Search results for: clinical prediction rule

Authors: Champika S. Kariyawasam

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The distribution of gorse (Ulex europaeus) plants in South Australia has been modelled using 126 presence-only location data as a function of seven climate parameters. The predicted range of U. europaeus is mainly along the Mount Lofty Ranges in the Adelaide Hills and on Kangaroo Island. Annual precipitation and yearly average aridity index appeared to be the highest contributing variables to the final model formulation. The Jackknife procedure was employed to identify the contribution of different variables to gorse model outputs and response curves were used to predict changes with changing environmental variables. Based on this analysis, it was revealed that the combined effect of one or more variables could make a completely different impact to the original variables on their own to the model prediction. This work also demonstrates the need for a careful approach when selecting environmental variables for projecting correlative models to climatically distinct area. Maxent acts as a robust model when projecting the fitted species distribution model to another area with changing climatic conditions, whereas the generalized linear model, bioclim, and domain models to be less robust in this regard. These findings are important not only for predicting and managing invasive alien gorse in South Australia and Sri Lanka but also in other countries of the invasive range.

Keywords: invasive species, Maxent, species distribution modelling, Ulex europaeus

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Authors: Amina Sultan, Mohamed Ghonim, Eman Oweida, Aya Abdelaziz

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Objective: To assess the diagnostic reliability of multi-detector CT in pre and post-operative evaluation of cochlear implant candidates. Material and Methods: The study includes 40 patients (18 males and 22 females); mean age 5.6 years. They were classified into two groups: Group A (20 patients): cochlear implant device was Nucleus-22 and Group B (20 patients): the device was MED-EL. Cochlear length (CL) and cochlear height (CH) were measured pre-operatively by 128-multidetector CT. Electrode length (EL) and insertion depth angle (α) were measured post-operatively by MDCT. Results: For Group A mean CL was 9.1 mm ± 0.4 SD; mean CH was 4.1 ± 0.3 SD; mean EL was 18 ± 2.7 SD; mean α angle was 299.05 ± 37 SD. Significant statistical correlation (P < 0.05) was found between preoperative CL and post-operative EL (r²=0.6); as well as EL and α angle (r²=0.7). Group B's mean CL was 9.1 mm ± 0.3 SD; mean CH was 4.1 ± 0.4 SD; mean EL was 27 ± 2.1 SD; mean α angle was 287.6 ± 41.7 SD. Significant statistical correlation was found between CL and EL (r²= 0.6) and α angle (r²=0.5). Also, a strong correlation was found between EL and α angle (r²=0.8). Significant statistical difference was detected between the two devices as regards to the electrode length. Conclusion: Multidetector CT is a reliable tool for preoperative planning and post-operative evaluation of the outcomes of cochlear implant operations. Cochlear length is a valuable prognostic parameter for prediction of the depth of electrode array insertion which can influence criteria of device selection.

Keywords: angle of insertion (α angle), cochlear implant (CI), cochlear length (CL), Multidetector Computed Tomography (MDCT)

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Authors: Matza Gusto Andika, Malinda Sabrina, Syarie Fatunnisa

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Aerodynamic and aeroelastics studies on the hanger bridge profile are important to analyze the aerodynamic phenomenon and Aeroelastics stability of hanger. Wind tunnel tests were conducted on a model of H-beam profile from hanger bridge. The purpose of this study is to investigate steady aerodynamic characteristics such as lift coefficient (Cl), drag coefficient (Cd), and moment coefficient (Cm) under the different angle of attack for preliminary prediction of aeroelastics stability problems. After investigation the steady aerodynamics characteristics from the model, dynamic testing is also conducted in wind tunnel to know the aeroelastics phenomenon which occurs at the H-beam hanger bridge profile. The studies show that the torsional vortex induced vibration occur when the wind speed is 7.32 m/s until 9.19 m/s with maximum amplitude occur when the wind speed is 8.41 m/s. The result of wind tunnel testing is matching to hanger vibration where occur in the field, so wind tunnel studies has successful to model the problem. In order that the H-beam profile is not good enough for the hanger bridge and need to be modified to minimize the Aeroelastics problem. The modification can be done with structure dynamics modification or aerodynamics modification.

Keywords: aerodynamics, aeroelastic, hanger bridge, h-beam profile, vortex induced vibration, wind tunnel

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Authors: J. Sharon Mano Pappu, Sathyanarayana N. Gummadi

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Growth of Debaryomyces nepalensis on mixed substrates in batch culture follows diauxic pattern of completely utilizing glucose during the first exponential growth phase, followed by an intermediate lag phase and a second exponential growth phase consuming xylose. The present study deals with the development of cybernetic mathematical model for prediction of xylitol production and yield. Production of xylitol from xylose in batch fermentation is investigated in the presence of glucose as the co-substrate. Different ratios of glucose and xylose concentrations are assessed to study the impact of multi substrate on production of xylitol in batch reactors. The parameters in the model equations were estimated from experimental observations using integral method. The model equations were solved simultaneously by numerical technique using MATLAB. The developed cybernetic model of xylose fermentation in the presence of a co-substrate can provide answers about how the ratio of glucose to xylose influences the yield and rate of production of xylitol. This model is expected to accurately predict the growth of microorganism on mixed substrate, duration of intermediate lag phase, consumption of substrate, production of xylitol. The model developed based on cybernetic modelling framework can be helpful to simulate the dynamic competition between the metabolic pathways.

