Search results for: variability

Authors: Ayon Mukherjee

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Covariate-adjusted response-adaptive (CARA) designs use the available responses to skew the treatment allocation in a clinical trial in towards treatment found at an interim stage to be best for a given patient's covariate profile. Extensive research has been done on various aspects of CARA designs with the patient responses assumed to follow a parametric model. However, ranges of application for such designs are limited in real-life clinical trials where the responses infrequently fit a certain parametric form. On the other hand, robust estimates for the covariate-adjusted treatment effects are obtained from the parametric assumption. To balance these two requirements, designs are developed which are free from distributional assumptions about the survival responses, relying only on the assumption of proportional hazards for the two treatment arms. The proposed designs are developed by deriving two types of optimum allocation designs, and also by using a distribution function to link the past allocation, covariate and response histories to the present allocation. The optimal designs are based on biased coin procedures, with a bias towards the better treatment arm. These are the doubly-adaptive biased coin design (DBCD) and the efficient randomized adaptive design (ERADE). The treatment allocation proportions for these designs converge to the expected target values, which are functions of the Cox regression coefficients that are estimated sequentially. These expected target values are derived based on constrained optimization problems and are updated as information accrues with sequential arrival of patients. The design based on the link function is derived using the distribution function of a probit model whose parameters are adjusted based on the covariate profile of the incoming patient. To apply such designs, the treatment allocation probabilities are sequentially modified based on the treatment allocation history, response history, previous patients’ covariates and also the covariates of the incoming patient. Given these information, an expression is obtained for the conditional probability of a patient allocation to a treatment arm. Based on simulation studies, it is found that the ERADE is preferable to the DBCD when the main aim is to minimize the variance of the observed allocation proportion and to maximize the power of the Wald test for a treatment difference. However, the former procedure being discrete tends to be slower in converging towards the expected target allocation proportion. The link function based design achieves the highest skewness of patient allocation to the best treatment arm and thus ethically is the best design. Other comparative merits of the proposed designs have been highlighted and their preferred areas of application are discussed. It is concluded that the proposed CARA designs can be considered as suitable alternatives to the traditional balanced randomization designs in survival trials in terms of the power of the Wald test, provided that response data are available during the recruitment phase of the trial to enable adaptations to the designs. Moreover, the proposed designs enable more patients to get treated with the better treatment during the trial thus making the designs more ethically attractive to the patients. An existing clinical trial has been redesigned using these methods.

Keywords: censored response, Cox regression, efficiency, ethics, optimal allocation, power, variability

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Authors: Ho Yin Wickson Cheung, Liora Malki-Epshtein

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Outdoor air pollution presents a significant challenge for public health globally, especially in urban areas, with road traffic acting as the primary contributor to air pollution. Several studies have documented the antagonistic relation between traffic-related air pollution (TRAP) and the impact on health, especially to the vulnerable group of population, particularly young pupils. Generally, TRAP could cause damage to their brain, restricting the ability of children to learn and, more importantly, causing detrimental respiratory issues in later life. Butlittle is known about the specific exposure of children at school during the school day and the impact this may have on their overall exposure to pollution at a crucial time in their development. This project has set out to examine the air quality across primary schools in South-East London and assesses the variability of data found based on their geographic location and surroundings. Nitrogen dioxide, PM contaminants, and carbon dioxide were collected with diffusion tubes and portable monitoring equipment for eight schools across three local areas, that are Greenwich, Lewisham, and Tower Hamlets. This study first examines the geographical features of the schools surrounding (E.g., coverage of urban road structure and green infrastructure), then utilize three different methods to capture pollutants data. Moreover, comparing the obtained results with existing data from monitoring stations to understand the differences in air quality before and during the pandemic. Furthermore, most studies in this field have unfortunately neglected human exposure to pollutants and calculated based on values from fixed monitoring stations. Therefore, this paper introduces an alternative approach by calculating human exposure to air pollution from real-time data obtained when commuting within related areas (Driving routes and field walking). It is found that schools located highly close to motorways are generally not suffering from the most air pollution contaminants. Instead, one with the worst traffic congested routes nearby might also result in poor air quality. Monitored results also indicate that the annual air pollution values have slightly decreased during the pandemic. However, the majority of the data is currently still exceeding the WHO guidelines. Finally, the total human exposures for NO2 during commuting in the two selected routes were calculated. Results illustrated the total exposure for route 1 were 21,730 μm/m3 and 28,378.32 μm/m3, and for route 2 were 30,672 μm/m3 and 16,473 μm/m3. The variance that occurred might be due to the difference in traffic volume that requires further research. Exposure for NO2 during commuting was plotted with detailed timesteps that have shown their peak usually occurred while commuting. These have consolidated the initial assumption to the extremeness of TRAP. To conclude, this paper has yielded significant benefits to understanding air quality across schools in London with the new approach of capturing human exposure (Driving routes). Confirming the severity of air pollution and promoting the necessity of considering environmental sustainability for policymakers during decision making to protect society's future pillars.

Keywords: air pollution, schools, pupils, congestion

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Authors: Prescilla Lambert, Christophe Roos, Mounim Lebrini

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For several years, the Caribbean islands and West Africa have had to deal with the massive arrival of the brown seaweed Sargassum. Overall, this macroalgae, which constitutes a habitat for a great diversity of marine organisms, is also an additional stress factor for the marine environment (e.g., coral reefs). In addition, the accumulation followed by the significant decomposition of the Sargassum spp. biomass on the coast leads to the release of toxic gases (H₂S and NH₃), which calls into question the functioning of the economic, health and tourist life of the island and the other interested territories. Originally, these algae are formed by the eutrophication of the oceans accentuated by global warming. Unfortunately, scientists predict a significant recurrence of these Sargassum strandings for years to come. It is therefore more than necessary to find solutions by putting in place a sustainable management plan for this phenomenon. Martinique, a small island in the Caribbean arc, is one of the many areas impacted by Sargassum seaweed strandings. Since 2011, there has been a constant increase in the degradation of the materials present in this region, largely due to toxic/corrosive gases released by the algae decomposition. In order to protect the structures and the vulnerable building materials while limiting the use of synthetic/petroleum based molecules as much as possible, research is being conducted on molecules of natural origin. Thus, thanks to the chemical composition, which comprise molecules with interesting properties, algae such as Sargassum could potentially help to solve many issues. Therefore, this study focuses on the green extraction and characterization of molecules from the species Sargassum fluitans and Sargassum natans present in Martinique. The secondary metabolites found in these extracts showed variability in yield rates due to local climatic conditions. The tests carried out shed light on the anticorrosive and antibacterial potential of the algae. These extracts can thus be described as natural inhibitors. The effect of variation in inhibitor concentrations was tested in electrochemistry using electrochemical impedance spectroscopy and polarization curves. The analysis of electrochemical results obtained by direct immersion in the extracts and self-assembled molecular layers (SAMs) for Sargassum fluitans III, Sargassum natans I and VIII species was conclusive in acid and alkaline environments. The excellent results obtained reveal an inhibitory efficacy of 88% at 50mg/L for the crude extract of Sargassum fluitans III and efficacies greater than 97% for the chemical families of Sargassum fluitans III. Similarly, microbiological tests also suggest a bactericidal character. Results for Sargassum fluitans III crude extract show a minimum inhibitory concentration (MIC) of 0.005 mg/mL on Gram-negative bacteria and a MIC greater than 0.6 mg/mL on Gram-positive bacteria. These results make it possible to consider the management of local and international issues while valuing a biomass rich in biodegradable molecules. The next step in this study will therefore be the evaluation of the toxicity of Sargassum spp..

