Search results for: predict

Authors: Jamel Slaughter

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Father involvement across the development of a child has been linked to children’s psychological adjustment, fewer behavioral problems, and higher educational attainment. Conversely, there is much less research that highlights father involvement in relation to childhood development during early childhood period prior to preschool age (ages 1-3 years). Most research on fathers and child outcomes have been limited by its focus on the stages of adolescence, middle childhood, and infancy. This study examined the influence of father involvement, during the toddler stage, on 5th grade cognitive development, rule-breaking, and behavior outcomes measured by Child Behavior Checklist (CBCL) scores. Using data from the Early Head Start Research and Evaluation (EHSRE) Study, 1996-2010: United States, a total of 3,001 children and families were identified in 17 sites (cities), representing a diverse demographic sample. An independent samples t-test was run to compare cognitive development, aggressive, and rule-breaking behavior mean scores among children who had early continuous father involvement for the first 14 – 36 months to children who did not have early continuous father involvement for the first 14 – 36 months. Multiple linear regression was conducted to determine if continuous, or non-continuous father involvement (14 month-36 months), can be used to predict outcome scores on the Child Behavior Checklist in aggressive behavior, rule-breaking behavior, and cognitive development, at 5th grade. A statistically significant mean difference in cognitive development scores were found for children who had continuous father involvement (M=1.92, SD=2.41, t (1009) =2.81, p =.005, 95% CI=.146 to .828) compared to those who did not (M=2.60, SD=3.06, t (1009) =-2.38, p=.017, 95% CI= -1.08 to -.105). There was also a statistically significant mean difference in rule-breaking behavior scores between children who had early continuous father involvement (M=1.95, SD=2.33, t (1009) = 3.69, p <.001, 95% CI= .287 to .940), compared to those that did not (M=2.87, SD=2.93, t (1009) = -3.49, p =.001, 95% CI= -1.30 to -.364). No statistically significant difference was found in aggressive behavior scores. Multiple linear regression was performed using continuous father involvement to determine which has the largest relationship to rule-breaking behavior and cognitive development based on CBCL scores. Rule-breaking behavior was found to be significant (F (2, 1008) = 8.353, p<.001), with an R2 of .016. Cognitive development was also significant (F (2, 1008) = 4.44, p=.012), with an R2 of .009. Early continuous father involvement was a significant predictor of rule-breaking behavior and cognitive development at middle childhood. Findings suggest early continuous father involvement during the first 14 – 36 months of their children’s life, may lead to lower levels of rule-breaking behaviors and thought problems at 5th grade.

Keywords: cognitive development, early continuous father involvement, middle childhood, rule-breaking behavior

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Authors: Timothy Quinn, Ronald Croce, Aliaksandr Leuchanka, Justin Walker

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Many endurance athletes train at or just below an intensity associated with their lactate threshold (LT) and often the heart rate (HR) that these athletes use for their LT are above their true LT-HR measured in a laboratory. Training above their true LT-HR may lead to overtraining and injury. Few athletes have the capability of measuring their LT in a laboratory and rely on perception to guide them, as accurate field tests to determine LT are limited. Therefore, the purpose of this study was to determine if an 8-minute field test could accurately define the HR associated with LT as measured in the laboratory. On Day 1, fifteen male runners (mean±SD; age, 27.8±4.1 years; height, 177.9±7.1 cm; body mass, 72.3±6.2 kg; body fat, 8.3±3.1%) performed a discontinuous treadmill LT/maximal oxygen consumption (LT/VO2max) test using a portable metabolic gas analyzer (Cosmed K4b2) and a lactate analyzer (Analox GL5). The LT (and associated HR) was determined using the 1/+1 method, where blood lactate increased by 1 mMol•L-1 over baseline followed by an additional 1 mMol•L-1 increase. Days 2 and 3 were randomized, and the athletes performed either an 8-minute run on the treadmill (TM) or on a 160-m indoor track (TR) in an effort to cover as much distance as possible while maintaining a high intensity throughout the entire 8 minutes. VO2, HR, ventilation (VE), and respiratory exchange ratio (RER) were measured using the Cosmed system, and rating of perceived exertion (RPE; 6-20 scale) was recorded every minute. All variables were averaged over the 8 minutes. The total distance covered over the 8 minutes was measured in both conditions. At the completion of the 8-minute runs, blood lactate was measured. Paired sample t-tests and pairwise Pearson correlations were computed to determine the relationship between variables measured in the field tests versus those obtained in the laboratory at LT. An alpha level of <0.05 was required for statistical significance. The HR (mean +SD) during the TM (167+9 bpm) and TR (172+9 bpm) tests were strongly correlated to the HR measured during the laboratory LT (169+11 bpm) test (r=0.68; p<0.03 and r=0.88; p<0.001, respectively). Blood lactate values during the TM and TR tests were not different from each other but were strongly correlated with the laboratory LT (r=0.73; p<0.04 and r=0.66; p<0.05, respectively). VE (Lmin-1) was significantly greater during the TR (134.8+11.4 Lmin-1) as compared to the TM (123.3+16.2 Lmin-1) with moderately strong correlations to the laboratory threshold values (r=0.38; p=0.27 and r=0.58; p=0.06, respectively). VO2 was higher during TR (51.4 mlkg-1min-1) compared to TM (47.4 mlkg-1min-1) with correlations of 0.33 (p=0.35) and 0.48 (p=0.13), respectively to threshold values. Total distance run was significantly greater during the TR (2331.6+180.9 m) as compared to the TM (2177.0+232.6 m), but they were strongly correlated with each other (r=0.82; p<0.002). These results suggest that an 8-minute running field test can accurately predict the HR associated with the LT and may be a simple test that athletes and coaches could implement to aid in training techniques.