Keywords: co-substrate, cybernetic model, diauxic growth, xylose, xylitol

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Authors: Adeola Adejumo, Oluwole David Adebayo, Muraina Kamilu Olanrewaju

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The purpose of this study was to investigate the influence of some psychological factors (i.e, school climate, parental involvement and classroom interaction) on the learning gains of university undergraduates in Mathematics in Ibadan, Nigeria. Three hundred undergraduates who are on open distance learning education programme in the University of Ibadan and thirty mathematics lecturers constituted the study’s sample. Both the independent and dependent variables were measured with relevant standardized instruments and the data obtained was analyzed using multiple regression statistical method. The instruments used were school climate scale, parental involvement scale and classroom interaction scale. Three research questions were answered in the study. The result showed that there was significant relationship between the three independent variables (school climate, parental involvement and classroom interaction) on the students’ learning gain in mathematics and that the independent variables both jointly and relatively contributed significantly to the prediction of students’ learning gain in mathematics. On the strength of these findings, the need to enhance the school climate, improve the parents’ involvement in the student’s education and encourage students’ classroom interaction were stressed and advocated.

Keywords: school climate, parental involvement, ODL, learning gains, mathematics

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Authors: P. Karpagam, Babu Joseph, P. Ashok Kumar

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The incidence of Candida infections is increasing worldwide. The serious nature of these infections is compounded by increasing levels of drug resistance. Pure cultures of the Candida sp. were obtained from clinical isolates and fresh garlic extracts were obtained by extraction techniques. The antifungal activity of garlic extract was investigated in an in vitro system. The extract (100%, 75% and 50%) showed significant antifungal activity against Candida, whereas, low concentration (25%) of the extract showed less antifungal activity against the test organism. Antifungal activities of honey and lemon juice were tested against the Candida; however, the growth was not inhibited by these extracts. On the other hand honey and lemon when combined with garlic exhibited a good antifungal activity. The study thus confirms the antifungal properties of garlic extract along with additives like honey and lemon have significant antifungal activity against isolates of Candida species.

Keywords: Candida, garlic extract, lemon, synergitic antifungal activity

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Authors: Vesna Karapetkovska Hristova, M. Ayaz Ahmad, Julijana Tomovska, Biljana Bogdanova Popov, Blagojce Najdovski

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The average total concentrations of heavy metals; (cadmium [Cd], copper [Cu], nickel [Ni], lead [Pb], and zinc [Zn]) were analyzed in soil and vegetables samples collected from the different region of Macedonia during the years 2010-2012. Basic soil properties such as pH, organic matter and clay content were also included in the study. The average concentrations of Cd, Cu, Ni, Pb, Zn in the A horizon (0-30 cm) of agricultural soils were as follows, respectively: 0.25, 5.3, 6.9, 15.2, 26.3 mg kg-1 of soil. We have found that neural networking model can be considered as a tool for prediction and spatial analysis of the processes controlling the metal transfer within the soil-and vegetables. The predictive ability of such models is well over 80% as compared to 20% for typical regression models. A radial basic function network reflects good predicting accuracy and correlation coefficients between soil properties and metal content in vegetables much better than the back-propagation method. Neural Networking / soft computing can support the decision-making processes at different levels, including agro ecology, to improve crop management based on monitoring data and risk assessment of metal transfer from soils to vegetables.

Keywords: soft computing approach, total concentrations, heavy metals, agricultural soils

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Authors: Vijayalakshmi Marella, NageswaraRaoPilli

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This paper describes a simple, rapid and sensitive liquid chromatography / tandem mass spectrometry assay for the determination of montelukast in human plasma using montelukast d6 as an internal standard. Analyte and the internal standard were extracted from 50 µL of human plasma via solid phase extraction technique without evaporation, drying and reconstitution steps. The chromatographic separation was achieved on a C18 column by using a mixture of methanol and 5mM ammonium acetate (80:20, v/v) as the mobile phase at a flow rate of 0.8 mL/min. Good linearity results were obtained during the entire course of validation. Method validation was performed as per FDA guidelines and the results met the acceptance criteria. A run time of 2.5 min for each sample made it possible to analyze more number of samples in short time, thus increasing the productivity. The proposed method was found to be applicable to clinical studies.

