Search results for: numerical simulation

Authors: Dinesh Munjurulimana, Anil Tiwari, Tingwen Li, Carlos Pereira, Sreekanth Pannala, John Waters

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With the rapid transition to electric vehicles (EVs) across the globe, car manufacturers are in need of integrated and lightweight solutions for the battery packs of these vehicles. An integral part of a battery pack is the battery tray, which constitutes a significant portion of the pack’s overall weight. Based on the functional requirements, cost targets, and packaging space available, a range of materials –from metals, composites, and plastics– are often used to develop these battery trays. This paper considers the design and development of integrated thermoplastic-intensive battery trays, using the available packaging space from a representative EV battery pack. Presented as a proposed alternative are multiple concepts to integrate several connected systems such as cooling plates and underbody impact protection parts of a multi-piece incumbent battery pack. The resulting digital prototype was evaluated for several mechanical performance measures such as mechanical shock, drop, crush resistance, modal analysis, and torsional stiffness. The performance of this alternative design is then compared with the incumbent solution. In addition, insights are gleaned into how these novel approaches can be optimized to meet or exceed the performance of incumbent designs. Preliminary manufacturing feasibility of the optimal solution using injection molding and other commonly used manufacturing methods for thermoplastics is briefly explained. Then numerical and analytical evaluations are performed to show a representative Pareto front of cost vs. volume of the production parts. The proposed solution is observed to offer weight savings of up to 40% on a component level and part elimination of up to two systems in the battery pack of a typical battery EV while offering the potential to meet the required performance measures highlighted above. These conceptual solutions are also observed to potentially offer secondary benefits such as improved thermal and electrical isolations and be able to achieve complex geometrical features, thus demonstrating the ability to use the complete packaging space available in the vehicle platform considered. The detailed study presented in this paper serves as a valuable reference for researches across the globe working on the development of EV battery packs – especially those with an interest in the potential of employing alternate solutions as part of a mixed-material system to help capture untapped opportunities to optimize performance and meet critical application requirements.

Keywords: thermoplastics, lightweighting, part integration, electric vehicle battery packs

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Authors: Jiazhen Chen, Yue Sun, Joanne Yip, Kit-Lun Yick

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The design of sports bras poses a considerable challenge due to the difficulty in accurately predicting the wearing result after computer-aided design (CAD). It needs repeated physical try-on or virtual try-on to obtain a comfortable pressure range during motion. Specifically, in the context of running, the exact support area and force exerted on the breasts remain unclear. Consequently, obtaining an effective method to design the sports bra pads shape becomes particularly challenging. This predicament hinders the successful creation and production of sports bras that cater to women's health needs. The purpose of this study is to propose an effective method to obtain the 3D shape of sports bra pads and to understand the relationship between the supporting force and the 3D shape parameters of the pads. Firstly, the static 3D shape of the sports bra pad and human motion data (Running) are obtained by using the 3D scanner and advanced 4D scanning technology. The 3D shape of the sports bra pad is parameterised and simplified by Free-form Deformation (FFD). Then the sub-models of sports bra and human body are constructed by segmenting and meshing them with MSC Apex software. The material coefficient of sports bras is obtained by material testing. The Marc software is then utilised to establish a dynamic contact model between the human breast and the sports bra pad. To realise the reverse design of the sports bra pad, this contact model serves as a forward model for calculating the inverse problem. Based on the forward contact model, the inverse problem of the 3D shape parameters of the sports bra pad with the target bra-wearing pressure range as the boundary condition is solved. Finally, the credibility and accuracy of the simulation are validated by comparing the experimental results with the simulations by the FE model on the pressure distribution. On the one hand, this research allows for a more accurate understanding of the support area and force distribution on the breasts during running. On the other hand, this study can contribute to the customization of sports bra pads for different individuals. It can help to obtain sports bra pads with comfortable dynamic pressure.

Keywords: sports bra design, breast motion, running, inverse problem, finite element dynamic model

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Authors: Jaroslav Kraus

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Spatial information about demographic processes are a standard part of outputs in the Czech Republic. That was also the case of Population and Housing Census which was held on 2011. This is a starting point for a follow up study devoted to two basic types of households: single person households and households of one completed family. Single person households and one family households create more than 80 percent of all households, but the share and spatial structure is in long-term changing. The increase of single households is results of long-term fertility decrease and divorce increase, but also possibility of separate living. There are regions in the Czech Republic with traditional demographic behavior, and regions like capital Prague and some others with changing pattern. Population census is based - according to international standards - on the concept of currently living population. Three types of geospatial approaches will be used for analysis: (i) firstly measures of geographic distribution, (ii) secondly mapping clusters to identify the locations of statistically significant hot spots, cold spots, spatial outliers, and similar features and (iii) finally analyzing pattern approach as a starting point for more in-depth analyses (geospatial regression) in the future will be also applied. For analysis of this type of data, number of households by types should be distinct objects. All events in a meaningful delimited study region (e.g. municipalities) will be included in an analysis. Commonly produced measures of central tendency and spread will include: identification of the location of the center of the point set (by NUTS3 level); identification of the median center and standard distance, weighted standard distance and standard deviational ellipses will be also used. Identifying that clustering exists in census households datasets does not provide a detailed picture of the nature and pattern of clustering but will be helpful to apply simple hot-spot (and cold spot) identification techniques to such datasets. Once the spatial structure of households will be determined, any particular measure of autocorrelation can be constructed by defining a way of measuring the difference between location attribute values. The most widely used measure is Moran’s I that will be applied to municipal units where numerical ratio is calculated. Local statistics arise naturally out of any of the methods for measuring spatial autocorrelation and will be applied to development of localized variants of almost any standard summary statistic. Local Moran’s I will give an indication of household data homogeneity and diversity on a municipal level.

Keywords: census, geo-demography, households, the Czech Republic

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Authors: M. Paraipan, V. M. Javadova, S. I. Tyutyunnikov

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The concept of energy amplifier or accelerator driven system (ADS) involves the use of a particle accelerator coupled with a nuclear reactor. The accelerated particle beam generates a supplementary source of neutrons, which allows the subcritical functioning of the reactor, and consequently a safe exploitation. The harder neutron spectrum realized ensures a better incineration of the actinides. The almost generalized opinion is that the optimal beam for ADS is represented by protons with energy around 1 GeV (gigaelectronvolt). In the present work, a systematic analysis of the energy gain for proton beams with energy from 0.5 to 3 GeV and ion beams from deuteron to neon with energies between 0.25 and 2 AGeV is performed. The target is an assembly of metallic U-Pu-Zr fuel rods in a bath of lead-bismuth eutectic coolant. The rods length is 150 cm. A beryllium converter with length 110 cm is used in order to maximize the energy released in the target. The case of a linear accelerator is considered, with a beam intensity of 1.25‧10¹⁶ p/s, and a total accelerator efficiency of 0.18 for proton beam. These values are planned to be achieved in the European Spallation Source project. The energy gain G is calculated as the ratio between the energy released in the target to the energy spent to accelerate the beam. The energy released is obtained through simulation with the code Geant4. The energy spent is calculating by scaling from the data about the accelerator efficiency for the reference particle (proton). The analysis concerns the G values, the net power produce, the accelerator length, and the period between refueling. The optimal energy for proton is 1.5 GeV. At this energy, G reaches a plateau around a value of 8 and a net power production of 120 MW (megawatt). Starting with alpha, ion beams have a higher G than 1.5 GeV protons. A beam of 0.25 AGeV(gigaelectronvolt per nucleon) ⁷Li realizes the same net power production as 1.5 GeV protons, has a G of 15, and needs an accelerator length 2.6 times lower than for protons, representing the best solution for ADS. Beams of ¹⁶O or ²⁰Ne with energy 0.75 AGeV, accelerated in an accelerator with the same length as 1.5 GeV protons produce approximately 900 MW net power, with a gain of 23-25. The study of the evolution of the isotopes composition during irradiation shows that the increase in power production diminishes the period between refueling. For a net power produced of 120 MW, the target can be irradiated approximately 5000 days without refueling, but only 600 days when the net power reaches 1 GW (gigawatt).

