Search results for: prediction interval

Authors: Arpit Kumar Shrivastava, Subrat Kumar, Rajani Kanta Mohapatra, Priyadarshi Soumyaranjan Sahu

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Computational approaches to predict structure, function and other biological characteristics of proteins are becoming more common in comparison to the traditional methods in drug discovery. Cryptosporidiosis is a major zoonotic diarrheal disease particularly in children, which is caused primarily by Cryptosporidium hominis and Cryptosporidium parvum. Currently, there are no vaccines for cryptosporidiosis and recommended drugs are not effective. With the availability of complete genome sequence of C. hominis, new targets have been recognized for the development of effective and better drugs and/or vaccines. We identified a unique hypothetical epitopic protein in C. hominis genome through BLASTP analysis. A 3D model of the hypothetical protein was generated using I-Tasser server through threading methodology. The quality of the model was validated through Ramachandran plot by PROCHECK server. The functional annotation of the hypothetical protein through DALI server revealed structural similarity with human Transportin 3. Phylogenetic analysis for this hypothetical protein also showed C. hominis hypothetical protein (CUV04613) was the closely related to human transportin 3 protein. The 3D protein model is further subjected to virtual screening study with inhibitors from the Zinc Database by using Dock Blaster software. Docking study reported N-(3-chlorobenzyl) ethane-1,2-diamine as the best inhibitor in terms of docking score. Docking analysis elucidated that Leu 525, Ile 526, Glu 528, Glu 529 are critical residues for ligand–receptor interactions. The molecular dynamic simulation was done to access the reliability of the binding pose of inhibitor and protein complex using GROMACS software at 10ns time point. Trajectories were analyzed at each 2.5 ns time interval, among which, H-bond with LEU-525 and GLY- 530 are significantly present in MD trajectories. Furthermore, antigenic determinants of the protein were determined with the help of DNA Star software. Our study findings showed a great potential in order to provide insights in the development of new drug(s) or vaccine(s) for control as well as prevention of cryptosporidiosis among humans and animals.

Keywords: cryptosporidium hominis, hypothetical protein, molecular docking, molecular dynamics simulation

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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: 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: Chaoge Wang, Xiquan Weng, Yan Meng, Wentao Lin

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Objective: Cold exposure and exercise serve as two physiological stimuli to glycolipid metabolism and insulin sensitivity. So far, it remains to be elucidated whether exercise plus cold exposure can produce an addictive effect on promoting glycolipid metabolism and insulin sensitivity. Methods: 64 SD rats were subjected to high-fat and high-sugar diets for 9-week and sucessfully to establish an obesity model. They were randomly divided into 8 groups: normal control group (NC), normal exercise group (NE), continuous cold control group (CC), continuous cold exercise group (CE), acute clod control group (AC), acute cold exercise group (AE), intermittent cold control group (IC) and intermittent cold exercise group (IE). For continuous cold exposure, the rats stayed in a cold environment all day; for acute cold exposure, the rats were exposed to cold for only 4h before the end of the experiment; for intermittent cold exposure, the rats were exposed to cold for 4h per day. The protocol for treadmill runnings were as follows: 25m/min (speed), 0°C (slope), 30 mins each time, an interval for 10 mins between two runnings, twice/two days, lasting for 5 weeks. Sampling were conducted on the 5th weekend. Blood lipids, free fatty acids, blood glucose (FBG), and serum insulin (FINS) were examined, and the insulin resistance index (HOMA-IR = FBG (mmol/L)×FINS(mIU/L)/22.5) was calculated. SPSS 22.0 was used for statistical analysis of the experimental results, and the ANOVA analysis was performed between groups (p < 0.05 was significant). Results: (1) Compared with the NC group, the FBG of the rats was significantly declined in the NE, CE, AC, AE, and IE groups (p < 0.05), the FINS of the rats was significantly declined in the AE group (p < 0.05), the HOMA-IR of the rats was significantly declined in the NE, CE, AC, AE and IE groups (p < 0.05). Compared with the NE group, the FBG of the rats was significantly declined in the CE, AE, and IE groups (p < 0.05), the FINS and HOMA-IR of the rats were significantly declined in the AE group (p < 0.05). (2) Compared with the NC group, the CHO, TG, LDL-C, and FFA of the rats were significantly declined in CE and IE groups (p < 0.05), the HDL-C of the rats was significantly higher in NE, CC, CE, AE, and IE groups (p < 0.05). Compared with the NE group, the HDL-C of the rats was significantly higher in the CE and IE groups (p < 0.05). Conclusions: Sedentariness or exercise in the acute cold doesn't make sense in the treatment of type 2 diabetes, which led to one-off increases of the body's insulin sensitivity. Exercise in the continuous and intermittent cold can effectively decline the FBG, TC, TG, LDL-C, and FFA levels and increase the HDL-C level and insulin sensitivity in obese rats. These results can impact the prevention and treatment of type 2 diabetes.

Keywords: cold, exercise, insulin sensitivity, obesity

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Authors: Anwar Ul Haque, Waqar Asrar, Erwin Sulaeman, Jaffar S. M. Ali

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Pressure distribution data of the standard airfoils is usually used for the calibration purposes in subsonic wind tunnels. Results of such experiments are quite old and obtained by using the model in the spanwise direction. In this manuscript, pressure distribution over NACA 4412 airfoil model was presented by placing the 3D model in the lateral direction. The model is made of metal with pressure ports distributed longitudinally as well as in the lateral direction. The pressure model was attached to the floor of the tunnel with the help of the base plate to give the specified angle of attack to the model. Before the start of the experiments, the pressure tubes of the respective ports of the 128 ports pressure scanner are checked for leakage, and the losses due to the length of the pipes were also incorporated in the results for the specified pressure range. Growth rate maps of the boundary layer thickness were also plotted. It was found that with the increase in the velocity, the dynamic pressure distribution was also increased for the alpha seep. Plots of pressure distribution so obtained were overlapped with those obtained by using XFLR software, a low fidelity tool. It was found that at moderate and high angles of attack, the distribution of the pressure coefficients obtained from the experiments is high when compared with the XFLR ® results obtained along with the span of the wing. This under-prediction by XFLR ® is more obvious on the windward than on the leeward side.