Keywords: Montelukast, tandem mass spectrometry, montelukast d6, FDA guidelines

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Authors: Lucia Liu, Matthew Koziol

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Obsessive-compulsive disorder (OCD), a long-lasting anxiety disorder involving recurrent, intrusive thoughts, affects over 2 million adults in the United States. Transcranial magnetic stimulation (TMS) stands out as a noninvasive, cutting-edge therapy that has been shown to reduce symptoms in patients with treatment-resistant OCD. The Food and Drug Administration (FDA) approved protocol pairs TMS sessions with individualized symptom provocation, aiming to improve the susceptibility of brain circuits to stimulation. However, limited standardization or guidance exists on how to conduct symptom provocation and which methods are most effective. This study aims to compare the effect of internal versus external techniques to induce obsessional stress in a clinical setting during TMS therapy. Two symptom provocation methods, (i) Asking patients thought-provoking questions about their obsessions (internal) and (ii) Requesting patients to perform obsession-related tasks (external), were employed in a crossover design with repeated measurement. Thirty-six treatments of NeuroStar TMS were administered to each of two patients over 8 weeks in an outpatient clinic. Patient One received 18 sessions of internal provocation followed by 18 sessions of external provocation, while Patient Two received 18 sessions of external provocation followed by 18 sessions of internal provocation. The primary outcome was the level of self-reported obsessional stress on a visual analog scale from 1 to 10. The secondary outcome was self-reported OCD severity, collected biweekly in a four-level Likert-scale (1 to 4) of bad, fair, good and excellent. Outcomes were compared and tested between provocation arms through repeated measures ANOVA, accounting for intra-patient correlations. Ages were 42 for Patient One (male, White) and 57 for Patient Two (male, White). Both patients had similar moderate symptoms at baseline, as determined through the Yale-Brown Obsessive Compulsive Scale (YBOCS). When comparing obsessional stress induced across the two arms of internal and external provocation methods, the mean (SD) was 6.03 (1.18) for internal and 4.01 (1.28) for external strategies (P=0.0019); ranges were 3 to 8 for internal and 2 to 8 for external strategies. Internal provocation yielded 5 (31.25%) bad, 6 (33.33%) fair, 3 (18.75%) good, and 2 (12.5%) excellent responses for OCD status, while external provocation yielded 5 (31.25%) bad, 9 (56.25%) fair, 1 (6.25%) good, and 1 (6.25%) excellent responses (P=0.58). Internal symptom provocation tactics had a significantly stronger impact on inducing obsessional stress and led to better OCD status (non-significant). This could be attributed to the fact that answering questions may prompt patients to reflect more on their lived experiences and struggles with OCD. In the future, clinical trials with larger sample sizes are warranted to validate this finding. Results support the increased integration of internal methods into structured provocation protocols, potentially reducing the time required for provocation and achieving greater treatment response to TMS.

Keywords: obsessive-compulsive disorder, transcranial magnetic stimulation, mental health, symptom provocation

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Authors: Areej Al-Khabbaz, Swapna Goerge, Majedah Abdul-Rasoul

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Introduction: The most prevalent periodontal disease among children is gingivitis, and it usually becomes more severe in adolescence. A number of intervention studies suggested that resolution of periodontal inflammation can improve metabolic control in patients diagnosed with diabetes mellitus. Aim: to assess the effect of non-surgical periodontal therapy on glycemic control of children diagnosed with diabetes mellitus. Method: Twenty-eight children diagnosed with diabetes mellitus were recruited with established diagnosis diabetes for at least 1 year. Informed consent and child assent form were obtained from children and parents prior to enrolment. The dental examination for the participants was performed on the same week directly following their annual medical assessment. All patients had their glycosylated hemoglobin (HbA1c%) test one week prior to their annual medical and dental visit and 3 months following non-surgical periodontal therapy. All patients received a comprehensive periodontal examination The periodontal assessment included clinical attachment loss, bleeding on probing, plaque score, plaque index and gingival index. All patients were referred for non-surgical periodontal therapy, which included oral hygiene instruction and motivation followed by supra-gingival and subg-ingival scaling using ultrasonic and hand instruments. Statistical Analysis: Data were entered and analyzed using the Statistical Package for Social Science software (SPSS, Chicago, USA), version 18. Statistical analysis of clinical findings was performed to detect differences between the two groups in term of periodontal findings and HbA1c%. Binary logistic regression analysis was performed in order to examine which factors were significant in multivariate analysis after adjusting for confounding between effects. The regression model used the dependent variable ‘Improved glycemic control’, and the independent variables entered in the model were plaque index, gingival index, bleeding %, plaque Statistical significance was set at p < 0.05. Result: A total of 28 children. The mean age of the participants was 13.3±1.92 years. The study participants were divided into two groups; Compliant group (received dental scaling) and non-complaints group (received oral hygiene instructions only). No statistical difference was found between compliant and non-compliant group in age, gender distribution, oral hygiene practice and the level of diabetes control. There was a significant difference between compliant and non-compliant group in term of improvement of HBa1c before and after periodontal therapy. Mean gingival index was the only significant variable associated with improved glycemic control level. In conclusion, this study has demonstrated that non-surgical mechanical periodontal therapy can improve HbA1c% control. The result of this study confirmed that children with diabetes mellitus who are compliant to dental care and have routine professional scaling may have better metabolic control compared to diabetic children who are erratic with dental care.