Keywords: accelerator driven system, ion beam, electrical power, energy gain

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Authors: M. Beusink, E. W. C. Coenen

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The increased application of novel structural materials, such as high grade asphalt, concrete and laminated composites, has sparked the need for a better understanding of the often complex, non-linear mechanical behavior of such materials. The effective macroscopic mechanical response is generally dependent on the applied load path. Moreover, it is also significantly influenced by the microstructure of the material, e.g. embedded fibers, voids and/or grain morphology. At present, multiscale techniques are widely adopted to assess micro-macro interactions in a numerically efficient way. Computational homogenization techniques have been successfully applied over a wide range of engineering cases, e.g. cases involving first order and second order continua, thin shells and cohesive zone models. Most of these homogenization methods rely on Representative Volume Elements (RVE), which model the relevant microstructural details in a confined volume. Imposed through kinematical constraints or boundary conditions, a RVE can be subjected to a microscopic load sequence. This provides the RVE's effective stress-strain response, which can serve as constitutive input for macroscale analyses. Simultaneously, such a study of a RVE gives insight into fine scale phenomena such as microstructural damage and its evolution. It has been reported by several authors that the type of boundary conditions applied to the RVE affect the resulting homogenized stress-strain response. As a consequence, dedicated boundary conditions have been proposed to appropriately deal with this concern. For the specific case of a planar assumption for the analyzed structure, e.g. plane strain, axisymmetric or plane stress, this assumption needs to be addressed consistently in all considered scales. Although in many multiscale studies a planar condition has been employed, the related impact on the multiscale solution has not been explicitly investigated. This work therefore focuses on the influence of the planar assumption for multiscale modeling. In particular the plane stress case is highlighted, by proposing three different implementation strategies which are compatible with a first-order computational homogenization framework. The first method consists of applying classical plane stress theory at the microscale, whereas with the second method a generalized plane stress condition is assumed at the RVE level. For the third method, the plane stress condition is applied at the macroscale by requiring that the resulting macroscopic out-of-plane forces are equal to zero. These strategies are assessed through a numerical study of a thin walled structure and the resulting effective macroscale stress-strain response is compared. It is shown that there is a clear influence of the length scale at which the planar condition is applied.

Keywords: first-order computational homogenization, planar analysis, multiscale, microstrucutures

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Authors: Katarzyna Pasternak

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Experiencing the meaning of life is widely recognised as a vital element of well-being and central human motivation. Studies have shown that a higher meaning of life is associated, among other things, with a higher quality of life, higher levels of happiness and better declared health. The subject of the study is the meaning in life measured with The Meaning in Life Questionnaire and the presence of such emotions as nostalgia, awe and hope, and the content of imaginations measured after temporal mental simulations in Americans and Poles. The respondents had to imagine themselves in future, in 40 years and describe two events that would take place at that time. Next, participants assessed the importance of the events described by them, recognised whether during their journey through time they felt awe, hope and nostalgia, and answered the questionnaire examining the meaning in life. 204 (102 from Poland 102 from the USA ) people aged 21 to 60 participated in the study. The study checked whether there were differences in the content of the imaginations of the respondents from Poland and USA, and whether there were statistically significant difference between the declared sense of meaning in life among participants from both countries. The result of the study hane shown that there were no differences in the overall result obtained by the participants in The Meaning in Life Questionnaire , while there were statistically significant differences among the subscales of the questionnaire. It turned out that Americans have a higher presence of meaning in life than Poles, but they obtained lower results in searching of meaning in life. Studies have also shown that there was a statistically significant difference between Poles and Americans in feeling awe after a mental simulation. Poles felt higher level of awe. Images about the future differed between Poles and Americans. Poles judged that the events they described were very important to them. Interestingly, the content of American participants’ imaginations was dominated by topics related to the future of the world, ecology and world peace. There were also ideas about nice moments spent with friends and family. Among Poles, ideas related to professional career and development as well as family events dominated. Research shows that despite the lack of differences in the general meaning in life, Poles are more focused on searching for meaning in life than Americans. The study shows interesting differences between the two cultures.

Keywords: meaning in life, mental simulations, imaginations, temporal mental simulations, future, cultural differences

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Authors: I. Diamant, R. Srouji

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Applicant perceptions are attitudes, feelings, and cognitions which individuals have about selection procedures and have been mostly studied in the context of personnel selection. The main aim of the present study is to expand the understanding of applicant perceptions, using the framework of Organizational Justice Theory, in the domain of selection for higher education. The secondary aim is to explore the relationships between individual differences in social anxiety and applicants’ perceptions. The selection process is an accept/reject situation; it was hypothesized that applicants with higher social anxiety would experience negative perceptions and a lower success estimation, especially when subjected to social interaction elements in the process (interview and group simulation). Also, the effects of prior preparation and post-process explanations offered at the end of the selection process were explored. One hundred sixty psychology M.A. program applicants participated in this research, and following the selection process completed questionnaires measuring social anxiety, social exclusion, ratings on several justice dimensions for each of the methods in the selection process, feelings of success, and self-estimation of compatibility. About half of the applicants also received explanations regarding the significance and the aims of the selection process. Results provided support for most of our hypotheses: applicants with higher social anxiety experienced an increased level of social exclusion in the selection process, perceived the selection as less fair and ended with a lower feeling of success relative to those applicants without social anxiety. These relationships were especially salient in the selection procedures which included social interaction. Additionally, preparation for the selection process was positively related to the favorable perception of fairness in the selection process. Finally, contrary to our hypothesis, it was found that explanations did not affect the applicant’s perceptions. The results enhance our understanding of which factors affect applicant perceptions in applicants to higher education studies and contribute uniquely to the understanding of the effect of social anxiety on different aspects of selection experienced by applicants. The findings clearly show that some individuals may be predisposed to react unfavorably to certain selection situations. In an age of increasing awareness towards fairness in evaluation and selection and hiring procedures, these findings may be of relevance and may contribute to the design of future personnel selection methods in general and of higher education selection in particular.

Keywords: applicant perceptions, selection and assessment, organizational justice theory, social anxiety

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Authors: Liqin Zhi, Lin Xu, Wenxia Wei, Yaqi Cai

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Recently, replacing some of the methyl groups in polydimethylsiloxanes with other functional groups has been extensively explored to obtain modified polymethylsiloxanes with special properties that enable new industrial applications. Fluorinated polysiloxanes, one type of these modified polysiloxanes, are based on a siloxane backbone with fluorinated groups attached to the side chains of polysiloxanes. As a commercially significant material, poly[methyl(trifluoropropyl)siloxane] (PMTFPS) has sufficient fluorine content to be useful as a fuel-and oil-resistant elastomer, which combines both the chemical and solvent resistance of fluorocarbons and the wide temperature range applicability of organosilicones. PMTFPS products can be used in many applications in which resistance to fuel, oils and hydrocarbon solvents is required, including use as lubricants in bearings, sealants, and elastomers for aerospace and automotive fuel systems. Fluorinated methylsiloxanes, a type of modified methylsiloxane, include tris(trifluoropropyl)trimethylcyclotrisiloxane (D3F) and tetrakis(trifluoropropyl)tetramethylcyclotetrasiloxane (D4F), both of which contain trifluoropropyl groups in the side chains of cyclic methylsiloxanes. D3F, as an important monomer in the manufacture of PMTFPS, is often present as an impurity in PMTFPS. In addition, the synthesis of PMTFPS from D3F could form other fluorinated methylsiloxanes with low molecular weights (such as D4F). The yearly demand and production volumes of D3F increased rapidly all over world. Fluorinated methylsiloxanes might be released into the environment via different pathways during the production and application of PMTFPS. However, there is a lack of data concerning the emission, environmental occurrence and potential environmental impacts of fluorinated methylsiloxanes. Here, we report fluorinated methylsiloxanes (D3F and D4F) in surface water and sediment samples collected near a fluorinated methylsiloxane manufacturing plant in Weihai, China. The concentrations of D3F and D4F in surface water ranged from 3.29 to 291 ng/L and from 7.02 to 168 ng/L, respectively. The concentrations of D3F and D4F in sediment ranged from 11.8 to 5478 ng/g and from 17.2 to 6277 ng/g, respectively. In simulation experiment, the half-lives of D3F and D4F at different pH values (5.2, 6.4, 7.2, 8.3 and 9.2) varied from 80.6 to 154 h and from 267 to 533 h respectively. CF₃(CH₂)₂MeSi(OH)₂ was identified as one of the main hydrolysis products of fluorinated methylsiloxanes. It was also detected in the river samples at concentrations of 72.1-182.9 ng/L. In addition, the slow rearrangement of D3F (spiked concentration = 500 ng/L) to D4F (concentration = 11.0-22.7 ng/L) was also found during 336h hydrolysis experiment.

