Search results for: probability estimation

Authors: Vinay C. Doranalu, Amba Shetty

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In recent days due to climate change, there is a large variation in spatial distribution of daily rainfall within a small region. Rainfall is one of the main end climatic variables which affect spatio-temporal patterns of water availability. The real task postured by the change in climate is identification, estimation and understanding the uncertainty of rainfall. This study intended to analyze the spatial variations and temporal trends of daily precipitation using high resolution (0.25º x 0.25º) gridded data of Indian Meteorological Department (IMD). For the study, 38 grid points were selected in the study area and analyzed for daily precipitation time series (113 years) over the period 1901-2013. Grid points were divided into two zones based on the elevation and situated location of grid points: Low Land (exposed to sea and low elevated area/ coastal region) and High Land (Interior from sea and high elevated area/western Ghats). Time series were applied to examine the spatial analysis and temporal trends in each grid points by non-parametric Mann-Kendall test and Theil-Sen estimator to perceive the nature of trend and magnitude of slope in trend of rainfall. Pettit-Mann-Whitney test is applied to detect the most probable change point in trends of the time period. Results have revealed remarkable monotonic trend in each grid for daily precipitation of the time series. In general, by the regional cluster analysis found that increasing precipitation trend in shoreline region and decreasing trend in Western Ghats from recent years. Spatial distribution of rainfall can be partly explained by heterogeneity in temporal trends of rainfall by change point analysis. The Mann-Kendall test shows significant variation as weaker rainfall towards the rainfall distribution over eastern parts of the Western Ghats region of Karnataka.

Keywords: change point analysis, coastal region India, gridded rainfall data, non-parametric

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Authors: Ahmed Mantawy, James C. Anderson

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Numerous cases of earthquakes (main-shocks) that were followed by aftershocks have been recorded in California. In 1992 a pair of strong earthquakes occurred within three hours of each other in Southern California. The first shock occurred near the community of Landers and was assigned a magnitude of 7.3 then the second shock occurred near the city of Big Bear about 20 miles west of the initial shock and was assigned a magnitude of 6.2. In the same year, a series of three earthquakes occurred over two days in the Cape-Mendocino area of Northern California. The main-shock was assigned a magnitude of 7.0 while the second and the third shocks were both assigned a value of 6.6. This paper investigates the effect of a main-shock accompanied with aftershocks of significant intensity on reinforced concrete (RC) frame buildings to indicate nonlinear behavior using PERFORM-3D software. A 6-story building in San Bruno and a 20-story building in North Hollywood were selected for the study as both of them have RC moment resisting frame systems. The buildings are also instrumented at multiple floor levels as a part of the California Strong Motion Instrumentation Program (CSMIP). Both buildings have recorded responses during past events such as Loma-Prieta and Northridge earthquakes which were used in verifying the response parameters of the numerical models in PERFORM-3D. The verification of the numerical models shows good agreement between the calculated and the recorded response values. Then, different scenarios of a main-shock followed by a series of aftershocks from real cases in California were applied to the building models in order to investigate the structural behavior of the moment-resisting frame system. The behavior was evaluated in terms of the lateral floor displacements, the ductility demands, and the inelastic behavior at critical locations. The analysis results showed that permanent displacements may have happened due to the plastic deformation during the main-shock that can lead to higher displacements during after-shocks. Also, the inelastic response at plastic hinges during the main-shock can change the hysteretic behavior during the aftershocks. Higher ductility demands can also occur when buildings are subjected to trains of ground motions compared to the case of individual ground motions. A general conclusion is that the occurrence of aftershocks following an earthquake can lead to increased damage within the elements of an RC frame buildings. Current code provisions for seismic design do not consider the probability of significant aftershocks when designing a new building in zones of high seismic activity.

Keywords: reinforced concrete, existing buildings, aftershocks, damage accumulation

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Authors: Abram Zoltan

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Over the past hundred years, human activities, particularly the burning of fossil fuels, have released enough carbon dioxide and other greenhouse gases to dissipate additional heat into the lower atmosphere and affect the global climate. Climate change affects many social and environmental determinants of health: clean air, safe drinking water, and adequate food. Our aim is to draw attention to the effects of climate change on the health and health care system. Improving climate awareness and the knowledge related to climate change's health impacts are essential among medical students and practicing medical doctors. Therefore, in their everyday practice, they also need some assistance and up-to-date knowledge of how climate change can endanger human health and deal with these novel health problems. Our activity, based on the cooperation of more universities, aims to develop new curriculum outlines and learning materials on climate change's health impacts for medical schools. Special attention is intended to pay to the possible preventative measures against these impacts. For all of this, the project plans to create new curriculum outlines and learning materials for medical students, elaborate methodological guidelines and create training materials for medical doctors' postgraduate learning programs. The target groups of the project are medical students, educational staff of medical schools and universities, practicing medical doctors with special attention to the general practitioners and family doctors. We had searched various surveys, domestic and international studies about the effects of climate change and statistical estimation of the possible consequences. The health effects of climate change can be measured only approximately by considering only a fraction of the potential health effects and assuming continued economic growth and health progress. We can estimate that climate change is expected to cause about 250,000 more deaths. We conclude that climate change is one of the most serious problems of the 21st century, affecting all populations. In the short- to medium-term, the health effects of climate change will be determined mainly by human vulnerability. In the longer term, the effects depend increasingly on the extent to which transformational action is taken now to reduce emissions. We can contribute to reducing environmental pollution by raising awareness and by educating the population.

Keywords: climate change, health impacts, medical students, education

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Authors: Amiya Kumar Pati, Spandan Sahu, Kishanjit Kumar Khatua

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River is our main source of water which is a form of open channel flow and the flow in the open channel provides with many complex phenomena of sciences that needs to be tackled such as the critical flow conditions, boundary shear stress, and depth-averaged velocity. The development of society, more or less solely depends upon the flow of rivers. The rivers are major sources of many sediments and specific ingredients which are much essential for human beings. A river flow consisting of small and shallow channels sometimes divide and recombine numerous times because of the slow water flow or the built up sediments. The pattern formed during this process resembles the strands of a braid. Braided streams form where the sediment load is so heavy that some of the sediments are deposited as shifting islands. Braided rivers often exist near the mountainous regions and typically carry coarse-grained and heterogeneous sediments down a fairly steep gradient. In this paper, the apparent shear stress formulae were suitably modified, and the Energy Concept Method (ECM) was applied for the prediction of discharges at the junction of a two-flow braided compound channel. The Energy Concept Method has not been applied for estimating the discharges in the braided channels. The energy loss in the channels is analyzed based on mechanical analysis. The cross-section of channel is divided into two sub-areas, namely the main-channel below the bank-full level and region above the bank-full level for estimating the total discharge. The experimental data are compared with a wide range of theoretical data available in the published literature to verify this model. The accuracy of this approach is also compared with Divided Channel Method (DCM). From error analysis of this method, it is observed that the relative error is less for the data-sets having smooth floodplains when compared to rough floodplains. Comparisons with other models indicate that the present method has reasonable accuracy for engineering purposes.