Keywords: blood lactate, heart rate, running, training

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Authors: Yu-Wen Lin, Chin-Sheng Tang, Wan-Yi Chen

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Various biological specimens, drugs, and chemicals exist in the hospital. The medical staffs and hypersensitive inpatients expose might expose to multiple hazards while they work or stay in the hospital. Therefore, the indoor air quality (IAQ) of the hospital should be paid more attention. Respiratory care wards (RCW) are responsible for caring the patients who cannot spontaneously breathe without the ventilators. The patients in RCW are easy to be infected. Compared to the bacteria concentrations of other hospital units, RCW came with higher values in other studies. This research monitored the IAQ of the RCW and checked the compliances of the indoor air quality standards of Taiwan Indoor Air Quality Act. Meanwhile, the influential factors of IAQ and the impacts of ventilator modules, with humidifier or with filter, were investigated. The IAQ of two five-bed wards and one nurse station of a RCW in a regional hospital were monitored. The monitoring was proceeded for 16 hours or 24 hours during the sampling days with a sampling frequency of 20 minutes per hour. The monitoring was performed for two days in a row and the AIQ of the RCW were measured for eight days in total. The concentrations of carbon dioxide (CO₂), carbon monoxide (CO), particulate matter (PM), nitrogen oxide (NOₓ), total volatile organic compounds (TVOCs), relative humidity (RH) and temperature were measured by direct reading instruments. The bioaerosol samples were taken hourly. The hourly air change rate (ACH) was calculated by measuring the air ventilation volume. Human activities were recorded during the sampling period. The linear mixed model (LMM) was applied to illustrate the impact factors of IAQ. The concentrations of CO, CO₂, PM, bacterial and fungi exceeded the Taiwan IAQ standards. The major factors affecting the concentrations of CO, PM₁ and PM₂.₅ were location and the number of inpatients. The significant factors to alter the CO₂ and TVOC concentrations were location and the numbers of in-and-out staff and inpatients. The number of in-and-out staff and the level of activity affected the PM₁₀ concentrations statistically. The level of activity and the numbers of in-and-out staff and inpatients are the significant factors in changing the bacteria and fungi concentrations. Different models of the patients’ ventilators did not affect the IAQ significantly. The results of LMM can be utilized to predict the pollutant concentrations under various environmental conditions. The results of this study would be a valuable reference for air quality management of RCW.

Keywords: respiratory care ward, indoor air quality, linear mixed model, bioaerosol

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Authors: Roseliza Mat Alipiah, David Raffaelli, J. C. R. Smart

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This research is a new approach as it integrates information from both environmental and social sciences to inform effective management of the wetlands. A three-stage research framework was developed for modelling the drivers and pressures imposed on the wetlands and their impacts to the ecosystem and the local communities. Firstly, a Bayesian Belief Network (BBN) was used to predict the probability of anthropogenic activities affecting the delivery of different key wetland ecosystem services under different management scenarios. Secondly, Choice Experiments (CEs) were used to quantify the relative preferences which key wetland stakeholder group (aquaculturists) held for delivery of different levels of these key ecosystem services. Thirdly, a Multi-Criteria Decision Analysis (MCDA) was applied to produce an ordinal ranking of the alternative management scenarios accounting for their impacts upon ecosystem service delivery as perceived through the preferences of the aquaculturists. This integrated ecosystem management approach was applied to a wetland ecosystem in Setiu, Terengganu, Malaysia which currently supports a significant level of aquaculture activities. This research has produced clear guidelines to inform policy makers considering alternative wetland management scenarios: Intensive Aquaculture, Conservation or Ecotourism, in addition to the Status Quo. The findings of this research are as follows: The BBN revealed that current aquaculture activity is likely to have significant impacts on water column nutrient enrichment, but trivial impacts on caged fish biomass, especially under the Intensive Aquaculture scenario. Secondly, the best fitting CE models identified several stakeholder sub-groups for aquaculturists, each with distinct sets of preferences for the delivery of key ecosystem services. Thirdly, the MCDA identified Conservation as the most desirable scenario overall based on ordinal ranking in the eyes of most of the stakeholder sub-groups. Ecotourism and Status Quo scenarios were the next most preferred and Intensive Aquaculture was the least desirable scenario. The methodologies developed through this research provide an opportunity for improving planning and decision making processes that aim to deliver sustainable management of wetland ecosystems in Malaysia.