Keywords: children, diabetes, metabolic control, periodontal therapy

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Authors: Grace Omowunmi Soyebi

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A computerized medical record system is a collection of medical information about a person that is stored on a computer. One principal problem of most hospitals in rural areas is using the file management system for keeping records. A lot of time is wasted when a patient visits the hospital, probably in an emergency, and the nurse or attendant has to search through voluminous files before the patient's file can be retrieved; this may cause an unexpected to happen to the patient. This data mining application is to be designed using a structured system analysis and design method which will help in a well-articulated analysis of the existing file management system, feasibility study, and proper documentation of the design and implementation of a computerized medical record system. This computerized system will replace the file management system and help to quickly retrieve a patient's record with increased data security, access clinical records for decision-making, and reduce the time range at which a patient gets attended to.

Keywords: programming, data, software development, innovation

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Authors: M. Khalid, W. Rashmi, L. L. Kwan

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This work provides an overview on the experimental and numerical simulations of various nanofluids and their flow and heat transfer behavior. It was further extended to study the effect of nanoparticle concentration, fluid flow rates and thermo-physical properties on the heat transfer enhancement of Al2O3/water nanofluid in a turbulent flow circular conduit using ANSYS FLUENT™ 14.0. Single-phase approximation (homogeneous model) and two-phase (mixture and Eulerian) models were used to simulate the nanofluid flow behavior in the 3-D horizontal pipe. The numerical results were further validated with experimental correlations reported in the literature. It was found that heat transfer of nanofluids increases with increasing particle volume concentration and Reynolds number, respectively. Results showed good agreement (~9% deviation) with the experimental correlations, especially for a single-phase model with constant properties. Among two-phase models, mixture model (~14% deviation) showed better prediction compared to Eulerian-dispersed model (~18% deviation) when temperature independent properties were used. Non-drag forces were also employed in the Eulerian two-phase model. However, the two-phase mixture model with temperature dependent nanofluid properties gave slightly closer agreement (~12% deviation).

Keywords: nanofluid, CFD, heat transfer, forced convection, circular conduit

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Authors: Valentina Di Maria, Anton Ianakiev

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The use of adhesive anchors for wooden constructions is an efficient technology to connect and design timber members in new timber structures and to rehabilitate the damaged structural members of historical buildings. Due to the lack of standard regulation in this specific area of structural design, designers’ choices are still supported by test analysis that enables knowledge, and the prediction, of the structural behavior of glued in rod joints. The paper outlines an experimental research activity aimed at identifying the tensile resistance capacity of several new adhesive joint prototypes made of epoxy resin, steel bar and timber, Oak and Douglas Fir species. The development of new adhesive connectors has been carried out by using epoxy to glue stainless steel bars into pre-drilled holes, characterized by smooth and rough internal surfaces, in timber samples. The realization of a threaded contact surface using a specific drill bit has led to an improved bond between wood and epoxy. The applied changes have also reduced the cost of the joints’ production. The paper presents the results of this parametric analysis and a Finite Element analysis that enables identification and study of the internal stress distribution in the proposed adhesive anchors.

Keywords: glued in rod joints, adhesive anchors, timber, epoxy, rough contact surface, threaded hole shape

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Authors: Arian Hosseini, Mahmudul Hasan

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To address the increasing need for efficient and accurate content moderation, we propose an efficient and lightweight deep classification ensemble structure. Our approach is based on a combination of simple visual features, designed for high-accuracy classification of violent content with low false positives. Our ensemble architecture utilizes a set of lightweight models with narrowed-down color features, and we apply it to both images and videos. We evaluated our approach using a large dataset of explosion and blast contents and compared its performance to popular deep learning models such as ResNet-50. Our evaluation results demonstrate significant improvements in prediction accuracy, while benefiting from 7.64x faster inference and lower computation cost. While our approach is tailored to explosion detection, it can be applied to other similar content moderation and violence detection use cases as well. Based on our experiments, we propose a "think small, think many" philosophy in classification scenarios. We argue that transforming a single, large, monolithic deep model into a verification-based step model ensemble of multiple small, simple, and lightweight models with narrowed-down visual features can possibly lead to predictions with higher accuracy.

Keywords: deep classification, content moderation, ensemble learning, explosion detection, video processing

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Authors: Heba Kamal, Ghada Saudi

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Due to The rapid expansion of energy and growing number of wind turbines construction in earthquake areas, a design method for simple and accurate evaluation of seismic load to ensure structural integrity is required. In Egypt, there are some appropriate places to build wind turbine towers lie in active seismically regions, so accurate analysis is necessary for prediction of seismic loads with consideration of intensity of the earthquake, soil and structural characteristics. In this research, seismic behavior of wind turbine towers Gamesa Type G52 in Zafarana Wind Farm Egypt is investigated using experimental work by ambient vibration test, and fully dynamic analysis based on time history from El Aqaba Earthquake 1995 using 3D by PLAXIS 3D software, including the soil structure interaction effect. The results obtained from dynamic analyses are discussed. From this study, it is concluded that, the fully dynamic seismic analysis based on used PLAXIS 3D with the aid of the full scale ambient vibration test gives almost good simulation for the seismic loads that can be applied to wind turbine tower design in Egypt.