Keywords: critical flow, energy concept, open channel flow, sediment, two-flow braided compound channel

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Authors: Kevin Brochard, Herve Le Sourne, Guillaume Barras

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The design of a submarine’s hull is crucial for its operability and crew’s safety, but also complex. Indeed, engineers need to balance lightness, acoustic discretion and resistance to both immersion pressure and environmental attacks. Submarine explosions represent a first-rate threat for the integrity of the hull, whose behavior needs to be properly analyzed. The presented work is focused on the development of a simplified analytical method to study the structural response of a deeply immersed cylinder submitted to an underwater explosion. This method aims to provide engineers a quick estimation of the resulting damage, allowing them to simulate a large number of explosion scenarios. The present research relies on the so-called plastic string on plastic foundation model. A two-dimensional boundary value problem for a cylindrical shell is converted to an equivalent one-dimensional problem of a plastic string resting on a non-linear plastic foundation. For this purpose, equivalence parameters are defined and evaluated by making assumptions on the shape of the displacement and velocity field in the cross-sectional plane of the cylinder. Closed-form solutions for the deformation and velocity profile of the shell are obtained for explosive loading, and compare well with numerical and experimental results. However, the plastic-string model has not yet been adapted for a cylinder in immersion subjected to an explosive loading. In fact, the effects of fluid-structure interaction have to be taken into account. Moreover, when an underwater explosion occurs, several pressure waves are emitted by the gas bubble pulsations, called secondary waves. The corresponding loads, which may produce significant damages to the cylinder, must also be accounted for. The analytical developments carried out to solve the above problem of a shock wave impacting a cylinder, considering fluid-structure interaction will be presented for an unstiffened cylinder. The resulting deformations are compared to experimental and numerical results for different shock factors and different standoff distances.

Keywords: immersed cylinder, rigid plastic material, shock loading, underwater explosion

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Authors: Vaishali Patil, Neeraj Masand

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In older people, Alzheimer’s disease (AD) is turning out to be a lethal disease. According to the amyloid hypothesis, aggregation of the amyloid β–protein (Aβ), particularly its 42-residue variant (Aβ42), plays direct role in the pathogenesis of AD. Aβ is generated through sequential cleavage of amyloid precursor protein (APP) by β–secretase (BACE) and γ–secretase (GS). Thus in the treatment of AD, γ-secretase modulators (GSMs) are potential disease-modifying as they selectively lower pathogenic Aβ42 levels by shifting the enzyme cleavage sites without inhibiting γ–secretase activity. This possibly avoids known adverse effects observed with complete inhibition of the enzyme complex. Virtual screening, via drug-like ADMET filter, QSAR and molecular docking analyses, has been utilized to identify novel γ–secretase modulators with sulphonamide nucleus. Based on QSAR analyses and docking score, some novel analogs have been synthesized. The results obtained by in silico studies have been validated by performing in vivo analysis. In the first step, behavioral assessment has been carried out using Scopolamine induced amnesia methodology. Later the same series has been evaluated for neuroprotective potential against the oxidative stress induced by Scopolamine. Biochemical estimation was performed to evaluate the changes in biochemical markers of Alzheimer’s disease such as lipid peroxidation (LPO), Glutathione reductase (GSH), and Catalase. The Scopolamine induced amnesia model has shown increased Acetylcholinesterase (AChE) levels and the inhibitory effect of test compounds in the brain AChE levels have been evaluated. In all the studies Donapezil (Dose: 50µg/kg) has been used as reference drug. The reduced AChE activity is shown by compounds 3f, 3c, and 3e. In the later stage, the most potent compounds have been evaluated for Aβ42 inhibitory profile. It can be hypothesized that this series of alkyl-aryl sulphonamides exhibit anti-AD activity by inhibition of Acetylcholinesterase (AChE) enzyme as well as inhibition of plaque formation on prolong dosage along with neuroprotection from oxidative stress.

Keywords: gamma-secretase inhibitors, Alzzheimer's disease, sulphonamides, QSAR

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Authors: Radyah Ivan, Khanferyan Roman

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Milk and dairy products are an important component of a balanced diet. Dairy products represent a heterogeneous food group of solid, semi-solid and liquid, fermented or non-fermented foods, each differing in nutrients such as fat and micronutrient content. Deficiency of milk and dairy products contributes a impact on the main health parameters of the various age groups of the population. The goal of this study was to analyze of the frequency of the consumption of milk and various groups of dairy products by students and its association with their body mass index (BMI), body composition and other physiological parameters. 388 full-time students of the Medical Institute of RUDN University (185 male and 203 female, average age was 20.4+2.2 and 21.9+1.7 y.o., respectively) took part in the cross-sectional study. Anthropometric measurements, estimation of BMI and body composition were analyzed by bioelectrical impedance analysis. The frequency of consumption of the milk and various groups of dairy products was studied using a modified questionnaire on the frequency of consumption of products. Due to the questionnaire data on the frequency of consumption of the diary products, it have been demonstrated that only 11% of respondents consume milk daily, 5% - cottage cheese, 4% and 1% - fermented natural and with fillers milk products, respectively, hard cheese -4%. The study demonstrated that about 16% of the respondents did not consume milk at all over the past month, about one third - cottage cheese, 22% - natural sour-milk products and 18% - sour-milk products with various fillers. hard cheeses and pickled cheeses didn’t consume 9% and 26% of respondents, respectively. We demonstrated the gender differences in the characteristics of consumer preferences were revealed. Thus female students are less likely to use cream, sour cream, soft cheese, milk comparing to male students. Among female students the prevalence of persons with overweight was higher (25%) than among male students (19%). A modest inverse relationship was demonstrated between daily milk intake, BMI, body composition parameters and diary products consumption (r=-0.61 and r=-0.65). The study showed daily insufficient milk and dairy products consumption by students and due to this it have been demonstrated the relationship between the low and rare consumption of diary products and main parameters of indicators of physical activity and health indicators.

Keywords: frequency of consumption, milk, dairy products, physical development, nutrition, body mass index.

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Authors: Mahtab Makaremi Masouleh, Günter Wozniak

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This research is part of an interdisciplinary project, promoting the design of a light temporary installable textile defence system against flood. In case river water levels increase abruptly especially in winter time, one can expect massive extra load on a textile protective structure in term of impact as a result of floating debris and even tree trunks. Estimation of this impulsive force on such structures is of a great importance, as it can ensure the reliability of the design in critical cases. This fact provides the motivation for the numerical analysis of a fluid structure interaction application, comprising flexible slender shaped and free-surface water flow, where an accelerated heavy flotsam tends to approach the membrane. In this context, the analysis on both the behavior of the flexible membrane and its interaction with moving flotsam is conducted by finite elements based solvers of the explicit solver and implicit Abacus solver available as products of SIMULIA software. On the other hand, a study on how free surface water flow behaves in response to moving structures, has been investigated using the finite volume solver of Star CCM+ from Siemens PLM Software. An automatic communication tool (CSE, SIMULIA Co-Simulation Engine) and the implementation of an effective partitioned strategy in form of an implicit coupling algorithm makes it possible for partitioned domains to be interconnected powerfully. The applied procedure ensures stability and convergence in the solution of these complicated issues, albeit with high computational cost; however, the other complexity of this study stems from mesh criterion in the fluid domain, where the two structures approach each other. This contribution presents the approaches for the establishment of a convergent numerical solution and compares the results with experimental findings.