Keywords: subsonic flow, boundary layer, wind tunnel, pressure testing

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Authors: E. Vlachopapadopoulou, E. Dikaiakou, E. Anagnostou, I. Panagiotopoulos, E. Kaloumenou, M. Kafetzi, A. Fotinou, S. Michalacos

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Aim: To investigate the relation and impact of Body Mass Index (BMI), Waist Circumference (WC) and Body Fat Weight (BFW) on insulin resistance (MATSUDA INDEX < 2.5) in children and adolescents. Methods: Data from 95 overweight and obese children (47 boys and 48 girls) with mean age 10.7 ± 2.2 years were analyzed. ROC analysis was used to investigate the predictive ability of BMI, WC and BFW for insulin resistance and find the optimal cut-offs. The overall performance of the ROC analysis was quantified by computing area under the curve (AUC). Results: ROC curve analysis indicated that the optimal-cut off of WC for the prediction of insulin resistance was 97 cm with sensitivity equal to 75% and specificity equal to 73.1%. AUC was 0.78 (95% CI: 0.63-0.92, p=0.001). The sensitivity and specificity of obesity for the discrimination of participants with insulin resistance from those without insulin resistance were equal to 58.3% and 75%, respectively (AUC=0.67). BFW had a borderline predictive ability for insulin resistance (AUC=0.58, 95% CI: 0.43-0.74, p=0.101). The predictive ability of WC was equivalent with the correspondence predictive ability of BMI (p=0.891). Obese subjects had 4.2 times greater odds for having insulin resistance (95% CI: 1.71-10.30, p < 0.001), while subjects with WC more than 97 had 8.1 times greater odds for having insulin resistance (95% CI: 2.14-30.86, p=0.002). Conclusion: BMI and WC are important clinical factors that have significant clinical relation with insulin resistance in children and adolescents. The cut off of 97 cm for WC can identify children with greater likelihood for insulin resistance.

Keywords: body fat weight, body mass index, insulin resistance, obese children, waist circumference

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Authors: Jinu Phonamontham

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Coronavirus, also referred to as COVID-19, is a virus caused by the SARS-Cov-2 virus. The pandemic has caused over 10 million cases and 500,000 deaths worldwide through the end of June 2020. In a previous study, HLA-DQA1*01:02 allele was associated with COVID-19 disease (p-value = 0.0121). Furthermore, there was a statistical significance between HLA- DQB1*06:02 and COVID-19 in the Italian population by Bonferroni’s correction (p-value = 0.0016). Nevertheless, there is no data describing the distribution of HLA alleles as a valid marker for prediction of COVID-19 in the Thai population. We want to investigate the prevalence of HLA-DQA1*01:02 and HLA-DQB1*06:02 alleles that are associated with severe COVID-19 in the Thai population. In this study, we recruited 200 healthy Thai individuals. Genomic DNA samples were isolated from EDTA blood using Genomic DNA Mini Kit. HLA genotyping was conducted using the Lifecodes HLA SSO typing kits (Immucor, West Avenue, Stamford, USA). The frequency of HLA-DQA1 alleles in Thai population, consisting of HLA-DQA1*01:01 (27.75%), HLA-DQA1*01:02 (24.50%), HLA-DQA1*03:03 (13.00%), HLA-DQA1*06:01 (10.25%) and HLA-DQA1*02:01 (6.75%). Furthermore, the distributions of HLA-DQB1 alleles were HLA-DQB1*05:02 (21.50%), HLA-DQB1*03:01 (15.75%), HLA-DQB1*05:01 (14.50%), HLA-DQB1*03:03 (11.00%) and HLA-DQB1*02:02 (8.25%). Particularly, HLA- DQA1*01:02 (29.00%) allele was the highest frequency in the NorthEast group, but there was not significant difference when compared with the other regions in Thais (p-value = 0.4202). HLA-DQB1*06:02 allele was similarly distributed in Thai population and there was no significant difference between Thais and China (3.8%) and South Korea (6.4%) and Japan (8.2%) with p-value > 0.05. Whereas, South Africa (15.7%) has a significance with Thais by p-value of 0.0013. This study supports the specific genotyping of the HLA-DQA1*01:02 and HLA-DQB1*06:02 alleles to screen severe COVID-19 in Thai and many populations.

Keywords: HLA-DQA1*01:02, HLA-DQB1*06:02, Asian, Thai population

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Authors: Arturs Macanovskis, Vitalijs Lusis, Andrejs Krasnikovs

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Work has three stages. In the first stage was examined pull-out process for steel fiber was embedded into a concrete by one end and was pulled out of concrete under the angle to pulling out force direction. Angle was varied. Length of steel fiber was 26 mm, diameter 0.5 mm. On the obtained force- displacement diagrams were observed jumps. For such mechanical behavior explanation, fiber channel in concrete surface microscopical experimental investigation, using microscope KEYENCE VHX2000, was performed. Surface of fiber channel in concrete matrix after pull-out test (fiber angle to pulling out force direction 70°). At the second stage were obtained diagrams for load- crack opening displacement for breaking homogeneously reinforced and layered fiber concrete prisms (with dimensions 10x10x40 cm) subjected to 4-point bending. After testing was analyzed main crack. On the main crack’s both surfaces were recognized all pulled out fibers their locations, angles to crack surface and lengths of pull-out fibers parts. At the third stage elaborated prediction model for the fiber-concrete beam, failure under bending, using the following data: a) diagrams for fibers pulling out at different angles; b) experimental data about steel-straight fibers locations in the main crack.