Keywords: Wind turbine towers, Zafarana Wind Farm, Gamesa Type G52, ambient vibration test

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Authors: Rebiahi Sid Ahmed, Boutarfi Zakaria, Rahmoun Malika, Antonio Galvez

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The objective of this study was to explore the possible correlation between resistance to antibiotics and tolerance to biocides in Gram-negative bacilli isolated from the University Hospital Center of Tlemcen. This study focused on 175 clinical isolates of Gram-negative bacilli, it is a question of exploring: their level and profile of resistance to antibiotics, their tolerance to biocides, as well as the identification of the genetic supports of this resistance. Enterobacter spp. was the most predominant bacterial genus, all isolates harbored at least one of the studied genes with significant resistance capacity. Our results show, in some cases, a possible positive correlation between the presence of biocide tolerance genes and those of antibiotic resistance; in fact, tolerance to biocides could be one of the co-selection factors for antibiotic resistance. The results of this study should encourage the good practice of hygiene measures as well as the rational use of antimicrobials in order to hinder the development and emergence of resistance in our hospital departments.Mots clés : Antibiotiques, Biocides, Enterobacter, Hôpital, Résistance,

Keywords: antibiotic, biocides, enterobacter, hospital, resistance

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Authors: Tejeet Singh, Virat Khanna

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Composite materials have drawn considerable attention of engineers due to their light weight and application at high thermo-mechanical loads. With regard to the prediction of the life of high temperature structural components like rotating cylinders and the evaluation of their deterioration with time, it is essential to have a full knowledge of creep characteristics of these materials. Therefore, in the present study the secondary stage creep stresses and strain rates are estimated in thick-walled composite cylinders subjected to rotary inertia at different angular speeds. The composite cylinder is composed of aluminum matrix (Al) and reinforced with silicon carbide (SiC) particles which are uniformly mixed. The creep response of the material of the cylinder is described by threshold stress based creep law. The study indicates that with the increase in angular speed, the radial, tangential, axial and effective stress increases to a significant value. However, the radial stress remains zero at inner radius and outer radius due to imposed boundary conditions of zero pressure. Further, the stresses are tensile in nature throughout the entire radius of composite cylinder. The strain rates are also influenced in the same manner as that of creep stresses. The creep rates will increase significantly with the increase of centrifugal force on account of rotation.

Keywords: composite, creep, rotating cylinder, angular speed

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Authors: Strahinja Z. Kovačević, Lidija R. Jevrić, Sanja O. Podunavac Kuzmanović

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The relationship between antibacterial activity of eighteen different substituted benzimidazole derivatives and their molecular characteristics was studied using chemometric QSAR (Quantitative Structure–Activity Relationships) approach. QSAR analysis has been carried out on inhibitory activity towards Staphylococcus aureus, by using molecular descriptors, as well as minimal inhibitory activity (MIC). Molecular descriptors were calculated from the optimized structures. Principal component analysis (PCA) followed by hierarchical cluster analysis (HCA) and multiple linear regression (MLR) was performed in order to select molecular descriptors that best describe the antibacterial behavior of the compounds investigated, and to determine the similarities between molecules. The HCA grouped the molecules in separated clusters which have the similar inhibitory activity. PCA showed very similar classification of molecules as the HCA, and displayed which descriptors contribute to that classification. MLR equations, that represent MIC as a function of the in silico molecular descriptors were established. The statistical significance of the estimated models was confirmed by standard statistical measures and cross-validation parameters (SD = 0.0816, F = 46.27, R = 0.9791, R2CV = 0.8266, R2adj = 0.9379, PRESS = 0.1116). These parameters indicate the possibility of application of the established chemometric models in prediction of the antibacterial behaviour of studied derivatives and structurally very similar compounds.

Keywords: antibacterial, benzimidazole, molecular descriptors, QSAR

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Authors: Nur Mohammad Ali, Md. Shafiqul Alam, Jayanta Bhusan Deb, Nowrin Sharmin

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The past ten years have seen significant expansion in the aviation sector, which during the previous five years has steadily pushed emerging countries closer to economic independence. It is crucial to accurately forecast the potential demand for air travel to make long-term financial plans. To forecast market demand for low-cost passenger carriers, this study suggests working with low-cost airlines, airports, consultancies, and governmental institutions' strategic planning divisions. The study aims to develop an artificial intelligence-based methods, notably fuzzy inference systems (FIS), to determine the most accurate forecasting technique for domestic low-cost carrier demand in Bangladesh. To give end users real-world applications, the study includes nine variables, two sub-FIS, and one final Mamdani Fuzzy Inference System utilizing a graphical user interface (GUI) made with the app designer tool. The evaluation criteria used in this inquiry included mean square error (MSE), accuracy, precision, sensitivity, and specificity. The effectiveness of the developed air passenger demand prediction FIS is assessed using 240 data sets, and the accuracy, precision, sensitivity, specificity, and MSE values are 90.83%, 91.09%, 90.77%, and 2.09%, respectively.