Keywords: fluorinated methylsiloxanes, environmental matrices, hydrolysis, sediment

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Authors: Sisira Edirippulige

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Background: Digital health education and training (DHET) is imperative for preparing current and future clinicians to work competently in digitally enabled environments. Despite rapid integration of digital health in modern health services, systematic education and training opportunities for health workers is still lacking. Objectives: This study aimed to investigate healthcare professionals’ perspectives and expectations regarding the knowledge, skills and competency needs in digital health for current and future healthcare workforce. Methods: A qualitative study design with semi-structured individual interviews was employed. A purposive sample method was adopted to collect relevant information from the health workers. Inductive thematic analysis was used to analyse data. Interviews were audio-recorded and transcribed verbatim. Consolidated Criteria for Reporting Qualitative Research (COREQ) was followed when we reported this study. Results: Two themes emerged while analysing the data: (1) what to teach in DHET and (2) how to teach DHET. Overall, healthcare professionals agreed that DHET is important for preparing current and future clinicians for working competently in digitally enabled environments. Knowledge relating to what is digital health, types of digital health, use of technology and human factors in digital health were considered as important to be taught in DHET. Skills relating to digital health consultations, clinical information system management and remote monitoring were considered important to be taught. Blended learning which combined e-learning and classroom-based teaching, simulation sessions and clinical rotations were suggested by healthcare professionals as optimal approaches to deliver the above-mentioned content. Conclusions: This study is the first of its kind to investigate health professionals’ perspectives and expectations relating to the knowledge, skills and competency needs in digital health for current and future healthcare workforce. Healthcare workers are keen to acquire relevant knowledge, skills and competencies related to digital health. Different modes of education delivery is of interest to fit in with busy schedule of health workers.

Keywords: digital health, telehealth, telemedicine, education, curriculum

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Authors: S. Curteanu, F. Leon, M. Gavrilescu, S. A. Floria

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Optimization algorithms inspired from human behavior were applied in this approach, associated with neural networks models. The algorithms belong to human behaviors of learning and cooperation and human competitive behavior classes. For the first class, the main strategies include: random learning, individual learning, and social learning, and the selected algorithms are: simplified human learning optimization (SHLO), social learning optimization (SLO), and teaching-learning based optimization (TLBO). For the second class, the concept of learning is associated with competitiveness, and the selected algorithms are sports-inspired algorithms (with Football Game Algorithm, FGA and Volleyball Premier League, VPL) and Imperialist Competitive Algorithm (ICA). A real process, the synthesis of polyacrylamide-based multicomponent hydrogels, where some parameters are difficult to obtain experimentally, is considered as a case study. Reaction yield and swelling degree are predicted as a function of reaction conditions (acrylamide concentration, initiator concentration, crosslinking agent concentration, temperature, reaction time, and amount of inclusion polymer, which could be starch, poly(vinyl alcohol) or gelatin). The experimental results contain 175 data. Artificial neural networks are obtained in optimal form with biologically inspired algorithm; the optimization being perform at two level: structural and parametric. Feedforward neural networks with one or two hidden layers and no more than 25 neurons in intermediate layers were obtained with values of correlation coefficient in the validation phase over 0.90. The best results were obtained with TLBO algorithm, correlation coefficient being 0.94 for an MLP(6:9:20:2) – a feedforward neural network with two hidden layers and 9 and 20, respectively, intermediate neurons. Good results obtained prove the efficiency of the optimization algorithms. More than the good results, what is important in this approach is the simulation methodology, including neural networks and optimization biologically inspired algorithms, which provide satisfactory results. In addition, the methodology developed in this approach is general and has flexibility so that it can be easily adapted to other processes in association with different types of models.

Keywords: artificial neural networks, human behaviors of learning and cooperation, human competitive behavior, optimization algorithms

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Authors: Nursyafiq Ali Shibramulisi, Nor Farah Fauzi, Nur Azniza Zawin Anuar, Nurul Atikah Azmi, Janice Hew Pei Fang

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Background: Work related musculoskeletal disorders (WRMD) among dental personnel have been underestimated and under-reported worldwide and specifically in Malaysia. The problem will arise and progress slowly over time, as it results from accumulated injury throughout the period of work. Several risk factors, such as repetitive movement, static posture, vibration, and adapting poor working postures, have been identified to be contributing to WRMSD in dental practices. Dental personnel is at higher risk of getting this problem as it is their working nature and core business. This would cause pain and dysfunction syndrome among them and result in absence from work and substandard services to their patients. Methodology: A cross-sectional study involving 19 government dental clinics in Perak was done over the period of 3 months. Those who met the criteria were selected to participate in this study. Malay version of the Self-Reported Nordic Musculoskeletal Discomfort Form was used to identify the prevalence of WRMSD, while the intensity of pain in the respective regions was evaluated using a 10-point scale according to ‘Pain as The 5ᵗʰ Vital Sign’ by MOH Malaysia and later on were analyzed using SPSS version 25. Descriptive statistics, including mean and SD and median and IQR, were used for numerical data. Categorical data were described by percentage. Pearson’s Chi-Square Test and Spearman’s Correlation were used to find the association between the prevalence of WRMSD and other socio-demographic data. Results: 159 dentists, 73 dental therapists, 26 dental lab technicians, 81 dental surgery assistants, and 23 dental attendants participated in this study. The mean age for the participants was 34.9±7.4 and their mean years of service was 9.97±7.5. Most of them were female (78.5%), Malay (71.3%), married (69.6%) and right-handed (90.1%). The highest prevalence of WRMSD was neck (58.0%), followed by shoulder (48.1%), upper back (42.0%), lower back (40.6%), hand/wrist (31.5%), feet (21.3%), knee (12.2%), thigh 7.7%) and lastly elbow (6.9%). Most of those who reported having neck pain scaled their pain experiences at 2 out of 10 (19.5%), while for those who suffered upper back discomfort, most of them scaled their pain experience at 6 out of 10 (17.8%). It was found that there was a significant relationship between age and pain at neck (p=0.007), elbow (p=0.027), lower back (p=0.032), thigh (p=0.039), knee (p=0.001) and feet (p=0.000) regions. Job position also had been found to be having a significant relationship with pain experienced at the lower back (p=0.018), thigh (p=0.011), knee, and feet (p=0.000). Conclusion: The prevalence of WRMSD among dental personnel in Perak was found to be high. Age and job position were found to be having a significant relationship with pain experienced in several regions. Intervention programs should be planned and conducted to prevent and reduce the occurrence of WRMSD, as all harmful or unergonomic practices should be avoided at all costs.