Keywords: fiberconcrete, pull-out, fiber channel, layered fiberconcrete

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Authors: Cornelia-Eugenia Munteanu

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The goal of this paper is to present the diagnostic contribution that the screening instrument, Mini-Mental State Examination-2: Expanded Version (MMSE-2:EV), brings in detecting the cognitive impairment or in monitoring the progress of degenerative disorders. The diagnostic signification is underlined by the interpretation of the MMSE-2:EV scores, resulted from the test application to patients with mild and major neurocognitive disorders. The original MMSE is one of the most widely used screening tools for detecting the cognitive impairment, in clinical settings, but also in the field of neurocognitive research. Now, the practitioners and researchers are turning their attention to the MMSE-2. To enhance its clinical utility, the new instrument was enriched and reorganized in three versions (MMSE-2:BV, MMSE-2:SV and MMSE-2:EV), each with two forms: blue and red. The MMSE-2 was adapted and used successfully in Romania since 2013. The cases were selected from current practice, in order to cover vast and significant neurocognitive pathology: mild cognitive impairment, Alzheimer’s disease, vascular dementia, mixed dementia, Parkinson’s disease, conversion of the mild cognitive impairment into Alzheimer’s disease. The MMSE-2:EV version was used: it was applied one month after the initial assessment, three months after the first reevaluation and then every six months, alternating the blue and red forms. Correlated with age and educational level, the raw scores were converted in T scores and then, with the mean and the standard deviation, the z scores were calculated. The differences of raw scores between the evaluations were analyzed from the point of view of statistic signification, in order to establish the progression in time of the disease. The results indicated that the psycho-diagnostic approach for the evaluation of the cognitive impairment with MMSE-2:EV is safe and the application interval is optimal. The alternation of the forms prevents the learning phenomenon. The diagnostic accuracy and efficient therapeutic conduct derive from the usage of the national test norms. In clinical settings with a large flux of patients, the application of the MMSE-2:EV is a safe and fast psycho-diagnostic solution. The clinicians can draw objective decisions and for the patients: it doesn’t take too much time and energy, it doesn’t bother them and it doesn’t force them to travel frequently.

Keywords: MMSE-2, dementia, cognitive impairment, neuropsychology

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Authors: Pawan Kumar, Dhananjoy Shaw

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Heart rate variability (HRV) is the physiological phenomenon of variation in the time interval between heartbeats and is alterable with fitness, age and different medical conditions including withdrawal/retirement from games/sports. Objectives of the study were to develop (a) percentile norms of heart rate variability (HRV) variables derived from time domain analysis of the Indian sportspersons withdrawn from competitive games/sports pertaining to sympathetic and parasympathetic activity (b) percentile norms of heart rate variability (HRV) variables derived from frequency domain analysis of the Indian sportspersons withdrawn from competitive games/sports pertaining to sympathetic and parasympathetic activity. The study was conducted on 430 males. Ages of the sample ranged from 30 to 35 years of same socio-economic status. Date was collected using ECG polygraphs. Data were processed and extracted using frequency domain analysis and time domain analysis. Collected data were computed with percentile from one to hundred. The finding showed that the percentile norms of heart rate variability (HRV) variables derived from time domain analysis of the Indian sportspersons withdrawn from competitive games/sports pertaining to sympathetic and parasympathetic activity namely, NN50 count (ranged from 1 to 189 score as percentile range). pNN50 count (ranged from .24 to 60.80 score as percentile range). SDNN (ranged from 17.34 to 167.29 score as percentile range). SDSD (ranged from 11.14 to 120.46 score as percentile range). RMMSD (ranged from 11.19 to 120.24 score as percentile range) and SDANN (ranged from 4.02 to 88.75 score as percentile range). The percentile norms of heart rate variability (HRV) variables derived from frequency domain analysis of the Indian sportspersons withdrawn from competitive games/sports pertaining to sympathetic and parasympathetic activity namely Low Frequency (Normalized Power) ranged from 20.68 to 90.49 score as percentile range. High Frequency (Normalized Power) ranged from 14.37 to 81.60 score as percentile range. LF/ HF ratio(ranged from 0.26 to 9.52 score as percentile range). LF (Absolute Power) ranged from 146.79 to 5669.33 score as percentile range. HF (Absolute Power) ranged from 102.85 to 10735.71 score as percentile range and Total Power (Absolute Power) ranged from 471.45 to 25879.23 score as percentile range. Conclusion: The analysis documented percentile norms for time domain analysis and frequency domain analysis for versatile use and evaluation.

