Search results for: prediction modelling

Authors: E. H. Walsh, J. McMahon, M. P. Herring

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Post-primary school-based suicide prevention (PSSP) is a valuable avenue to reduce suicidal behaviours in adolescents. The aims of this meta-analysis and meta-regression were 1) to quantify the effect of PSSP interventions on adolescent suicide ideation (SI) and suicide attempts (SA), and 2) to explore how intervention effects may vary based on important contextual and intervention factors. This study provides further support to the benefits of PSSP by demonstrating lower suicide outcomes in over 30,000 adolescents following PSSP and mental health interventions and tentatively suggests that intervention effectiveness may potentially vary based on intervention factors. The protocol for this study is registered on PROSPERO (ID=CRD42020168883). Population, intervention, comparison, outcomes, and study design (PICOs) defined eligible studies as cluster randomised studies (n=12) containing PSSP and measuring suicide outcomes. Aggregate electronic database EBSCO host, Web of Science, and Cochrane Central Register of Controlled Trials databases were searched. Cochrane bias tools for cluster randomised studies demonstrated that half of the studies were rated as low risk of bias. The Egger’s Regression Test adapted for multi-level modelling indicated that publication bias was not an issue (all ps > .05). Crude and corresponding adjusted pooled log odds ratios (OR) were computed using the Metafor package in R, yielding 12 SA and 19 SI effects. Multi-level random-effects models accounting for dependencies of effects from the same study revealed that in crude models, compared to controls, interventions were significantly associated with 13% (OR=0.87, 95% confidence interval (CI), [0.78,0.96], Q18 =15.41, p=0.63) and 34% (OR=0.66, 95%CI [0.47,0.91], Q10=16.31, p=0.13) lower odds of SI and SA, respectively. Adjusted models showed similar odds reductions of 15% (OR=0.85, 95%CI[0.75,0.95], Q18=10.04, p=0.93) and 28% (OR=0.72, 95%CI[0.59,0.87], Q10=10.46, p=0.49) for SI and SA, respectively. Within-cluster heterogeneity ranged from no heterogeneity to low heterogeneity for SA across crude and adjusted models (0-9%). No heterogeneity was identified for SI across crude and adjusted models (0%). Pre-specified univariate moderator analyses were not significant for SA (all ps < 0.05). Variations in average pooled SA odds reductions across categories of various intervention characteristics were observed (all ps < 0.05), which preliminarily suggests that the effectiveness of interventions may potentially vary across intervention factors. These findings have practical implications for researchers, clinicians, educators, and decision-makers. Further investigation of important logical, theoretical, and empirical moderators on PSSP intervention effectiveness is recommended to establish how and when PSSP interventions best reduce adolescent suicidal behaviour.

Keywords: adolescents, contextual factors, post-primary school-based suicide prevention, suicide ideation, suicide attempts

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Authors: Phillipa Evans, Christopher Trotter

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Aims: This study aims to examine the effectiveness of a practice skills framework implemented in three youth detention centres in Juvenile Justice in New South Wales (NSW), Australia. The study is supported by a grant from and Australian Research Council and NSW Juvenile Justice. Recent years have seen a number of incidents in youth detention centres in Australia and other places. These have led to inquiries and reviews with some suggesting that detention centres often do not even meet basic human rights and do little in terms of providing opportunities for rehabilitation of residents. While there is an increasing body of research suggesting that community based supervision can be effective in reducing recidivism if appropriate skills are used by supervisors, there has been less work considering worker skills in youth detention settings. The research that has been done, however, suggest that teaching interpersonal skills to youth officers may be effective in enhancing the rehabilitation culture of centres. Positive outcomes have been seen in a UK detention centre for example, from teaching staff to do five-minute problem-solving interventions. The aim of this project is to examine the effectiveness of training and coaching youth detention staff in three NSW detention centres in interpersonal practice skills. Effectiveness is defined in terms of reductions in the frequency of critical incidents and improvements in the well-being of staff and young people. The research is important as the results may lead to the development of more humane and rehabilitative experiences for young people. Method: The study involves training staff in core effective practice skills and supporting staff in the use of those skills through supervision and de-briefing. The core effective practice skills include role clarification, pro-social modelling, brief problem solving, and relationship skills. The training also addresses some of the background to criminal behaviour including trauma. Data regarding critical incidents and well-being before and after the program implementation are being collected. This involves interviews with staff and young people, the completion of well-being scales, and examination of departmental records regarding critical incidents. In addition to the before and after comparison a matched control group which is not offered the intervention is also being used. The study includes more than 400 young people and 100 youth officers across 6 centres including the control sites. Data collection includes interviews with workers and young people, critical incident data such as assaults, use of lock ups and confinement and school attendance. Data collection also includes analysing video-tapes of centre activities for changes in the use of staff skills. Results: The project is currently underway with ongoing training and supervision. Early results will be available for the conference.

Keywords: custody, practice skills, training, youth workers

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Authors: Elizabeth Parrott, Harry Pointon, Frederic Bezombes, Heather Panter

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Unmanned aerial vehicles (UAV’s) are making a profound effect within policing, forensic and fire service procedures worldwide. These intelligent devices have already proven useful in photographing and recording large-scale outdoor and indoor sites using orthomosaic and three-dimensional (3D) modelling techniques, for the purpose of capturing and recording sites during and post-incident. UAV’s are becoming an established tool as they are extending the reach of the photographer and offering new perspectives without the expense and restrictions of deploying full-scale aircraft. 3D reconstruction quality is directly linked to the resolution of captured images; therefore, close proximity flights are required for more detailed models. As technology advances deployment of UAVs in confined spaces is becoming more common. With this in mind, this study investigates the effects of UAV operation within active crimes scenes with regard to the dispersal of particulate evidence. To date, there has been little consideration given to the potential effects of using UAV’s within active crime scenes aside from a legislation point of view. Although potentially the technology can reduce the likelihood of contamination by replacing some of the roles of investigating practitioners. There is the risk of evidence dispersal caused by the effect of the strong airflow beneath the UAV, from the downwash of the propellers. The initial results of this study are therefore presented to determine the height of least effect at which to fly, and the commercial propeller type to choose to generate the smallest amount of disturbance from the dataset tested. In this study, a range of commercially available 4-inch propellers were chosen as a starting point due to the common availability and their small size makes them well suited for operation within confined spaces. To perform the testing, a rig was configured to support a single motor and propeller powered with a standalone mains power supply and controlled via a microcontroller. This was to mimic a complete throttle cycle and control the device to ensure repeatability. By removing the variances of battery packs and complex UAV structures to allow for a more robust setup. Therefore, the only changing factors were the propeller and operating height. The results were calculated via computer vision analysis of the recorded dispersal of the sample particles placed below the arm-mounted propeller. The aim of this initial study is to give practitioners an insight into the technology to use when operating within confined spaces as well as recognizing some of the issues caused by UAV’s within active crime scenes.

Keywords: dispersal, evidence, propeller, UAV

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Authors: Arindam Chaudhuri

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Classification of data has been actively used for most effective and efficient means of conveying knowledge and information to users. The prima face has always been upon techniques for extracting useful knowledge from data such that returns are maximized. With emergence of huge datasets the existing classification techniques often fail to produce desirable results. The challenge lies in analyzing and understanding characteristics of massive data sets by retrieving useful geometric and statistical patterns. We propose a supervised parallel fuzzy rough support vector machine (PFRSVM) for data classification in cloud environment. The classification is performed by PFRSVM using hyperbolic tangent kernel. The fuzzy rough set model takes care of sensitiveness of noisy samples and handles impreciseness in training samples bringing robustness to results. The membership function is function of center and radius of each class in feature space and is represented with kernel. It plays an important role towards sampling the decision surface. The success of PFRSVM is governed by choosing appropriate parameter values. The training samples are either linear or nonlinear separable. The different input points make unique contributions to decision surface. The algorithm is parallelized with a view to reduce training times. The system is built on support vector machine library using Hadoop implementation of MapReduce. The algorithm is tested on large data sets to check its feasibility and convergence. The performance of classifier is also assessed in terms of number of support vectors. The challenges encountered towards implementing big data classification in machine learning frameworks are also discussed. The experiments are done on the cloud environment available at University of Technology and Management, India. The results are illustrated for Gaussian RBF and Bayesian kernels. The effect of variability in prediction and generalization of PFRSVM is examined with respect to values of parameter C. It effectively resolves outliers’ effects, imbalance and overlapping class problems, normalizes to unseen data and relaxes dependency between features and labels. The average classification accuracy for PFRSVM is better than other classifiers for both Gaussian RBF and Bayesian kernels. The experimental results on both synthetic and real data sets clearly demonstrate the superiority of the proposed technique.