Keywords: co-simulation, flexible thin structure, fluid-structure interaction, implicit coupling algorithm, moving flotsam

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Authors: Heewon Jeong, Seongpyo Kim, Joon Ha Kim

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Over the last few tens of years, the coastal regions of Korea have experienced red tide algal blooms, which are harmful and toxic to both humans and marine organisms due to their potential threat. It was accelerated owing to eutrophication by human activities, certain oceanic processes, and climate change. Previous studies have tried to monitoring and predicting the algae concentration of the ocean with the bio-optical algorithms applied to color images of the satellite. However, the accurate estimation of algal blooms remains problems to challenges because of the complexity of coastal waters. Therefore, this study suggests a new method to identify the concentration of red tide algal bloom from images of geostationary ocean color imager (GOCI) which are representing the water environment of the sea in Korea. The method employed GOCI images, which took the water leaving radiances centered at 443nm, 490nm and 660nm respectively, as well as observed weather data (i.e., humidity, temperature and atmospheric pressure) for the database to apply optical characteristics of algae and train deep learning algorithm. Convolution neural network (CNN) was used to extract the significant features from the images. And then artificial neural network (ANN) was used to estimate the concentration of algae from the extracted features. For training of the deep learning model, backpropagation learning strategy is developed. The established methods were tested and compared with the performances of GOCI data processing system (GDPS), which is based on standard image processing algorithms and optical algorithms. The model had better performance to estimate algae concentration than the GDPS which is impossible to estimate greater than 5mg/m³. Thus, deep learning model trained successfully to assess algae concentration in spite of the complexity of water environment. Furthermore, the results of this system and methodology can be used to improve the performances of remote sensing. Acknowledgement: This work was supported by the 'Climate Technology Development and Application' research project (#K07731) through a grant provided by GIST in 2017.

Keywords: deep learning, algae concentration, remote sensing, satellite

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Authors: Fikru Fentaw Abera

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Climate changes alter regional hydrologic conditions and results in a variety of impacts on water resource systems. Such hydrologic changes will affect almost every aspect of human well-being. The goal of this paper is to assess the impact of climate change on the hydrology of Upper Guder catchment located in northwest of Ethiopia. The GCM derived scenarios (HadCM3 A2a & B2a SRES emission scenarios) experiments were used for the climate projection. The statistical downscaling model (SDSM) was used to generate future possible local meteorological variables in the study area. The down-scaled data were then used as input to the soil and water assessment tool (SWAT) model to simulate the corresponding future stream flow regime in Upper Guder catchment of the Abay River Basin. A semi distributed hydrological model, SWAT was developed and Generalized Likelihood Uncertainty Estimation (GLUE) was utilized for uncertainty analysis. GLUE is linked with SWAT in the Calibration and Uncertainty Program known as SWAT-CUP. Three benchmark periods simulated for this study were 2020s, 2050s and 2080s. The time series generated by GCM of HadCM3 A2a and B2a and Statistical Downscaling Model (SDSM) indicate a significant increasing trend in maximum and minimum temperature values and a slight increasing trend in precipitation for both A2a and B2a emission scenarios in both Gedo and Tikur Inch stations for all three bench mark periods. The hydrologic impact analysis made with the downscaled temperature and precipitation time series as input to the hydrological model SWAT suggested for both A2a and B2a emission scenarios. The model output shows that there may be an annual increase in flow volume up to 35% for both emission scenarios in three benchmark periods in the future. All seasons show an increase in flow volume for both A2a and B2a emission scenarios for all time horizons. Potential evapotranspiration in the catchment also will increase annually on average 3-15% for the 2020s and 7-25% for the 2050s and 2080s for both A2a and B2a emissions scenarios.

Keywords: climate change, Guder sub-basin, GCM, SDSM, SWAT, SWAT-CUP, GLUE

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Authors: Chetra Mang, Ahmadali Tahmasebimoradi, Xavier Lorang

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Lattice structures are widely used in various applications, especially in aeronautic, aerospace, and medical applications because of their high performance properties. Thanks to advancement of the additive manufacturing technology, the lattice structures can be manufactured by different methods such as laser beam melting technology. However, the presence of geometric defects in the lattice structures is inevitable due to the manufacturing process. The geometric defects may have high impact on the mechanical strength of the structures. This work analyzes the correlation between the manufacturing parameters and the mechanical strengths of the lattice structures. To do that, two types of the lattice structures; body-centered cubic with z-struts (BCCZ) structures made of Inconel718, and body-centered cubic (BCC) structures made of Scalmalloy, are manufactured by laser melting beam machine using Taguchi design of experiment. Each structure is placed on the substrate with a specific position and orientation regarding the roller direction of deposed metal powder. The position and orientation are considered as the manufacturing parameters. The geometric defects of each beam in the lattice are characterized and used to build the geometric model in order to perform simulations. Then, the mechanical strengths are defined by the homogeneous response as Young's modulus and yield strength. The distribution of mechanical strengths is observed as a function of manufacturing parameters. The mechanical response of the BCCZ structure is stretch-dominated, i.e., the mechanical strengths are directly dependent on the strengths of the vertical beams. As the geometric defects of vertical beams are slightly changed based on their position/orientation on the manufacturing substrate, the mechanical strengths are less dispersed. The manufacturing parameters are less influenced on the mechanical strengths of the structure BCCZ. The mechanical response of the BCC structure is bending-dominated. The geometric defects of inclined beam are highly dispersed within a structure and also based on their position/orientation on the manufacturing substrate. For different position/orientation on the substrate, the mechanical responses are highly dispersed as well. This shows that the mechanical strengths are directly impacted by manufacturing parameters. In addition, this work is carried out to study the uncertainty propagation of the geometric defects on the mechanical strength of the BCC lattice structure made of Scalmalloy. To do that, we observe the distribution of mechanical strengths of the lattice according to the distribution of the geometric defects. A probability density law is determined based on a statistical hypothesis corresponding to the geometric defects of the inclined beams. The samples of inclined beams are then randomly drawn from the density law to build the lattice structure samples. The lattice samples are then used for simulation to characterize the mechanical strengths. The results reveal that the distribution of mechanical strengths of the structures with the same manufacturing parameters is less dispersed than one of the structures with different manufacturing parameters. Nevertheless, the dispersion of mechanical strengths due to the structures with the same manufacturing parameters are unneglectable.

Keywords: geometric defects, lattice structure, mechanical strength, uncertainty propagation

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Authors: Maria Miranda-Medina, Sara Salopek, Andras Vernes, Martin Jech

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Sliding lubricated surfaces induce the formation of tribofilms that reduce friction, wear and prevent large-scale damage of contact parts. Engine oils and lubricants use antiwear and antioxidant additives such as zinc dialkyldithiophosphate (ZDDP) from where protective tribofilms are formed by degradation. The ZDDP tribofilms are described as a two-layer structure composed of inorganic polymer material. On the top surface, the long chain polyphosphate is a zinc phosphate and in the bulk, the short chain polyphosphate is a mixed Fe/Zn phosphate with a gradient concentration. The polyphosphate chains are partially adherent to steel surface through a sulfide and work as anti-wear pads. In this contribution, ZDDP tribofilms formed on gray cast iron surfaces are studied. The tribofilms were generated in a reciprocating sliding tribometer with a piston ring-cylinder liner configuration. Fully formulated oil of SAE grade 5W-30 was used as lubricant during two tests at 40Hz and 50Hz. For the estimation of the tribofilm thicknesses, spectroscopic ellipsometry was used due to its high accuracy and non-destructive nature. Ellipsometry works under an optical principle where the change in polarisation of light reflected by the surface, is associated with the refractive index of the surface material or to the thickness of the layer deposited on top. Ellipsometrical responses derived from tribofilms are modelled by effective medium approximation (EMA), which includes the refractive index of involved materials, homogeneity of the film and thickness. The materials composition was obtained from x-ray photoelectron spectroscopic studies, where the presence of ZDDP, O and C was confirmed. From EMA models it was concluded that tribofilms formed at 40 Hz are thicker and more homogeneous than the ones formed at 50 Hz. In addition, the refractive index of each material is mixed to derive an effective refractive index that describes the optical composition of the tribofilm and exhibits a maximum response in the UV range, being a characteristic of glassy semitransparent films.