Keywords: RMSSD, Percentile, SDANN, HF, LF

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Authors: Picha Sriprachan

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The increase in electricity prices affects the cost of high-rise building construction. The objectives of this research are to study the electrical cost, trend of electrical cost and to forecast electrical cost of high-rise building construction. The methods of this research are: 1) to study electrical payment formats, cost data collection methods, and the factors affecting electrical cost of high-rise building construction, 2) to study the quantity and trend of cumulative percentage of the electrical cost, and 3) to forecast the electrical cost for different types of high-rise buildings. The results of this research show that the average proportion between electrical cost and the value of the construction project is 0.87 percent. The proportion of electrical cost for residential, office and commercial, and hotel buildings are closely proportional. If construction project value increases, the proportion of electrical cost and the value of the construction project will decrease. However, there is a relationship between the amount of electrical cost and the value of the construction project. During the structural construction phase, the amount of electrical cost will increase and during structural and architectural construction phase, electrical cost will be maximum. The cumulative percentage of the electrical cost is related to the cumulative percentage of the high-rise building construction cost in the same direction. The amount of service space of the building, number of floors and the duration of the construction affect the electrical cost of construction. The electrical cost of construction forecasted by using linear regression equation is close to the electrical cost forecasted by using the proportion of electrical cost and value of the project.

Keywords: high-rise building construction, electrical cost, construction phase, architectural phase

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Authors: Raymond Limen Njinga, Adeneye Samuel Olaolu, Akinyode Ojumoola Ajimo

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Introduction: Measurement of entrance dose and dose at different depths is essential to avoid overdose and underdose of patients. The aim of this study is to verify the variation in the absorbed dose using a water-equivalent material. Materials and Methods: The plastic phantom was arranged on the couch of the halcyon linear accelerator by Varian, with the farmer ionization chamber inserted and connected to the electrometer. The image of the setup was taken using the High-Quality Single 1280x1280x16 higher on the service mode to check the alignment with the isocenter. The beam quality TPR₂₀,₁₀ (Tissue phantom ratio) was done to check the beam quality of the machine at a field size of 10 cm x 10 cm. The calibration was done using SAD type set-up at a depth of 5 cm. This process was repeated for ten consecutive weeks, and the values were recorded. Results: The results of the beam output for the teletherapy machine were satisfactory and accepted in comparison with the commissioned measurement of 0.62. The beam quality TPR₂₀,₁₀ (Tissue phantom ratio) was reasonable with respect to the beam quality of the machine at a field size of 10 cm x 10 cm. Conclusion: The results of the beam quality and the absorbed dose rate showed a good consistency over the period of ten weeks with the commissioned measurement value.

Keywords: linear accelerator, absorbed dose rate, isocenter, phantom, ionization chamber

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Authors: S. Obregon-Cano, R. Moreno-Rojas, E. Cartea-Gonzalez, A. De Haro-Bailon

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The potential of near-infrared spectroscopy (NIRS) for screening the total glucosinolate (t-GSL) content, and also, the aliphatic glucosinolates gluconapin (GNA), progoitrin (PRO) and glucobrassicanapin (GBN) in turnip greens and turnip tops was assessed. This crop is grown for edible leaves and stems for human consumption. The reference values for glucosinolates, as they were obtained by high performance liquid chromatography on the vegetable samples, were regressed against different spectral transformations by modified partial least-squares (MPLS) regression (calibration set of samples n= 350). The resulting models were satisfactory, with calibration coefficient values from 0.72 (GBN) to 0.98 (tGSL). The predictive ability of the equations obtained was tested using a set of samples (n=70) independent of the calibration set. The determination coefficients and prediction errors (SEP) obtained in the external validation were: GNA=0.94 (SEP=3.49); PRO=0.41 (SEP=1.08); GBN=0.55 (SEP=0.60); tGSL=0.96 (SEP=3.28). These results show that the equations developed for total glucosinolates, as well as for gluconapin can be used for screening these compounds in the leaves and stems of this species. In addition, the progoitrin and glucobrassicanapin equations obtained can be used to identify those samples with high, medium and low contents. The calibration equations obtained were accurate enough for a fast, non-destructive and reliable analysis of the content in GNA and tGSL directly from NIR spectra. The equations for PRO and GBN can be employed to identify samples with high, medium and low contents.

Keywords: brassica rapa, glucosinolates, gluconapin, NIRS, turnip greens

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Authors: Shun-Qi Zhang, Shu-Yang Zhang, Min Chen, , Jing Bai

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Piezoelectric laminated smart structures can be subjected to the strong driving electric field, which may result in large displacements and rotations. In one hand, piezoelectric materials usually behave very significant material nonlinear effects under strong electric fields. On the other hand, thin-walled structures undergoing large displacements and rotations exist nonnegligible geometric nonlinearity. In order to give a precise prediction of piezo laminated smart structures under the large electric field, this paper develops a finite element (FE) model accounting for material nonlinearity (piezoelectric part) and geometric nonlinearity based on the first order shear deformation (FSOD) hypothesis. The proposed FE model is first validated by both experimental and numerical examples from the literature. Afterwards, it is applied to simulate for plate and shell structures with multiple piezoelectric patches under the strong applied electric field. From the simulation results, it shows that large discrepancies occur between linear and nonlinear predictions for piezoelectric laminated structures driving at the strong electric field. Therefore, both material and geometric nonlinearities should be taken into account for piezoelectric structures under strong electric.

Keywords: piezoelectric smart structures, finite element analysis, geometric nonlinearity, electroelastic material nonlinearities

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Authors: Khalid Ali, Manar Jammal

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In order to meet the stringent latency and reliability requirements of the upcoming 5G networks, Open Radio Access Networks (O-RAN) have been proposed. The virtualization of O-RAN has allowed it to be treated as a Network Function Virtualization (NFV) architecture, while its components are considered Virtualized Network Functions (VNFs). Hence, intelligent Machine Learning (ML) based solutions can be utilized to apply different resource management and allocation techniques on O-RAN. However, intelligently allocating resources for O-RAN VNFs can prove challenging due to the dynamicity of traffic in mobile networks. Network providers need to dynamically scale the allocated resources in response to the incoming traffic. Elastically allocating resources can provide a higher level of flexibility in the network in addition to reducing the OPerational EXpenditure (OPEX) and increasing the resources utilization. Most of the existing elastic solutions are reactive in nature, despite the fact that proactive approaches are more agile since they scale instances ahead of time by predicting the incoming traffic. In this work, we propose and evaluate traffic forecasting models based on the ML algorithm. The algorithms aim at predicting future O-RAN traffic by using previous traffic data. Detailed analysis of the traffic data was carried out to validate the quality and applicability of the traffic dataset. Hence, two ML models were proposed and evaluated based on their prediction capabilities.