Keywords: FRSVM, Hadoop, MapReduce, PFRSVM

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Authors: N. C. van der Merwe, J. Oosthuizen, M. F. Makhetha, J. Adams, B. K. Dajee, S-R. Schneider

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Women representing high-risk breast cancer families, who tested negative for pathogenic mutations in BRCA1 and BRCA2, are four times more likely to develop breast cancer compared to women in the general population. Sequencing of genes involved in genomic stability and DNA repair led to the identification of novel contributors to familial breast cancer risk. These include BLM and PALB2. Bloom's syndrome is a rare homozygous autosomal recessive chromosomal instability disorder with a high incidence of various types of neoplasia and is associated with breast cancer when in a heterozygous state. PALB2, on the other hand, binds to BRCA2 and together, they partake actively in DNA damage repair. Archived DNA samples of 66 BRCA1/2 negative high-risk breast cancer patients were retrospectively selected based on the presence of an extensive family history of the disease ( > 3 affecteds per family). All coding regions and splice-site boundaries of both genes were screened using High-Resolution Melting Analysis. Samples exhibiting variation were bi-directionally automated Sanger sequenced. The clinical significance of each variant was assessed using various in silico and splice site prediction algorithms. Comprehensive screening identified a total of 11 BLM and 26 PALB2 variants. The variants detected ranged from global to rare and included three novel mutations. Three BLM and two PALB2 likely pathogenic mutations were identified that could account for the disease in these extensive breast cancer families in the absence of BRCA mutations (BLM c.11T > A, p.V4D; BLM c.2603C > T, p.P868L; BLM c.3961G > A, p.V1321I; PALB2 c.421C > T, p.Gln141Ter; PALB2 c.508A > T, p.Arg170Ter). Conclusion: The study confirmed the contribution of pathogenic mutations in BLM and PALB2 to the familial breast cancer burden in South Africa. It explained the presence of the disease in 7.5% of the BRCA1/2 negative families with an extensive family history of breast cancer. Segregation analysis will be performed to confirm the clinical impact of these mutations for each of these families. These results justify the inclusion of both these genes in a comprehensive breast and ovarian next generation sequencing cancer panel and should be screened simultaneously with BRCA1 and BRCA2 as it might explain a significant percentage of familial breast and ovarian cancer in South Africa.

Keywords: Bloom Syndrome, familial breast cancer, PALB2, South Africa

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Authors: Narendra Akhadkar, Philippe Delcambre

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In power protection and control products, assembly process variations due to the individual parts manufactured from single or multi-cavity tooling is a major problem. The dimensional and geometrical variations on the individual parts, in the form of manufacturing tolerances and assembly tolerances, are sources of clearance in the kinematic joints, polarization effect in the joints, and tolerance stack-up. All these variations adversely affect the quality of product, functionality, cost, and time-to-market. Variation simulation analysis may be used in the early product design stage to predict such uncertainties. Usually, variations exist in both manufacturing processes and materials. In the tolerance analysis, the effect of the dimensional and geometrical variations of the individual parts on the functional characteristics (conditions) of the final assembled products are studied. A functional characteristic of the product may be affected by a set of interrelated dimensions (functional parameters) that usually form a geometrical closure in a 3D chain. In power protection and control products, the prerequisite is: when a fault occurs in the electrical network, the product must respond quickly to react and break the circuit to clear the fault. Usually, the response time is in milliseconds. Any failure in clearing the fault may result in severe damage to the equipment or network, and human safety is at stake. In this article, we have investigated two important functional characteristics that are associated with the robust performance of the product. It is demonstrated that the experimental data obtained at the Schneider Electric Laboratory prove the very good prediction capabilities of the variation simulation performed using CETOL (tolerance analysis software) in an industrial context. Especially, this study allows design engineers to better understand the critical parts in the product that needs to be manufactured with good, capable tolerances. On the contrary, some parts are not critical for the functional characteristics (conditions) of the product and may lead to some reduction of the manufacturing cost, ensuring robust performance. The capable tolerancing is one of the most important aspects in product and manufacturing process design. In the case of miniature circuit breaker (MCB), the product's quality and its robustness are mainly impacted by two aspects: (1) allocation of design tolerances between the components of a mechanical assembly and (2) manufacturing tolerances in the intermediate machining steps of component fabrication.

Keywords: geometrical variation, product robustness, tolerance analysis, variation simulation

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Authors: Aude Gounelle, Gloria Senfaute, Ludivine Saint-Mard, Thomas Chartier

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The long-term deformation rates of faults are not fully captured by Probabilistic Seismic Hazard Assessment (PSHA). PSHA that use catalogues to develop area or smoothed-seismicity sources is limited by the data available to constraint future earthquakes activity rates. The integration of faults in PSHA can at least partially address the long-term deformation. However, careful treatment of fault sources is required, particularly, in low strain rate regions, where estimated seismic hazard levels are highly sensitive to assumptions concerning fault geometry, segmentation and slip rate. When integrating faults in PSHA various constraints on earthquake rates from geologic and seismologic data have to be satisfied. For low strain rate regions where such data is scarce it would be especially challenging. Faults in PSHA requires conversion of the geologic and seismologic data into fault geometries, slip rates and then into earthquake activity rates. Several approaches exist for translating slip rates into earthquake activity rates. In the most frequently used approach, the background earthquakes are handled using a truncated approach, in which earthquakes with a magnitude lower or equal to a threshold magnitude (Mw) occur in the background zone, with a rate defined by the rate in the earthquake catalogue. Although magnitudes higher than the threshold are located on the fault with a rate defined using the average slip rate of the fault. As high-lighted by several research, seismic events with magnitudes stronger than the selected magnitude threshold may potentially occur in the background and not only at the fault, especially in regions of slow tectonic deformation. It also has been known that several sections of a fault or several faults could rupture during a single fault-to-fault rupture. It is then essential to apply a consistent modelling procedure to allow for a large set of possible fault-to-fault ruptures to occur aleatory in the hazard model while reflecting the individual slip rate of each section of the fault. In 2019, a tool named SHERIFS (Seismic Hazard and Earthquake Rates in Fault Systems) was published. The tool is using a methodology to calculate the earthquake rates in a fault system where the slip-rate budget of each fault is conversed into rupture rates for all possible single faults and faultto-fault ruptures. The objective of this paper is to compare the SHERIFS method with one other frequently used model to analyse the impact on the seismic hazard and through sensibility studies better understand the influence of key parameters and assumptions. For this application, a simplified but realistic case study was selected, which is in an area of moderate to hight seismicity (South Est of France) and where the fault is supposed to have a low strain.