Keywords: Sargassum, secondary metabolites, anticorrosive, antibacterial, natural inhibitors

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Authors: Staci McGill, Morgan Hayes, Robert Coleman, Kimberly Tumlin

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The interaction and relationships between horses and humans have been shown to be positive for physical, mental, and emotional wellbeing, however equine spaces where these interactions occur do include some environmental risks. There are 1.7 million jobs associated with the equine industry in the United States in addition to recreational riders, owners, and volunteers who interact with horses for substantial amounts of time daily inside built structures. One specialized facility, an “indoor arena” is a semi-indoor structure used for exercising horses and exhibiting skills during competitive events. Typically, indoor arenas have a sand or sand mixture as the footing or surface over which the horse travels, and increasingly, silica sand is being recommended due to its durable nature. It was previously identified in a semi-qualitative survey that the majority of individuals using indoor arenas have environmental concerns with dust. 27% (90/333) of respondents reported respiratory issues or allergy-like symptoms while riding with 21.6% (71/329) of respondents reporting these issues while standing on the ground observing or teaching. Frequent headaches and/or lightheadedness was reported in 9.9% (33/333) of respondents while riding and in 4.3% 14/329 while on the ground. Horse respiratory health is also negatively impacted with 58% (194/333) of respondents indicating horses cough during or after time in the indoor arena. Instructors who spent time in indoor arenas self-reported more respiratory issues than those individuals who identified as smokers, highlighting the health relevance of understanding these unique structures. To further elucidate environmental concerns and self-reported health issues, 35 facility assessments were conducted in a cross-sectional sampling design in the states of Kentucky and Ohio (USA). Data, including air speeds, were collected in a grid fashion at 15 points within the indoor arenas and then mapped spatially using krigging in ARCGIS. From the spatial maps, standard variances were obtained and differences were analyzed using multivariant analysis of variances (MANOVA) and analysis of variances (ANOVA). There were no differences for the variance of the air speeds in the spaces for facility orientation, presence and type of roof ventilation, climate control systems, amount of openings, or use of fans. Variability of the air speeds in the indoor arenas was 0.25 or less. Further analysis yielded that average air speeds within the indoor arenas were lower than 100 ft/min (0.51 m/s) which is considered still air in other animal facilities. The lack of air movement means that dust clearance is reliant on particle size and weight rather than ventilation. While further work on respirable dust is necessary, this characterization of the semi-indoor environment where animals and humans interact indicates insufficient air flow to eliminate or reduce respiratory hazards. Finally, engineering solutions to address air movement deficiencies within indoor arenas or mitigate particulate matter are critical to ensuring exposures do not lead to adverse health outcomes for equine professionals, volunteers, participants, and horses within these spaces.

Keywords: equine, indoor arena, ventilation, particulate matter, respiratory health

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Authors: I. M. L. Nadeesha Jayaweera, Adao Alex Trindade

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In choosing a candidate model in likelihood-based modeling via an information criterion, the practitioner is often faced with the difficult task of deciding just how far up the ranked list to look. Motivated by this pragmatic necessity, we construct an uncertainty band for a generalized (model selection) information criterion (GIC), defined as a criterion for which the limit in probability is identical to that of the normalized log-likelihood. This includes common special cases such as AIC & BIC. The method starts from the asymptotic normality of the GIC for the joint distribution of the candidate models in an independent and identically distributed (IID) data framework and proceeds by deriving the (asymptotically) exact distribution of the minimum. The calculation of an upper quantile for its distribution then involves the computation of multivariate Gaussian integrals, which is amenable to efficient implementation via the R package "mvtnorm". The performance of the methodology is tested on simulated data by checking the coverage probability of nominal upper quantiles and compared to the bootstrap. Both methods give coverages close to nominal for large samples, but the bootstrap is two orders of magnitude slower. The methodology is subsequently extended to two other commonly used model structures: regression and time series. In the regression case, we derive the corresponding asymptotically exact distribution of the minimum GIC invoking Lindeberg-Feller type conditions for triangular arrays and are thus able to similarly calculate upper quantiles for its distribution via multivariate Gaussian integration. The bootstrap once again provides a default competing procedure, and we find that similar comparison performance metrics hold as for the IID case. The time series case is complicated by far more intricate asymptotic regime for the joint distribution of the model GIC statistics. Under a Gaussian likelihood, the default in most packages, one needs to derive the limiting distribution of a normalized quadratic form for a realization from a stationary series. Under conditions on the process satisfied by ARMA models, a multivariate normal limit is once again achieved. The bootstrap can, however, be employed for its computation, whence we are once again in the multivariate Gaussian integration paradigm for upper quantile evaluation. Comparisons of this bootstrap-aided semi-exact method with the full-blown bootstrap once again reveal a similar performance but faster computation speeds. One of the most difficult problems in contemporary statistical methodological research is to be able to account for the extra variability introduced by model selection uncertainty, the so-called post-model selection inference (PMSI). We explore ways in which the GIC uncertainty band can be inverted to make inferences on the parameters. This is being attempted in the IID case by pivoting the CDF of the asymptotically exact distribution of the minimum GIC. For inference one parameter at a time and a small number of candidate models, this works well, whence the attained PMSI confidence intervals are wider than the MLE-based Wald, as expected.