Keywords: O-RAN, traffic forecasting, NFV, ARIMA, LSTM, elasticity

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Authors: Pedro P. Drumond, Priscilla M. Moura, Marcia M. L. P. Coelho

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The purpose of On-site Stormwater Detention (OSD) system is to promote the detention of addition stormwater runoff caused by impervious areas, in order to maintain the peak flow the same as the pre-urbanization condition. In recent decades, these systems have been built in many cities around the world. However, its real efficiency continues to be unknown due to the lack of research, especially with regard to monitoring its real performance. Thus, this study aims to compare the water level monitoring data of an OSD built in Belo Horizonte/Brazil with the results of theoretical methods simulations, usually adopted in OSD design. There were made two theoretical simulations, one using the Rational Method and Modified Puls method and another using the Soil Conservation Service (SCS) method and Modified Puls method. The monitoring data were obtained with a water level sensor, installed inside the reservoir and connected to a data logger. The comparison of OSD performance was made for 48 rainfall events recorded from April/2015 to March/2017. The comparison of maximum water levels in the OSD showed that the results of the simulations with Rational/Puls and SCS/Puls methods were, on average 33% and 73%, respectively, lower than those monitored. The Rational/Puls results were significantly higher than the SCS/Puls results, only in the events with greater frequency. In the events with average recurrence interval of 5, 10 and 200 years, the maximum water heights were similar in both simulations. Also, the results showed that the duration of rainfall events was close to the duration of monitored hydrograph. The rising time and recession time of the hydrographs calculated with the Rational Method represented better the monitored hydrograph than SCS Method. The comparison indicates that the real discharge coefficient value could be higher than 0.61, adopted in Puls simulations. New researches evaluating OSD real performance should be developed. In order to verify the peak flow damping efficiency and the value of the discharge coefficient is necessary to monitor the inflow and outflow of an OSD, in addition to monitor the water level inside it.

Keywords: best management practices, on-site stormwater detention, source control, urban drainage

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Authors: Attila Kiss, Erzsébet Némedi, Zoltán Naár

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Due to the rapidly growing number of conscious customers in the recent years, more and more people look for products with positive physiological effects which may contribute to the preservation of their health. In response to these demands Food Science Research Institute of Budapest develops and introduces into the market new functional foods of guaranteed positive effect that contain bioactive agents. New, efficient technologies are also elaborated in order to preserve the maximum biological effect of the produced foods. The main objective of our work was the development of new functional biscuits fortified with physiologically beneficial ingredients. Bakery products constitute the base of the food nutrients’ pyramid, thus they might be regarded as foodstuffs of the largest consumed quantity. In addition to the well-known and certified physiological benefits of lysine, as an essential amino acid, a series of antioxidant type compounds is formed as a consequence of the occurring Maillard-reaction. Progress of the evoked Maillard-reaction was studied by applying diverse sugars (glucose, fructose, saccharose, isosugar) and lysine at several temperatures (120-170°C). Interval of thermal treatment was also varied (10-30 min). The composition and production technologies were tailored in order to reach the maximum of the possible biological benefits, so as to the highest antioxidant capacity in the biscuits. Out of the examined sugar components, theextent of the Maillard-reaction-driven transformation of glucose was the most pronounced at both applied temperatures. For the precise assessment of the antioxidant activity of the products FRAP and DPPH methods were adapted and optimised. To acquire an authentic and extensive mechanism of the occurring transformations, Maillard-reaction products were identified, and relevant reaction pathways were revealed. GC-MS and HPLC-MS techniques were applied for the analysis of the 60 generated MRPs and characterisation of actual transformation processes. 3 plausible major transformation routes might have been suggested based on the analytical result and the deductive sequence of possible occurring conversions between lysine and the sugars.

Keywords: Maillard-reaction, lysine, antioxidant activity, GC-MS and HPLC-MS techniques

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Authors: Ariel Vilchez, Francisca Acevedo, Rodrigo Navia