Keywords: aviation industry, fuzzy inference system, membership function, graphical user interference

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Authors: Teng Zhang, Xuelin Zhang, Shouxiang Lu, Changhai Li

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The existence of animal groups and collective migration are common in nature, and collective behavior is attracting more and more attention of researchers. Previous results have shown that architectural design had an important effect on the process of crowd evacuation. In this paper, collective behavior of mice passing through a middle-exit or corner-exit under panic was investigated. Selfish behavior and herd behavior were easily observed in our video, which caused the congregation with high density near the exit. Triangle structure of congregation formed near the middle-exit while arch structure formed near the corner-exit. It is noteworthy that the exit located at the middle of the wall was more effective for evacuation than at the corner. Meanwhile, the escape sequence of mouse passing through the exit was investigated, and the result showed that the priority depends largely on its location in the congregation. With the level of stimulus increasing, these phenomena still exist. The frequency distributions of time intervals and the burst sizes were also analyzed in this study to explore the secret of collective behavior of mice. These results could provide evidence for the hypothesis or prediction about human behavior in crowd evacuation. However, it is not clear whether the simulated results from different species can correspond to reality or not. Broader comparison among different species about this topic will be eager to be conducted to deepen our understanding of collective behavior in nature.

Keywords: collective behavior, mice, evacuation, exit location

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Authors: Bin Liu

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MicroRNAs (miRNAs) are small non-coding RNA molecules, functioning in transcriptional and post-transcriptional regulation of gene expression. The discrimination of the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops) is necessary for the understanding of miRNAs’ role in the control of cell life and death. Since both their small size and sequence specificity, it cannot be based on sequence information alone but requires structure information about the miRNA precursor to get satisfactory performance. Kmers are convenient and widely used features for modeling the properties of miRNAs and other biological sequences. However, Kmers suffer from the inherent limitation that if the parameter K is increased to incorporate long range effects, some certain Kmer will appear rarely or even not appear, as a consequence, most Kmers absent and a few present once. Thus, the statistical learning approaches using Kmers as features become susceptible to noisy data once K becomes large. In this study, we proposed a Gapped k-mer approach to overcome the disadvantages of Kmers, and applied this method to the field of miRNA prediction. Combined with the structure status composition, a classifier called imiRNA-GSSC was proposed. We show that compared to the original imiRNA-kmer and alternative approaches. Trained on human miRNA precursors, this predictor can achieve an accuracy of 82.34 for predicting 4022 pre-miRNA precursors from eleven species.

Keywords: gapped k-mer, imiRNA-GSSC, microRNA precursor, support vector machine

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Authors: Alejandra Sierra, Gloria Valadez, Adriana Dávalos, Mirliana Ramírez

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For years, the Pan American Health Organization/World Health Organization and other organizations have made efforts to the improve access and the quality of prenatal care as part of comprehensive programs for maternal and neonatal health, the standards of care have been renewed in order to migrate from a medical perspective to a holistic perspective. However, despite the efforts currently antenatal care models have not been verified by a scientific evaluation in order to determine their effectiveness. The teenage pregnancy is considered as a very important phenomenon since it has been strongly associated with inequalities, poverty and the lack of gender quality; therefore it is important to analyze the antenatal care that’s been given, including not only the clinical intervention but also the activities surrounding the advertising and the health education. In this study, the objective was to describe if the previously established activities (on the prenatal care models) are being performed in the care of pregnant teenagers attending prenatal care in health institutions in two cities in México and Chile during 2013. Methods: Observational and descriptive study, of a transversal cohort. 170 pregnant women (13-19 years) were included in prenatal care in two health institutions (100 women from León-Mexico and 70 from Chile-Coquimbo). Data collection: direct survey, perinatal clinical record card which was used as checklists: WHO antenatal care model WHO-2003, Official Mexican Standard NOM-007-SSA2-1993 and Personalized Service Manual on Reproductive Process- Chile Crece Contigo; for data analysis descriptive statistics were used. The project was approved by the relevant ethics committees. Results: Regarding the fulfillment of interventions focused on physical, gynecological exam, immunizations, monitoring signs and biochemical parameters in both groups was met by more than 84%; the activities of guidance and counseling pregnant teenagers in Leon compliance rates were below 50%, on the other hand, although pregnant women in Coquimbo had a higher percentage of compliance, no one reached 100%. The topics that less was oriented were: family planning, signs and symptoms of complications and labor. Conclusions: Although the coverage of the interventions indicated in the prenatal care models was high, there were still shortcomings in the fulfillment of activities to orientation, education and health promotion. Deficiencies in adherence to prenatal care guidelines could be due to different circumstances such as lack of registration or incomplete filling of medical records, lack of medical supplies or health personnel, absences of people at prenatal check-up appointments, among many others. Therefore, studies are required to evaluate the quality of prenatal care and the effectiveness of existing models, considering the role of the different actors (pregnant women, professionals and health institutions) involved in the functionality and quality of prenatal care models, in order to create strategies to design or improve the application of a complete process of promotion and prevention of maternal and child health as well as sexual and reproductive health in general.