Keywords: effective medium approximation, reciprocating sliding tribometer, spectroscopic ellipsometry, zinc dialkyldithiophosphate

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Authors: Sara Tokhi Arab, Rozo Noguchi, Tofeal Ahamed

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Drought is a natural disaster that occurs in a region due to a lack of precipitation and high temperatures over a continuous period or in a single season as a consequence of climate change. Precipitation deficits and prolonged high temperatures mostly affect the agricultural sector, water resources, socioeconomics, and the environment. Consequently, it causes agricultural product loss, food shortage, famines, migration, and natural resources degradation in a region. Agriculture is the first sector affected by drought. Therefore, it is important to develop an agricultural drought risk and loss assessment to mitigate the drought impact in the agriculture sector. In this context, the main purpose of this study was to assess yield loss using composite drought indices in the drought-affected vineyards. In this study, the CDI was developed for the years 2016 to 2020 by comprising five indices: the vegetation condition index (VCI), temperature condition index (TCI), deviation of NDVI from the long-term mean (NDVI DEV), normalized difference moisture index (NDMI) and precipitation condition index (PCI). Moreover, the quantitative principal component analysis (PCA) approach was used to assign a weight for each input parameter, and then the weights of all the indices were combined into one composite drought index. Finally, Bayesian regularized artificial neural networks (BRANNs) were used to evaluate the yield variation in each affected vineyard. The composite drought index result indicated the moderate to severe droughts were observed across the Kabul Province during 2016 and 2018. Moreover, the results showed that there was no vineyard in extreme drought conditions. Therefore, we only considered the severe and moderated condition. According to the BRANNs results R=0.87 and R=0.94 in severe drought conditions for the years of 2016 and 2018 and the R= 0.85 and R=0.91 in moderate drought conditions for the years of 2016 and 2018, respectively. In the Kabul Province within the two years drought periods, there was a significate deficit in the vineyards. According to the findings, 2018 had the highest rate of loss almost -7 ton/ha. However, in 2016 the loss rates were about – 1.2 ton/ha. This research will support stakeholders to identify drought affect vineyards and support farmers during severe drought.

Keywords: grapes, composite drought index, yield loss, satellite remote sensing

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Authors: Ashu Rastogi, Uttam Thakur, Jimmy Pathak, Rajesh Gupta, Anil Bhansali

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Introduction: Liraglutide is known to induce weight loss and metabolic benefits in obese individuals. However, its effect after sleeve gastrectomy are not known. Methods: People with obesity (BMI>27.5 kg/m2) underwent LSG. Subsequently, participants were randomized to receive either 0.6mg liraglutide subcutaneously daily from 6 week post to be continued till 24 week (L-L group) or placebo (L-P group). Patients were assessed before surgery (baseline) and 6 weeks, 12weeks, 18weeks and 24weeks after surgery for height, weight, waist and hip circumference, BMI, body fat percentage, HbA1c, fasting C-peptide, fasting insulin, HOMA-IR, HOMA-β, GLP-1 levels (after standard OGTT). MRI abdomen was performed prior to surgery and at 24weeks post operatively for the estimation of intrapancreatic and intrahepatic fat content. Outcome measures: Primary outcomes were changes in metabolic variables of fasting and stimulated GLP-1 levels, insulin, c-peptide, plasma glucose levels. Secondary variables were indices of insulin resistance HOMA-IR, Matsuda index; and pancreatic and hepatic steatosis. Results: Thirty-eight patients undergoing LSG were screened and 29 participants were enrolled. Two patients withdrew consent and one patient died of acute coronary event. 26 patients were randomized and data analysed. Median BMI was 40.73±3.66 and 46.25±6.51; EBW of 49.225±11.14 and 651.48±4.85 in the L-P and L-L group, respectively. Baseline FPG was 132±51.48, 125±39.68; fasting insulin 21.5±13.99, 13.15±9.20, fasting GLP-1 2.4± .37, 2.4± .32, AUC GLP-1 340.78± 44 and 332.32 ± 44.1, HOMA-IR 7.0±4.2 and 4.42±4.5 in the L-P and L-L group, respectively. EBW loss was 47± 13.20 and 65.59± 24.20 (p<0.05) in the placebo versus liraglutide group. However, we did not observe inter-group difference in metabolic parameters between the groups in spite of significant intra-group changes after 6 months of LSG. Intra-pancreatic fat prior to surgery was 3.21±1.7 and 2.2±0.9 (p=0.38) that decreased to 2.14±1.8 and 1.06±0.8 (p=0.25) at 6 months in L-P and L-L group, respectively. Similarly, intra-pancreatic fat was 1.97±0.27 and 1.88±0.36 (p=0.361) at baseline that decreased to 1.14±0.44 and 1.36±0.47 (p=0.465) at 6 months in L-P and L-L group, respectively. Conclusion: Liraglutide augments extra body weight loss after sleeve gastrectomy. A decrease in intra-pancreatic and intra-hepatic fat is noticed after bariatric surgery without additive benefit of liraglutide administration.

Keywords: sleeve gastrectomy, liraglutide, intra-pancreatic fat, insulin

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Authors: Roya Yumul, Ofelia Loani Elvir-Lazo, Sevan Komshian, Ruby Wang, Jun Tang

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BACKGROUND: An objective means for monitoring the anti-nociceptive effects of perioperative medications has long been desired as a way to provide anesthesiologists information regarding a patient’s level of antinociception and preclude any untoward autonomic responses and reflexive muscular movements from painful stimuli intraoperatively. To this end, electroencephalogram (EEG) based tools including BIS and qCON were designed to provide information about the depth of sedation while qNOX was produced to inform on the degree of antinociception. The goal of this study was to compare the reliability of qCON/qNOX to BIS as specific indicators of response to nociceptive stimulation. METHODS: Sixty-two patients undergoing general anesthesia with LMA were included in this study. Institutional Review Board (IRB) approval was obtained, and informed consent was acquired prior to patient enrollment. Inclusion criteria included American Society of Anesthesiologists (ASA) class I-III, 18 to 80 years of age, and either gender. Exclusion criteria included the inability to consent. Withdrawal criteria included conversion to the endotracheal tube and EEG malfunction. BIS and qCON/qNOX electrodes were simultaneously placed on all patients prior to induction of anesthesia and were monitored throughout the case, along with other perioperative data, including patient response to noxious stimuli. All intraoperative decisions were made by the primary anesthesiologist without influence from qCON/qNOX. Student’s t-distribution, prediction probability (PK), and ANOVA were used to statistically compare the relative ability to detect nociceptive stimuli for each index. Twenty patients were included for the preliminary analysis. RESULTS: A comparison of overall intraoperative BIS, qCON and qNOX indices demonstrated no significant difference between the three measures (N=62, p> 0.05). Meanwhile, index values for qNOX (62±18) were significantly higher than those for BIS (46±14) and qCON (54±19) immediately preceding patient responses to nociceptive stimulation in a preliminary analysis (N=20, * p= 0.0408). Notably, certain hemodynamic measurements demonstrated a significant increase in response to painful stimuli (MAP increased from 74 ±13 mm Hg at baseline to 84 ± 18 mm Hg during noxious stimuli [p= 0.032] and HR from 76 ± 12 BPM at baseline to 80 ± 13 BPM during noxious stimuli [p=0.078] respectively). CONCLUSION: In this observational study, BIS and qCON/qNOX provided comparable information on patients’ level of sedation throughout the course of an anesthetic. Meanwhile, increases in qNOX values demonstrated a superior correlation to an imminent response to stimulation relative to all other indices