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The wound healing process can be accelerated and improved by the action of antioxidants such as curcumin (Cur) over the tissues; however, the efficacy of curcumin used through the digestive system is not enough to exploit its benefits. Electrospinning presents an alternative to carry curcumin directly to the wounds, and polyhydroxybutyrate (PHB) is proposed as the matrix to load curcumin owing to its biodegradable and biocompatible properties. PHB is among 150 types of Polyhydroxyalkanoates (PHAs) identified, it is a natural thermoplastic polyester produced by microbial fermentation obtained from microorganisms. The proposed objective is to develop electrospun bacterial PHB-based microfibers containing curcumin for possible biomedical applications. Commercial PHB was solved in Chloroform: Dimethylformamide (4:1) to a final concentration of 7% m/V. Curcumin was added to the polymeric solution at 1%, and 7% m/m regarding PHB. The electrospinning equipment (NEU-BM, China) with a rotary collector was used to obtain Cur-PHB fibers at different voltages and flow rate of the polymeric solution considering a distance of 20 cm from the needle to the collector. Scanning electron microscopy (SEM) was used to determine the diameter and morphology of the obtained fibers. Thermal stability was obtained from Thermogravimetric (TGA) analysis, and Fourier Transform Infrared Spectroscopy (FT-IR) was carried out in order to study the chemical bonds and interactions. A preliminary curcumin release to Phosphate Buffer Saline (PBS) pH = 7.4 was obtained in vitro and measured by spectrophotometry. PHB fibers presented an intact chemical composition regarding the original condition (dust) according to FTIR spectra, the diameter fluctuates between 0.761 ± 0.123 and 2.157 ± 0.882 μm, with different qualities according to their morphology. The best fibers in terms of quality and diameter resulted in sample 2 and sample 6, obtained at 0-10kV and 0.5 mL/hr, and 0-10kV and 1.5 mL/hr, respectively. The melting temperature resulted near 178 °C, according to the bibliography. The crystallinity of fibers decreases while curcumin concentration increases for the studied interval. The curcumin release reaches near 14% at 37 °C at 54h in PBS adjusted to a quasi-Fickian Diffusion. We conclude that it is possible to load curcumin in PHB to obtain continuous, homogeneous, and solvent-free microfibers by electrospinning. Between 0% and 7% of curcumin, the crystallinity of fibers decreases as the concentration of curcumin increases. Thus, curcumin enhances the flexibility of the obtained material. HPLC should be used in further analysis of curcumin release.

Keywords: antioxidant, curcumin, polyhydroxybutyrate, wound healing

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Authors: Hui-Min Lai, Chin-Pin Chen, Yung-Fu Chang

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The expectation-confirmation model (ECM) is one of the most widely used models for evaluating information system continuance, and this model has been extended to other study backgrounds, or expanded with other theoretical perspectives. However, combining ECM with other theories or investigating the background problem may produce some disparities, thus generating inaccurate conclusions. Habit is considered to be an important factor that influences the user’s continuance behavior. This paper thus critically examines seven pairs of relationships from the original ECM and the habit variable. A meta-analysis was used to tackle the development of ECM research over the last 10 years from a range of journals and conference papers published in 2005–2014. Forty-six journal articles and 19 conference papers were selected for analysis. The results confirm our prediction that a high effect size for the seven pairs of relationships was obtained (ranging from r=0.386 to r=0.588). Furthermore, a meta-analytic structural equation modeling was performed to simultaneously test all relationships. The results show that habit had a significant positive effect on continuance intention at p<=0.05 and that the six other pairs of relationships were significant at p<0.10. Based on the findings, we refined our original research model and an alternative model was proposed for understanding and predicting information system continuance. Some theoretical implications are also discussed.

Keywords: Expectation-confirmation theory, Expectation-confirmation model, Meta-analysis, meta-analytic structural equation modeling.

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Authors: Regina Chua

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To reduce the costs of re-fielding a large survey, a Random Forest classifier was applied to measure the accuracy of classifying individuals into their assigned segments with the fewest possible questions. Given a long survey, one needed to determine the most predictive ten or fewer questions that would accurately assign new individuals to custom segments. Furthermore, the solution needed to be quick in its classification and usable in non-Python environments. In this paper, a supervised Random Forest classifier was modeled on a dataset with 7,000 individuals, 60 questions, and 254 features. The Random Forest consisted of an iterative collection of individual decision trees that result in a predicted segment with robust precision and recall scores compared to a single tree. A random 70-30 stratified sampling for training the algorithm was used, and accuracy trade-offs at different depths for each segment were identified. Ultimately, the Random Forest classifier performed at 87% accuracy at a depth of 10 with 20 instead of 254 features and 10 instead of 60 questions. With an acceptable accuracy in prioritizing feature selection, new tools were developed for non-Python environments: a worksheet with a formulaic version of the algorithm and an embedded function to predict the segment of an individual in real-time. Random Forest was determined to be an optimal classification model by its feature selection, performance, processing speed, and flexible application in other environments.

Keywords: machine learning, supervised learning, data science, random forest, classification, prediction, predictive modeling

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Authors: Jagruti Barot

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The textile industry plays a major role in the economy of India and on the other side of the coin it is the major source for water pollution. As azo dyes is the largest dye class they are extensively used in many fields such as textile industry, leather tanning industry, paper production, food, colour photography, pharmaceuticals and medicine, cosmetic, hair colourings, wood staining, agricultural, biological and chemical research etc. In addition to these, they can have acute and/or chronic effects on organisms depending on their concentration and length of exposure when they discharged as effluent in the environment. The aim of this study was to assess the genotoxic and histotoxic potentials of environmentally relevant concentrations of RR 120 on Catla catla, important edible freshwater fingerlings. For this, healthy Catla catla fingerlings were procured from the Government Fish Farm and acclimatized in 100 L capacity and continuously aerated glass aquarium in laboratory for 15 days. According to APHA some physic-chemical parameters were measured and maintained such as temperature, pH, dissolve oxygen, alkalinity, total hardness. Water along with excreta had been changed every 24 hrs. All fingerlings were fed artificial food palates once a day @ body weight. After 15 days fingerlings were grouped in 5 (10 in each) and exposed to various concentrations of RR 120 (Control, 10, 20, 30 and 40 mg/L) and samples (peripheral blood and gills, kidney) were collected and analyzed at 96 hrs. of interval. All results were compared with the control. Micronuclei (MN), nuclear buds (NB), fragmented-apoptotic (FA) and bi-nucleated (BN) cells in blood cells and in tissues (gills and kidney cells) were observed. Prominent histopathological alterations were noticed in gills such as aneurism, hyperplasia, degenerated central axis, lifting of gill epithelium, curved secondary gill lamellae etc. Similarly kidney showed some detrimental changes like shrunken glomeruli with increased periglomerular space, degenerated renal tubules etc. Both haematological and histopathological changes clearly reveal the toxic potential of RR 120. This work concludes that water pollution assessment can be done by these two biomarkers which provide baseline to the further chromosomal or molecular work.