Keywords: WRMSD, ergonomic, dentistry, dental

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Authors: Madia Safdar, G. Amjad Hussain, Mashhood Ahmad

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One of the major challenges of today’s era is peak demand which causes stress on the transmission lines and also raises the cost of energy generation and ultimately higher electricity bills to the end users, and it was used to be managed by the supply side management. However, nowadays this has been withdrawn because of existence of potential in the demand side management (DSM) having its economic and- environmental advantages. DSM in domestic load can play a vital role in reducing the peak load demand on the network provides a significant cost saving. In this paper the potential of demand response (DR) in reducing the peak load demands and electricity bills to the electric users is elaborated. For this purpose the domestic appliances are modeled in MATLAB Simulink and controlled by a module called energy management controller. The devices are categorized into controllable and uncontrollable loads and are operated according to real-time tariff pricing pattern instead of fixed time pricing or variable pricing. Energy management controller decides the switching instants of the controllable appliances based on the results from optimization algorithms. In GAMS software, the MILP (mixed integer linear programming) algorithm is used for optimization. In different cases, different constraints are used for optimization, considering the comforts, needs and priorities of the end users. Results are compared and the savings in electricity bills are discussed in this paper considering real time pricing and fixed tariff pricing, which exhibits the existence of potential to reduce electricity bills and peak loads in demand side management. It is seen that using real time pricing tariff instead of fixed tariff pricing helps to save in the electricity bills. Moreover the simulation results of the proposed energy management system show that the gained power savings lie in high range. It is anticipated that the result of this research will prove to be highly effective to the utility companies as well as in the improvement of domestic DR.

Keywords: controllable and uncontrollable domestic loads, demand response, demand side management, optimization, MILP (mixed integer linear programming)

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Authors: H. Jung, N. Kim, B. Kang, J. Choe

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History matching is a crucial procedure for predicting reservoir performances and making future decisions. However, it is difficult due to uncertainties of initial reservoir models. Therefore, it is important to have reliable initial models for successful history matching of highly heterogeneous reservoirs such as channel reservoirs. In this paper, we proposed a novel scheme for regenerating geological models using support vector machine (SVM) and principal component analysis (PCA). First, we perform PCA for figuring out main geological characteristics of models. Through the procedure, permeability values of each model are transformed to new parameters by principal components, which have eigenvalues of large magnitude. Secondly, the parameters are projected into two-dimensional plane by multi-dimensional scaling (MDS) based on Euclidean distances. Finally, we train an SVM classifier using 20% models which show the most similar or dissimilar well oil production rates (WOPR) with the true values (10% for each). Then, the other 80% models are classified by trained SVM. We select models on side of low WOPR errors. One hundred channel reservoir models are initially generated by single normal equation simulation. By repeating the classification process, we can select models which have similar geological trend with the true reservoir model. The average field of the selected models is utilized as a probability map for regeneration. Newly generated models can preserve correct channel features and exclude wrong geological properties maintaining suitable uncertainty ranges. History matching with the initial models cannot provide trustworthy results. It fails to find out correct geological features of the true model. However, history matching with the regenerated ensemble offers reliable characterization results by figuring out proper channel trend. Furthermore, it gives dependable prediction of future performances with reduced uncertainties. We propose a novel classification scheme which integrates PCA, MDS, and SVM for regenerating reservoir models. The scheme can easily sort out reliable models which have similar channel trend with the reference in lowered dimension space.

Keywords: history matching, principal component analysis, reservoir modelling, support vector machine

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Authors: R. Abdulrahman, A. Eardley, A. Soliman

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The proliferation in the development of mobile learning (m-learning) has played a vital role in the rapidly growing electronic learning market. This relatively new technology can help to encourage the development of in learning and to aid knowledge transfer a number of areas, by familiarizing students with innovative information and communications technologies (ICT). M-learning plays a substantial role in the deployment of learning methods for nursing students by using the Internet and portable devices to access learning resources ‘anytime and anywhere’. However, acceptance of m-learning by students is critical to the successful use of m-learning systems. Thus, there is a need to study the factors that influence student’s intention to use m-learning. This paper addresses this issue. It outlines the outcomes of a study that evaluates the unified theory of acceptance and use of technology (UTAUT) model as applied to the subject of user acceptance in relation to m-learning activity in nurse education. The model integrates the significant components across eight prominent user acceptance models. Therefore, a standard measure is introduced with core determinants of user behavioural intention. The research model extends the UTAUT in the context of m-learning acceptance by modifying and adding individual innovativeness (II) and quality of service (QoS) to the original structure of UTAUT. The paper goes on to add the factors of previous experience (of using mobile devices in similar applications) and the nursing students’ readiness (to use the technology) to influence their behavioural intentions to use m-learning. This study uses a technique called ‘convenience sampling’ which involves student volunteers as participants in order to collect numerical data. A quantitative method of data collection was selected and involves an online survey using a questionnaire form. This form contains 33 questions to measure the six constructs, using a 5-point Likert scale. A total of 42 respondents participated, all from the Nursing Institute at the Armed Forces Hospital in Saudi Arabia. The gathered data were then tested using a research model that employs the structural equation modelling (SEM), including confirmatory factor analysis (CFA). The results of the CFA show that the UTAUT model has the ability to predict student behavioural intention and to adapt m-learning activity to the specific learning activities. It also demonstrates satisfactory, dependable and valid scales of the model constructs. This suggests further analysis to confirm the model as a valuable instrument in order to evaluate the user acceptance of m-learning activity.

Keywords: mobile learning, nursing institute students’ acceptance of m-learning activity in Saudi Arabia, unified theory of acceptance and use of technology model (UTAUT), structural equation modelling (SEM)

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Authors: Ibrahim Rassoul, A. Serir, E-K. Si Ahmed, J. Legrand

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The acronym PEM refers to Proton Exchange Membrane or alternatively Polymer Electrolyte Membrane. Due to its high efficiency, low operating temperature (30–80 °C), and rapid evolution over the past decade, PEMFCs are increasingly emerging as a viable alternative clean power source for automobile and stationary applications. Before PEMFCs can be employed to power automobiles and homes, several key technical challenges must be properly addressed. One technical challenge is elucidating the mechanisms underlying water transport in and removal from PEMFCs. On one hand, sufficient water is needed in the polymer electrolyte membrane or PEM to maintain sufficiently high proton conductivity. On the other hand, too much liquid water present in the cathode can cause “flooding” (that is, pore space is filled with excessive liquid water) and hinder the transport of the oxygen reactant from the gas flow channel (GFC) to the three-phase reaction sites. The experimental transparent fuel cell used in this work was designed to represent actual full scale of fuel cell geometry. According to the operating conditions, a number of flow regimes may appear in the microchannel: droplet flow, blockage water liquid bridge /plug (concave and convex forms), slug/plug flow and film flow. Some of flow patterns are new, while others have been already observed in PEMFC microchannels. An algorithm in MATLAB was developed to automatically determine the flow structure (e.g. slug, droplet, plug, and film) of detected liquid water in the test microchannels and yield information pertaining to the distribution of water among the different flow structures. A video processing algorithm was developed to automatically detect dynamic and static liquid water present in the gas channels and generate relevant quantitative information. The potential benefit of this software allows the user to obtain a more precise and systematic way to obtain measurements from images of small objects. The void fractions are also determined based on images analysis. The aim of this work is to provide a comprehensive characterization of two-phase flow in an operating fuel cell which can be used towards the optimization of water management and informs design guidelines for gas delivery microchannels for fuel cells and its essential in the design and control of diverse applications. The approach will combine numerical modeling with experimental visualization and measurements.