Keywords: antinociception, BIS, general anesthesia, LMA, qCON/qNOX

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Authors: Xiaobo Liu, Rakesh Mishra, Yun Zhang

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Continuous monitoring of forest canopy height with large coverage is essential for obtaining forest carbon stocks and emissions, quantifying biomass estimation, analyzing vegetation coverage, and determining biodiversity. LiDAR can be used to collect accurate woody vegetation structure such as canopy height. However, LiDAR’s coverage is usually limited because of its high cost and limited maneuverability, which constrains its use for dynamic and large area forest canopy monitoring. On the other hand, optical satellite images, like Sentinel-2, have the ability to cover large forest areas with a high repeat rate, but they do not have height information. Hence, exploring the solution of integrating LiDAR data and Sentinel-2 images to enlarge the coverage of forest canopy height prediction and increase the prediction repeat rate has been an active research topic in the environmental remote sensing community. In this study, we explore the potential of training a Random Forest Regression (RFR) model and a Convolutional Neural Network (CNN) model, respectively, to develop two predictive models for predicting and validating the forest canopy height of the Acadia Forest in New Brunswick, Canada, with a 10m ground sampling distance (GSD), for the year 2018 and 2021. Two 10m airborne LiDAR-derived canopy height models, one for 2018 and one for 2021, are used as ground truth to train and validate the RFR and CNN predictive models. To evaluate the prediction performance of the trained RFR and CNN models, two new predicted canopy height maps (CHMs), one for 2018 and one for 2021, are generated using the trained RFR and CNN models and 10m Sentinel-2 images of 2018 and 2021, respectively. The two 10m predicted CHMs from Sentinel-2 images are then compared with the two 10m airborne LiDAR-derived canopy height models for accuracy assessment. The validation results show that the mean absolute error (MAE) for year 2018 of the RFR model is 2.93m, CNN model is 1.71m; while the MAE for year 2021 of the RFR model is 3.35m, and the CNN model is 3.78m. These demonstrate the feasibility of using the RFR and CNN models developed in this research for predicting large-coverage forest canopy height at 10m spatial resolution and a high revisit rate.

Keywords: remote sensing, forest canopy height, LiDAR, Sentinel-2, artificial intelligence, random forest regression, convolutional neural network

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Authors: Stephanie Watson, Fabrice Stephenson, Conrad Pilditch, Carolyn Lundquist

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Spatial data detailing both the distribution and density of functionally important marine species are needed to inform management decisions. Species distribution models (SDMs) have proven helpful in this regard; however, models often focus only on species occurrences derived from spatially expansive datasets and lack the resolution and detail required to inform regional management decisions. Boosted regression trees (BRT) were used to produce high-resolution SDMs (250 m) at two spatial scales predicting probability of occurrence, abundance (count per sample unit), density (count per km2) and uncertainty for seven coastal seafloor taxa that vary in habitat usage and distribution to examine prediction differences and implications for coastal management. We investigated if small scale regionally focussed models (82,000 km2) can provide improved predictions compared to data-rich national scale models (4.2 million km2). We explored the variability in predictions across model type (occurrence vs abundance) and model scale to determine if specific taxa models or model types are more robust to geographical variability. National scale occurrence models correlated well with broad-scale environmental predictors, resulting in higher AUC (Area under the receiver operating curve) and deviance explained scores; however, they tended to overpredict in the coastal environment and lacked spatially differentiated detail for some taxa. Regional models had lower overall performance, but for some taxa, spatial predictions were more differentiated at a localised ecological scale. National density models were often spatially refined and highlighted areas of ecological relevance producing more useful outputs than regional-scale models. The utility of a two-scale approach aids the selection of the most optimal combination of models to create a spatially informative density model, as results contrasted for specific taxa between model type and scale. However, it is vital that robust predictions of occurrence and abundance are generated as inputs for the combined density model as areas that do not spatially align between models can be discarded. This study demonstrates the variability in SDM outputs created over different geographical scales and highlights implications and opportunities for managers utilising these tools for regional conservation, particularly in data-limited environments.

Keywords: Benthic ecology, spatial modelling, multi-scalar modelling, marine conservation.

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Authors: Woldegerima G. Medhin, Tadele Araya

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Background: Rabies is acute viral encephalitis affecting mainly carnivores and insectivorous but can affect any mammal. Case fatality rate is 100% once clinical signs appear. Rabies has a worldwide distribution in continental regions of Asia and Africa. Globally, rabies is responsible for more than 61000 human deaths annually. An estimation of human mortality rabies in Asia and Africa annually exceed 35172 and 21476 respectively. Ethiopia approximately 2900 people were estimated to die of rabies annually, Tigary region approximately 98 people were estimated to die annually. The aim of this study is to analyze trends, describe, and evaluate the ten years rabies data in Tigray, Ethiopia. Methods: We conducted descriptive epidemiological study from 15-30 February, 2023 of rabies exposure and death in humans by reviewing the health management information system report from Tigray Regional Health Bureau and vaccination coverage of dog population from 2013 to 2022. We used case definition, suspected cases are those bitten by the dogs displaying clinical signs consistent with rabies and confirmed cases were deaths from rabies at time of the exposure. Results: A total 21031 dog bites and 375 deaths report of rabies and 18222 post exposure treatments for humans in Tigray region were used. A suspected rabies patients had shown an increasing trend from 2013 to 2015 and 2018 to 2019. Overall mortality rate was 19/1000 in Tigray. Majority of suspected patients (45%) were age <15 years old. An estimated by Agriculture Bureau of Tigray Region about 12000 owned and 2500 stray dogs are available in the region, but yearly dog vaccination remains low (50%). Conclusion: Rabies is a public health problem in Tigray region. It is highly recommended to vaccinate individually owned dogs and concerned sectors should eliminate stray dogs. Surveillance system should strengthen for estimating the real magnitude, launch preventive and control measures.

Keywords: rabies, Virus, transmision, prevalence

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Authors: G. Amariei, K. Boltes, R. Rosal, P. Leton

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Wastewater treatment plants (WWTPs) are supposed to hold an important place in the reduction of emerging contaminants, but provide an environment that has potential for the development and/or spread of adaptation, as bacteria are continuously mixed with contaminants at sub-inhibitory concentrations. Reviewing the literature, there are little data available regarding the use of adapted bacteria forming activated sludge community for toxicity assessment, and only individual validations have been performed. Therefore, the aim of this work was to study the toxicity of Triclosan (TCS) and Ibuprofen (IBU), individually and in binary combination, on adapted activated sludge (AS). For this purpose a battery of biomarkers were assessed, involving oxidative stress and cytotoxicity responses: glutation-S-transferase (GST), catalase (CAT) and viable cells with FDA. In addition, we compared the toxic effects on adapted bacteria with unadapted bacteria, from a previous research. Adapted AS comes from three continuous-flow AS laboratory systems; two systems received IBU and TCS, individually; while the other received the binary combination, for 14 days. After adaptation, each bacterial culture condition was exposure to IBU, TCS and the combination, at 12 h. The concentration of IBU and TCS ranged 0.5-4mg/L and 0.012-0.1 mg/L, respectively. Batch toxicity experiments were performed using Oxygraph system (Hansatech), for determining the activity of CAT enzyme based on the quantification of oxygen production rate. Fluorimetric technique was applied as well, using a Fluoroskan Ascent Fl (Thermo) for determining the activity of GST enzyme, using monochlorobimane-GSH as substrate, and to the estimation of viable cell of the sludge, by fluorescence staining using Fluorescein Diacetate (FDA). For IBU adapted sludge, CAT activity it was increased at low concentration of IBU, TCS and mixture. However, increasing the concentration the behavior was different: while IBU tends to stabilize the CAT activity, TCS and the mixture decreased this one. GST activity was significantly increased by TCS and mixture. For IBU, no variations it was observed. For TCS adapted sludge, no significant variations on CAT activity it was observed. GST activity it was significant decreased for all contaminants. For mixture adapted sludge the behaviour of CAT activity it was similar to IBU adapted sludge. GST activity it was decreased at all concentration of IBU. While the presence of TCS and mixture, respectively, increased the GST activity. These findings were consistent with the viability cells evaluation, which clearly showed a variation of sludge viability. Our results suggest that, compared with unadapted bacteria, the adapted bacteria conditions plays a relevant role in the toxicity behaviour towards activated sludge communities.