Keywords: model selection inference, generalized information criteria, post model selection, Asymptotic Theory

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Authors: Vaibhav Baliyan, S. S. Parihar

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With the rise in temperature during reproductive phase and moisture stress, winter wheat yields are likely to decrease because of limited plant growth, higher rate of night respiration, higher spikelet sterility or number of grains per spike and restricted embryo development thereby reducing grain number. Crop management practices play a pivotal role in minimizing adverse effects of terminal heat stress on wheat production. Amongst various agronomic management practices, adjusting sowing date, crop cultivars and irrigation scheduling have been realized to be simple yet powerful, implementable and eco-friendly mitigation strategies to sustain yields under elevated temperature conditions. Taking into account, large variability in wheat production in space and time, a study was conducted to identify the suitable wheat varieties under both early and late planting with suitable irrigation schedule for minimizing terminal heat stress effect and thereby improving wheat production. Experiments were conducted at research farms of Indian Agricultural Research Institute, New Delhi, India, separately for timely and late sown conditions with suitable varieties with staggering dates of sowing from 1st November to 30th November in case of timely sown and from 1st December to 31st December for late sown condition. The irrigation schedule followed for both the experiments were 100% of ETc (evapotranspiration of crop), 80% of ETc and 60% of ETc. Results of the timely sown experiment indicated that 1st November sowing resulted in higher grain yield followed by 10th November. However, delay in sowing thereafter resulted in gradual decrease in yield and the maximum reduction was noticed under 30th November sowing. Amongst the varieties, HD3086 produced higher grain yield compared to other varieties. Irrigation applied based on 100% of ETc gave higher yield comparable to 80% of ETc but both were significantly higher than 60% of ETc. It was further observed that even liberal irrigation under 100% of ETc could not compensate the yield under delayed sowing suggesting that rise in temperature beyond January adversely affected the growth and development of crop as well as forced maturity resulting in significant reduction of yield attributing characters due to terminal heat stress. Similar observations were recorded under late sown experiment too. Planting on 1st December along with 100% ETc of irrigation schedule resulted in significantly higher grain yield as compared to other dates and irrigation regimes. Further, it was observed that reduction in yield under late sown conditions was significantly large than the timely sown conditions irrespective of the variety grown and irrigation schedule followed. Delayed sowing resulted in reducing crop growth period and forced maturity in turn led to significant deterioration in all the yield attributing characters and there by reduction in yield suggesting that terminal heat stress had greater impact on yield under late sown crop than timely sown due to temperature rise coinciding with reproductive phase of the crop.

Keywords: climate, irrigation, mitigation, wheat

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Authors: Sheikh Mohammed Rafiul Huque

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Bangladesh since its inception has been an economy that is fuelled by agriculture and agriculture has significant contribution to the GDP of Bangladesh. The agriculture of Bangladesh predominantly and historically dependent on organic sources of raw material though the place has taken in decades by inorganic sources of raw materials due to the high demand of food for rapidly growing of population. Meanwhile, a new market segment, which is niche market, has been evolving in the urban area in favor of organic products, though 71.1% population living in rural areas is dependent mainly on conventional products. The new market segment is search of healthy and safer source of food and they could believe that organic products are the solution of that. In Bangladesh, food adulteration is very common practices among the shop-keepers to extend the shelf life of raw vegetables and fruits. The niche group of city dwellers is aware about the fact and gradually shifting their buying behavior to organic products. A recent survey on organic farming revealed that 16,200 hectares under organic farming in recent time, which was only 2,500 hectares in 2008. This study is focused on consumer awareness of organic products and tried to explore the factors affecting organic food consumption among high income group of people. The hypothesis is developed to explore the effect of gender (GENDER), ability to purchase (ABILITY) and health awareness (HEALTH) on purchase intention (INTENTION). A snowball sampling was administered among the high income group of people in Dhaka city among 150 respondents. In this sampling process the study could identify only those samples who has consume organic products. A Partial Least Square (PLS) method was used to analyze data using path analysis. It was revealed from the analysis that coefficient determination R2 is 0.829 for INTENTION endogenous latent variable. This means that three latent variables (GENDER, ABILITY, and HEALTH) significantly explain 82.9% of the variance in INTENTION of purchasing organic products. Moreover, GENDER solely explains 6.3% and 8.6% variability of ABILITY and HEALTH respectively. The inner model suggests that HEALTH has strongest negative effect on INTENTION (-0.647) followed by ABILITY (0.344) and GENDER (0.246). The hypothesized path relationship between ABILITY->INTENTION, HEALTH->INTENTION and GENDER->INTENTION are statistically significant. Furthermore, the hypothesized path relationship between GENDER->ABILITY (0.262) and GENDER->HEALTH (-0.292) also statistically significant. The purpose of the study is to demonstrate how an organic product producer can improve his participatory guarantee system (PGS) while marketing the products. The study focuses on understanding gender (GENDER), ability (ABILITY) and health (HEALTH) factors while positioning the products (INTENTION) in the mind of the consumer. In this study, the respondents are found to care about high price and ability to purchase variables with loading -0.920 and 0.898. They are good indicators of ability to purchase (ABILITY). The marketers should consider about price of organic comparing to conventional products while marketing, otherwise, that will create negative intention to buy with a loading of -0.939. Meanwhile, it is also revealed that believability of chemical free component in organic products and health awareness affects health (HEALTH) components with high loading -0.941 and 0.682. The study analyzes that low believability of chemical free component and high price of organic products affects intension to buy. The marketers should not overlook this point while targeting the consumers in Bangladesh.

Keywords: health awareness, organic products, purchase ability, purchase intention

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Authors: Sonia Boscenco, Zihan Wang, Euclides José de Mendoça Filho, João Paulo Hoppe, Irina Pokhvisneva, Geoffrey B.C. Hall, Michael J. Meaney, Patricia Pelufo Silveira

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Entropy can be described as a measure of the number of states of a system, and when used in the context of physiological time-based signals, it serves as a measure of complexity. In functional connectivity data, entropy can account for the moment-to-moment variability that is neglected in traditional functional magnetic resonance imaging (fMRI) analyses. While brain fMRI resting state entropy has been associated with some pathological conditions like schizophrenia, no investigations have explored the association between brain entropy measures and individual differences in child behavior in healthy children. We describe a novel exploratory approach to evaluate brain fMRI resting state data in two child cohorts, and MAVAN (N=54, 4.5 years, 48% males) and GUSTO (N = 206, 4.5 years, 48% males) and its associations to child behavior, that can be used in future research in the context of child exposures and long-term health. Following rs-fMRI data pre-processing and Shannon entropy calculation across 32 network regions of interest to acquire 496 unique functional connections, partial correlation coefficient analysis adjusted for sex was performed to identify associations between entropy data and Strengths and Difficulties questionnaire in MAVAN and Child Behavior Checklist domains in GUSTO. Significance was set at p < 0.01, and we found eight significant associations in GUSTO. Negative associations were found between two frontoparietal regions and cerebellar posterior and oppositional defiant problems, (r = -0.212, p = 0.006) and (r = -0.200, p = 0.009). Positive associations were identified between somatic complaints and four default mode connections: salience insula (r = 0.202, p < 0.01), dorsal attention intraparietal sulcus (r = 0.231, p = 0.003), language inferior frontal gyrus (r = 0.207, p = 0.008) and language posterior superior temporal gyrus (r = 0.210, p = 0.008). Positive associations were also found between insula and frontoparietal connection and attention deficit / hyperactivity problems (r = 0.200, p < 0.01), and insula – default mode connection and pervasive developmental problems (r = 0.210, p = 0.007). In MAVAN, ten significant associations were identified. Two positive associations were found = with prosocial scores: the salience prefrontal cortex and dorsal attention connection (r = 0.474, p = 0.005) and the salience supramarginal gyrus and dorsal attention intraparietal sulcus (r = 0.447, p = 0.008). The insula and prefrontal connection were negatively associated with peer problems (r = -0.437, p < 0.01). Conduct problems were negatively associated with six separate connections, the left salience insula and right salience insula (r = -0.449, p = 0.008), left salience insula and right salience supramarginal gyrus (r = -0.512, p = 0.002), the default mode and visual network (r = -0.444, p = 0.009), dorsal attention and language network (r = -0.490, p = 0.003), and default mode and posterior parietal cortex (r = -0.546, p = 0.001). Entropy measures of resting state functional connectivity can be used to identify individual differences in brain function that are correlated with variation in behavioral problems in healthy children. Further studies applying this marker into the context of environmental exposures are warranted.