Keywords: adolescent health, health systems, maternal health, primary health care

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Authors: Raphael Tuor, Denis Lalanne

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The ATM research community is missing suitable tools to design, test, and validate new UI prototypes. Important stakes underline the implementation of both DSS and XAI methods into current systems. ML-based DSS are gaining in relevance as ATFM becomes increasingly complex. However, these systems only prove useful if a human can understand them, and thus new XAI methods are needed. The human-machine dyad should work as a team and should understand each other. We present xSky, a configurable benchmark tool that allows us to compare different versions of an ATC interface in conflict detection tasks. Our main contributions to the ATC research community are (1) a conflict detection task simulator (xSky) that allows to test the applicability of visual prototypes on scenarios of varying difficulty and outputting relevant operational metrics (2) a theoretical approach to the explanations of AI-driven trajectory predictions. xSky addresses several issues that were identified within available research tools. Researchers can configure the dimensions affecting scenario difficulty with a simple CSV file. Both the content and appearance of the XAI elements can be customized in a few steps. As a proof-of-concept, we implemented an XAI prototype inspired by the maritime field.

Keywords: air traffic control, air traffic simulation, conflict detection, explainable artificial intelligence, explainability, human-automation collaboration, human factors, information visualization, interpretability, trajectory prediction

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Authors: Rajat Mittal, Harshal Raut, Jung Hee Seo

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Wave-assisted propulsion (WAP) systems convert wave energy into thrust using elastically mounted hydrofoils. We employ sharp-interface immersed boundary simulations to examine the effect of two key parameters on the flow physics, the fluid-structure interaction, as well as thrust performance of these systems - the stiffness of the torsional spring and the location of the rotational center. The variation in spring stiffness leads to different amplitude of pitch motion, phase difference with respect to heaving motion and thrust coefficient and we show the utility of ‘maps’ of energy exchange between the flow and the hydrofoil system, as a way to understand and predict this behavior. The Force Partitioning Method (FPM) is used to decompose the pressure forces into individual components and understand the mechanism behind increase in thrust. Next, a scaling law is presented for the thrust coefficient generated by heaving and pitching foil. The parameters within the scaling law are calculated based on direct-numerical simulations based parametric study utilized to generate the energy maps. The predictions of the proposed scaling law are then compared with those of a similar model from the literature, showing a noticeable improvement in the prediction of the thrust coefficient.

Keywords: propulsion, flapping foils, hydrodynamics, wave power

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Authors: Ravi Teja Mandapaka, Prasanna Kumar Kukkamalla

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Poor absorption of calcium, elevated levels in serum and loss of bone are major problems of astronauts during space travel. Supplementation of calcium could not reveal this problem. In normal condition only 33% of calcium is absorbed from dietary sources. In this paper effect of space environment on calcium metabolism was discussed. Many surprising study findings were found during literature survey. Clinical trials on ovariectomized mice showed that reduction of calcium particles to nano level make them more absorbable and bioavailable. Control of bone loss in astronauts in critical important In Fortification of milk with nana calcium particles showed reduces urinary pyridinoline, deoxypyridinoline levels. Dietary calcium and supplementation do not show much retention of calcium in zero gravity environment where absorption is limited. So, the fortification of foods with nano calcium particles seemed beneficial for astronauts during and after space travel in their speedy recovery.

Keywords: nano calcium, astronauts, fortification, supplementation

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Authors: Sujoy Das, M. M. Ghosh

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The thermal conductivity of a fluid can be significantly enhanced by dispersing nano-sized particles in it, and the resultant fluid is termed as "nanofluid". A theoretical model for estimating the thermal conductivity of a nanofluid has been proposed here. It is based on the mechanism that evenly dispersed nanoparticles within a nanofluid undergo Brownian motion in course of which the nanoparticles repeatedly collide with the heat source. During each collision a rapid heat transfer occurs owing to the solid-solid contact. Molecular dynamics (MD) simulation of the collision of nanoparticles with the heat source has shown that there is a pulse-like pick up of heat by the nanoparticles within 20-100 ps, the extent of which depends not only on thermal conductivity of the nanoparticles, but also on the elastic and other physical properties of the nanoparticle. After the collision the nanoparticles undergo Brownian motion in the base fluid and release the excess heat to the surrounding base fluid within 2-10 ms. The Brownian motion and associated temperature variation of the nanoparticles have been modeled by stochastic analysis. Repeated occurrence of these events by the suspended nanoparticles significantly contributes to the characteristic thermal conductivity of the nanofluids, which has been estimated by the present model for a ethylene glycol based nanofluid containing Cu-nanoparticles of size ranging from 8 to 20 nm, with Gaussian size distribution. The prediction of the present model has shown a reasonable agreement with the experimental data available in literature.

Keywords: brownian dynamics, molecular dynamics, nanofluid, thermal conductivity

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Authors: Mohammad Namazi, Mohammad Sadeghzadeh Maharluie

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Predicting earnings management is vital for the capital market participants, financial analysts and managers. The aim of this research is attempting to respond to this query: Is there a significant difference between the regression model and neural networks’ models in predicting earnings management, and which one leads to a superior prediction of it? In approaching this question, a Linear Regression (LR) model was compared with two neural networks including Multi-Layer Perceptron (MLP), and Generalized Regression Neural Network (GRNN). The population of this study includes 94 listed companies in Tehran Stock Exchange (TSE) market from 2003 to 2011. After the results of all models were acquired, ANOVA was exerted to test the hypotheses. In general, the summary of statistical results showed that the precision of GRNN did not exhibit a significant difference in comparison with MLP. In addition, the mean square error of the MLP and GRNN showed a significant difference with the multi variable LR model. These findings support the notion of nonlinear behavior of the earnings management. Therefore, it is more appropriate for capital market participants to analyze earnings management based upon neural networks techniques, and not to adopt linear regression models.