Keywords: adapted sludge community, mixture, PPCPs, toxicity

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Authors: Dávid Kovács, Bálint Csanády, Dániel Boros, Iván Ivkovic, Lóránt Nagy, Dalma Tóth-Lakits, László Márkus, András Lukács

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The modeling practice of financial instruments has seen significant change over the last decade due to the recognition of time-dependent and stochastically changing correlations among the market prices or the prices and market characteristics. To represent this phenomenon, the Stochastic Correlation Process (SCP) has come to the fore in the joint modeling of prices, offering a more nuanced description of their interdependence. This approach has allowed for the attainment of realistic tail dependencies, highlighting that prices tend to synchronize more during intense or volatile trading periods, resulting in stronger correlations. Evidence in statistical literature suggests that, similarly to the volatility, the SCP of certain stock prices follows rough paths, which can be described using fractional differential equations. However, estimating parameters for these equations often involves complex and computation-intensive algorithms, creating a necessity for alternative solutions. In this regard, the Fractional Ornstein-Uhlenbeck (fOU) process from the family of fractional processes offers a promising path. We can effectively describe the rough SCP by utilizing certain transformations of the fOU. We employed neural networks to understand the behavior of these processes. We had to develop a fast algorithm to generate a valid and suitably large sample from the appropriate process to train the network. With an extensive training set, the neural network can estimate the process parameters accurately and efficiently. Although the initial focus was the fOU, the resulting model displayed broader applicability, thus paving the way for further investigation of other processes in the realm of financial mathematics. The utility of SCP extends beyond its immediate application. It also serves as a springboard for a deeper exploration of fractional processes and for extending existing models that use ordinary Wiener processes to fractional scenarios. In essence, deploying both SCP and fractional processes in financial models provides new, more accurate ways to depict market dynamics.

Keywords: fractional Ornstein-Uhlenbeck process, fractional stochastic processes, Heston model, neural networks, stochastic correlation, stochastic differential equations, stochastic volatility

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Authors: Sultan Ahmad Azizi, Gaurang J. Joshi

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Urban transportation has come to lime light in recent times due to deteriorating travel quality. The economic growth of India has boosted significant rise in private vehicle ownership in cities, whereas public transport systems have largely been ignored in metropolitan cities. Even though there is latent demand for public transport systems like organized bus services, most of the metropolitan cities have unsustainably low share of public transport. Unfortunately, Indian metropolitan cities have failed to maintain balance in mode share of various travel modes in absence of timely introduction of mass transit system of required capacity and quality. As a result, personalized travel modes like two wheelers have become principal modes of travel, which cause significant environmental, safety and health hazard to the citizens. Of late, the policy makers have realized the need to improve public transport system in metro cities for sustaining the development. However, the challenge to the transit planning authorities is to design a transit system for cities that may attract people to switch over from their existing and rather convenient mode of travel to the transit system under the influence of household socio-economic characteristics and the given travel pattern. In this context, the fast-growing industrial city of Surat is taken up as a case for the study of likely shift to bus transit. Deterioration of public transport system of bus after 1998, has led to tremendous growth in two-wheeler traffic on city roads. The inadequate and poor service quality of present bus transit has failed to attract the riders and correct the mode use balance in the city. The disaggregate travel behavior for trip generations and the travel mode choice has been studied for the West Adajan residential sector of city. Mode specific utility functions are calibrated under multi-nominal logit environment for two-wheeler, cars and auto rickshaws with respect to bus transit using SPSS. Estimation of shift to bus transit is carried indicate an average 30% of auto rickshaw users and nearly 5% of 2W users are likely to shift to bus transit if service quality is improved. However, car users are not expected to shift to bus transit system.

Keywords: bus transit, disaggregate travel nehavior, mode choice Behavior, public transport

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: deep learning, long short term memory, energy, renewable energy load forecasting

Procedia PDF Downloads 237

Authors: Kyomin Lee, Joohee Kim, Sangho Kang

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The commercial nuclear power reactor in South Korea, Kori Unit 1, which was a 587 MWe pressurized water reactor that started operation since 1978, was permanently shut down in June 2017 without an additional operating license extension. The Kori 1 Unit is scheduled to become the nuclear power unit to enter the decommissioning phase. In this study, the preliminary evaluation of the decommissioning wastes for the Kori Unit 1 was performed based on the following series of process: firstly, the plant inventory is investigated based on various documents (i.e., equipment/ component list, construction records, general arrangement drawings). Secondly, the radiological conditions of systems, structures and components (SSCs) are established to estimate the amount of radioactive waste by waste classification. Third, the waste management strategies for Kori Unit 1 including waste packaging are established. Forth, selection of the proper decontamination and dismantling (D&D) technologies is made considering the various factors. Finally, the amount of decommissioning waste by classification for Kori 1 is estimated using the DeCAT program, which was developed by KEPCO-E&C for a decommissioning cost estimation. The preliminary evaluation results have shown that the expected amounts of decommissioning wastes were less than about 2% and 8% of the total wastes generated (i.e., sum of clean wastes and radwastes) before/after waste processing, respectively, and it was found that the majority of contaminated material was carbon or alloy steel and stainless steel. In addition, within the range of availability of information, the results of the evaluation were compared with the results from the various decommissioning experiences data or international/national decommissioning study. The comparison results have shown that the radioactive waste amount from Kori Unit 1 decommissioning were much less than those from the plants decommissioned in U.S. and were comparable to those from the plants in Europe. This result comes from the difference of disposal cost and clearance criteria (i.e., free release level) between U.S. and non-U.S. The preliminary evaluation performed using the methodology established in this study will be useful as a important information in establishing the decommissioning planning for the decommissioning schedule and waste management strategy establishment including the transportation, packaging, handling, and disposal of radioactive wastes.