Keywords: child behaviour, functional connectivity, imaging, Shannon entropy

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Authors: U.K. Sarkar, G. Karnatak, P. Mishal, Lianthuamluaia, S. Kumari, S.K. Das, B.K. Das

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Inland open water fisheries provide food, income, livelihood and nutritional security to millions of fishers across the globe. However, the open water ecosystem and fisheries are threatened due to climate change and anthropogenic pressures, which are more visible in the recent six decades, making the resources vulnerable. Understanding the interaction between meteorological parameters and inland fisheries is imperative to develop mitigation and adaptation strategies. As per IPCC 5th assessment report, the earth is warming at a faster rate in recent decades. Global mean surface temperature (GMST) for the decade 2006–2015 (0.87°C) was 6 times higher than the average over the 1850–1900 period. The direct and indirect impacts of climatic parameters on the ecology of fisheries ecosystem have a great bearing on fisheries due to alterations in fish physiology. The impact of meteorological factors on ecosystem health and fish food organisms brings about changes in fish diversity, assemblage, reproduction and natural recruitment. India’s average temperature has risen by around 0.7°C during 1901–2018. The studies show that the mean air temperature in the Ganga basin has increased in the range of 0.20 - 0.47 °C and annual rainfall decreased in the range of 257-580 mm during the last three decades. The studies clearly indicate visible impacts of climatic and environmental factors on inland open water fisheries. Besides, a significant reduction in-depth and area (37.20–57.68% reduction), diversity of natural indigenous fish fauna (ranging from 22.85 to 54%) in wetlands and progression of trophic state from mesotrophic to eutrophic were recorded. In this communication, different applications of biometeorology in inland fisheries management with special reference to the assessment of ecosystem and species vulnerability to climatic variability and change have been discussed. Further, the paper discusses the impact of climate anomaly and extreme climatic events on inland fisheries and emphasizes novel modeling approaches for understanding the impact of climatic and environmental factors on reproductive phenology for identification of climate-sensitive/resilient fish species for the adoption of climate-smart fisheries in the future. Adaptation and mitigation strategies to enhance fish production and the role of culture-based fisheries and enclosure culture in converting sequestered carbon into blue carbon have also been discussed. In general, the type and direction of influence of meteorological parameters on fish biology in open water fisheries ecosystems are not adequately understood. The optimum range of meteorological parameters for sustaining inland open water fisheries is yet to be established. Therefore, the application of biometeorology in inland fisheries offers ample scope for understanding the dynamics in changing climate, which would help to develop a database on such least, addressed research frontier area. This would further help to project fisheries scenarios in changing climate regimes and develop adaptation and mitigation strategies to cope up with adverse meteorological factors to sustain fisheries and to conserve aquatic ecosystem and biodiversity.

Keywords: biometeorology, inland fisheries, aquatic ecosystem, modeling, India

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Authors: Nishimwe Clemence, Mchunu Gugu, Mukamusoni Dariya

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Background: Community health workers and extension workers can play an important role in supporting families to adopt health practices, encourage delivery in a health care facility, and ensure time referral of mothers and newborns if needed. Saving the lives of neonates should, therefore, be a significant health outcome in any maternal and newborn health program that is being implemented. Furthermore, about half of a million mothers die from pregnancy-related causes. Maternal and newborn deaths related to the period of postnatal care are neglected. Some authors emphasized that in developing countries, newborn mortality rates have been reduced much more slowly because of the lack of many necessary facility-based and outreach service. The aim of this review was to critically analyze the implementation and integration process of the maternal and newborn health care program by maternal community health workers, into the health care system, in Africa. Furthermore, it aims to reduce maternal and newborn mortality. We addressed the following review question: (1) what process is involved in the implementation and integration of the maternal and newborn health care program by maternal community health workers during antenatal, delivery and postnatal care into health system care in Africa? Methods: The database searched was from Health Source: Nursing/Academic Edition through academic search complete via EBSCO Host. An iterative approach was used to go through Google scholarly papers. The reviewers considered adapted Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidance, and the Mixed Methods Appraisal Tool (MMAT) was used. Synthesis method in integrative review following elements of noting patterns and themes, seeing plausibility, clustering, counting, making contrasts and comparisons, discerning commons and unusual patterns, subsuming particulars into general, noting relations between variability, finding intervening factors and building a logical chain of evidence, using data–based convergent synthesis design. Results: From the seventeen of studies included, results focused on three dimensions inspired by the literature on antenatal, delivery, and postnatal interventions. From this, further conceptual framework was elaborated. The conceptual framework process of implementation and integration of maternal and newborn health care program by maternal community health workers was elaborated in order to ensure the sustainability of community based intervention. Conclusions: the review revealed that the implementation and integration of maternal and newborn health care program require planning. We call upon governments, non-government organizations, the global health community, all stakeholders including policy makers, program managers, evaluators, educators, and providers to be involved in implementation and integration of maternal and newborn health program in updated policy and community-based intervention. Furthermore, emphasis should be placed on competence, responsibility, and accountability of maternal community health workers, their training and payment, collaboration with health professionals in health facilities, and reinforcement of outreach service. However, the review was limited in focus to the African context, where the process of maternal and newborn health care program has been poorly implemented.

Keywords: Africa, implementation of integration, maternal, newborn

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Authors: Masood Otarod, Ronald M. Supkowski