Keywords: Bayesian belief network (BBN), choice experiments (CE), multi-criteria decision analysis (MCDA), aquaculture

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Authors: Eqqab Maree, Habil Jurgen Bast, Zana K. Shakir

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Passive damping, once properly characterized and incorporated into the structure design is an autonomous mechanism. Passive damping can be achieved by applying layers of a polymeric material, called viscoelastic layers (VEM), to the base structure. This type of configuration is known as free or unconstrained layer damping treatment. A shear or constrained damping treatment uses the idea of adding a constraining layer, typically a metal, on top of the polymeric layer. Constrained treatment is a more efficient form of damping than the unconstrained damping treatment. In constrained damping treatment a sandwich is formed with the viscoelastic layer as the core. When the two outer layers experience bending, as they would if the structure was oscillating, they shear the viscoelastic layer and energy is dissipated in the form of heat. This form of energy dissipation allows the structural oscillations to attenuate much faster. The purpose behind this study is to predict damping effects by using two methods of passive viscoelastic constrained layer damping. First method is Euler-Bernoulli beam theory; it is commonly used for predicting the vibratory response of beams. Second method is Finite Element software packages provided in this research were obtained by using two-dimensional solid structural elements in ANSYS14 specifically eight nodded (SOLID183) and the output results from ANSYS 14 (SOLID183) its damped natural frequency values and mode shape for first five modes. This method of passive damping treatment is widely used for structural application in many industries like aerospace, automobile, etc. In this paper, take a steel cantilever sandwich beam with viscoelastic core type 3M-468 by using methods of passive viscoelastic constrained layer damping. Also can proved that, the percentage reduction of modal frequency between undamped and damped steel sandwich cantilever beam 8mm thickness for each mode is very high, this is due to the effect of viscoelastic layer on damped beams. Finally this types of damped sandwich steel cantilever beam with viscoelastic materials core type (3M468) is very appropriate to use in automotive industry and in many mechanical application, because has very high capability to reduce the modal vibration of structures.

Keywords: steel cantilever, sandwich beam, viscoelastic materials core type (3M468), ANSYS14, Euler-Bernoulli beam theory

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Authors: Ruchi Pathania, Ahmad Ahmad, Shireesh Srivastava

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Cyanobacteria is a promising microbe that can capture and convert atmospheric CO₂ and light into valuable industrial bio-products like biofuels, biodegradable plastics, etc. Among their most attractive traits are faster autotrophic growth, whole year cultivation using non-arable land, high photosynthetic activity, much greater biomass and productivity and easy for genetic manipulations. Cyanobacteria store carbon in the form of glycogen which can be hydrolyzed to release glucose and fermented to form bioethanol or other valuable products. Marine cyanobacterial species are especially attractive for countries with scarcity of freshwater. We recently identified a marine native cyanobacterium Synechococcus sp. BDU 130192 which has good growth rate and high level of polyglucans accumulation compared to Synechococcus PCC 7002. In this study, firstly we sequenced the whole genome and the sequences were annotated using the RAST server. Genome scale metabolic model (GSMM) was reconstructed through COBRA toolbox. GSMM is a computational representation of the metabolic reactions and metabolites of the target strain. GSMMs construction through the application of Flux Balance Analysis (FBA), which uses external nutrient uptake rates and estimate steady state intracellular and extracellular reaction fluxes, including maximization of cell growth. The model, which we have named isyn942, includes 942 reactions and 913 metabolites having 831 metabolic, 78 transport and 33 exchange reactions. The phylogenetic tree obtained by BLAST search revealed that the strain was a close relative of Synechococcus PCC 7002. The flux balance analysis (FBA) was applied on the model iSyn942 to predict the theoretical yields (mol product produced/mol CO₂ consumed) for native and non-native products like acetone, butanol, etc. under phototrophic condition by applying metabolic engineering strategies. The reported strain can be a viable strain for biotechnological applications, and the model will be helpful to researchers interested in understanding the metabolism as well as to design metabolic engineering strategies for enhanced production of various bioproducts.