Keywords: deformation rates, faults, probabilistic seismic hazard, PSHA

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Authors: Hanwei Gao, Louis Jezequel, Eric Cabrol, Bernard Vitry

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The chassis system is composed of complex elements that take up all the loads from the tire-ground contact area and thus it plays an important role in numerous specifications such as durability, comfort, crash, etc. During the development of new vehicle projects in Renault, durability validation is always the main focus while deployment of comfort comes later in the project. Therefore, sometimes design choices have to be reconsidered because of the natural incompatibility between these two specifications. Besides, robustness is also an important point of concern as it is related to manufacturing costs as well as the performance after the ageing of components like shock absorbers. In this paper an approach is proposed aiming to realize a multi-objective optimization between chassis endurance and comfort while taking the random factors into consideration. The adaptive-sparse polynomial chaos expansion method (PCE) with Chebyshev polynomial series has been applied to predict responses’ uncertainty intervals of a system according to its uncertain-but-bounded parameters. The approach can be divided into three steps. First an initial design of experiments is realized to build the response surfaces which represent statistically a black-box system. Secondly within several iterations an optimum set is proposed and validated which will form a Pareto front. At the same time the robustness of each response, served as additional objectives, is calculated from the pre-defined parameter intervals and the response surfaces obtained in the first step. Finally an inverse strategy is carried out to determine the parameters’ tolerance combination with a maximally acceptable degradation of the responses in terms of manufacturing costs. A quarter car model has been tested as an example by applying the road excitations from the actual road measurements for both endurance and comfort calculations. One indicator based on the Basquin’s law is defined to compare the global chassis durability of different parameter settings. Another indicator related to comfort is obtained from the vertical acceleration of the sprung mass. An optimum set with best robustness has been finally obtained and the reference tests prove a good robustness prediction of Chebyshev PCE method. This example demonstrates the effectiveness and reliability of the approach, in particular its ability to save computational costs for a complex system.

Keywords: chassis durability, Chebyshev polynomials, multi-objective optimization, polynomial chaos expansion, ride comfort, robust design

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Authors: Mustafa Alhamdi

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Industrial application to classify gamma rays and neutron events is investigated in this study using deep machine learning. The identification using a convolutional neural network and recursive neural network showed a significant improvement in predication accuracy in a variety of applications. The ability to identify the isotope type and activity from spectral information depends on feature extraction methods, followed by classification. The features extracted from the spectrum profiles try to find patterns and relationships to present the actual spectrum energy in low dimensional space. Increasing the level of separation between classes in feature space improves the possibility to enhance classification accuracy. The nonlinear nature to extract features by neural network contains a variety of transformation and mathematical optimization, while principal component analysis depends on linear transformations to extract features and subsequently improve the classification accuracy. In this paper, the isotope spectrum information has been preprocessed by finding the frequencies components relative to time and using them as a training dataset. Fourier transform implementation to extract frequencies component has been optimized by a suitable windowing function. Training and validation samples of different isotope profiles interacted with CdTe crystal have been simulated using Geant4. The readout electronic noise has been simulated by optimizing the mean and variance of normal distribution. Ensemble learning by combing voting of many models managed to improve the classification accuracy of neural networks. The ability to discriminate gamma and neutron events in a single predication approach using deep machine learning has shown high accuracy using deep learning. The paper findings show the ability to improve the classification accuracy by applying the spectrogram preprocessing stage to the gamma and neutron spectrums of different isotopes. Tuning deep machine learning models by hyperparameter optimization of neural network models enhanced the separation in the latent space and provided the ability to extend the number of detected isotopes in the training database. Ensemble learning contributed significantly to improve the final prediction.

Keywords: machine learning, nuclear physics, Monte Carlo simulation, noise estimation, feature extraction, classification

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Authors: Markus Rütten, Olaf Wünsch

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Non-Newtonian fluid properties can change the flow behaviour significantly, its prediction is more difficult when thermal effects come into play. Hence, the focal point of this work is the wake flow behind a heated circular cylinder in the laminar vortex shedding regime for thermo-viscous shear thinning fluids. In the case of isothermal flows of Newtonian fluids the vortex shedding regime is characterised by a distinct Reynolds number and an associated Strouhal number. In the case of thermo-viscous shear thinning fluids the flow regime can significantly change in dependence of the temperature of the viscous wall of the cylinder. The Reynolds number alters locally and, consequentially, the Strouhal number globally. In the present CFD study the temperature dependence of the Reynolds and Strouhal number is investigated for the flow of a Carreau fluid around a heated cylinder. The temperature dependence of the fluid viscosity has been modelled by applying the standard Williams-Landel-Ferry (WLF) equation. In the present simulation campaign thermal boundary conditions have been varied over a wide range in order to derive a relation between dimensionless heat transfer, Reynolds and Strouhal number. Together with the shear thinning due to the high shear rates close to the cylinder wall this leads to a significant decrease of viscosity of three orders of magnitude in the nearfield of the cylinder and a reduction of two orders of magnitude in the wake field. Yet the shear thinning effect is able to change the flow topology: a complex K´arm´an vortex street occurs, also revealing distinct characteristic frequencies associated with the dominant and sub-dominant vortices. Heating up the cylinder wall leads to a delayed flow separation and narrower wake flow, giving lesser space for the sequence of counter-rotating vortices. This spatial limitation does not only reduce the amplitude of the oscillating wake flow it also shifts the dominant frequency to higher frequencies, furthermore it damps higher harmonics. Eventually the locally heated wake flow smears out. Eventually, the CFD simulation results of the systematically varied thermal flow parameter study have been used to describe a relation for the main characteristic order parameters.

Keywords: heat transfer, thermo-viscous fluids, shear thinning, vortex shedding

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Authors: Taylan Kabbani, Ekrem Duman

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The design of adaptive systems that take advantage of financial markets while reducing the risk can bring more stagnant wealth into the global market. However, most efforts made to generate successful deals in trading financial assets rely on Supervised Learning (SL), which suffered from various limitations. Deep Reinforcement Learning (DRL) offers to solve these drawbacks of SL approaches by combining the financial assets price "prediction" step and the "allocation" step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with its environment to make optimal decisions through trial and error. In this paper, a continuous action space approach is adopted to give the trading agent the ability to gradually adjust the portfolio's positions with each time step (dynamically re-allocate investments), resulting in better agent-environment interaction and faster convergence of the learning process. In addition, the approach supports the managing of a portfolio with several assets instead of a single one. This work represents a novel DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem, or what is referred to as The Agent Environment as Partially observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. More specifically, we design an environment that simulates the real-world trading process by augmenting the state representation with ten different technical indicators and sentiment analysis of news articles for each stock. We then solve the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which can learn policies in high-dimensional and continuous action spaces like those typically found in the stock market environment. From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of deep reinforcement learning in financial markets over other types of machine learning such as supervised learning and proves its credibility and advantages of strategic decision-making.

Keywords: the stock market, deep reinforcement learning, MDP, twin delayed deep deterministic policy gradient, sentiment analysis, technical indicators, autonomous agent

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Authors: Olga A. Burukina

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Tourism is a complex socio-economic phenomenon, partly regulated by national tourism industries. The sustainable development of tourism in a region, country or in tourist destination depends on a number of factors (political, economic, social, cultural, legal and technological), the understanding and correct interpretation of which is invariably anthropocentric. It is logical that for the successful functioning of a tour operating company, it is necessary to ensure its sustainable development. Sustainable tourism is defined as tourism that fully considers its current and future economic, social and environmental impacts, taking into account the needs of the industry, the environment and the host communities. For the business enterprise, sustainable development is defined as adopting business strategies and activities that meet the needs of the enterprise and its stakeholders today while protecting, sustaining and enhancing the human and natural resources that will be needed in the future. In addition to a systemic approach to the analysis of tourist destinations, each tourism project can and should be considered as a system characterized by a very high degree of variability, since each particular case of its implementation differs from the previous and subsequent ones, sometimes in a cardinal way. At the same time, it is important to understand that this variability is predominantly of anthropogenic nature (except for force majeure situations that are considered separately and afterwards). Knowledge management is the process of creating, sharing, using and managing the knowledge and information of an organization. It refers to a multidisciplinary approach to achieve organisational objectives by making the best use of knowledge. Knowledge management is seen as a key systems component that allows obtaining, storing, transferring, and maintaining information and knowledge in particular, in a long-term perspective. The study aims, firstly, to identify (1) the dynamic changes in the Italian travel industry in the last 5 years before the COVID19 pandemic, which can be considered the scope of force majeure circumstances, (2) the impact of the pandemic on the industry and (3) efforts required to restore it, and secondly, how project management tools can help to improve knowledge management in tour operating companies to maintain their sustainability, diminish potential risks and restore their pre-pandemic performance level as soon as possible. The pilot research is based upon a systems approach and has employed a pilot survey, semi-structured interviews, prior research analysis (aka literature review), comparative analysis, cross-case analysis, and modelling. The results obtained are very encouraging: PM tools can improve knowledge management in tour operating companies and secure the more sustainable development of the Italian tourism industry based on proper knowledge management and risk management.