Keywords: polymer electrolyte fuel cell, air-water two phase flow, gas diffusion layer, microchannels, advancing contact angle, receding contact angle, void fraction, surface tension, image processing

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Authors: F. DelGaudio, H. Gill

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Resuscitation during cardiopulmonary (CPA) arrest is dynamic, high stress, high acuity situation, which can easily lead to communication breakdown, and errors. The care of these high acuity patients has also been shown to increase physiologic stress and task saturation of providers, which can negatively impact the care being provided. These difficulties are further complicated during a global pandemic and pose a significant safety risk to bedside providers. Nurse-led codes are a relatively new concept that may be a potential solution for alleviating some of these difficulties. An experienced nurse who has completed advanced cardiac life support (ACLS), and additional training, assumed the responsibility of directing the mechanics of the appropriate ACLS algorithm. This was done in conjunction with a physician who also acted as a physician leader. The additional nurse-led code training included a multi-disciplinary in situ simulation of a CPA on a suspected COVID-19 patient. During the CPA, the nurse leader’s responsibilities include: ensuring adequate compression depth and rate, minimizing interruptions in chest compressions, the timing of rhythm/pulse checks, and appropriate medication administration. In addition, the nurse leader also functions as a last line safety check for appropriate personal protective equipment and limiting exposure of staff. The use of nurse-led codes for CPA has shown to decrease the cognitive overload and task saturation for the physician, as well as limiting the number of staff being exposed to a potentially infectious patient. The real-world application has allowed physicians to perform and oversee high-risk procedures such as intubation, line placement, and point of care ultrasound, without sacrificing the integrity of the resuscitation. Nurse-led codes have also given the physician the bandwidth to review pertinent medical history, advanced directives, determine reversible causes, and have the end of life conversations with family. While there is a paucity of research on the effectiveness of nurse-led codes, there are many potentially significant benefits. In addition to its value during a pandemic, it may also be beneficial during complex circumstances such as extracorporeal cardiopulmonary resuscitation.

Keywords: cardiopulmonary arrest, COVID-19, nurse-led code, task saturation

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Authors: Ben Soltane Cheima, Ittansa Yonas Kelbesa

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Speaker Identification (SI) is the task of establishing identity of an individual based on his/her voice characteristics. The SI task is typically achieved by two-stage signal processing: training and testing. The training process calculates speaker specific feature parameters from the speech and generates speaker models accordingly. In the testing phase, speech samples from unknown speakers are compared with the models and classified. Even though performance of speaker identification systems has improved due to recent advances in speech processing techniques, there is still need of improvement. In this paper, a Closed-Set Tex-Independent Speaker Identification System (CISI) based on a Multiple Classifier System (MCS) is proposed, using Mel Frequency Cepstrum Coefficient (MFCC) as feature extraction and suitable combination of vector quantization (VQ) and Gaussian Mixture Model (GMM) together with Expectation Maximization algorithm (EM) for speaker modeling. The use of Voice Activity Detector (VAD) with a hybrid approach based on Short Time Energy (STE) and Statistical Modeling of Background Noise in the pre-processing step of the feature extraction yields a better and more robust automatic speaker identification system. Also investigation of Linde-Buzo-Gray (LBG) clustering algorithm for initialization of GMM, for estimating the underlying parameters, in the EM step improved the convergence rate and systems performance. It also uses relative index as confidence measures in case of contradiction in identification process by GMM and VQ as well. Simulation results carried out on voxforge.org speech database using MATLAB highlight the efficacy of the proposed method compared to earlier work.

Keywords: feature extraction, speaker modeling, feature matching, Mel frequency cepstrum coefficient (MFCC), Gaussian mixture model (GMM), vector quantization (VQ), Linde-Buzo-Gray (LBG), expectation maximization (EM), pre-processing, voice activity detection (VAD), short time energy (STE), background noise statistical modeling, closed-set tex-independent speaker identification system (CISI)

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Authors: Hossam N. Alhawatmeh, Areen I. Albustanji

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Hemodialysis negatively affects physical and psychological health. Pain, poor sleep quality, and low self-esteem are highly prevalent among patients with end-stage renal disease (ESRD) who receive hemodialysis, significantly increasing mortality and morbidity of those patients. Mind-body interventions (MBI), such as mindfulness meditation, have been recently gaining popularity that improved pain, sleep quality, and self-esteem in different populations. However, to our best knowledge, its effects on these health problems in patients receiving hemodialysis have not been studied in Jordan. Thus, the purpose of the study was to examine the effect of mindfulness meditation on pain, sleep quality, and self-esteem in patients with ESR receiving hemodialysis in Jordan. An experimental repeated-measures, randomized, parallel control design was conducted on (n =60) end-stage renal disease patients undergoing hemodialysis between March and June 2023 in the dialysis center at a public hospital in Jordan. Participants were randomly assigned to the experimental (n =30) and control groups (n =30) using a simple random assignment method. The experimental group practiced mindfulness meditation for 30 minutes three times per week for five weeks during their hemodialysis treatments. The control group's patients continued to receive hemodialysis treatment as usual for five weeks during hemodialysis sessions. The study variables for both groups were measured at baseline (Time 0), two weeks after intervention (Time 1), and at the end of intervention (Time 3). The numerical rating scale (NRS), the Rosenberg Self-Esteem Scale (RSES-M), and the Pittsburgh Sleep Quality Index (PSQI) were used to measure pain, self-esteem, and sleep quality, respectively. SPSS version 25 was used to analyze the study data. The sample was described by frequency, mean, and standard deviation as an appropriate. The repeated measures analysis of variance (ANOVA) tests were run to test the study hypotheses. The results of repeated measures ANOVA (within-subject) revealed that mindfulness meditation significantly decrease pain by the end of the intervention in the experimental group. Additionally, mindfulness meditation improved sleep quality and self-esteem in the experimental group, and these improvements occurred significantly after two weeks of the intervention and at the end of the intervention. The results of repeated measures ANOVA (within and between-subject) revealed that the experimental group, compared to the control group, experienced lower levels of pain and higher levels of sleep quality and self-esteem over time. In conclusion, the results provided substantial evidence supporting the positive impacts of mindfulness meditation on pain, sleep quality, and self-esteem in patients with ESRD undergoing hemodialysis. These results highlight the potential of mindfulness meditation as an adjunctive therapy in the comprehensive care of this patient population. Incorporating mindfulness meditation into the treatment plan for patients receiving hemodialysis may contribute to improved well-being and overall quality of life.

Keywords: hemodialysis, pain, sleep quality, self-esteem, mindfulness

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Authors: Chatrabhuti Pipop, Visessri Supattra, Charinpanitkul Tawatchai

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Durian is one of the top agricultural products exported by Thailand. There is the massive market potential for the durian industry. While the global demand for Thai durians, especially the demand from China, is very high, Thailand's durian supply is far from satisfying strong demand. Poor agricultural practices result in low yields and poor quality of fruit. Most irrigation systems currently used by the farmers are fixed schedule or fixed rates that ignore actual weather conditions and crop water requirements. In addition, the technologies emerging are too difficult and complex and prices are too high for the farmers to adopt and afford. Many farmers leave the durian trees to grow naturally. With improper irrigation and nutrient management system, durians are vulnerable to a variety of issues, including stunted growth, not flowering, diseases, and death. Technical development or research for durian is much needed to support the wellbeing of the farmers and the economic development of the country. However, there are a limited number of studies or development projects for durian because durian is a perennial crop requiring a long time to obtain the results to report. This study, therefore, aims to address the problem of durian production by developing an autonomous and precision irrigation system. The system is designed and equipped with an industrial programmable controller, a weather station, and a digital flow meter. Daily water requirements are computed based on weather data such as rainfall and evapotranspiration for daily irrigation with variable flow rates. A prediction model is also designed as a part of the system to enhance the irrigation schedule. Before the system was installed in the field, a simulation model was built and tested in a laboratory setting to ensure its accuracy. Water consumption was measured daily before and after the experiment for further analysis. With this system, the crop water requirement is precisely estimated and optimized based on the data from the weather station. Durian will be irrigated at the right amount and at the right time, offering the opportunity for higher yield and higher income to the farmers.