Keywords: cyanobacteria, flux balance analysis, genome scale metabolic model, metabolic engineering

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Authors: Nicolò Vaiana, Filip C. Filippou, Giorgio Serino

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The solution of the nonlinear dynamic equilibrium equations of base-isolated structures adopting a conventional monolithic solution approach, i.e. an implicit single-step time integration method employed with an iteration procedure, and the use of existing nonlinear analytical models, such as differential equation models, to simulate the dynamic behavior of seismic isolators can require a significant computational effort. In order to reduce numerical computations, a partitioned solution method and a one dimensional nonlinear analytical model are presented in this paper. A partitioned solution approach can be easily applied to base-isolated structures in which the base isolation system is much more flexible than the superstructure. Thus, in this work, the explicit conditionally stable central difference method is used to evaluate the base isolation system nonlinear response and the implicit unconditionally stable Newmark’s constant average acceleration method is adopted to predict the superstructure linear response with the benefit in avoiding iterations in each time step of a nonlinear dynamic analysis. The proposed mathematical model is able to simulate the dynamic behavior of seismic isolators without requiring the solution of a nonlinear differential equation, as in the case of widely used differential equation model. The proposed mixed explicit-implicit time integration method and nonlinear exponential model are adopted to analyze a three dimensional seismically isolated structure with a lead rubber bearing system subjected to earthquake excitation. The numerical results show the good accuracy and the significant computational efficiency of the proposed solution approach and analytical model compared to the conventional solution method and mathematical model adopted in this work. Furthermore, the low stiffness value of the base isolation system with lead rubber bearings allows to have a critical time step considerably larger than the imposed ground acceleration time step, thus avoiding stability problems in the proposed mixed method.

Keywords: base-isolated structures, earthquake engineering, mixed time integration, nonlinear exponential model

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Authors: Nikkey Keshri

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Arctic is a polar region located at the north of the earth. This consists of the Arctic Ocean and other parts of Canada, Russia, the United States, Denmark, Norway, Sweden, Finland, and Iceland. Arctic has vast natural resources which are exploited with modern technology, and the economic opening up of Russia has given new opportunities. All these states have connected with the Arctic region for economic activities and this effect the region ecology. The pollution problem is a serious threat to the people health living around pollution sources. Due to the prevailing worldwide sea and air currents, the Arctic area is the fallout region for long-range transport pollutants, and in some places the concentrations exceed the levels of densely populated urban areas. The Arctic is especially vulnerable to the effects of global warming, as has become apparent in the melting sea ice in recent years. Climate models predict much greater warming in the Arctic than the global average, resulting in significant international attention to the region. The global warming has an adverse impact on the climate, indigenous people, wildlife, and infrastructure. However, there are several opportunities that have emerged in the form of shipping routes, resources, and new territories. The shipping route through the Arctic is a reality and is currently navigable for a few weeks during summers. There are large deposits of oil and gas, minerals and fish and the surrounding countries with Arctic coastlines are becoming quite assertive about exercising their sovereignty over the newfound wealth. The main part of the research is that how the opening of Northern Sea Route is providing opportunities or problem in the Arctic and it is becoming geopolitically important. It focuses on the interest Arctic and non Arctic states, their present and anticipated global geopolitical aims. The Northern Sea Route might open up due to climate changes and that Iceland might benefit or has an impact from the situation. Efforts will be made to answer the research question: ‘Whether Opening of North Sea Route is providing opportunities or becoming a risk for Arctic region?’ Every research has a structure which usually called design. In this research, both Qualitative and Quantitative method is used in terms of various literature, maps, pie- charts, etc to find out the answer for the research question. The aim of this research is to find out the impact of Opening of North Sea Route over Arctic region and how this make arctic geopolitically important. The aim behind this research is to find out the impact of climate change and how the particular geographical area is being affected.

Keywords: climate change, geopolitics, international relation, Northern Sea Route

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Authors: Mustafa M. Donma, Orkide Donma

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A growing list of cancers might be influenced by obesity. Obesity is associated with an increased risk for the occurrence and development of some cancers. Inflammation can lead to cancer. It is one of the characteristic features of cancer and plays a critical role in cancer development. C-reactive protein (CRP) is under evaluation related to the new and simple prognostic factors in patients with metastatic renal cell cancer. Obesity can predict and promote systemic inflammation in healthy adults. BMI is correlated with hs-CRP. In this study, SII index and CRP values were evaluated in children with normal BMI and those within the range of different obesity grades to detect the tendency towards cancer in pediatric obesity. A total of one hundred and ninety-four children; thirty-five children with normal BMI, twenty overweight (OW), forty-seven obese (OB) and ninety-two morbid obese (MO) participated in the study. Age- and sex-matched groups were constituted using BMI-for age percentiles. Informed consent was obtained. Ethical Committee approval was taken. Weight, height, waist circumference (C), hip C, head C and neck C of the children were measured. The complete blood count test was performed. C-reactive protein analysis was performed. Statistical analyses were performed using SPSS. The degree for statistical significance was p≤0.05. SII index values were progressively increasing starting from normal weight (NW) to MO children. There is a statistically significant difference between NW and OB as well as MO children. No significant difference was observed between NW and OW children, however, a correlation was observed between NW and OW children. MO constitutes the only group, which exhibited a statistically significant correlation between SII index and CRP. Obesity-related bladder, kidney, cervical, liver, colorectal, endometrial cancers are still being investigated. Obesity, characterized as a chronic low-grade inflammation, is a crucial risk factor for colon cancer. Elevated childhood BMI values may be indicative of processes leading to cancer, initiated early in life. Prevention of childhood adiposity may decrease the cancer incidence in adults. To authors’ best knowledge, this study is the first to introduce SII index values during obesity of varying degrees of severity. It is suggested that this index seems to affect all stages of obesity with an increasing tendency and may point out the concomitant status of obesity and cancer starting from very early periods of life.