Keywords: knowledge management, project management, sustainable development, tourism industr

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Authors: Malika Souada, Juanita Rausch, Benjamin Guinot, Christine Bugajny

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The increase of global commerce and tourism makes the shipping sector an important contributor of atmospheric pollution. Both, airborne particles and gaseous pollutants have negative impact on health and climate. This is especially the case in port cities, due to the proximity of the exposed population to the shipping emissions in addition to other multiple sources of pollution linked to the surrounding urban activity. The objective of this study is to determine the concentrations of fine particles (immission), specifically PM2.5, PM1, PM0.3, BC and sulphates, in a context where maritime passenger traffic plays an important role (port area of Bordeaux centre). The methodology is based on high temporal resolution measurements of pollutants, correlated with meteorological and ship movements data. Particles and gaseous pollutants from seven maritime passenger ships were sampled and analysed during the docking, manoeuvring and berthing phases. The particle mass measurements were supplemented by measurements of the number concentration of ultrafine particles (<300 nm diameter). The different measurement points were chosen by taking into account the local meteorological conditions and by pre-modelling the dispersion of the smoke plumes. The results of the measurement campaign carried out during the summer of 2021 in the port of Bordeaux show that the detection of concentrations of particles emitted by ships proved to be punctual and stealthy. Punctual peaks of ultrafine particle concentration in number (P#/m3) and BC (ng/m3) were measured during the docking phases of the ships, but the concentrations returned to their background level within minutes. However, it appears that the influence of the docking phases does not significantly affect the air quality of Bordeaux centre in terms of mass concentration. Additionally, no clear differences in PM2.5 concentrations between the periods with and without ships at berth were observed. The urban background pollution seems to be mainly dominated by exhaust and non-exhaust road traffic emissions. However, temporal high-resolution measurements suggest a probable emission of gaseous precursors responsible for the formation of secondary aerosols related to the ship activities. This was evidenced by the high values of the PM1/BC and PN/BC ratios, tracers of non-primary particle formation, during periods of ship berthing vs. periods without ships at berth. The research findings from this study provide robust support for port area air quality assessment and source apportionment.

Keywords: characterization, fine particulate matter, harbour air quality, shipping impacts

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Authors: Ruzanna Muradyan

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Medical data mining is a crucial field in contemporary healthcare that offers cutting-edge tactics with enormous potential to transform patient care. This abstract examines how sophisticated data mining techniques could transform the healthcare industry, with a special focus on how they might improve patient outcomes. Healthcare data repositories have dynamically evolved, producing a rich tapestry of different, multi-dimensional information that includes genetic profiles, lifestyle markers, electronic health records, and more. By utilizing data mining techniques inside this vast library, a variety of prospects for precision medicine, predictive analytics, and insight production become visible. Predictive modeling for illness prediction, risk stratification, and therapy efficacy evaluations are important points of focus. Healthcare providers may use this abundance of data to tailor treatment plans, identify high-risk patient populations, and forecast disease trajectories by applying machine learning algorithms and predictive analytics. Better patient outcomes, more efficient use of resources, and early treatments are made possible by this proactive strategy. Furthermore, data mining techniques act as catalysts to reveal complex relationships between apparently unrelated data pieces, providing enhanced insights into the cause of disease, genetic susceptibilities, and environmental factors. Healthcare practitioners can get practical insights that guide disease prevention, customized patient counseling, and focused therapies by analyzing these associations. The abstract explores the problems and ethical issues that come with using data mining techniques in the healthcare industry. In order to properly use these approaches, it is essential to find a balance between data privacy, security issues, and the interpretability of complex models. Finally, this abstract demonstrates the revolutionary power of modern data mining methodologies in transforming the healthcare sector. Healthcare practitioners and researchers can uncover unique insights, enhance clinical decision-making, and ultimately elevate patient care to unprecedented levels of precision and efficacy by employing cutting-edge methodologies.

Keywords: data mining, healthcare, patient care, predictive analytics, precision medicine, electronic health records, machine learning, predictive modeling, disease prognosis, risk stratification, treatment efficacy, genetic profiles, precision health

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Authors: Md. Fazlul Karim, Ashik Sharfaraz, Aysha Ferdoushi

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Background: Computational medicinal chemistry approaches are used for designing and identifying new drug-like molecules, predicting properties and pharmacological activities, and optimizing lead compounds in drug development. SIRT7, a nicotinamide adenine dinucleotide (NAD+)-dependent deacylase which regulates aging, is an emerging target for cancer therapy with mounting evidence that SIRT7 downregulation plays important roles in reversing cancer phenotypes and suppressing tumor growth. Activation or altered expression of SIRT7 is associated with the progression and invasion of various cancers, including liver, breast, gastric, prostate, and non-small cell lung cancer. Objectives: The goal of this work was to identify potent and selective bioactive candidate inhibitors of SIRT7 by in silico screening of small molecule compounds obtained from Nigella sativa (N. sativa). Methods: SIRT7 structure was retrieved from The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), and its active site was identified using CASTp and metaPocket. Molecular docking simulation was performed with PyRx 0.8 virtual screening software. Drug-likeness properties were tested using SwissADME and pkCSM. In silico toxicity was evaluated by Osiris Property Explorer. Bioactivity was predicted by Molinspiration software. Antitumor activity was screened for Prediction of Activity Spectra for Substances (PASS) using Way2Drug web server. Molecular dynamics (MD) simulation was carried out by Desmond v3.6 package. Results: A total of 159 bioactive compounds from the N. Sativa were screened against the SIRT7 enzyme. Five bioactive compounds: chrysin (CID:5281607), pinocembrin (CID:68071), nigellidine (CID:136828302), nigellicine (CID:11402337), and epicatechin (CID:72276) were identified as potent SIRT7 anti-cancer candidates after docking score evaluation and applying Lipinski's Rule of Five. Finally, MD simulation identified Chrysin as the top SIRT7 anti-cancer candidate molecule. Conclusion: Chrysin, which shows a potential inhibitory effect against SIRT7, can act as a possible anti-cancer drug candidate. This inhibitor warrants further evaluation to check its pharmacokinetics and pharmacodynamics properties both in vitro and in vivo.

Keywords: SIRT7, antitumor, molecular docking, molecular dynamics simulation

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Authors: Christian Ihwiwhu, Nnamdi Obioha, Udeme John, Edward Bobade, Oghenerunor Bekibele, Adedeji Awujoola, Ibi-Ada Itotoi

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Understanding the complexity in the distribution of hydrocarbon in a simple structure with flow baffles and connectivity issues is critical in targeting and developing the remaining pay in a mature asset. Subtle facies changes (heterogeneity) can have a drastic impact on reservoir fluids movement, and this can be crucial to identifying sweet spots in mature fields. This study aims to evaluate selected reservoirs in Ovhor Field, Niger Delta, Nigeria, with the objective of optimising production from the field by targeting undeveloped oil reserves, bypassed pay, and gaining an improved understanding of the selected reservoirs to increase the company’s reservoir limits. The task at the Ovhor field is complicated by poor stratigraphic seismic resolution over the field. 3-D geological (sedimentology and stratigraphy) interpretation, use of results from quantitative interpretation, and proper understanding of production data have been used in recognizing flow baffles and undeveloped compartments in the field. The full field 3-D model has been constructed in such a way as to capture heterogeneities and the various compartments in the field to aid the proper simulation of fluid flow in the field for future production prediction, proper history matching and design of good trajectories to adequately target undeveloped oil in the field. Reservoir property models (porosity, permeability, and net-to-gross) have been constructed by biasing log interpreted properties to a defined environment of deposition model whose interpretation captures the heterogeneities expected in the studied reservoirs. At least, two scenarios have been modelled for most of the studied reservoirs to capture the range of uncertainties we are dealing with. The total original oil in-place volume for the four reservoirs studied is 157 MMstb. The cumulative oil and gas production from the selected reservoirs are 67.64 MMstb and 9.76 Bscf respectively, with current production rate of about 7035 bopd and 4.38 MMscf/d (as at 31/08/2019). Dynamic simulation and production forecast on the 4 reservoirs gave an undeveloped reserve of about 3.82 MMstb from two (2) identified oil restoration activities. These activities include side-tracking and re-perforation of existing wells. This integrated approach led to the identification of bypassed oil in some areas of the selected reservoirs and an improved understanding of the studied reservoirs. New wells have/are being drilled now to test the results of our studies, and the results are very confirmatory and satisfying.