Keywords: characterization, classification, decommissioning, decontamination and dismantling, Kori 1, radioactive waste

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Authors: Mohamed Korany, Azza Gazy, Essam Khamis, Marwa Adel, Miranda Fawzy

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Background: Two new, simple and specific methods; First, a Zero-crossing first-derivative technique and second, a chemometric-assisted spectrophotometric artificial neural network (ANN) were developed and validated in accordance with ICH guidelines. Both methods were used for the simultaneous estimation of the two closely related antioxidant nutraceuticals ; Coenzyme Q10 (Q) ; also known as Ubidecarenone or Ubiquinone-10, and Vitamin E (E); alpha-tocopherol acetate, in their pharmaceutical binary mixture. Results: For first method: By applying the first derivative, both Q and E were alternatively determined; each at the zero-crossing of the other. The D1 amplitudes of Q and E, at 285 nm and 235 nm respectively, were recorded and correlated to their concentrations. The calibration curve is linear over the concentration range of 10-60 and 5.6-70 μg mL-1 for Q and E, respectively. For second method: ANN (as a multivariate calibration method) was developed and applied for the simultaneous determination of both analytes. A training set (or a concentration set) of 90 different synthetic mixtures containing Q and E, in wide concentration ranges between 0-100 µg/mL and 0-556 µg/mL respectively, were prepared in ethanol. The absorption spectra of the training sets were recorded in the spectral region of 230–300 nm. A Gradient Descend Back Propagation ANN chemometric calibration was computed by relating the concentration sets (x-block) to their corresponding absorption data (y-block). Another set of 45 synthetic mixtures of the two drugs, in defined range, was used to validate the proposed network. Neither chemical separation, preparation stage nor mathematical graphical treatment were required. Conclusions: The proposed methods were successfully applied for the assay of Q and E in laboratory prepared mixtures and combined pharmaceutical tablet with excellent recoveries. The ANN method was superior over the derivative technique as the former determined both drugs in the non-linear experimental conditions. It also offers rapidity, high accuracy, effort and money saving. Moreover, no need for an analyst for its application. Although the ANN technique needed a large training set, it is the method of choice in the routine analysis of Q and E tablet. No interference was observed from common pharmaceutical additives. The results of the two methods were compared together

Keywords: coenzyme Q10, vitamin E, chemometry, quantitative analysis, first derivative spectrophotometry, artificial neural network

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Authors: Mohammed Abdullah Alshahrani

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The research focuses on investigating the use of partial least squares (PLS) methodology for addressing challenges associated with high-dimensional correlated data. Recent technological advancements have led to experiments producing data characterized by a large number of variables compared to observations, with substantial inter-variable correlations. Such data patterns are common in chemometrics, where near-infrared (NIR) spectrometer calibrations record chemical absorbance levels across hundreds of wavelengths, and in genomics, where thousands of genomic regions' copy number alterations (CNA) are recorded from cancer patients. PLS serves as a widely used method for analyzing high-dimensional data, functioning as a regression tool in chemometrics and a classification method in genomics. It handles data complexity by creating latent variables (components) from original variables. However, applying PLS can present challenges. The study investigates key areas to address these challenges, including unifying interpretations across three main PLS algorithms and exploring unusual negative shrinkage factors encountered during model fitting. The research presents an alternative approach to addressing the interpretation challenge of predictor weights associated with PLS. Sparse estimation of predictor weights is employed using a penalty function combining a lasso penalty for sparsity and a Cauchy distribution-based penalty to account for variable dependencies. The results demonstrate sparse and grouped weight estimates, aiding interpretation and prediction tasks in genomic data analysis. High-dimensional data scenarios, where predictors outnumber observations, are common in regression analysis applications. Ordinary least squares regression (OLS), the standard method, performs inadequately with high-dimensional and highly correlated data. Copy number alterations (CNA) in key genes have been linked to disease phenotypes, highlighting the importance of accurate classification of gene expression data in bioinformatics and biology using regularized methods like PLS for regression and classification.

Keywords: partial least square regression, genetics data, negative filter factors, high dimensional data, high correlated data

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Authors: Ian Christopher Rocha

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This study aimed to determine the association of the quality of life (QOL) of the caregivers of children in need of special protection (CNSP) in child-caring institutions in Metro Manila with the levels of their role overload (RO) and role distress (RD). The CNSP in this study covered the orphaned, abandoned, abused, neglected, exploited, and mentally-challenged children. In this study, the domains of QOL included physical health (PH), psychological health, social health (SH), and living conditions (LC). It also intended to ascertain the association of their personal and work-related characteristics with their RO and RD levels. The respondents of this study were 130 CNSP caregivers in 17 residential child-rearing institutions in Metro Manila. A purposive non-probability sampling was used. Using a quantitative methodological approach, the survey method was utilized to gather data with the use of a self-administered structured questionnaire. Data were analyzed using both descriptive and inferential statistics. Results revealed that the level of RO, the level of RD, and the QOL of the CNSP caregivers were all moderate. Data also suggested that there were significant positive relationships between the RO level and the caregivers’ characteristics, such as age, the number of training, and years of service in the institution. At the same time, the findings revealed that there were significant positive relationships between the RD level and the caregivers’ characteristics, such as age and hours of care rendered to their care recipients. In addition, the findings suggested that all domains of their QOL obtained significant relationships with their RO level. For the correlations of their level of RO and their QOL domains, the PH and the LC obtained a moderate negative correlation with the RO level while the rest of the domains obtained weak negative correlations with RO level. For the correlations of their level of RD and the QOL domains, all domains, except SH, obtained strong negative correlations with the level of RD. The SH revealed to have a moderate negative correlation with RD level. In conclusion, caregivers who are older experience higher levels of RO and RD; caregivers who have more training and years of service experience the higher level of RO; and caregivers who have longer hours of rendered care experience the higher level of RD. In addition, the study affirmed that if the levels of RO and RD are high, the QOL is low, and vice versa. Therefore, the RO and RD levels are reliable predictors of the caregivers’ QOL. In relation, the caregiving situation in the Philippines revealed to be unique and distinct from other countries because the levels of RO and RD and the QOL of Filipino CNSP caregivers were all moderate in contrast with their foreign counterparts who experience high caregiving RO and RD leading to low QOL.

Keywords: quality of life, caregivers, children in need of special protection, physical health, psychological health, social health, living conditions, role overload, role distress

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Authors: M. R. Aher, R. B. Laware, G. S. Asane, B. S. Kuchekar

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Objective: Studies have shown that a new combination therapy with Artemisinin derivatives and curcumin is unique, with potential advantages over known ACTs. In present study an attempt was made to prepare microparticulate drug delivery system of Curcumin-Zn complex and evaluate it in combination with artemether for antimalarial activity. Material and method: Curcumin Zn complex was prepared and encapsulated using sodium alginate. Microparticles thus obtained are further coated with various enteric polymers at different coating thickness to control the release. Microparticles are evaluated for encapsulation efficiency, drug loading and in vitro drug release. Roentgenographic Studies was conducted in rabbits with BaSO 4 tagged formulation. Optimized formulation was screened for antimalarial activity using P. berghei-infected mice survival test and % paracetemia inhibition, alone (three oral dose of 5mg/day) and in combination with arthemether (i.p. 500, 1000 and 1500µg). Curcumin-Zn(II) was estimated in serum after oral administration to rats by using spectroflurometry. Result: Microparticles coated with Cellulose acetate phthalate showed most satisfactory and controlled release with 479 min time for 60% drug release. X-ray images taken at different time intervals confirmed the retention of formulation in GI tract. Estimation of curcumin in serum by spectroflurometry showed that drug concentration is maintained in the blood for longer time with tmax of 6 hours. The survival time (40 days post treatment) of mice infected with P. berghei was compared to survival after treatment with either Curcumin-Zn(II) microparticles artemether combination, curcumin-Zn complex and artemether. Oral administration of Curcumin-Zn(II)-artemether prolonged the survival of P.berghei-infected mice. All the mice treated with Curcumin-Zn(II) microparticles (5mg/day) artemether (1000µg) survived for more than 40 days and recovered with no detectable parasitemia. Administration of Curcumin-Zn(II) artemether combination reduced the parasitemia in mice by more than 90% compared to that in control mice for the first 3 days after treatment. Conclusion: Antimalarial activity of the curcumin Zn-artemether combination was more pronounced than mono therapy. A single dose of 1000µg of artemether in curcumin-Zn combination gives complete protection in P. berghei-infected mice. This may reduce the chances of drug resistance in malaria management.

