIV were: exposure to HIV was considered unlikely (48%), HIV test was not offered (36%) and unawareness of HIV testing centres (16%). On HIV/AIDS-related knowledge, the male participants scored higher than the females as the mean score for males was (M = 6.4, SD = 2.4) while for females it was (M 5.7, SD 2.5). In terms of risk perception, female participants appeared to have lower levels of risk perception than male participants, with the mean score for males being (M 11.7, SD 2.5) and (M 10.5, SD 2.4) for females. The female participants showed slightly more positive attitudes towards HIV testing than male participants: the mean score for males was (M = 108.14, SD = 17.9) and was (M = 111.32, SD = 17.3) for females. Conclusions: The data reveal that misconceptions about HIV/AIDS in Saudi Arabia are still a challenge. Although the attitudes towards HIV testing were reasonably positive, the utilization of the HIV test was low. Thus, tailoring HIV/AIDS preventive strategies in Saudi Arabia should focus on the needs of young people and other high risk groups in the country.

Keywords: attitude toward hiv testing, hiv testing, hiv/aids related knowledge, risk perception

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Authors: Moustafa Osman Mohammed

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Construction industry, as one of the main contributor in depletion of natural resources, influences climate change. This paper discusses incremental and evolutionary development of the proposed models for optimization of a life-cycle analysis to explicit strategy for evaluation systems. The main categories are virtually irresistible for introducing uncertainties, uptake composite structure model (CSM) as environmental management systems (EMSs) in a practice science of evaluation small and medium-sized enterprises (SMEs). The model simplified complex systems to reflect nature systems’ input, output and outcomes mode influence “framework measures” and give a maximum likelihood estimation of how elements are simulated over the composite structure. The traditional knowledge of modeling is based on physical dynamic and static patterns regarding parameters influence environment. It unified methods to demonstrate how construction systems ecology interrelated from management prospective in procedure reflects the effect of the effects of engineering systems to ecology as ultimately unified technologies in extensive range beyond constructions impact so as, - energy systems. Sustainability broadens socioeconomic parameters to practice science that meets recovery performance, engineering reflects the generic control of protective systems. When the environmental model employed properly, management decision process in governments or corporations could address policy for accomplishment strategic plans precisely. The management and engineering limitation focuses on autocatalytic control as a close cellular system to naturally balance anthropogenic insertions or aggregation structure systems to pound equilibrium as steady stable conditions. Thereby, construction systems ecology incorporates engineering and management scheme, as a midpoint stage between biotic and abiotic components to predict constructions impact. The later outcomes’ theory of environmental obligation suggests either a procedures of method or technique that is achieved in sustainability impact of construction system ecology (SICSE), as a relative mitigation measure of deviation control, ultimately.

Keywords: sustainability, environmental impact assessment, environemtal management, construction ecology

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Authors: Orkide Donma, Mustafa M. Donma