Keywords: facies, flow baffle, bypassed pay, heterogeneities, history matching, reservoir limit

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Authors: Ali H. Daraji, Jack M. Hale, Ye Jianqiao

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With the extensive use of high specific strength structures to optimise the loading capacity and material cost in aerospace and most engineering applications, much effort has been expended to develop intelligent structures for active vibration reduction and structural health monitoring. These structures are highly flexible, inherently low internal damping and associated with large vibration and long decay time. The modification of such structures by adding lightweight piezoelectric sensors and actuators at efficient locations integrated with an optimal control scheme is considered an effective solution for structural vibration monitoring and controlling. The size and location of sensor and actuator are important research topics to investigate their effects on the level of vibration detection and reduction and the amount of energy provided by a controller. Several methodologies have been presented to determine the optimal location of a limited number of sensors and actuators for small-scale structures. However, these studies have tackled this problem directly, measuring the fitness function based on eigenvalues and eigenvectors achieved with numerous combinations of sensor/actuator pair locations and converging on an optimal set using heuristic optimisation techniques such as the genetic algorithms. This is computationally expensive for small- and large-scale structures subject to optimise a number of s/a pairs to suppress multiple vibration modes. This paper proposes an efficient method to determine optimal locations for a limited number of sensor/actuator pairs for active vibration reduction of a flexible structure based on finite element method and Hamilton’s principle. The current work takes the simplified approach of modelling a structure with sensors at all locations, subjecting it to an external force to excite the various modes of interest and noting the locations of sensors giving the largest average percentage sensors effectiveness measured by dividing all sensor output voltage over the maximum for each mode. The methodology was implemented for a cantilever plate under external force excitation to find the optimal distribution of six sensor/actuator pairs to suppress the first six modes of vibration. It is shown that the results of the optimal sensor locations give good agreement with published optimal locations, but with very much reduced computational effort and higher effectiveness. Furthermore, it is shown that collocated sensor/actuator pairs placed in these locations give very effective active vibration reduction using optimal linear quadratic control scheme.

Keywords: optimisation, plate, sensor effectiveness, vibration control

Procedia PDF Downloads 214

Authors: Sandra Smart-Akande, Chaminda Hewage, Imtiaz Khan, Thanuja Mallikarachchi

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According to UK police-recorded data, there has been a substantial increase in knife-related crime and youth violence in the UK since 2014 particularly in the London boroughs. These crime rates are disproportionally distributed across London with the majority of these crimes occurring in the highly deprived areas of London and among young people aged 11 to 24 with large discrepancies across ethnicity, age, gender and borough of residence. Comprehensive studies and literature have identified risk factors associated with a knife carrying among youth to be Adverse Childhood Experience (ACEs), poor mental health, school or social exclusion, drug dealing, drug using, victim of violent crime, bullying, peer pressure or gang involvement, just to mention a few. ACEs are potentially traumatic events that occur in childhood, this can be experiences or stressful events in the early life of a child and can lead to an increased risk of damaging health or social outcomes in the latter life of the individual. Research has shown that children or youths involved in youth violence have had childhood experience characterised by disproportionate adverse childhood experiences and substantial literature link ACEs to be associated with criminal or delinquent behavior. ACEs are commonly grouped by researchers into: Abuse (Physical, Verbal, Sexual), Neglect (Physical, Emotional) and Household adversities (Mental Illness, Incarcerated relative, Domestic violence, Parental Separation or Bereavement). To the author's best knowledge, no study to date has investigated how household mental health (mental health of a parent or mental health of a child) and domestic violence (domestic violence on a parent or domestic violence on a child) is related to knife homicides across the local authorities areas of London. This study seeks to address the gap by examining a large sample of data from the London Metropolitan Police Force and Characteristics of Children in Need data from the UK Department for Education. The aim of this review is to identify and synthesise evidence from data and a range of literature to identify the relationship between adverse childhood experiences and youth violence in the UK. Understanding the link between ACEs and future outcomes can support preventative action.

Keywords: adverse childhood experiences, domestic violence, mental health, youth violence, prediction analysis, London knife crime

Procedia PDF Downloads 101

Authors: Belcher Fulele, W. A. A. Ddamba

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Study of solvent systems contributes to the understanding of intermolecular interactions that occur in binary mixtures. These interactions involves among others strong dipole-dipole interactions and weak van de Waals interactions which are of significant application in pharmaceuticals, solvent extractions, design of reactors and solvent handling and storage processes. Binary mixtures of solvents can thus be used as a model to interpret thermodynamic behavior that occur in a real solution mixture. Densities of pure DFM, n-alkanes (n-pentane, n-hexane, n-heptane and n-octane) and amides (N-methylformamide, N-ethylformamide, N,N-dimethylformamide and N,N-dimethylacetamide) as well as their [DFM + ((C5-C8) n-alkane or amide)] binary mixtures over the entire composition range, have been reported at temperature 293.15, 298.15 and 303.15 K and atmospheric pressure. These data has been used to derive the thermodynamic properties: the excess molar volume of solution, apparent molar volumes, excess partial molar volumes, limiting excess partial molar volumes, limiting partial molar volumes of each component of a binary mixture. The results are discussed in terms of possible intermolecular interactions and structural effects that occur in the binary mixtures. The variation of excess molar volume with DFM composition for the [DFM + (C5-C7) n-alkane] binary mixture exhibit a sigmoidal behavior while for the [DFM + n-octane] binary system, positive deviation of excess molar volume function was observed over the entire composition range. For each of the [DFM + (C5-C8) n-alkane] binary mixture, the excess molar volume exhibited a fall with increase in temperature. The excess molar volume for each of [DFM + (NMF or NEF or DMF or DMA)] binary system was negative over the entire DFM composition at each of the three temperatures investigated. The negative deviations in excess molar volume values follow the order: DMA > DMF > NEF > NMF. Increase in temperature has a greater effect on component self-association than it has on complex formation between molecules of components in [DFM + (NMF or NEF or DMF or DMA)] binary mixture which shifts complex formation equilibrium towards complex to give a drop in excess molar volume with increase in temperature. The Prigogine-Flory-Patterson model has been applied at 298.15 K and reveals that the free volume is the most important contributing term to the excess experimental molar volume data for [DFM + (n-pentane or n-octane)] binary system. For [DFM + (NMF or DMF or DMA)] binary mixture, the interactional term and characteristic pressure term contributions are the most important contributing terms in describing the sign of experimental excess molar volume. The mixture systems contributed to the understanding of interactions of polar solvents with proteins (amides) with non-polar solvents (alkanes) in biological systems.