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This abstract discusses a method that reduces the general dispersion model in cylindrical coordinates to a second order linear ordinary differential equation with constant coefficients so that it can be utilized to conduct kinetic studies in packed bed tubular catalytic reactors at a broad range of Reynolds numbers. The model was tested by 13CO isotope transient tracing of the CO adsorption of Boudouard reaction in a differential reactor at an average Reynolds number of 0.2 over Pd-Al2O3 catalyst. Detailed experimental results have provided evidence for the validity of the theoretical framing of the model and the estimated parameters are consistent with the literature. The solution of the general dispersion model requires the knowledge of the radial distribution of axial velocity. This is not always known. Hence, up until now, the implementation of the dispersion model has been largely restricted to the plug-flow regime. But, ideal plug-flow is impossible to achieve and flow regimes approximating plug-flow leave much room for debate as to the validity of the results. The reduction of the general dispersion model transpires as a result of the application of a factorization theorem. Factorization theorem is derived from the observation that a cross section of a catalytic bed consists of a solid phase across which the reaction takes place and a void or porous phase across which no significant measure of reaction occurs. The disparity in flow and the heterogeneity of the catalytic bed cause the concentration of reacting compounds to fluctuate radially. These variabilities signify the existence of radial positions at which the radial gradient of concentration is zero. Succinctly, factorization theorem states that a concentration function of axial and radial coordinates in a catalytic bed is factorable as the product of the mean radial cup-mixing function and a contingent dimensionless function. The concentration of adsorbed compounds are also factorable since they are piecewise continuous functions and suffer the same variability but in the reverse order of the concentration of mobile phase compounds. Factorability is a property of packed beds which transforms the general dispersion model to an equation in terms of the measurable mean radial cup-mixing concentration of the mobile phase compounds and mean cross-sectional concentration of adsorbed species. The reduced model does not require the knowledge of the radial distribution of the axial velocity. Instead, it is characterized by new transport parameters so denoted by Ωc, Ωa, Ωc, and which are respectively denominated convection coefficient cofactor, axial dispersion coefficient cofactor, and radial dispersion coefficient cofactor. These cofactors adjust the dispersion equation as compensation for the unavailability of the radial distribution of the axial velocity. Together with the rest of the kinetic parameters they can be determined from experimental data via an optimization procedure. Our data showed that the estimated parameters Ωc, Ωa Ωr, are monotonically correlated with the Reynolds number. This is expected to be the case based on the theoretical construct of the model. Computer generated simulations of methanation reaction on nickel provide additional support for the utility of the newly conceptualized dispersion model.

Keywords: factorization, general dispersion model, isotope transient kinetic, partial differential equations

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Authors: Rao Singh Priyanka, Jayangondaperumal R.

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The Himalayan active fault has the potential to generate great earthquakes in the future, posing a biggest existential threat to humans in the Himalayan and adjacent region. Quantitative evaluation of accumulated and released interseismic strain is crucial to assess the magnitude and spatio-temporal variability of future great earthquakes along the Himalayan arc. To mitigate the destruction and hazards associated with such earthquakes, it is important to understand their recurrence cycle. The eastern Himalayan and Indo-Burman plate boundary systems offers an oblique convergence across two orthogonal plate boundaries, resulting in a zone of distributed deformation both within and away from the plate boundary and clockwise rotation of fault-bounded blocks. This seismically active region has poorly documented historical archive of the past large earthquakes. Thus, paleoseismologicalstudies confirm the surface rupture evidences of the great continental earthquakes (Mw ≥ 8) along the Himalayan Frontal Thrust (HFT), which along with the Geodetic studies, collectively provide the crucial information to understand and assess the seismic potential. These investigations reveal the rupture of 3/4th of the HFT during great events since medieval time but with debatable opinions for the timing of events due to unclear evidences, ignorance of transverse segment boundaries, and lack of detail studies. Recent paleoseismological investigations in the eastern Himalaya and Mishmi ranges confirms the primary surface ruptures of the 1950 C.E. great earthquake (M>8). However, a seismic gap exists between the 1714 C.E. and 1950 C.E. Assam earthquakes that did not slip since 1697 C.E. event. Unlike the latest large blind 2015 Gorkha earthquake (Mw 7.8), the 1950 C.E. event is not triggered by a large event of 1947 C.E. that occurred near the western edge of the great upper Assam event. Moreover, the western segment of the eastern Himalayadid not witness any surface breaking earthquake along the HFT for over the past 300 yr. The frontal fault excavations reveal that during the 1950 earthquake, ~3.1-m-high scarp along the HFT was formed due to the co-seismic slip of 5.5 ± 0.7 m at Pasighat in the Eastern Himalaya and a 10-m-high-scarp at a Kamlang Nagar along the Mishmi Thrust in the Eastern Himalayan Syntaxis is an outcome of a dip-slip displacement of 24.6 ± 4.6 m along a 25 ± 5°E dipping fault. This event has ruptured along the two orthogonal fault systems in the form of oblique thrust fault mechanism. Approx. 130 km west of Pasighat site, the Himebasti village has witnessed two earthquakes, the historical 1697 Sadiya earthquake, and the 1950 event, with a cumulative dip-slip displacement of 15.32 ± 4.69 m. At Niglok site, Arunachal Pradesh, a cumulative slip of ~12.82 m during at least three events since pre 19585 B.P. has produced ~6.2-m high scarp while the youngest scarp of ~2.4-m height has been produced during 1697 C.E. The site preserves two deformational events along the eastern HFT, providing an idea of last serial ruptures at an interval of ~850 yearswhile the successive surface rupturing earthquakes lacks in the Mishmi Range to estimate the recurrence cycle.

Keywords: paleoseismology, surface rupture, recurrence interval, Eastern Himalaya

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Authors: Michal Geca, Grzegorz Baranski, Ksenia Siadkowska

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The work is a part of the project which aims at developing innovative power and control systems for the high power aircraft piston engine ASz62IR. Developed electronically controlled ignition system will reduce emissions of toxic compounds as a result of lowered fuel consumption, optimized combustion and engine capability of efficient combustion of ecological fuels. The tested unit is an air-cooled four-stroke gasoline engine of 9 cylinders in a radial setup, mechanically charged by a radial compressor powered by the engine crankshaft. The total engine cubic capac-ity is 29.87 dm3, and the compression ratio is 6.4:1. The maximum take-off power is 1000 HP at 2200 rpm. The maximum fuel consumption is 280 kg/h. Engine powers aircrafts: An-2, M-18 „Dromader”, DHC-3 „OTTER”, DC-3 „Dakota”, GAF-125 „HAWK” i Y5. The main problems of the engine includes the imbalanced work of cylinders. The non-uniformity value in each cylinder results in non-uniformity of their work. In radial engine cylinders arrangement causes that the mixture movement that takes place in accordance (lower cylinder) or the opposite (upper cylinders) to the direction of gravity. Preliminary tests confirmed the presence of uneven workflow of individual cylinders. The phenomenon is most intense at low speed. The non-uniformity is visible on the waveform of cylinder pressure. Therefore two studies were conducted to determine the impact of this phenomenon on the engine performance: simulation and real tests. Simplified simulation was conducted on the element of the intake system coated with fuel film. The study shows that there is an effect of gravity on the movement of the fuel film inside the radial engine intake channels. Both in the lower and the upper inlet channels the film flows downwards. It follows from the fact that gravity assists the movement of the film in the lower cylinder channels and prevents the movement in the upper cylinder channels. Real tests on aircraft engine ASz62IR was conducted in transients condition (rapid change of the excess air in each cylinder were performed. Calculations were conducted for mass of fuel reaching the cylinders theoretically and really and on this basis, the factors of fuel evaporation “x” were determined. Therefore a simplified model of the fuel supply to cylinder was adopted. Model includes time constant of the fuel film τ, the number of engine transport cycles of non-evaporating fuel along the intake pipe γ and time between next cycles Δt. The calculation results of identification of the model parameters are presented in the form of radar graphs. The figures shows the averages declines and increases of the injection time and the average values for both types of stroke. These studies shown, that the change of the position of the cylinder will cause changes in the formation of fuel-air mixture and thus changes in the combustion process. Based on the results of the work of simulation and experiments was possible to develop individual algorithms for ignition control. This work has been financed by the Polish National Centre for Research and Development, INNOLOT, under Grant Agreement No. INNOLOT/I/1/NCBR/2013.