Keywords: micronuclei, genotoxicity, RR 120, Catla catla

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Authors: Yan Cheng, Yijun Shao, James Rudolph, Charlene R. Weir, Beth Sahlmann, Qing Zeng-Treitler

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Supervised predictive models require labeled data for training purposes. Complete and accurate labeled data, i.e., a ‘gold standard’, is not always available, and imperfectly labeled data may need to serve as an alternative. An important question is if the accuracy of the labeled data creates a performance ceiling for the trained model. In this study, we trained several models to recognize the presence of delirium in clinical documents using data with annotations that are not completely accurate (i.e., weakly-supervised learning). In the external evaluation, the support vector machine model with a linear kernel performed best, achieving an area under the curve of 89.3% and accuracy of 88%, surpassing the 80% accuracy of the training sample. We then generated a set of simulated data and carried out a series of experiments which demonstrated that models trained on imperfect data can (but do not always) outperform the accuracy of the training data, e.g., the area under the curve for some models is higher than 80% when trained on the data with an error rate of 40%. Our experiments also showed that the error resistance of linear modeling is associated with larger sample size, error type, and linearity of the data (all p-values < 0.001). In conclusion, this study sheds light on the usefulness of imperfect data in clinical research via weakly-supervised learning.

Keywords: weakly-supervised learning, support vector machine, prediction, delirium, simulation

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Authors: Mohammad Hossein Modarressi, Mozhgan Mondeali, Khabat Barkhordari, Ali Etemadi

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The COVID-19 pandemic, caused by SARS-CoV-2, has led to significant concerns worldwide due to its catastrophic effects on public health. The SARS-CoV-2 infection is initiated with the binding of the receptor-binding domain (RBD) in its spike protein to the ACE2 receptor in the host cell membrane. Due to the error-prone entity of the viral RNA-dependent polymerase complex, the virus genome, including the coding region for the RBD, acquires new mutations, leading to the appearance of multiple variants. These variants can potentially impact transmission, virulence, antigenicity and evasive immune properties. S477N mutation located in the RBD has been observed in the SARS-CoV-2 omicron (B.1.1. 529) variant. In this study, we investigated the consequences of S477N mutation at the molecular level using computational approaches such as molecular dynamics simulation, protein-protein interaction analysis, immunoinformatics and free energy computation. We showed that displacement of Ser with Asn increases the stability of the spike protein and its affinity to ACE2 and thus increases the transmission potential of the virus. This mutation changes the folding and secondary structure of the spike protein. Also, it reduces antibody neutralization, raising concern about re-infection, vaccine breakthrough and therapeutic values.

Keywords: S477N, COVID-19, molecular dynamic, SARS-COV2 mutations

Procedia PDF Downloads 177

Authors: Siti N. I. Mat Kamal, Othman Ibrahim, Mehrbakhsh Nilashi, Jafalizan M. Jali

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The use of digital forensics (DF) is nowadays essential for law enforcement agencies to identify analysis and interpret the digital information derived from digital sources. In Malaysia, the engagement of Malaysian Law Enforcement Agencies (MLEA) with this new technology is not evenly distributed. To investigate the factors influencing the adoption of DF in Malaysia law enforcement agencies’ operational environment, this study proposed the initial theoretical framework based on the integration of technology organization environment (TOE), institutional theory, and human organization technology (HOT) fit model. A questionnaire survey was conducted on selected law enforcement agencies in Malaysia to verify the validity of the initial integrated framework. Relative advantage, compatibility, coercive pressure, normative pressure, vendor support and perceived technical competence of technical staff were found as the influential factors on digital forensics adoption. In addition to the only moderator of this study (agency size), any significant moderating effect on the perceived technical competence and the decision to adopt digital forensics by Malaysian law enforcement agencies was found insignificant. Thus, these results indicated that the developed integrated framework provides an effective prediction of the digital forensics adoption by Malaysian law enforcement agencies.

Keywords: digital forensics, digital forensics adoption, digital information, law enforcement agency

Procedia PDF Downloads 154

Authors: Anna Hadamus, Patryk Wasowski, Anna Mosiolek, Zbigniew Wronski, Sebastian Wojtowicz, Dariusz Bialoszewski

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Introduction: Kinesiology Taping is a very popular physiotherapy method, often used for healthy people, especially athletes, in order to stimulate the muscles and improve their performance. The aim of this study was to determine the effect of the muscle application of Kinesiology Taping on the joint position sense in active motion. Material and Methods: The study involved 50 healthy people - 30 men and 20 women, mean age was 23.2 years (range 18-30 years). The exclusion criteria were injuries and operations of the knee, which could affect the test results. The participants were divided randomly into two equal groups. The first group consisted of individuals with the applied Kinesiology Taping muscle application (KT group), whereas in the rest of the individuals placebo application from red adhesive tape was used (placebo group). Both applications were to enhance the effects of quadriceps muscle activity. Joint position sense (JPS) was evaluated in this study. Error of Active Reproduction of the Joint Position (EARJP) of the knee was measured in 45° flexion. The test was performed prior to applying the patch, with the applied application, then 24 hours after wearing, and after removing the tape. The interval between trials was not less than 30 minutes. Statistical analysis was performed using Statistica 12.0. We calculated distribution characteristics, Wilcoxon test, Friedman‘s ANOVA and Mann-Whitney U test. Results. In the KT group and the placebo group average test score of JPS before applying application KT were 3.48° and 5.16° respectively, after its application it was 4.84° and 4.88°, then after 24 hours of experiment JPS was 5.12° and 4.96°, and after application removal we measured 3.84° and 5.12° respectively. Differences over time in any of the groups were not statistically significant. There were also no significant differences between the groups. Conclusions: 1. Applying Kinesiology Taping to quadriceps muscle had no significant effect on the knee joint proprioception. Its use in order to improve sensorimitor skills seems therefore to be unreasonable. 2. No differences between applications of KT and placebo indicates that the clinical effect of stretch tape is minimal or absent. 3. The results are the basis for the continuation of prospective, randomized trials of numerous study groups.