Keywords: children, C-reactive protein, systemic immune-inflammation index, obesity

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Authors: Yori Gidron, Maartje Mol, Norbert Foudraine, Frits Van Osch, Joop Van Den Bergh, Moshe Farchi, Maud Straus

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Background: The worldwide pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-COV2), which began in 2019, also known as Covid-19, has infected over 136 million people and tragically took the lives of over 2.9 million people worldwide. Many of the complications and deaths are predicted by the inflammatory “cytokine storm.” One way to progress in the prevention of death is by finding a predictive and protective factor that inhibits inflammation, on the one hand, and which also increases anti-viral immunity on the other hand. The vagal nerve does precisely both actions. This study examined whether vagal nerve activity, indexed by heart-rate variability (HRV), predicts survival in patients with Covid-19. Method: We performed a pseudo-prospective study, where we retroactively obtained ECGs of 271 Covid-19 patients arriving at a large regional hospital in The Netherlands. HRV was indexed by the standard deviation of the intervals between normal heartbeats (SDNN). We examined patients’ survival at 3 weeks and took into account multiple confounders and known prognostic factors (e.g., age, heart disease, diabetes, hypertension). Results: Patients’ mean age was 68 (range: 25-95) and nearly 22% of the patients had died by 3 weeks. Their mean SDNN (17.47msec) was far below the norm (50msec). Importantly, relatively higher HRV significantly predicted a higher chance of survival, after statistically controlling for patients’ age, cardiac diseases, hypertension and diabetes (relative risk, H.R, and 95% confidence interval (95%CI): H.R = 0.49, 95%CI: 0.26 – 0.95, p < 0.05). However, since HRV declines rapidly with age and since age is a profound predictor in Covid-19, we split the sample by median age (40). Subsequently, we found that higher HRV significantly predicted greater survival in patients older than 70 (H.R = 0.35, 95%CI: 0.16 – 0.78, p = 0.01), but HRV did not predict survival in patients below age 70 years (H.R = 1.11, 95%CI: 0.37 – 3.28, p > 0.05). Conclusions: To the best of our knowledge, this is the first study showing that higher vagal nerve activity, as indexed by HRV, is an independent predictor of higher chances for survival in Covid-19. The results are in line with the protective role of the vagal nerve in diseases and extend this to a severe infectious illness. Studies should replicate these findings and then test in controlled trials whether activating the vagus nerve may prevent mortality in Covid-19.

Keywords: Covid-19, heart-rate Variability, prognosis, survival, vagal nerve

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Authors: Ammar Y. Alqahtani

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In order to minimize the negative impact on the environment, it is essential to manage the waste that generated from the premature disposal of end-of-life (EOL) products properly. Consequently, government and international organizations introduced new policies and regulations to minimize the amount of waste being sent to landfills. Moreover, the consumers’ awareness regards environment has forced original equipment manufacturers to consider being more environmentally conscious. Therefore, manufacturers have thought of different ways to deal with waste generated from EOL products viz., remanufacturing, reusing, recycling, or disposing of EOL products. The rate of depletion of virgin natural resources and their dependency on the natural resources can be reduced by manufacturers when EOL products are treated as remanufactured, reused, or recycled, as well as this will cut on the amount of harmful waste sent to landfills. However, disposal of EOL products contributes to the problem and therefore is used as a last option. Number of EOL need to be estimated in order to fulfill the components demand. Then, disassembly process needs to be performed to extract individual components and subassemblies. Smart products, built with sensors embedded and network connectivity to enable the collection and exchange of data, utilize sensors that are implanted into products during production. These sensors are used for remanufacturers to predict an optimal warranty policy and time period that should be offered to customers who purchase remanufactured components and products. Sensor-provided data can help to evaluate the overall condition of a product, as well as the remaining lives of product components, prior to perform a disassembly process. In this paper, a multi-period disassembly-to-order (DTO) model is developed that takes into consideration the different system uncertainties. The DTO model is solved using Nonlinear Programming (NLP) in multiple periods. A DTO system is considered where a variety of EOL products are purchased for disassembly. The model’s main objective is to determine the best combination of EOL products to be purchased from every supplier in each period which maximized the total profit of the system while satisfying the demand. This paper also addressed the impact of sensor embedded products on the cost of warranties. Lastly, this paper presented and analyzed a case study involving various simulation conditions to illustrate the applicability of the model.