Keywords: radial engine, ignition system, non-uniformity, combustion process

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Authors: Gabriela Borilova, Monika Petrakova, Petr Kralik

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Nowadays, European producers are increasingly interested in the production of halal meat products. Halal meat has been increasingly appearing in the EU's market network and meat products from European producers are being exported to Islamic countries. Halal criteria are mainly related to the origin of muscle used in production, and also to the way products are obtained and processed. Although the EU has legislatively addressed the question of food authenticity, the circumstances of previous years when products with undeclared horse or poultry meat content appeared on EU markets raised the question of the effectiveness of control mechanisms. Replacement of expensive or not-available types of meat for low-priced meat has been on a global scale for a long time. Likewise, halal products may be contaminated (falsified) by pork or food components obtained from pigs. These components include collagen, offal, pork fat, mechanically separated pork, emulsifier, blood, dried blood, dried blood plasma, gelatin, and others. These substances can influence sensory properties of the meat products - color, aroma, flavor, consistency and texture or they are added for preservation and stabilization. Food manufacturers sometimes access these substances mainly due to their dense availability and low prices. However, the use of these substances is not always declared on the product packaging. Verification of the presence of declared ingredients, including the detection of undeclared ingredients, are among the basic control procedures for determining the authenticity of food. Molecular biology methods, based on DNA analysis, offer rapid and sensitive testing. The PCR method and its modification can be successfully used to identify animal species in single- and multi-ingredient raw and processed foods and qPCR is the first choice for food analysis. Like all PCR-based methods, it is simple to implement and its greatest advantage is the absence of post-PCR visualization by electrophoresis. qPCR allows detection of trace amounts of nucleic acids, and by comparing an unknown sample with a calibration curve, it can also provide information on the absolute quantity of individual components in the sample. Our study addresses a problem that is related to the fact that the molecular biological approach of most of the work associated with the identification and quantification of animal species is based on the construction of specific primers amplifying the selected section of the mitochondrial genome. In addition, the sections amplified in conventional PCR are relatively long (hundreds of bp) and unsuitable for use in qPCR, because in DNA fragmentation, amplification of long target sequences is quite limited. Our study focuses on finding a suitable genomic DNA target and optimizing qPCR to reduce variability and distortion of results, which is necessary for the correct interpretation of quantification results. In halal products, the impact of falsification of meat products by the addition of components derived from pigs is all the greater that it is not just about the economic aspect but above all about the religious and social aspect. This work was supported by the Ministry of Agriculture of the Czech Republic (QJ1530107).

Keywords: food fraud, halal food, pork, qPCR

Procedia PDF Downloads 219

Authors: Ashok Kumar, Parveen Parveen

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India is bestowed with an extremely high population of plant species with medicinal value and even has two biodiversity hotspots. Indian systems of medicine including Ayurveda, Siddha and Unani have historically been serving humankind across the world since time immemorial. About 1500 plant species have well been documented in Ayurvedic Nighantus as official medicinal plants. Additionally, several hundred species of plants are being routinely used as medicines by local people especially tribes living in and around forests. The natural resources for medicinal plants have unscientifically been over-exploited forcing rapid depletion in their genetic diversity. Moreover, renewed global interest in herbal medicines may even lead to additional depletion of medicinal plant wealth of the country, as about 95% collection of medicinal plants for pharmaceutical preparation is being carried out from natural forests. On the other hand, huge export market of medicinal and aromatic plants needs to be seriously tapped for enhancing inflow of foreign currency. Asparagus racemosus Willd., a member of family Liliaceae, is one of thirty-two plant species that have been identified as priority species for cultivation and conservation by the National Medicinal Plant Board (NMPB), Government of India. Though attention is being focused on standardization of agro-techniques and extraction methods, little has been designed on genetic improvement and selection of desired types with higher root production and saponin content, a basic ingredient of medicinal value. The saponin not only improves defense mechanisms and controls diabetes but the roots of this species promote secretion of breast milk, improved lost body weight and considered as an aphrodisiac. There is ample scope for genetic improvement of this species for enhancing productivity substantially, qualitatively and quantitatively. It is emphasized to select desired genotypes with sufficient genetic diversity for important economic traits. Hybridization between two genetically divergent genotypes could result in the synthesis of new F1 hybrids consisting of useful traits of both the parents. The evaluation of twenty seed sources of Asparagus racemosus assembled different geographical locations of India revelled high degree of variability for traits of economic importance. The maximum genotypic and phenotypic variance was observed for shoot height among shoot related traits and for root length among root related traits. The shoot height, genotypic variance, phenotypic variance, genotypic coefficient of variance, the phenotypic coefficient of variance was recorded to be 231.80, 3924.80, 61.26 and 1037.32, respectively, where those of the root length were 9.55, 16.80, 23.46 and 41.27, respectively. The maximum genetic advance and genetic gain were obtained for shoot height among shoot-related traits and root length among root-related traits. Index values were developed for all seed sources based on the four most important traits, and Panthnagar (Uttrakhand), Jodhpur (Rajasthan), Dehradun (Uttarakhand), Chandigarh (Punjab), Jammu (Jammu & Kashmir) and Solan (Himachal Pradesh) were found to be promising seed sources.