Keywords: alkanes, amides, excess thermodynamic parameters, Prigogine-Flory-Patterson model

Procedia PDF Downloads 337

Authors: Benjamin Mauck, Kevin Winter

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The city of Cape Town in South Africa, has shown consistent economic and population growth in the last few decades and that growth is expected to continue to increase into the future. These projected economic and population growth rates are set to place additional pressure on the city’s already strained water supply system. Thus, given Cape Town’s water scarcity, increasing water demands and stressed water supply system, coupled with global awareness around the issues of sustainable development, environmental protection and climate change, alternative water management strategies are required to ensure water is sustainably managed. Water Sensitive Urban Design (WSUD) is an approach to sustainable urban water management that attempts to assign a resource value to all forms of water in the urban context, viz. stormwater, wastewater, potable water and groundwater. WSUD employs a wide range of strategies to improve the sustainable management of urban water such as the water reuse, developing alternative available supply sources, sustainable stormwater management and enhancing the aesthetic and recreational value of urban water. Managed Aquifer Recharge (MAR) is one WSUD strategy which has proven to be a successful reuse strategy in a number of places around the world. MAR is the process where an aquifer is intentionally or artificially recharged, which provides a valuable means of water storage while enhancing the aquifers supply potential. This paper investigates the feasibility of implementing MAR in the sandy, unconfined Cape Flats Aquifer (CFA) in Cape Town. The main objective of the study is to assess if MAR is a viable strategy for stormwater management on the Cape Flats, aiding the prevention or mitigation of the seasonal flooding that occurs on the Cape Flats, while also improving the supply potential of the aquifer. This involves the infiltration of stormwater into the CFA during the wet winter months and in turn, abstracting from the CFA during the dry summer months for fit-for-purpose uses in order to optimise the recharge and storage capacity of the CFA. The fully-integrated MIKE SHE model is used in this study to simulate both surface water and groundwater hydrology. This modelling approach enables the testing of various potential recharge and abstraction scenarios required for implementation of MAR on the Cape Flats. Further MIKE SHE scenario analysis under projected future climate scenarios provides insight into the performance of MAR as a stormwater management strategy under climate change conditions. The scenario analysis using an integrated model such as MIKE SHE is a valuable tool for evaluating the feasibility of the MAR as a stormwater management strategy and its potential to contribute towards improving Cape Town’s water security into the future.

Keywords: managed aquifer recharge, stormwater management, cape flats aquifer, MIKE SHE

Procedia PDF Downloads 230

Authors: Matthias Stange

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Building Information Modelling (BIM) is one of the most promising methods in the building design process and plays an important role in the digitalization of the Architectural, Engineering, and Construction (AEC) Industry. The application of BIM is seen as the key enabler for increasing productivity in the construction industry. The model-based collaboration using the BIM method is intended to significantly reduce cost increases, schedule delays, and quality problems in the planning and construction of buildings. Numerous qualitative studies based on expert interviews support this theory and report perceived benefits from the use of BIM in terms of achieving project objectives related to cost, schedule, and quality. However, there is a large research gap in analysing quantitative data collected from real construction projects regarding the actual benefits of applying BIM based on representative sample size and different application regions as well as different project typologies. In particular, the influence of the project-related BIM maturity level is completely unexplored. This research project examines primary data from 105 construction projects worldwide using quantitative research methods. Projects from the areas of residential, commercial, and industrial construction as well as infrastructure and hydraulic engineering were examined in application regions North America, Australia, Europe, Asia, MENA region, and South America. First, a descriptive data analysis of 6 independent project variables (BIM maturity level, application region, project category, project type, project size, and BIM level) were carried out using statistical methods. With the help of statisticaldata analyses, the influence of the project-related BIM maturity level on 6 dependent project variables (deviation in planning time, deviation in construction time, number of planning collisions, frequency of rework, number of RFIand number of changes) was investigated. The study revealed that most of the benefits of using BIM perceived through numerous qualitative studies have not been confirmed. The results of the examined sample show that the application of BIM did not have an improving influence on the dependent project variables, especially regarding the quality of the planning itself and the adherence to the schedule targets. The quantitative research suggests the conclusion that the BIM planning method in its current application has not (yet) become a recognizable increase in productivity within the planning and construction process. The empirical findings indicate that this is due to the overall low level of BIM maturity in the projects of the examined sample. As a quintessence, the author suggests that the further implementation of BIM should primarily focus on an application-oriented and consistent development of the project-related BIM maturity level instead of implementing BIM for its own sake. Apparently, there are still significant difficulties in the interweaving of people, processes, and technology.

Keywords: AEC-process, building information modeling, BIM maturity level, project results, productivity of the construction industry

Procedia PDF Downloads 59

Authors: Emílie Kučerová, Tereza Veverková, Marina Morozovová, Eva Kudová, Jitka Viktorová

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The blood-brain barrier (BBB) is a key element regulating the transport of substances between the blood and the central nervous system (CNS). The BBB protects the CNS from potentially harmful substances and maintains a suitable environment for nervous activity in the CNS, but at the same time, it represents a significant obstacle to the entry of drugs into the CNS. Pharmacoresistant epilepsy is a form of epilepsy that cannot be suppressed using two (or more) appropriately chosen antiepileptic drugs. In many cases, pharmacoresistant epilepsy is characterized by an increased concentration of efflux pumps on the luminal sides of the endothelial cells that form the BBB and an increased number of drug-metabolizing enzymes in the BBB cells, thereby preventing the effective transport of antiepileptic drugs into the CNS. Currently, a number of scientific groups are focusing on the preparation and improvement of BBB models in vitro in order to study cell interactions or transport mechanisms. However, in pathological conditions such as pharmacoresistant epilepsy, there are changes in BBB structure, and current BBB models are insufficient for related research. Our goal is to develop a suitable BBB model for pharmacoresistant epilepsy in vitro and use it to test the transfer of potential antiepileptic drugs. This model is created by co-culturing immortalized human cerebral microvascular endothelial cells, human vascular pericytes and immortalized human astrocytes. The BBB in vitro is cultivated in the form of a 2D transwell model and the integrity of the barrier is verified by measuring transendothelial electrical resistance (TEER). From the current results, a contact cell arrangement with the cultivation of endothelial cells on the upper side of the insert and the co-cultivation of astrocytes and pericytes on the lower side of the insert is selected as the most promising for BBB model cultivation. The pharmacoresistance of the BBB model is achieved by long-term cultivation of endothelial cells in an increasing concentration of selected antiepileptic drugs, which should lead to increased production of efflux pumps and drug-metabolizing enzymes. The pharmacoresistant BBB model in vitro will be further used for the screening of substances that could act both as antiepileptics and at the same time as inhibitors of efflux pumps in endothelial cells. This project was supported by the Technology Agency of the Czech Republic (TACR), Personalized Medicine: Translational research towards biomedical applications, No. TN02000109 and by the Academy of Sciences of the Czech Republic (AS CR) – grant RVO 61388963.

Keywords: antiepileptic drugs, blood-brain barrier, efflux transporters, pharmacoresistance

Procedia PDF Downloads 43

Authors: Ciro Moreno-Ramirez, Maria Tomas-Rodriguez, Simos A. Evangelou

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A motorcycle's front end links the front wheel to the motorcycle's chassis and has two main functions: the front wheel suspension and the vehicle steering. Up to this date, several suspension systems have been developed in order to achieve the best possible front end behavior, being the telescopic fork the most common one and already subjected to several years of study in terms of its kinematics, dynamics, stability and control. A motorcycle telescopic fork suspension model consists of a couple of outer tubes which contain the suspension components (coil springs and dampers) internally and two inner tubes which slide into the outer ones allowing the suspension travel. The outer tubes are attached to the frame through two triple trees which connect the front end to the main frame through the steering bearings and allow the front wheel to turn about the steering axis. This system keeps the front wheel's displacement in a straight line parallel to the steering axis. However, there exist alternative suspension designs that allow different trajectories of the front wheel with the suspension travel. In this contribution, the authors investigate an alternative front suspension system (Hossack suspension) and its influence on the motorcycle nonlinear dynamics to identify and reduce stability risks that a new suspension systems may introduce in the motorcycle dynamics. Based on an existing high-fidelity motorcycle mathematical model, the front end geometry is modified to accommodate a Hossack suspension system. It is characterized by a double wishbone design that varies the front end geometry on certain maneuverings and, consequently, the machine's behavior/response. It consists of a double wishbone structure directly attached to the chassis. In here, the kinematics of this system and its impact on the motorcycle performance/stability are analyzed and compared to the well known telescopic fork suspension system. The framework of this research is the mathematical modelling and numerical simulation. Full stability analyses are performed in order to understand how the motorcycle dynamics may be affected by the newly introduced front end design. This study is carried out by a combination of nonlinear dynamical simulation and root-loci methods. A modal analysis is performed in order to get a deeper understanding of the different modes of oscillation and how the Hossack suspension system affects them. The results show that different kinematic designs of a double wishbone suspension systems do not modify the general motorcycle's stability. The normal modes properties remain unaffected by the new geometrical configurations. However, these normal modes differ from one suspension system to the other. It is seen that the normal modes behaviour depends on various important dynamic parameters, such as the front frame flexibility, the steering damping coefficient and the centre of mass location.