Keywords: closed-loop supply chains, environmentally conscious manufacturing, product recovery, reverse logistics

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Authors: Christos E. Lampropoulos, Maria Konsta, Tamta Sirbilatze, Ifigenia Apostolou, Vicky Dradaki, Konstantina Panouria, Irini Dri, Christina Kordali, Vaggelis Lambas, Georgios Mavras

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Objectives: Full Mini Nutritional Assessment (MNA) questionnaire is one of the most useful tools in diagnosis of malnutrition in hospitalized patients, which is related to increased morbidity and mortality. The purpose of our study was to assess the nutritional status of elderly, hospitalized patients and examine the hypothesis that MNA may predict mortality and extension of hospitalization. Methods: One hundred fifty patients (78 men, 72 women, mean age 80±8.2) were included in this cross-sectional study. The following data were taken into account in analysis: anthropometric and laboratory data, physical activity (International Physical Activity Questionnaires, IPAQ), smoking status, dietary habits, cause and duration of current admission, medical history (co-morbidities, previous admissions). Primary endpoints were mortality (from admission until 6 months afterwards) and duration of admission. The latter was compared to national guidelines for closed consolidated medical expenses. Logistic regression and linear regression analysis were performed in order to identify independent predictors for mortality and extended hospitalization respectively. Results: According to MNA, nutrition was normal in 54/150 (36%) of patients, 46/150 (30.7%) of them were at risk of malnutrition and the rest 50/150 (33.3%) were malnourished. After performing multivariate logistic regression analysis we found that the odds of death decreased 20% per each unit increase of full MNA score (OR=0.8, 95% CI 0.74-0.89, p < 0.0001). Patients who admitted due to cancer were 23 times more likely to die, compared to those with infection (OR=23, 95% CI 3.8-141.6, p=0.001). Similarly, patients who admitted due to stroke were 7 times more likely to die (OR=7, 95% CI 1.4-34.5, p=0.02), while these with all other causes of admission were less likely (OR=0.2, 95% CI 0.06-0.8, p=0.03), compared to patients with infection. According to multivariate linear regression analysis, each increase of unit of full MNA, decreased the admission duration on average 0.3 days (b:-0.3, 95% CI -0.45 - -0.15, p < 0.0001). Patients admitted due to cancer had on average 6.8 days higher extension of hospitalization, compared to those admitted for infection (b:6.8, 95% CI 3.2-10.3, p < 0.0001). Conclusion: Mortality and extension of hospitalization is significantly increased in elderly, malnourished patients. Full MNA score is a useful diagnostic tool of malnutrition.

Keywords: duration of admission, malnutrition, mini nutritional assessment score, prognostic factors for mortality

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Digitalisation in production technology is a driver for the application of machine learning methods. Through the application of predictive quality, the great potential for saving necessary quality control can be exploited through the data-based prediction of product quality and states. However, the serial use of machine learning applications is often prevented by various problems. Fluctuations occur in real production data sets, which are reflected in trends and systematic shifts over time. To counteract these problems, data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets to extract stable features. Successful process control of the target variables aims to centre the measured values around a mean and minimise variance. Competitive leaders claim to have mastered their processes. As a result, much of the real data has a relatively low variance. For the training of prediction models, the highest possible generalisability is required, which is at least made more difficult by this data availability. The implementation of a machine learning application can be interpreted as a production process. The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model with six phases that describes the life cycle of data science. As in any process, the costs to eliminate errors increase significantly with each advancing process phase. For the quality prediction of hydraulic test steps of directional control valves, the question arises in the initial phase whether a regression or a classification is more suitable. In the context of this work, the initial phase of the CRISP-DM, the business understanding, is critically compared for the use case at Bosch Rexroth with regard to regression and classification. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. Suitable methods for leakage volume flow regression and classification for inspection decision are applied. Impressively, classification is clearly superior to regression and achieves promising accuracies.