Keywords: Durian, precision irrigation, precision agriculture, smart farm

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Authors: Janneth Gonzalez, Andrés Pinzon Velasco, Maria Angarita

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Astrocytes play an important role in various processes in the brain, including pathological conditions such as neurodegenerative diseases. Recent studies have shown that the increase in saturated fatty acids such as palmitic acid (PA) triggers pro-inflammatorypathways in the brain. The use of synthetic neurosteroids such as tibolone has demonstrated neuro-protective mechanisms. However, broad studies with a systemic point of view on the neurodegenerative role of PA and the neuro-protective mechanisms of tibolone are lacking. In this study, we performed the integration of multi-omic data (transcriptome and proteome) into a human astrocyte genomic scale metabolic model to study the astrocytic response during palmitate treatment. We evaluated metabolic fluxes in three scenarios (healthy, induced inflammation by PA, and tibolone treatment under PA inflammation). We also applied a control theory approach to identify those reactions that exert more control in the astrocytic system. Our results suggest that PA generates a modulation of central and secondary metabolism, showing a switch in energy source use through inhibition of folate cycle and fatty acid β‐oxidation and upregulation of ketone bodies formation. We found 25 metabolic switches under PA‐mediated cellular regulation, 9 of which were critical only in the inflammatory scenario but not in the protective tibolone one. Within these reactions, inhibitory, total, and directional coupling profiles were key findings, playing a fundamental role in the (de)regulation of metabolic pathways that may increase neurotoxicity and represent potential treatment targets. Finally, the overall framework of our approach facilitates the understanding of complex metabolic regulation, and it can be used for in silico exploration of the mechanisms of astrocytic cell regulation, directing a more complex future experimental work in neurodegenerative diseases.

Keywords: astrocytes, data integration, palmitic acid, computational model, multi-omics

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Authors: Ahmed AlNouss, Salim Ahmed

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Process control refers to the techniques to control the variables in a process in order to maintain them at their desired values. Advanced process control (APC) is a broad term within the domain of control where it refers to different kinds of process control and control related tools, for example, model predictive control (MPC), statistical process control (SPC), fault detection and classification (FDC) and performance assessment. APC is often used for solving multivariable control problems and model predictive control (MPC) is one of only a few advanced control methods used successfully in industrial control applications. Advanced control is expected to bring many benefits to the plant operation; however, the extent of the benefits is plant specific and the application needs a large investment. This requires an analysis of the expected benefits before the implementation of the control. In a real plant simulation studies are carried out along with some experimentation to determine the improvement in the performance of the plant due to advanced control. In this research, such an exercise is undertaken to realize the needs of APC application. The main objectives of the paper are as follows: (1) To apply MPC to a number of simulations set up to realize the need of MPC by comparing its performance with that of proportional integral derivatives (PID) controllers. (2) To study the effect of controller parameters on control performance. (3) To develop appropriate performance index (PI) to compare the performance of different controller and develop novel idea to present tuning map of a controller. These objectives were achieved by applying PID controller and a special type of MPC which is dynamic matrix control (DMC) on the multi-tanks process simulated in loop-pro. Then the controller performance has been evaluated by changing the controller parameters. This performance was based on special indices related to the difference between set point and process variable in order to compare the both controllers. The same principle was applied for continuous stirred tank heater (CSTH) and continuous stirred tank reactor (CSTR) processes simulated in Matlab. However, in these processes some developed programs were written to evaluate the performance of the PID and MPC controllers. Finally these performance indices along with their controller parameters were plotted using special program called Sigmaplot. As a result, the improvement in the performance of the control loops was quantified using relevant indices to justify the need and importance of advanced process control. Also, it has been approved that, by using appropriate indices, predictive controller can improve the performance of the control loop significantly.

Keywords: advanced process control (APC), control loop, model predictive control (MPC), proportional integral derivatives (PID), performance indices (PI)

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Authors: Brianne Nichols, John A. Parkinson

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Positive Psychology has provided a rich understanding of the beneficial effects of positive emotions in relation to optimal functioning, and research has been devoted to promote states of positive feeling and thinking. While this is a worthwhile pursuit, positive emotions are not useful in all contexts - some situations may require the individual to make use of their negative emotions to reach a desired end state. To account for the potential value of a wider range of emotional experiences that are common to the human condition, Positive Psychology needs to expand its horizons and investigate how individuals achieve positive outcomes using varied means. The current research seeks to understand the positive psychology of fear of failure (FF), which is a commonly experienced negative emotion relevant to most life domains. On the one hand, this emotion has been linked with avoidance motivation and self-handicap behaviours, on the other; FF has been shown to act as a drive to move the individual forward. To fully capture the depth of this highly subjective emotional experience and understand the circumstances under which FF may be adaptive, this study adopted a mixed methods design using SenseMaker; a web-based tool that combines the richness of narratives with the objectivity of numerical data. Two hundred participants consisting mostly of undergraduate university students shared a story of a time in the recent past when they feared failure of achieving a valued goal. To avoid researcher bias in the interpretation of narratives, participants self-signified their stories in a tagging system that was based on researchers’ aim to explore the role of past failures, the cognitive, emotional and behavioural profile of individuals high and low in FF, and the relationship between these factors. In addition, the role of perceived personal control and self-esteem were investigated in relation to FF using self-report questionnaires. Results from quantitative analyses indicated that individuals with high levels of FF, compared to low, were strongly influenced by past failures and preoccupied with their thoughts and emotions relating to the fear. This group also reported an unwillingness to accept their internal experiences, which in turn was associated with withdrawal from goal pursuit. Furthermore, self-esteem was found to mediate the relationship between perceived control and FF, suggesting that self-esteem, with or without control beliefs, may have the potential to buffer against high FF. It is hoped that the insights provided by the current study will inspire future research to explore the ways in which ‘acceptance’ may help individuals keep moving towards a goal despite the presence of FF, and whether cultivating a non-contingent self-esteem is the key to resilience in the face of failures.

Keywords: fear of failure, goal-pursuit, negative emotions, optimal functioning, resilience

Procedia PDF Downloads 186

Authors: Ursula D. C. Resende, Fabiano S. Bicalho, Sandro T. M. Gonçalves

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The growing demand for cheap and clean energy sources have been motivated by the study and development of distinct technologies and devices able to provide different amounts of energy. In order to supply energy for small loads, the energy from the electromagnetic spectrum can be harvested. This possibility is particularly interesting because this kind of energy is constantly available in the environment and the number of radiofrequency sources is permanently increasing, due to advances in telecommunications services. A rectenna, which is a combination of an antenna and a rectifier circuit, is an equipment that can efficiently perform the electromagnetic energy harvesting. However, since the amount of electromagnetic energy available in the environment is very small, limited values of power can be harvested by the rectenna. Therefore, several technical strategies have been investigated in order to increase this amount of power. In this work, a metamaterial electromagnetic lens is used to improve the electromagnetic energy harvesting. The rectenna investigated was designed and optimized to charge a Li-Ion battery using the electromagnetic energy from an internet Wi-Fi commercial router model TL-WR841HP operating in 2.45 GHz with maximal output power equal to 18 dBm. The rectenna consists of a high directive antenna, a double voltage rectifier circuit and a metamaterial lens. The printed antenna, constituted of two rectangular radiator elements, was projected and optimized by using the Computer Simulation Software (CST) in order to obtain high directivities and values of S11 parameter below -10 dB in 2.45 GHz. The antenna was printed over a double-sided copper fiberglass substrate, FR4, with characterized relative electric permittivity εr = 4.3 and tangent of losses δ = 0.01. The rectifier circuit, which incorporates a circuit for impedance matching and uses the Schottky diode HSMS-2852, was projected and optimized by using Advanced Design Software (ADS) and built over the same FR4 substrate. The metamaterial cell is composed of two Square Split Ring Resonator (S-SRR) and a thin wire in order to operate with negative values of εr and relative magnetic permeability in 2.45 GHz. In order to evaluate the performance of the purposed rectenna two experimental charging tests were performed, one without and other with the metamaterial lens. The result obtained demonstrate that the electromagnetic lens was able to significantly increase the levels of electric current delivered to the battery, approximately 44%.