Keywords: formulation, microparticulate drug delivery, antimalarial, pharmaceutics

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Authors: Anne C. Richer-De-Forges, Dominique Arrouays, Songchao Chen, Mercedes Roman Dobarco

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Most of the digital soil mapping (DSM) products rely on machine learning (ML) prediction models and/or the use or pedotransfer functions (PTF) in which calibration data come from soil analyses performed in labs. However, many other observations (often qualitative, nominal, or ordinal) could be used as proxies of lab measurements or as input data for ML of PTF predictions. DSM and ML are briefly described with some examples taken from the literature. Then, we explore the potential of an ordinal qualitative variable, i.e., the hand-feel soil texture (HFST) estimating the mineral particle distribution (PSD): % of clay (0-2µm), silt (2-50µm) and sand (50-2000µm) in 15 classes. The PSD can also be measured by lab measurements (LAST) to determine the exact proportion of these particle-sizes. However, due to cost constraints, HFST are much more numerous and spatially dense than LAST. Soil texture (ST) is a very important soil parameter to map as it is controlling many of the soil properties and functions. Therefore, comes an essential question: is it possible to use HFST as a proxy of LAST for calibration and/or validation of DSM predictions of ST? To answer this question, the first step is to compare HFST with LAST on a representative set where both information are available. This comparison was made on ca 17,400 samples representative of a French region (34,000 km2). The accuracy of HFST was assessed, and each HFST class was characterized by a probability distribution function (PDF) of its LAST values. This enables to randomly replace HFST observations by LAST values while respecting the PDF previously calculated and results in a very large increase of observations available for the calibration or validation of PTF and ML predictions. Some preliminary results are shown. First, the comparison between HFST classes and LAST analyses showed that accuracies could be considered very good when compared to other studies. The causes of some inconsistencies were explored and most of them were well explained by other soil characteristics. Then we show some examples applying these relationships and the increase of data to several issues related to DSM. The first issue is: do the PDF functions that were established enable to use HSFT class observations to improve the LAST soil texture prediction? For this objective, we replaced all HFST for topsoil by values from the PDF 100 time replicates). Results were promising for the PTF we tested (a PTF predicting soil water holding capacity). For the question related to the ML prediction of LAST soil texture on the region, we did the same kind of replacement, but we implemented a 10-fold cross-validation using points where we had LAST values. We obtained only preliminary results but they were rather promising. Then we show another example illustrating the potential of using HFST as validation data. As in numerous countries, the HFST observations are very numerous; these promising results pave the way to an important improvement of DSM products in all the countries of the world.

Keywords: digital soil mapping, improvement of digital soil mapping predictions, potential of using hand-feel soil texture, soil texture prediction

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Authors: Priyanka Sharma, Manoj Jais, Poonam Gupta, Suraiya K. Ansari, Ravinder Kaur

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Introduction: As much as the Health Care is necessary for the population, so is the management of the Biomedical waste produced. Biomedical waste is a wide terminology used for the waste material produced during the diagnosis, treatment or immunization of human beings and animals, in research or in the production or testing of biological products. Biomedical waste management is a chain of processes from the point of generation of Biomedical waste to its final disposal in the correct and proper way, assigned for that particular type of waste. Any deviation from the said processes leads to improper disposal of Biomedical waste which itself is a major health hazard. Proper segregation of Biomedical waste is the key for Biomedical Waste management. Improper disposal of BMW can cause sharp injuries which may lead to HIV, Hepatitis-B virus, Hepatitis-C virus infections. Therefore, proper disposal of BMW is of upmost importance. Health care establishments segregate the Biomedical waste and dispose it as per the Biomedical waste management rules in India. Objectives: This study was done to observe the current trends of Biomedical waste generated in a tertiary care Hospital in Delhi. Methodology: Biomedical waste management rounds were conducted in the hospital wards. Relevant details were collected and analysed and sites with maximum Biomedical waste generation were identified. All the data was cross checked with the commons collection site. Results: The total amount of waste generated in the hospital during January 2014 till December 2014 was 6,39,547 kg, of which 70.5% was General (non-hazardous) waste and the rest 29.5% was BMW which consisted highly infectious waste (12.2%), disposable plastic waste (16.3%) and sharps (1%). The maximum quantity of Biomedical waste producing sites were Obstetrics and Gynaecology wards with a total Biomedical waste production of 45.8%, followed by Paediatrics, Surgery and Medicine wards with 21.2 %, 4.6% and 4.3% respectively. The maximum average Biomedical waste generated was by Obstetrics and Gynaecology ward with 0.7 kg/bed/day, followed by Paediatrics, Surgery and Medicine wards with 0.29, 0.28 and 0.18 kg/bed/day respectively. Conclusions: Hospitals should pay attention to the sites which produce a large amount of BMW to avoid improper segregation of Biomedical waste. Also, induction and refresher training Program of Biomedical waste management should be conducted to avoid improper management of Biomedical waste. Healthcare workers should be made aware of risks of poor Biomedical waste management.

Keywords: biomedical waste, biomedical waste management, hospital-tertiary care, New Delhi

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Authors: Erick R. Bandala, Kebret Kebede, Nicole Johnson, Rebecca Murray, Destiny Green, John Mejia, Polioptro Martinez-Austria

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Summertemperature data from Clark County was collected and used to estimate two different heat-related indexes: the heat index (HI) and excess heat factor (EHF). These two indexes were used jointly with data of health-related deaths in Clark County to assess the effect of extreme heat on the exposed population. The trends of the heat indexes were then analyzed for the 2007-2016 decadeandthe correlation between heat wave episodes and the number of heat-related deaths in the area was estimated. The HI showed that this value has increased significantly in June, July, and August over the last ten years. The same trend was found for the EHF, which showed a clear increase in the severity and number of these events per year. The number of heat wave episodes increased from 1.4 per year during the 1980-2016 period to 1.66 per yearduring the 2007-2016 period. However, a different trend was found for heat-wave-event duration, which decreasedfrom an average of 20.4 days during the trans-decadal period (1980-2016) to 18.1 days during the most recent decade(2007-2016). The number of heat-related deaths was also found to increase from 2007 to 2016, with 2016 with the highest number of heat-related deaths. Both HI and the number of deaths showeda normal-like distribution for June, July, and August, with the peak values reached in late July and early August. The average maximum HI values better correlated with the number of deaths registered in Clark County than the EHF, probably because HI uses the maximum temperature and humidity in its estimation,whereas EHF uses the average medium temperature. However, it is worth testing the EHF of the study zone because it was reported to fit properly in the case of heat-related morbidity. For the overall period, 437 heat-related deaths were registered in Clark County, with 20% of the deaths occurring in June, 52% occurring in July, 18% occurring in August,and the remaining 10% occurring in the other months of the year. The most vulnerable subpopulation was people over 50 years old, for which 76% of the heat-related deaths were registered.Most of the cases were associated with heart disease preconditions. The second most vulnerable subpopulation was young adults (20-50), which accounted for 23% of the heat-related deaths. These deathswere associated with alcoholic/illegal drug intoxication.

Keywords: heat, health, hazards, workforce

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