Keywords: classification, CRISP-DM, machine learning, predictive quality, regression

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Authors: Nurvadi Albekov

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Communicative situation is a basis, which designates potential models of ‘constructed forms’, a motivated basis of a text, for a text can be assumed as a product of the communicative situation. It is within the field of emergence the models of text, that can be potentially prognosticated in a certain communicative situation, are designated. Every text can be assumed as conceptual system structured on the base of certain communicative situation. However in the process of ‘structuring’ of a certain model of ‘conceptual system’ consciousness of a recipient is able act only within the border of the field of emergence for going out of this border indicates misunderstanding of the communicative situation. On the base of communicative situation we can witness the increment of meaning where the synergizing of the informative model of communication, formed by using of the invariant units of a language system, is a result of verbalization of the communicative situation. The potential of the models of a text, prognosticated within the field of emergence, also depends on the communicative situation. The conception ‘the field of emergence’ is interpreted as a unit of the language system, having poly-directed universal structure, implying the presence of the core, the center and the periphery, including different levels of means of a functioning system of language, both in terms of linguistic resources, and in terms of extra linguistic factors interaction of which results increment of a text. The conception ‘field of emergence’ is considered as the most promising in the analysis of texts: oral, written, printed and electronic. As a unit of the language system field of emergence has several properties that predict its use during the study of a text in different levels. This work is an attempt analysis of entropy in a text in the aspect of lingua-cultural code, prognosticated within the model of the field of emergence. The article describes the problem of entropy in the field of emergence, caused by influence of the extra-linguistic factors. The increasing of entropy is caused not only by the fact of intrusion of the language resources but by influence of the alien culture in a whole, and by appearance of non-typical for this very culture symbols in the field of emergence. The borrowing of alien lingua-cultural symbols into the lingua-culture of the author is a reason of increasing the entropy when constructing a text both in meaning and in structuring level. It is nothing but artificial formatting of lexical units that violate stylistic unity of a phrase. It is marked that one of the important characteristics descending the entropy in the field of emergence is a typical similarity of lexical and semantic resources of the different lingua-cultures in aspects of extra linguistic factors.

Keywords: communicative situation, field of emergence, lingua-culture, entropy

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Authors: E. H. Szeto, E. Ammendola, J. V. Tabares, A. Starkey, J. Hay, J. G. McClung, C. J. Bryan

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Introduction: Suicide is a leading cause of death globally, which accounts for more deaths annually than war, acquired immunodeficiency syndrome, homicides, and car accidents, while an estimated 140 million individuals have significant suicide ideation (SI) each year in the United States. Typical risk factors such as hopelessness, depression, and psychiatric disorders can predict suicide ideation but cannot distinguish between those who ideate from those who attempt suicide (SA). The Fluid Vulnerability Theory of suicide posits that a person’s activation of the suicidal mode is predicated on one’s predisposition, triggers, baseline/acute risk, and protective factors. The current study compares self-conscious cognitive-affective states (including guilt, shame, anger towards the self, and suicidal beliefs) among patients based on the endorsement of recent SI (i.e., past two weeks; acute risk) and lifetime SA (i.e., baseline risk). Method: A total of 2,722 individuals in an outpatient primary care setting were included in this cross-sectional, observational study; data for 2,584 were valid and retained for analysis. The Differential Emotions Scale measuring guilt, shame, and self-anger and the Suicide Cognitions Scale measuring suicide cognitions were administered. Results: A total of 2,222 individuals reported no recent SI or lifetime SA (Group 1), 161 reported recent SI only (Group 2), 145 reported lifetime SA only (Group 3), 56 reported both recent SI and lifetime SA (Group 4). The Kruskal-Wallis test showed that guilt, shame, self-anger, and suicide cognitions were the highest for Group 4 (both recent SI and lifetime SA), followed by Group 2 (recent SI-only), then Group 3 (lifetime SA-only), and lastly, Group 1 (no recent SI or lifetime SA). Conclusion: The results on recent SI-only versus lifetime SA-only contribute to the literature on the Fluid Vulnerability Theory of suicide by capturing SI and SA in two different time periods, which signify the acute risks and chronic baseline risks of the suicidal mode, respectively. It is also shown that: (a) people with a lifetime SA reported more severe symptoms than those without, (b) people with recent SI reported more severe symptoms than those without, and (c) people with both recent SI and lifetime SA were the most severely distressed. Future studies may replicate the findings here with other pertinent risk factors such as thwarted belongingness, perceived burdensomeness, and acquired capability, the last of which is consistently linked to attempting among ideators.