Keywords: electromagnetic energy harvesting, electromagnetic lens, metamaterial, rectenna

Procedia PDF Downloads 132

Authors: Hernández Marieta, Aguayo Mauricio, Pedreros María, Llompart Ovidio

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The hydrological cycle is influenced by multiple factors, including climate change, land use changes, and anthropogenic activities, all of which threaten water availability and quality worldwide. In recent decades, numerous investigations have used landscape metrics and hydrological modeling to demonstrate the influence of landscape patterns on the hydrological cycle components' natural dynamics. Many of these investigations have determined the repercussions on the quality and availability of water, sedimentation, and erosion regime, mainly in Asian basins. In fact, there is progress in this branch of science, but there are still unanswered questions for our region. This study examines the hydrological response in Chilean basins under various land use change scenarios (LUCC) and the influence of climate change. The components of the water cycle were modeled using a physically distributed type hydrological and hydraulic simulation model based on and oriented to mountain basins TETIS model. Future climate data were derived from Chilean regional simulations using the WRF-MIROC5 model, forced with the RCP 8.5 scenario, at a 25 km resolution for the periods 2030-2060 and 2061-2091. LUCC scenarios were designed based on nature-based solutions, landscape pattern influences, current national and international water conservation legislation, and extreme scenarios of non-preservation and conservation of native forests. The scenarios that demonstrate greater water availability, even under climate change, are those promoting the restoration of native forests in over 30% of the basins, even alongside agricultural activities. Current legislation promoting the restoration of native forests only in riparian zones (30-60 m or 200 m in steeper areas) will not be resilient enough to address future water shortages. Evapotranspiration, direct runoff, and water availability at basin outlets showed the greatest variations due to LUCC. The relationship between hydrological modeling and landscape configuration is an effective tool for establishing future territorial planning that prioritizes water resource protection.

Keywords: TETIS, landscape pattern, hydrological process, water availability, Chilean basins

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Authors: Neslihan Demirci, Serdar Durdağı

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Mycobacterium tuberculosis is still a worldwide disease-causing agent that, according to WHO, led to the death of 1.5 million people from tuberculosis (TB) in 2020. The bacteria reside in macrophages located specifically in the lung. There is a known quadruple drug therapy regimen for TB consisting of isoniazid (INH), rifampin (RIF), pyrazinamide (PZA), and ethambutol (EMB). Over the past 60 years, there have been great contributions to treatment options, such as recently approved delamanid (OPC67683) and bedaquiline (TMC207/R207910), targeting mycolic acid and ATP synthesis, respectively. Also, there are natural compounds that can block the tryptophanyl-tRNA synthetase (TrpRS) enzyme, chuangxinmycin, and indolmycin. Yet, already the drug resistance is reported for those agents. In this study, the newly released TrpRS enzyme structure is investigated for potential inhibitor drugs from already synthesized molecules to help the treatment of resistant cases and to propose an alternative drug for the quadruple drug therapy of tuberculosis. Maestro, Schrodinger is used for docking and molecular dynamic simulations. In-house library containing ~8000 compounds among FDA-approved indole-containing compounds, a total of 57 obtained from the ChemBL were used for both ATP and tryptophan binding pocket docking. Best of indole-containing 57 compounds were subjected to hit expansion and compared later with virtual screening workflow (VSW) results. After docking, VSW was done. Glide-XP docking algorithm was chosen. When compared, VSW alone performed better than the hit expansion module. Best scored compounds were kept for ten ns molecular dynamic simulations by Desmond. Further, 100 ns molecular dynamic simulation was performed for elected molecules according to Z-score. The top three MMGBSA-scored compounds were subjected to steered molecular dynamic (SMD) simulations by Gromacs. While SMD simulations are still being conducted, ponesimod (for multiple sclerosis), vilanterol (β₂ adrenoreceptor agonist), and silodosin (for benign prostatic hyperplasia) were found to have a significant affinity for tuberculosis TrpRS, which is the propulsive force for the urge to expand the research with in vitro studies. Interestingly, top-scored ponesimod has been reported to have a side effect that makes the patient prone to upper respiratory tract infections.

Keywords: drug repurposing, molecular dynamics, tryptophanyl-tRNA synthetase, tuberculosis

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Authors: Chloe E. Edwards, Yasaman Shahtaheri

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Risk management plays a fundamental role in project planning and delivery. The purpose of incorporating risk management into project management practices is to identify and address uncertainties related to key project-related activities. The uncertainties, known as risk events, can relate to project deliverables that are quantifiable and are often measured by impact to project schedule, cost, or environmental impact. Risk management should be incorporated as an iterative practice throughout the planning, execution, and commissioning phases of a project. This paper specifically examines how risk management contributes to effective project planning and delivery through a case study of a transportation project. This case study focused solely on impacts to project schedule regarding three milestones: readiness for delivery, readiness for testing and commissioning, and completion of the facility. The case study followed the ISO 31000: Risk Management – Guidelines. The key factors that are outlined by these guidelines include understanding the scope and context of the project, conducting a risk assessment including identification, analysis, and evaluation, and lastly, risk treatment through mitigation measures. This process requires continuous consultation with subject matter experts and monitoring to iteratively update the risks accordingly. The risk identification process led to a total of fourteen risks related to design, permitting, construction, and commissioning. The analysis involved running 1,000 Monte Carlo simulations through @RISK 8.0 Industrial software to determine potential milestone completion dates based on the project baseline schedule. These dates include the best case, most likely case, and worst case to provide an estimated delay for each milestone. Evaluation of these results provided insight into which risks were the highest contributors to the projected milestone completion dates. Based on the analysis results, the risk management team was able to provide recommendations for mitigation measures to reduce the likelihood of risks occurring. The risk management team also provided recommendations for managing the identified risks and project activities moving forward to meet the most likely or best-case milestone completion dates.

Keywords: construction management, monte carlo simulation, project delivery, risk assessment, transportation engineering

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Authors: Kamal Kaushik, Trisha, Dandapni, Sambit Nanda, A. Mukherjee, S. Pradhan

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INTRODUCTION: As complexity in radiotherapy practice and processes are increasing, there is a need to assure quality control to a greater extent. At present, no international literature available with regards to the optimal quality control indicators for radiotherapy; moreover, few clinical audits have been conducted in the field of radiotherapy. The primary aim is to improve the processes that directly impact clinical outcomes for patients in terms of patient safety and quality of care. PROCEDURE: A team of an Oncologist, a Medical Physicist and a Radiation Therapist was formed for weekly clinical audits of patient’s undergoing radiotherapy audits The stages for audits include Pre planning audits, Simulation, Planning, Daily QA, Implementation and Execution (with image guidance). Errors in all the parts of the chain were evaluated and recorded for the development of further departmental protocols for radiotherapy. EVALUATION: The errors at various stages of radiotherapy chain were evaluated and recorded for comparison before starting the clinical audits in the department of radiotherapy and after starting the audits. It was also evaluated to find the stage in which maximum errors were recorded. The clinical audits were used to structure standard protocols (in the form of checklist) in department of Radiotherapy, which may lead to further reduce the occurrences of clinical errors in the chain of radiotherapy. RESULTS: The aim of this study is to perform a comparison between number of errors in different part of RT chain in two groups (A- Before Audit and B-After Audit). Group A: 94 pts. (48 males,46 female), Total no. of errors in RT chain:19 (9 needed Resimulation) Group B: 94 pts. (61 males,33 females), Total no. of errors in RT chain: 8 (4 needed Resimulation) CONCLUSION: After systematic periodic clinical audits percentage of error in radiotherapy process reduced more than 50% within 2 months. There is a great need in improving quality control in radiotherapy, and the role of clinical audits can only grow. Although clinical audits are time-consuming and complex undertakings, the potential benefits in terms of identifying and rectifying errors in quality control procedures are potentially enormous. Radiotherapy being a chain of various process. There is always a probability of occurrence of error in any part of the chain which may further propagate in the chain till execution of treatment. Structuring departmental protocols and policies helps in reducing, if not completely eradicating occurrence of such incidents.