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Morbid obesity is a health threatening condition particularly in children. Generally, it leads to the development of metabolic syndrome (MetS) characterized by central obesity, elevated fasting blood glucose (FBG), triglyceride (TRG), blood pressure values and suppressed high density lipoprotein cholesterol (HDL-C) levels. However, some ambiguities exist during the diagnosis of MetS in children below 10 years of age. Therefore, clinicians are in the need of some surrogate markers for the laboratory assessment of pediatric MetS. In this study, the aim is to develop an index, which will be more helpful during the evaluation of further risks detected in morbid obese (MO) children. A total of 235 children with normal body mass index (N-BMI), with varying degrees of obesity; overweight (OW), obese (OB), MO as well as MetS participated in this study. The study was approved by the Institutional Ethical Committee. Informed consent forms were obtained from the parents of the children. Obesity states of the children were classified using BMI percentiles adjusted for age and sex. For the purpose, tabulated data prepared by WHO were used. MetS criteria were defined. Systolic and diastolic blood pressure values were measured. Parameters related to glucose and lipid metabolisms were determined. FBG, insulin (INS), HDL-C, TRG concentrations were determined. Diagnostic Obesity Notation Model Assessment Laboratory (DONMA_LAB) Index [ln TRG/HDL-C*INS] was introduced. Commonly used insulin resistance (IR) indices such as Homeostatic Model Assessment for IR (HOMA-IR) as well as ratios such as TRG/HDL-C, TRG/HDL-C*INS, HDL-C/TRG*INS, TRG/HDL-C*INS/FBG, log, and ln versions of these ratios were calculated. Results were interpreted using statistical package program (SPSS Version 16.0) for Windows. The data were evaluated using appropriate statistical tests. The degree for statistical significance was defined as 0.05. 35 N, 20 OW, 47 OB, 97 MO children and 36 with MetS were investigated. Mean ± SD values of TRG/HDL-C were 1.27 ± 0.69, 1.86 ± 1.08, 2.15 ± 1.22, 2.48 ± 2.35 and 4.61 ± 3.92 for N, OW, OB, MO and MetS children, respectively. Corresponding values for the DONMA_LAB index were 2.17 ± 1.07, 3.01 ± 0.94, 3.41 ± 0.93, 3.43 ± 1.08 and 4.32 ± 1.00. TRG/HDL-C ratio significantly differed between N and MetS groups. On the other hand, DONMA_LAB index exhibited statistically significant differences between N and all the other groups except the OW group. This index was capable of discriminating MO children from those with MetS. Statistically significant elevations were detected in MO children with MetS (p < 0.05). Multiple parameters are commonly used during the assessment of MetS. Upon evaluation of the values obtained for N, OW, OB, MO groups and for MO children with MetS, the [ln TRG/HDL-C*INS] value was unique in discriminating children with MetS.

Keywords: children, index, laboratory, metabolic syndrome, obesity

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Authors: Natalia Alonso-Alberca, Ana I. Vergara

Abstract:

From the very first years of their life, children are confronted with situations in which they need to deal with emotions. The family provides the first emotional experiences, and it is in the family context that children usually take their first steps towards acquiring emotion knowledge. Parents play a key role in this important task, helping their children develop emotional skills that they will need in challenging situations throughout their lives. Specifically, mothers are models imitated by their children. They create specific spatial and temporal contexts in which children learn about emotions, their causes, consequences, and complexity. This occurs not only through what mothers say or do directly to the child. Rather, it occurs, to a large extent, through the example that they set using their own emotional skills. The aim of the current study was to analyze how maternal abilities to perceive and to manage emotions influence children’s emotion knowledge, specifically, their emotion situation knowledge, taking into account the role played by the mother’s personality, the time spent together, and controlling the effect of age, sex and the child’s verbal abilities. Participants were 153 children from 4 schools in Spain, and their mothers. Children (41.8% girls)age range was 35 - 72 months. Mothers (N = 140) age (M = 38.7; R = 27-49). Twelve mothers had more than one child participating in the study. Main variables were the child´s emotion situation knowledge (ESK), measured by the Emotion Matching Task (EMT), and receptive language, using the Picture Vocabulary Test. Also, their mothers´ Emotional Intelligence (EI), through the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT) and personality, with The Big Five Inventory were analyzed. The results showed that the predictive power of maternal emotional skills on ESK was moderated by the mother’s personality, affecting both the direction and size of the relationships detected: low neuroticism and low openness to experience lead to a positive influence of maternal EI on children’s ESK, while high levels in these personality dimensions resulted in a negative influence on child´s ESK. The time that the mother and the child spend together was revealed as a positive predictor of this EK, while it did not moderate the influence of the mother's EI on child’s ESK. In light of the results, we can infer that maternal EI is linked to children’s emotional skills, though high level of maternal EI does not necessarily predict a greater degree of emotionknowledge in children, which seems rather to depend on specific personality profiles. The results of the current study indicate that a good level of maternal EI does not guarantee that children will learn the emotional skills that foster prosocial adaptation. Rather, EI must be accompanied by certain psychological characteristics (personality traits in this case).

Keywords: emotional intelligence, emotion situation knowledge, mothers, personality, young children

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