Keywords: asparagus, genetic, genotypes, variance

Procedia PDF Downloads 102

Authors: Melanie Redding

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Historic widespread nitrate contamination of the Lower Yakima Valley aquifer in Washington State initiated a community-based effort to reduce nitrate concentrations to below-drinking water standards. This group commissioned studies on characterizing local nitrogen sources, deep soil assessments, drinking water, and assessing nitrate concentrations at the water table. Nitrate is the most prevalent groundwater contaminant with common sources from animal and human waste, fertilizers, plants and precipitation. It is challenging to address groundwater contamination when common sources, such as agriculture, on-site sewage systems, and animal production, are widespread. Remediation is not possible, so mitigation is essential. The Lower Yakima Valley is located over 175,000 acres, with a population of 56,000 residents. Approximately 25% of the population do not have access to safe, clean drinking water, and 20% of the population is at or below the poverty level. Agriculture is the primary economic land-use activity. Irrigated agriculture and livestock production make up the largest percentage of acreage and nitrogen load. Commodities include apples, grapes, hops, dairy, silage corn, triticale, alfalfa and cherries. These commodities are important to the economic viability of the residents of the Lower Yakima Valley, as well as Washington State. Mitigation of nitrate in groundwater is challenging. The goal is to ensure everyone has safe drinking water. There are no easy remedies due to the extensive and pervasiveness of the contamination. Monitoring at the water table indicates that 45% of the 30 spatially distributed monitoring wells exceeded the drinking water standard. This indicates that there are multiple sources that are impacting water quality. Washington State has several areas which have extensive groundwater nitrate contamination. The groundwater in these areas continues to degrade over time. However, the Lower Yakima Valley is being successful in addressing this health issue because of the following reasons: the community is engaged and committed; there is one common goal; there has been extensive public education and outreach to citizens; and generating credible data using sound scientific methods. Work in this area is continuing as an ambient groundwater monitoring network is established to assess the condition of the aquifer over time. Nitrate samples are being collected from 170 wells, spatially distributed across the aquifer. This research entails quarterly sampling for two years to characterize seasonal variability and then continue annually afterward. This assessment will provide the data to statistically determine trends in nitrate concentrations across the aquifer, over time. Thirty-three of these wells are monitoring wells that are screened across the aquifer. The water quality from these wells are indicative of activities at the land surface. Additional work is being conducted to identify land use management practices that are effective in limiting nitrate migration through the soil column. Tracking nitrate in the soil column every season is an important component of bridging land-use practices with the fate and transport of nitrate through the subsurface. Patience, tenacity, and the ability to think outside the box are essential for dealing with widespread nitrate contamination of groundwater.

Keywords: community, groundwater, monitoring, nitrate

Procedia PDF Downloads 147

Authors: Massimiliano Biagini, Marco Spinsanti, Gabriella De Angelis, Sara Tomei, Ilaria Ferlenghi, Maria Scarselli, Alessia Biolchi, Alessandro Muzzi, Brunella Brunelli, Silvana Savino, Marzia M. Giuliani, Isabel Delany, Paolo Costantino, Rino Rappuoli, Vega Masignani, Nathalie Norais

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The gram-negative bacterium Neisseria meningitidis serogroup B (MenB) is an exclusively human pathogen representing the major cause of meningitides and severe sepsis in infants and children but also in young adults. This pathogen is usually present in the 30% of healthy population that act as a reservoir, spreading it through saliva and respiratory fluids during coughing, sneezing, kissing. Among surface-exposed protein components of this diplococcus, factor H binding protein is a lipoprotein proved to be a protective antigen used as a component of the recently licensed Bexsero vaccine. fHbp is a highly variable meningococcal protein: to reflect its remarkable sequence variability, it has been classified in three variants (or two subfamilies), and with poor cross-protection among the different variants. Furthermore, the level of fHbp expression varies significantly among strains, and this has also been considered an important factor for predicting MenB strain susceptibility to anti-fHbp antisera. Different methods have been used to assess fHbp expression on meningococcal strains, however, all these methods use anti-fHbp antibodies, and for this reason, the results are affected by the different affinity that antibodies can have to different antigenic variants. To overcome the limitations of an antibody-based quantification, we developed a quantitative Mass Spectrometry (MS) approach. Selected Reaction Monitoring (SRM) recently emerged as a powerful MS tool for detecting and quantifying proteins in complex mixtures. SRM is based on the targeted detection of ProteoTypicPeptides (PTPs), which are unique signatures of a protein that can be easily detected and quantified by MS. This approach, proven to be highly sensitive, quantitatively accurate and highly reproducible, was used to quantify the absolute amount of fHbp antigen in total extracts derived from 105 clinical isolates, evenly distributed among the three main variant groups and selected to be representative of the fHbp circulating subvariants around the world. We extended the study at the genetic level investigating the correlation between the differential level of expression and polymorphisms present within the genes and their promoter sequences. The implications of fHbp expression on the susceptibility of the strain to killing by anti-fHbp antisera are also presented. To date this is the first comprehensive fHbp expression profiling in a large panel of Neisseria meningitidis clinical isolates driven by an antibody-independent MS-based methodology, opening the door to new applications in vaccine coverage prediction and reinforcing the molecular understanding of released vaccines.

Keywords: quantitative mass spectrometry, Neisseria meningitidis, vaccines, bexsero, molecular epidemiology

Procedia PDF Downloads 277

Authors: T. C. C. Soo, S. Bhassu

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Unlike the common presence of both innate and adaptive immunity in vertebrates, crustaceans, in particular, shrimps, have been discovered to possess only innate immunity. This further emphasizes the importance of innate immunity within shrimps in pathogenic resistance. Under the study of pathogenic immune challenge, different shrimp species actually exhibit varying degrees of immune resistance towards the same pathogen. Furthermore, even within the same shrimp species, different batches of challenged shrimps can have different strengths of immune defence. Several important pathways are activated within shrimps during pathogenic infection. One of them is JAK-STAT pathway that is activated during bacterial, viral and fungal infections by which STAT(Signal Transducer and Activator of Transcription) gene is the core element of the pathway. Based on theory of Central Dogma, the genomic information is transmitted in the order of DNA, RNA and protein. This study is focused in uncovering the important evolutionary patterns present within the DNA (non-coding region) and RNA (coding region). The three shrimp species involved are Macrobrachium rosenbergii, Penaeus monodon and Litopenaeus vannamei which all possess commercial significance. The shrimp species were challenged with a famous penaeid shrimp virus called white spot syndrome virus (WSSV) which can cause serious lethality. Tissue samples were collected during time intervals of 0h, 3h, 6h, 12h, 24h, 36h and 48h. The DNA and RNA samples were then extracted using conventional kits from the hepatopancreas tissue samples. PCR technique together with designed STAT gene conserved primers were utilized for identification of the STAT coding sequences using RNA-converted cDNA samples and subsequent characterization using various bioinformatics approaches including Ramachandran plot, ProtParam and SWISS-MODEL. The varying levels of immune STAT gene activation for the three shrimp species during WSSV infection were confirmed using qRT-PCR technique. For one sample, three biological replicates with three technical replicates each were used for qRT-PCR. On the other hand, DNA samples were important for uncovering the structural variations within the genomic region of STAT gene which would greatly assist in understanding the STAT protein functional variations. The partially-overlapping primers technique was used for the genomic region sequencing. The evolutionary inferences and event predictions were then conducted through the Bayesian Inference method using all the acquired coding and non-coding sequences. This was supplemented by the construction of conventional phylogenetic trees using Maximum likelihood method. The results showed that adaptive evolution caused STAT gene sequence mutations between different shrimp species which led to evolutionary divergence event. Subsequently, the divergent sites were correlated to the differing expressions of STAT gene. Ultimately, this study assists in knowing the shrimp species innate immune variability and selection of disease resistant shrimps for breeding purpose. The deeper understanding of STAT gene evolution from the perspective of both purifying and adaptive approaches not only can provide better immunological insight among shrimp species, but also can be used as a good reference for immunological studies in humans or other model organisms.