Keywords: nonlinear mechanical systems, motorcycle dynamics, suspension systems, stability

Procedia PDF Downloads 209

Authors: Claudia García-García, Catalina B. García-García, Román Salmerón-Gómez

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Much economic-environmental research includes the analysis of possible interactions by using Moderated Regression Analysis (MRA), which is a specific application of multiple linear regression analysis. This methodology allows analyzing how the effect of one of the independent variables is moderated by a second independent variable by adding a cross-product term between them as an additional explanatory variable. Due to the very specification of the methodology, the moderated factor is often highly correlated with the constitutive terms. Thus, great multicollinearity problems arise. The appearance of strong multicollinearity in a model has important consequences. Inflated variances of the estimators may appear, there is a tendency to consider non-significant regressors that they probably are together with a very high coefficient of determination, incorrect signs of our coefficients may appear and also the high sensibility of the results to small changes in the dataset. Finally, the high relationship among explanatory variables implies difficulties in fixing the individual effects of each one on the model under study. These consequences shifted to the moderated analysis may imply that it is not worth including an interaction term that may be distorting the model. Thus, it is important to manage the problem with some methodology that allows for obtaining reliable results. After a review of those works that applied the MRA among the ten top journals of the field, it is clear that multicollinearity is mostly disregarded. Less than 15% of the reviewed works take into account potential multicollinearity problems. To overcome the issue, this work studies the possible application of recent methodologies to MRA. Particularly, the raised regression is analyzed. This methodology mitigates collinearity from a geometrical point of view: the collinearity problem arises because the variables under study are very close geometrically, so by separating both variables, the problem can be mitigated. Raise regression maintains the available information and modifies the problematic variables instead of deleting variables, for example. Furthermore, the global characteristics of the initial model are also maintained (sum of squared residuals, estimated variance, coefficient of determination, global significance test and prediction). The proposal is implemented to data from countries of the European Union during the last year available regarding greenhouse gas emissions, per capita GDP and a dummy variable that represents the topography of the country. The use of a dummy variable as the moderator is a special variant of MRA, sometimes called “subgroup regression analysis.” The main conclusion of this work is that applying new techniques to the field can improve in a substantial way the results of the analysis. Particularly, the use of raised regression mitigates great multicollinearity problems, so the researcher is able to rely on the interaction term when interpreting the results of a particular study.

Keywords: multicollinearity, MRA, interaction, raise

Procedia PDF Downloads 82

Authors: Virendra Nath, Vipin Kumar

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Background: TypeII Diabetes mellitus is a foremost health problem worldwide, predisposing to increased mortality and morbidity. Undesirable effects of the current medications have prompted the researcher to develop more potential drug(s) against the disease. The peroxisome proliferator-activated receptors (PPARs) are members of the nuclear receptors family and take part in a vital role in the regulation of metabolic equilibrium. They can induce or repress genes associated with adipogenesis, lipid, and glucose metabolism. Aims: Investigation of PPARα/γ agonistic hits were screened by hierarchical virtual screening followed by molecular dynamics simulation and knowledge-based structure-activity relation (SAR) analysis using approved PPAR α/γ dual agonist. Methods: The PPARα/γ agonistic activity of compounds was searched by using Maestro through structure-based virtual screening and molecular dynamics (MD) simulation application. Virtual screening of nuclear-receptor ligands was done, and the binding modes with protein-ligand interactions of newer entity(s) were investigated. Further, binding energy prediction, Stability studies using molecular dynamics (MD) simulation of PPARα and γ complex was performed with the most promising hit along with the structural comparative analysis of approved PPARα/γ agonists with screened hit was done for knowledge-based SAR. Results and Discussion: The silicone chip-based approach recognized the most capable nine hits and had better predictive binding energy as compared to the reference drug compound (Tesaglitazar). In this study, the key amino acid residues of binding pockets of both targets PPARα/γ were acknowledged as essential and were found to be associated in the key interactions with the most potential dual hit (ChemDiv-3269-0443). Stability studies using molecular dynamics (MD) simulation of PPARα and γ complex was performed with the most promising hit and found root mean square deviation (RMSD) stabile around 2Å and 2.1Å, respectively. Frequency distribution data also revealed that the key residues of both proteins showed maximum contacts with a potent hit during the MD simulation of 20 nanoseconds (ns). The knowledge-based SAR studies of PPARα/γ agonists were studied using 2D structures of approved drugs like aleglitazar, tesaglitazar, etc. for successful designing and synthesis of compounds PPARγ agonistic candidates with anti-hyperlipidimic potential.

Keywords: computational, diabetes, PPAR, simulation

Procedia PDF Downloads 78

Authors: M. Venegas, M. De Vega, N. García-Hernando

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Absorption cooling chillers have received growing attention over the past few decades as they allow the use of low-grade heat to produce the cooling effect. The combination of this technology with solar thermal energy in the summer period can reduce the electricity consumption peak due to air-conditioning. One of the main components, the absorber, is designed for simultaneous heat and mass transfer. Usually, shell and tubes heat exchangers are used, which are large and heavy. Cooling water from a cooling tower is conventionally used to extract the heat released during the absorption and condensation processes. These are clear inconvenient for the generalization of the absorption technology use, limiting its benefits in the contribution to the reduction in CO2 emissions, particularly for the H2O-LiBr solution which can work with low heat temperature sources as provided by solar panels. In the present work a promising new technology is under study, consisting in the use of membrane contactors in adiabatic microchannel mass exchangers. The configuration here proposed consists in one or several modules (depending on the cooling capacity of the chiller) that contain two vapour channels, separated from the solution by adjacent microporous membranes. The solution is confined in rectangular microchannels. A plastic or synthetic wall separates the solution channels between them. The solution entering the absorber is previously subcooled using ambient air. In this way, the need for a cooling tower is avoided. A model of the configuration proposed is developed based on mass and energy balances and some correlations were selected to predict the heat and mass transfer coefficients. The concentration and temperatures along the channels cannot be explicitly determined from the set of equations obtained. For this reason, the equations were implemented in a computer code using Engineering Equation Solver software, EES™. With the aim of minimizing the absorber volume to reduce the size of absorption cooling chillers, the ratio between the cooling power of the chiller and the absorber volume (R) is calculated. Its variation is shown along the solution channels, allowing its optimization for selected operating conditions. For the case considered the solution channel length is recommended to be lower than 3 cm. Maximum values of R obtained in this work are higher than the ones found in optimized horizontal falling film absorbers using the same solution. Results obtained also show the variation of R and the chiller efficiency (COP) for different ambient temperatures and desorption temperatures typically obtained using flat plate solar collectors. The configuration proposed of adiabatic membrane-based absorber using ambient air to subcool the solution is a good technology to reduce the size of the absorption chillers, allowing the use of low temperature solar heat and avoiding the need for cooling towers.