Keywords: suicide, guilt, shame, self-anger, suicide cognitions, suicide ideation, suicide attempt

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Authors: Jung Lee

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This study identifies how the cultivation capabilities of social capital influence the overall attitudes of social media users and how these influences differ across user groups. First, the cultivation capabilities of social capital are identified from three aspects, namely, social capital accessibility, potentiality and sensitivity. These three types of social capital acquisition capabilities collectively represent how the social media users perceive the social media environment in terms of possibilities for social capital creation. These three capabilities are hypothesized to influence social media satisfaction and continuing use intention. Next, two essential activities in social media are identified, namely, posting and replying, to categorise social media users based on behavioral patterns. Various social media activities consist of the combinations of these two basic activities. Posting represents the broadcasting aspect of social media, whereas replying represents the communicative aspect of social media. We categorize users into four from communicators to observers by using these two behaviors to develop usage pattern matrix. By applying the usage pattern matrix to the capability model, we argue that posting behavior generally has a positive moderating effect on the attitudes of social media users, whereas replying behavior occasionally exhibits the negative moderating effect. These different moderating effects of posting and replying behavior are explained based on the different levels of social capital sensitivity and expectation of individuals. When a person is highly expecting social capital from social media, he or she would post actively. However, when one is highly sensitive to social capital, he or she would actively respond and reply to postings of other people because such an act would create a longer and more interactive relationship. A total of 512 social media users are invited to answer the survey. They were asked about their attitudes toward the social media and how they expect social capital through this practice. They were asked to check their general social media usage pattern for user categorization. Result confirmed that most of the hypotheses were supported. Three types of social capital cultivation capabilities are significant determinants of social media attitudes, and two social media activities (i.e., posting and replying) exhibited different moderating effects on attitudes. This study provides following discussions. First, three types of social capital cultivation capabilities were identified. Despite the numerous concerns about social media, such as whether it is a decent and real environment that produces social capital, this study confirms that people explicitly expect and experience social capital values from social media. Second, posting and replying activities are two building blocks of social media activities. These two activities are useful in explaining different the attitudes of social media users and predict future usage.

Keywords: social media, social capital, social media satisfaction, social media use intention

Procedia PDF Downloads 179

Authors: Yeo Kyeong Lee, Hae Won Min, Ji Yeon Kang, Hee Sun Kim, Yeong Soo Shin

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Fire incidents have been steadily increased over the last year according to national emergency management agency of South Korea. Even though most of the fire incidents with property damage have been occurred in building, rehabilitation has not been properly done with consideration of structure safety. Therefore, this study aims at evaluating rehabilitation effects on fire damaged normal strength concrete beams through experiments and finite element analyses. For the experiments, reinforced concrete beams were fabricated having designed concrete strength of 21 MPa. Two different cover thicknesses were used as 40 mm and 50 mm. After cured, the fabricated beams were heated for 1hour or 2hours according to ISO-834 standard time-temperature curve. Rehabilitation was done by removing the damaged part of cover thickness and filling polymeric mortar into the removed part. Both fire damaged beams and rehabilitated beams were tested with four point loading system to observe structural behaviors and the rehabilitation effect. To verify the experiment, finite element (FE) models for structural analysis were generated using commercial software ABAQUS 6.10-3. For the rehabilitated beam models, integrated temperature-structural analyses were performed in advance to obtain geometries of the fire damaged beams. In addition to the fire damaged beam models, rehabilitated part was added with material properties of polymeric mortar. Three dimensional continuum brick elements were used for both temperature and structural analyses. The same loading and boundary conditions as experiments were implemented to the rehabilitated beam models and non-linear geometrical analyses were performed. Test results showed that maximum loads of the rehabilitated beams were 8~10% higher than those of the non-rehabilitated beams and even 1~6 % higher than those of the non-fire damaged beam. Stiffness of the rehabilitated beams were also larger than that of non-rehabilitated beams but smaller than that of the non-fire damaged beams. In addition, predicted structural behaviors from the analyses also showed good rehabilitation effect and the predicted load-deflection curves were similar to the experimental results. From this study, both experiments and analytical results demonstrated good rehabilitation effect on the fire damaged normal strength concrete beams. For the further, the proposed analytical method can be used to predict structural behaviors of rehabilitated and fire damaged concrete beams accurately without suffering from time and cost consuming experimental process.

Keywords: fire, normal strength concrete, rehabilitation, reinforced concrete beam

Procedia PDF Downloads 498

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

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

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

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Authors: A. P. Pashkov

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

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

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

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

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

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Authors: Jonathan Gong

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

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

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Authors: Sudha T., Naveen C.

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

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

Procedia PDF Downloads 39

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

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

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

Procedia PDF Downloads 134

Authors: Calvin Ralph, Edward Archer, Alistair McIlhagger

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

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

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

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

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

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Authors: Kurt Maxwell Kusterer, Sylvain Mignot, Annick Vignes

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

Keywords: YouTube, social networks, economics, consumer behaviour

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Authors: Marios Papaevripidou, Yvoni Pavlou, Zacharias Zacharia

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

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

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

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

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

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Authors: Soumita Banerjee

Abstract:

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

Keywords: dam failure, embankments, flood, rainfall

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Authors: Jithin S. Kumar, Ramesh Kannan Kandasami

Abstract:

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

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

Procedia PDF Downloads 59