Keywords: audit, clinical, radiotherapy, improving functionality

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Authors: Precious Ehiomogue, Ifechukwude Israel Ahuchaogu, Isiguzo Edwin Ahaneku

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Vermicompost is the product of the decomposition process using various species of worms, to create a mixture of decomposing vegetable or food waste, bedding materials, and vemicast. This process is called vermicomposting, while the rearing of worms for this purpose is called vermiculture. Several works have verified the adsorption of toxic metals using vermicompost but the application is still scarce for the retention of organic compounds. This research brings to knowledge the effectiveness of earthworm waste (vermicompost) for the remediation of crude oil contaminated soils. The remediation methods adopted in this study were two soil washing methods namely, batch and column process which represent laboratory and in-situ remediation. Characterization of the vermicompost and crude oil contaminated soil were performed before and after the soil washing using Fourier transform infrared (FTIR), scanning electron microscopy (SEM), X-ray fluorescence (XRF), X-ray diffraction (XRD) and Atomic adsorption spectrometry (AAS). The optimization of washing parameters, using response surface methodology (RSM) based on Box-Behnken Design was performed on the response from the laboratory experimental results. This study also investigated the application of machine learning models [Artificial neural network (ANN), Adaptive neuro fuzzy inference system (ANFIS). ANN and ANFIS were evaluated using the coefficient of determination (R²) and mean square error (MSE)]. Removal efficiency obtained from the Box-Behnken design experiment ranged from 29% to 98.9% for batch process remediation. Optimization of the experimental factors carried out using numerical optimization techniques by applying desirability function method of the response surface methodology (RSM) produce the highest removal efficiency of 98.9% at absorbent dosage of 34.53 grams, adsorbate concentration of 69.11 (g/ml), contact time of 25.96 (min), and pH value of 7.71, respectively. Removal efficiency obtained from the multilevel general factorial design experiment ranged from 56% to 92% for column process remediation. The coefficient of determination (R²) for ANN was (0.9974) and (0.9852) for batch and column process, respectively, showing the agreement between experimental and predicted results. For batch and column precess, respectively, the coefficient of determination (R²) for RSM was (0.9712) and (0.9614), which also demonstrates agreement between experimental and projected findings. For the batch and column processes, the ANFIS coefficient of determination was (0.7115) and (0.9978), respectively. It can be concluded that machine learning models can predict the removal of crude oil from polluted soil using vermicompost. Therefore, it is recommended to use machines learning models to predict the removal of crude oil from contaminated soil using vermicompost.

Keywords: ANFIS, ANN, crude-oil, contaminated soil, remediation and vermicompost

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Authors: A. Alyaaria, b, J. P. Wignerona, A. Ducharneb, Y. Kerrc, P. de Rosnayd, R. de Jeue, A. Govinda, A. Al Bitarc, C. Albergeld, J. Sabaterd, C. Moisya, P. Richaumec, A. Mialonc

Abstract:

Global Level-3 surface soil moisture (SSM) maps from the passive microwave soil moisture and Ocean Salinity satellite (SMOSL3) have been released. To further improve the Level-3 retrieval algorithm, evaluation of the accuracy of the spatio-temporal variability of the SMOS Level 3 products (referred to here as SMOSL3) is necessary. In this study, a comparative analysis of SMOSL3 with a SSM product derived from the observations of the Advanced Microwave Scanning Radiometer (AMSR-E) computed by implementing the Land Parameter Retrieval Model (LPRM) algorithm, referred to here as AMSRM, is presented. The comparison of both products (SMSL3 and AMSRM) were made against SSM products produced by a numerical weather prediction system (SM-DAS-2) at ECMWF (European Centre for Medium-Range Weather Forecasts) for the 03/2010-09/2011 period at global scale. The latter product was considered here a 'reference' product for the inter-comparison of the SMOSL3 and AMSRM products. Three statistical criteria were used for the evaluation, the correlation coefficient (R), the root-mean-squared difference (RMSD), and the bias. Global maps of these criteria were computed, taking into account vegetation information in terms of biome types and Leaf Area Index (LAI). We found that both the SMOSL3 and AMSRM products captured well the spatio-temporal variability of the SM-DAS-2 SSM products in most of the biomes. In general, the AMSRM products overestimated (i.e., wet bias) while the SMOSL3 products underestimated (i.e., dry bias) SSM in comparison to the SM-DAS-2 SSM products. In term of correlation values, the SMOSL3 products were found to better capture the SSM temporal dynamics in highly vegetated biomes ('Tropical humid', 'Temperate Humid', etc.) while best results for AMSRM were obtained over arid and semi-arid biomes ('Desert temperate', 'Desert tropical', etc.). When removing the seasonal cycles in the SSM time variations to compute anomaly values, better correlation with the SM-DAS-2 SSM anomalies were obtained with SMOSL3 than with AMSRM, in most of the biomes with the exception of desert regions. Eventually, we showed that the accuracy of the remotely sensed SSM products is strongly related to LAI. Both the SMOSL3 and AMSRM (slightly better) SSM products correlate well with the SM-DAS2 products over regions with sparse vegetation for values of LAI < 1 (these regions represent almost 50% of the pixels considered in this global study). In regions where LAI>1, SMOSL3 outperformed AMSRM with respect to SM-DAS-2: SMOSL3 had almost consistent performances up to LAI = 6, whereas AMSRM performance deteriorated rapidly with increasing values of LAI.

Keywords: remote sensing, microwave, soil moisture, AMSR-E, SMOS

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Authors: Najibullah M, Soumendra Nath Kuiry

Abstract:

Dams and reservoirs are perceived for their estimable alms to irrigation, water supply, flood control, electricity generation, etc. which civilize the prosperity and wealth of society across the world. Meantime the dam breach could cause devastating flood that can threat to the human lives and properties. Failures of large dams remain fortunately very seldom events. Nevertheless, a number of occurrences have been recorded in the world, corresponding in an average to one to two failures worldwide every year. Some of those accidents have caused catastrophic consequences. So it is decisive to predict the dam break flow for emergency planning and preparedness, as it poses high risk to life and property. To mitigate the adverse impact of dam break, modeling is necessary to gain a good understanding of the temporal and spatial evolution of the dam-break floods. This study will mainly deal with one-dimensional (1D) dam break modeling. Less commonly used in the hydraulic research community, another possible option for modeling the rapidly varied dam-break flows is the extended Boussinesq equations (BEs), which can describe the dynamics of short waves with a reasonable accuracy. Unlike the Shallow Water Equations (SWEs), the BEs taken into account the wave dispersion and non-hydrostatic pressure distribution. To capture the dam-break oscillations accurately it is very much needed of at least fourth-order accurate numerical scheme to discretize the third-order dispersion terms present in the extended BEs. The scope of this work is therefore to develop an 1D fourth-order accurate in both space and time Boussinesq model for dam-break flow analysis by using finite-volume / finite difference scheme. The spatial discretization of the flux and dispersion terms achieved through a combination of finite-volume and finite difference approximations. The flux term, was solved using a finite-volume discretization whereas the bed source and dispersion term, were discretized using centered finite-difference scheme. Time integration achieved in two stages, namely the third-order Adams Basforth predictor stage and the fourth-order Adams Moulton corrector stage. Implementation of the 1D Boussinesq model done using PYTHON 2.7.5. Evaluation of the performance of the developed model predicted as compared with the volume of fluid (VOF) based commercial model ANSYS-CFX. The developed model is used to analyze the risk of cascading dam failures similar to the Panshet dam failure in 1961 that took place in Pune, India. Nevertheless, this model can be used to predict wave overtopping accurately compared to shallow water models for designing coastal protection structures.

Keywords: Boussinesq equation, Coastal protection, Dam-break flow, One-dimensional model

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