Keywords: joint position sense, kinesiology taping, kinesiotaping, knee

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Authors: Hatthaphone Silimanotham, Michael Henry

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The effective maintenance of road bridge infrastructure is becoming a widely researched topic in the civil engineering field. Deterioration is one of the main issues in bridge performance, and it is necessary to understand how bridges deteriorate to optimally plan budget allocation for bridge maintenance. In Laos, many bridges are in a deteriorated state, which may affect the performance of the bridge. Due to bridge deterioration, the Ministry of Public Works and Transport is interested in the deterioration model to allocate the budget efficiently and support the bridge maintenance planning. A deterioration model can be used to predict the bridge condition in the future based on the observed behavior in the past. This paper analyzes the available inspection data of road bridges on the road classifications network to build deterioration prediction models for the main bridge type found at the provincial level (concrete slab, concrete girder, and steel truss) using probabilistic deterioration modeling by linear regression method. The analysis targets there has three bridge types in the 18 provinces of Laos and estimates the bridge deterioration rating for evaluating the bridge's remaining life. This research thus considers the relationship between the service period and the bridge condition to represent the probability of bridge condition in the future. The results of the study can be used for a variety of bridge management tasks, including maintenance planning, budgeting, and evaluating bridge assets.

Keywords: deterioration model, bridge condition, bridge management, probabilistic modeling

Procedia PDF Downloads 159

Authors: A. Soualem

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The design of multi stage deep drawing processes requires the evaluation of many process parameters such as the intermediate die geometry, the blank shape, the sheet thickness, the blank holder force, friction, lubrication etc..These process parameters have to be determined for the optimum forming conditions before the process design. In general sheet metal forming may involve stretching drawing or various combinations of these basic modes of deformation. It is important to determine the influence of the process variables in the design of sheet metal working process. Especially, the punch and die corner for deep drawing will affect the formability. At the same time the prediction of sheet metals springback after deep drawing is an important issue to solve for the control of manufacturing processes. Nowadays, the importance of this problem increases because of the use of steel sheeting with high stress and also aluminum alloys. The aim of this paper is to give a better understanding of the springback and its effect in various sheet metals forming process such as expansion and restraint deep drawing in the cup drawing process, by varying radius die, lubricant for two commercially available materials e.g. galvanized steel and Aluminum sheet. To achieve these goals experiments were carried out and compared with other results. The original of our purpose consist on tests which are ensured by adapting a U-type stretching-bending device on a tensile testing machine, where we studied and quantified the variation of the springback.

Keywords: springback, deep drawing, expansion, restricted deep drawing

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Authors: Cristian Crespo

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Conditional cash transfers (CCTs) have been targeted towards the poor. Thus, their targeting assessments check whether these schemes have been allocated to low-income households or individuals. However, CCTs have more than one goal and target group. An additional goal of CCTs is to increase school enrolment. Hence, students at risk of dropping out of school also are a target group. This paper analyses whether one of the most common targeting mechanisms of CCTs, a proxy means test (PMT), is suitable to identify the poor and future school dropouts. The PMT is compared with alternative approaches that use the outputs of a predictive model of school dropout. This model was built using machine learning algorithms and rich administrative datasets from Chile. The paper shows that using machine learning outputs in conjunction with the PMT increases targeting effectiveness by identifying more students who are either poor or future dropouts. This joint targeting approach increases effectiveness in different scenarios except when the social valuation of the two target groups largely differs. In these cases, the most likely optimal approach is to solely adopt the targeting mechanism designed to find the highly valued group.

Keywords: conditional cash transfers, machine learning, poverty, proxy means tests, school dropout prediction, targeting

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Authors: Z. N. Haji, S. O. Oyadiji, H. Samami, O. Farrell

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The prediction of the vibration response of rubber products by analytical or numerical method depends mainly on the predefined intrinsic material properties such as Young’s modulus, damping factor and Poisson’s ratio. Such intrinsic properties are determined experimentally by subjecting a bonded rubber sample to compression tests. The compression tests on such a sample yield an apparent Young’s modulus which is greater in magnitude than the intrinsic Young’s modulus of the rubber. As a result, many analytical equations have been developed to determine Young’s modulus from an apparent Young’s modulus of bonded rubber materials. In this work, the applicability of some of these analytical equations is assessed via experimental testing. The assessment is based on testing of vulcanized nitrile butadiene rubber (NBR70) samples using tensile test and compression test methods. The analytical equations are used to determine the intrinsic Young’s modulus from the apparent modulus that is derived from the compression test data of the bonded rubber samples. Then, these Young’s moduli are compared with the actual Young’s modulus that is derived from the tensile test data. The results show significant discrepancy between the Young’s modulus derived using the analytical equations and the actual Young’s modulus.

Keywords: bonded rubber, quasi-static test, shape factor, apparent Young’s modulus

Procedia PDF Downloads 173