Keywords: earnings management, generalized linear regression, neural networks multi-layer perceptron, Tehran stock exchange

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Authors: Lily Ingsrisawang, Tasanee Nacharoen

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Introduction: The problems of unbalanced data sets generally appear in real world applications. Due to unequal class distribution, many research papers found that the performance of existing classifier tends to be biased towards the majority class. The k -nearest neighbors’ nonparametric discriminant analysis is one method that was proposed for classifying unbalanced classes with good performance. Hence, the methods of discriminant analysis are of interest to us in investigating misclassification error rates for class-imbalanced data of three diabetes risk groups. Objective: The purpose of this study was to compare the classification performance between parametric discriminant analysis and nonparametric discriminant analysis in a three-class classification application of class-imbalanced data of diabetes risk groups. Methods: Data from a healthy project for 599 staffs in a government hospital in Bangkok were obtained for the classification problem. The staffs were diagnosed into one of three diabetes risk groups: non-risk (90%), risk (5%), and diabetic (5%). The original data along with the variables; diabetes risk group, age, gender, cholesterol, and BMI was analyzed and bootstrapped up to 50 and 100 samples, 599 observations per sample, for additional estimation of misclassification error rate. Each data set was explored for the departure of multivariate normality and the equality of covariance matrices of the three risk groups. Both the original data and the bootstrap samples show non-normality and unequal covariance matrices. The parametric linear discriminant function, quadratic discriminant function, and the nonparametric k-nearest neighbors’ discriminant function were performed over 50 and 100 bootstrap samples and applied to the original data. In finding the optimal classification rule, the choices of prior probabilities were set up for both equal proportions (0.33: 0.33: 0.33) and unequal proportions with three choices of (0.90:0.05:0.05), (0.80: 0.10: 0.10) or (0.70, 0.15, 0.15). Results: The results from 50 and 100 bootstrap samples indicated that the k-nearest neighbors approach when k = 3 or k = 4 and the prior probabilities of {non-risk:risk:diabetic} as {0.90:0.05:0.05} or {0.80:0.10:0.10} gave the smallest error rate of misclassification. Conclusion: The k-nearest neighbors approach would be suggested for classifying a three-class-imbalanced data of diabetes risk groups.

Keywords: error rate, bootstrap, diabetes risk groups, k-nearest neighbors

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Authors: Yina A. Cifuentes Triana, Andrés M. Pinzón Velásco, Marío E. Velásquez Lozano

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In this work the sequencing and genome characterization of a natural isolate of Saccharomyces cerevisiae yeast (strain 202-3), identified with potential for the production of second generation ethanol from sugarcane bagasse hydrolysates is presented. This strain was selected because its capability to consume xylose during the fermentation of sugarcane bagasse hydrolysates, taking into account that many strains of S. cerevisiae are incapable of processing this sugar. This advantage and other prominent positive aspects during fermentation profiles evaluated in bagasse hydrolysates made the strain 202-3 a candidate strain to improve the production of second-generation ethanol, which was proposed as a first step to study the strain at the genomic level. The molecular characterization was carried out by genome sequencing with the Illumina HiSeq 2000 platform paired end; the assembly was performed with different programs, finally choosing the assembler ABYSS with kmer 89. Gene prediction was developed with the approach of hidden Markov models with Augustus. The genes identified were scored based on similarity with public databases of nucleotide and protein. Records were organized from ontological functions at different hierarchical levels, which identified central metabolic functions and roles of the S. cerevisiae strain 202-3, highlighting the presence of four possible new proteins, two of them probably associated with the positive consumption of xylose.

Keywords: cellulosic ethanol, Saccharomyces cerevisiae, genome sequencing, xylose consumption

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Authors: A. K. M. Kamrul Islam, Abdelhamid Bouchachia, Suang Cang, Hongnian Yu

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Forecasting model has a great impact in terms of prediction and continues to do so into the future. Although many forecasting models have been studied in recent years, most researchers focus on different forecasting methods based on fuzzy time series to solve forecasting problems. The forecasted models accuracy fully depends on the two terms that are the length of the interval in the universe of discourse and the content of the forecast rules. Moreover, a hybrid forecasting method can be an effective and efficient way to improve forecasts rather than an individual forecasting model. There are different hybrids forecasting models which combined fuzzy time series with evolutionary algorithms, but the performances are not quite satisfactory. In this paper, we proposed a hybrid forecasting model which deals with the first order as well as high order fuzzy time series and particle swarm optimization to improve the forecasted accuracy. The proposed method used the historical enrollments of the University of Alabama as dataset in the forecasting process. Firstly, we considered an automatic clustering algorithm to calculate the appropriate interval for the historical enrollments. Then particle swarm optimization and fuzzy time series are combined that shows better forecasting accuracy than other existing forecasting models.

Keywords: fuzzy time series (fts), particle swarm optimization, clustering algorithm, hybrid forecasting model

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