Keywords: adiabatic absorption, air-cooled, membrane, solar thermal energy

Procedia PDF Downloads 264

Authors: Iris Vural Gursel, Jonathan Moncada, Ernst Worrell, Andrea Ramirez

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In order to achieve the transition from a fossil to bio-based economy, biomass needs to replace fossil resources in meeting the world’s energy and chemical needs. This calls for development of biorefinery systems allowing cost-efficient conversion of biomass to chemicals. In biorefinery systems, feedstock is converted to key intermediates called platforms which are converted to wide range of marketable products. The C6 sugars platform stands out due to its unique versatility as precursor for multiple valuable products. Among the different potential routes from C6 sugars to bio-based chemicals, 1,3-butadiene and ε-caprolactam appear to be of great interest. Butadiene is an important chemical for the production of synthetic rubbers, while caprolactam is used in production of nylon-6. In this study, ex-ante techno-economic performance of 1,3-butadiene and ε-caprolactam routes from C6 sugars were assessed. The aim is to provide insight from an early stage of development into the potential of these new technologies, and the bottlenecks and key cost-drivers. Two cases for each product line were analyzed to take into consideration the effect of possible changes on the overall performance of both butadiene and caprolactam production. Conceptual process design for the processes was developed using Aspen Plus based on currently available data from laboratory experiments. Then, operating and capital costs were estimated and an economic assessment was carried out using Net Present Value (NPV) as indicator. Finally, sensitivity analyses on processing capacity and prices was done to take into account possible variations. Results indicate that both processes perform similarly from an energy intensity point of view ranging between 34-50 MJ per kg of main product. However, in terms of processing yield (kg of product per kg of C6 sugar), caprolactam shows higher yield by a factor 1.6-3.6 compared to butadiene. For butadiene production, with the economic parameters used in this study, for both cases studied, a negative NPV (-642 and -647 M€) was attained indicating economic infeasibility. For the caprolactam production, one of the cases also showed economic infeasibility (-229 M€), but the case with the higher caprolactam yield resulted in a positive NPV (67 M€). Sensitivity analysis indicated that the economic performance of caprolactam production can be improved with the increase in capacity (higher C6 sugars intake) reflecting benefits of the economies of scale. Furthermore, humins valorization for heat and power production was considered and found to have a positive effect. Butadiene production was found sensitive to the price of feedstock C6 sugars and product butadiene. However, even at 100% variation of the two parameters, butadiene production remained economically infeasible. Overall, the caprolactam production line shows higher economic potential in comparison to that of butadiene. The results are useful in guiding experimental research and providing direction for further development of bio-based chemicals.

Keywords: bio-based chemicals, biorefinery, C6 sugars, economic analysis, process modelling

Procedia PDF Downloads 137

Authors: Zhihui Liu, Dongmei Hao, Qian Qiu, Yang An, Lin Yang, Song Zhang, Yimin Yang, Xuwen Li, Dingchang Zheng

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Premature birth, defined as birth before 37 completed weeks of gestation is a leading cause of neonatal morbidity and mortality and has long-term adverse consequences for health. It has recently been reported that the worldwide preterm birth rate is around 10%. The existing measurement techniques for diagnosing preterm delivery include tocodynamometer, ultrasound and fetal fibronectin. However, they are subjective, or suffer from high measurement variability and inaccurate diagnosis and prediction of preterm labor. Electrohysterography (EHG) method based on recording of uterine electrical activity by electrodes attached to maternal abdomen, is a promising method to assess uterine activity and diagnose preterm labor. The purpose of this study is to analyze the difference of EHG signal features between term labor and preterm labor. Free access database was used with 300 signals acquired in two groups of pregnant women who delivered at term (262 cases) and preterm (38 cases). Among them, EHG signals from 38 term labor and 38 preterm labor were preprocessed with band-pass Butterworth filters of 0.08–4Hz. Then, EHG signal features were extracted, which comprised classical time domain description including root mean square and zero-crossing number, spectral parameters including peak frequency, mean frequency and median frequency, wavelet packet coefficients, autoregression (AR) model coefficients, and nonlinear measures including maximal Lyapunov exponent, sample entropy and correlation dimension. Their statistical significance for recognition of two groups of recordings was provided. The results showed that mean frequency of preterm labor was significantly smaller than term labor (p < 0.05). 5 coefficients of AR model showed significant difference between term labor and preterm labor. The maximal Lyapunov exponent of early preterm (time of recording < the 26th week of gestation) was significantly smaller than early term. The sample entropy of late preterm (time of recording > the 26th week of gestation) was significantly smaller than late term. There was no significant difference for other features between the term labor and preterm labor groups. Any future work regarding classification should therefore focus on using multiple techniques, with the mean frequency, AR coefficients, maximal Lyapunov exponent and the sample entropy being among the prime candidates. Even if these methods are not yet useful for clinical practice, they do bring the most promising indicators for the preterm labor.

Keywords: electrohysterogram, feature, preterm labor, term labor

Procedia PDF Downloads 546

Authors: Elvis Nyirenda

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This research study work was done to evaluate and propose an optimum measure to enhance the uptake of clean energy technologies such as solar photovoltaics, the study also aims at enhancing the country’s energy mix from the overdependence on hydro power which is susceptible to droughts and climate change challenges The country in the years 2015 - 2016 and 2018 - 2019 had received rainfall below average due to climate change and a shift in the weather pattern; this resulted in prolonged power outages and load shedding for more than 10 hours per day. ZESCO Limited, the utility company that owns infrastructure in the generation, transmission, and distribution of electricity (state-owned), is seeking alternative sources of energy in order to reduce the over-dependence on hydropower stations. One of the alternative sources of energy is Solar Energy from the sun. However, solar power is intermittent in nature and to smoothen the load curve, investment in robust energy storage facilities is of great importance to enhance security and reliability of electricity supply in the country. The methodology of the study looked at the historical performance of the Kafue gorge upper power station and utilised the hourly generation figures as input data for generation modelling in Homer software. The average yearly demand was derived from the available data on the system SCADA. The two dams were modelled as natural battery with the absolute state of charging and discharging determined by the available water resource and the peak electricity demand. The software Homer Energy System is used to simulate the scheme incorporating a pumped storage facility and Solar photovoltaic systems. The pumped hydro scheme works like a natural battery for the conservation of water, with the only losses being evaporation and water leakages from the dams and the turbines. To address the problem of intermittency on the solar resource and the non-availability of water for hydropower generation, the study concluded that utilising the existing Hydro power stations, Kafue Gorge upper and Kafue Gorge Lower to work conjunctively with Solar energy will reduce power deficits and increase the security of supply for the country. An optimum capacity of 350MW of solar PV can be integrated while operating Kafue Gorge power station in both generating and pumping mode to enable efficient utilisation of water at Kafue Gorge upper Dam and Kafue Gorge Lower dam.

Keywords: hydropower, solar power systems, energy storage, photovoltaics, solar irradiation, pumped hydro storage system, supervisory control and data acquisition, Homer energy

Procedia PDF Downloads 95

Authors: Fuseina Mahama, Lieselot Vanhaverbeke

Abstract:

This research investigates the impact of patronage determinants on the distance consumers are willing to travel to patronize a tire shop. Although store patronage has been acknowledged as an important domain and has received substantial research interest, most of the studies so far conducted focus on grocery retail, leaving other categories of goods widely unexplored. In this study, we focus on car tires and provide a new perspective to the specific factors that influence tire shop patronage. An online survey of consumers’ tyre purchasing behaviour was conducted among private car owners in Belgium. A sample of 864 respondents was used in the study, with almost four out of five of them being male. 84% of the respondents had purchased a car tyre in the last 24 months and on average travelled 22.4kms to patronise a tyre shop. We tested the direct and mediated effects of store choice determinants on distance consumers are willing to travel. All hypotheses were tested using Structural Equation Modelling (SEM). Our findings show that with an increase in the consumer’s age the distance they were willing to travel to a tire shop decreased. Similarly, consumers who deemed proximity an important determinant of a tire shop our findings confirmed a negative effect on willingness to travel. On the other hand, the determinants price, personal contact and professionalism all had a positive effect on distance. This means that consumers actively sought out tire shops with these characteristics and were willing to travel longer distances in order to visit them. The indirect effects of the determinants flexible opening hours, family recommendation, dealer reputation, receiving auto service at home and availability of preferred brand on distance are mediated by dealer trust. Gender had a minimal effect on distance, with females exhibiting a stronger relation in terms of dealer trust as compared to males. Overall, we found that market relevant factors were better predictors of distance; and proximity, dealer trust and professionalism have the most profound effects on distance that consumers are willing to travel. This is related to the fact that the nature of shopping goods (among which are car tires) typically reinforces consumers to be more engaged in the shopping process, therefore factors that have to do with the store (e.g. location) and shopping process play a key role in store choice decision. These findings are very specific to shopping goods and cannot be generalized to other categories of goods. For marketers and retailers these findings can have direct implications on their location strategies. The factors found to be relevant to tire shop patronage will be used in our next study to calibrate a location model to be utilised to identify the optimum location for siting new tyre shop outlets and service centres.

Keywords: dealer trust, distance to store, tire store patronage, willingness to travel

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