Keywords: gene evolution, JAK-STAT pathway, immunology, STAT gene

Procedia PDF Downloads 117

Authors: B. R. Campomanes-Alvarez, P. Quiros, B. Fernandez

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Automatic Speech Recognition (ASR) is a machine-based process of decoding and transcribing oral speech. A typical ASR system receives acoustic input from a speaker or an audio file, analyzes it using algorithms, and produces an output in the form of a text. Some speech recognition systems use Hidden Markov Models (HMMs) to deal with the temporal variability of speech and Gaussian Mixture Models (GMMs) to determine how well each state of each HMM fits a short window of frames of coefficients that represents the acoustic input. Another way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition systems. Acoustic models for state-of-the-art ASR systems are usually training on massive amounts of data. However, audio files with their corresponding transcriptions can be difficult to obtain, especially in the Spanish language. Hence, in the case of these low-resource scenarios, building an ASR model is considered as a complex task due to the lack of labeled data, resulting in an under-trained system. Semi-supervised learning approaches arise as necessary tasks given the high cost of transcribing audio data. The main goal of this proposal is to develop a procedure based on acoustic semi-supervised learning for Spanish ASR systems by using DNNs. This semi-supervised learning approach consists of: (a) Training a seed ASR model with a DNN using a set of audios and their respective transcriptions. A DNN with a one-hidden-layer network was initialized; increasing the number of hidden layers in training, to a five. A refinement, which consisted of the weight matrix plus bias term and a Stochastic Gradient Descent (SGD) training were also performed. The objective function was the cross-entropy criterion. (b) Decoding/testing a set of unlabeled data with the obtained seed model. (c) Selecting a suitable subset of the validated data to retrain the seed model, thereby improving its performance on the target test set. To choose the most precise transcriptions, three confidence scores or metrics, regarding the lattice concept (based on the graph cost, the acoustic cost and a combination of both), was performed as selection technique. The performance of the ASR system will be calculated by means of the Word Error Rate (WER). The test dataset was renewed in order to extract the new transcriptions added to the training dataset. Some experiments were carried out in order to select the best ASR results. A comparison between a GMM-based model without retraining and the DNN proposed system was also made under the same conditions. Results showed that the semi-supervised ASR-model based on DNNs outperformed the GMM-model, in terms of WER, in all tested cases. The best result obtained an improvement of 6% relative WER. Hence, these promising results suggest that the proposed technique could be suitable for building ASR models in low-resource environments.

Keywords: automatic speech recognition, deep neural networks, machine learning, semi-supervised learning

Procedia PDF Downloads 316

Authors: X. Simon, D. Copena, M. Montero

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The last decades have witnessed a significant increase in renewable energy, especially wind energy, to achieve sustainable development. Specialized literature in this field has carried out interesting case studies to extensively analyze both the environmental benefits of this energy and its social acceptance. However, to the best of our knowledge, work to date makes no analysis of the role of private owners of lands with wind potential within a broader territory of strong wind implantation, nor does it estimate their economic incomes relating them to social acceptance. This work fills this gap by focusing on Galicia, territory housing over 4,000 wind turbines and almost 3,400 MW of power. The main difficulty in getting this financial information is that it is classified, not public. We develop methodological techniques (semi- structured interviews and work groups), inserted within the Participatory Research, to overcome this important obstacle. In this manner, the work directly compiles qualitative and quantitative information on the processes as well as the economic results derived from implementing wind energy in Galicia. During the field work, we held 106 semi-structured interviews and 32 workshops with owners of lands occupied by wind farms. The compiled information made it possible to create the socioeconomic database on wind energy in Galicia (SDWEG). This database collects a diversity of quantitative and qualitative information and contains economic information on the income received by the owners of lands occupied by wind farms. In the Galician case, regulatory framework prevented local participation under the community wind farm formula. The possibility of local participation in the new energy model narrowed down to companies wanting to install a wind farm and demanding land occupation. The economic mechanism of local participation begins here, thus explaining the level of acceptance of wind farms. Land owners can receive significant income given that these payments constitute an important source of economic resources, favor local economic activity, allow rural areas to develop productive dynamism projects and improve the standard of living of rural inhabitants. This work estimates that land owners in Galicia perceive about 10 million euros per year in total wind revenues. This represents between 1% and 2% of total wind farm invoicing. On the other hand, relative revenues (Euros per MW), far from the amounts reached in other spaces, show enormous payment variability. This signals the absence of a regulated market, the predominance of partial agreements, and the existence of asymmetric positions between owners and developers. Sustainable development requires the replacement of conventional technologies by low environmental impact technologies, especially those that emit less CO₂. However, this new paradigm also requires rural owners to participate in the income derived from the structural transformation processes linked to sustainable development. This paper demonstrates that regulatory framework may contribute to increasing sustainable technologies with high social acceptance without relevant local economic participation.

Keywords: regulatory framework, social acceptance, sustainable development, wind energy, wind income for landowners

Procedia PDF Downloads 121

Authors: Mohamed Emran, Giovanni Pardini, Maria Gispert, Mohamed Rashad

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Land use and abandonment in semi-arid degraded ecosystems may cause regressive dynamics in vegetation cover affecting organic matter contents, soil nutrients and structural stability, thus reducing soil resistance to erosion. Mediterranean areas are generally subjected to climatic fluctuations, which modify soil conditions and hydrological processes, such as runoff and water infiltration within the upper soil horizons. Low erosion rates occur in very fragile and shallow soils with minor clay content progressively decrease organic carbon C and nitrogen N pools in the upper soil horizons. Seven soils were selected representing variant context of land use and abandonment at the Cap de Creus Peninsula, Catalonia, NE Spain, from recent cultivated vines and olive groves, mid abandoned forests standing under cork and pine trees, pasture to late abandoned Cistus and Erica scrubs. The aim of this work was to study the effect of changes in land use and abandonment on the depletion of soil organic carbon and nitrogen transported by runoff water in shallow soils after natural rainfall events during two years with different rainfall patterns (1st year with low rainfall and 2nd year with high rainfall) by i) monitoring the most significant soil erosion parameters at recorded rainfall events, ii) studying the most relevant soil physical and chemical characteristics on seasonal basis and iii) analysing the seasonal trends of depleted carbon and nitrogen and their interaction with soil surface compaction parameters. Significant seasonal variability was observed in the relevant soil physical and chemical parameters and soil erosion parameters in all soils to establish their evolution under land use and abandonment during two years of different rainfall patterns (214 and 487 mm per year), giving important indications on soil response to rainfall impacts. Erosion rates decreased significantly with the increasing of soil C and N under low and high rainfall. In cultivated soils, C and N depletion increased by 144% and 115%, respectively by 13% increase in erosion rates during the 1st year with respect to the 2nd year. Depleted C and N were proportionally higher in soils under vines and olive with vulnerable soil structure and low soil resilience leading to degradation, altering nutrients cycles and causing adverse impact on environmental quality. Statistical analysis underlined that, during the 1st year, soil surface was less effective in preserving stocks of organic resources leading to higher susceptibility to erosion with consequent C and N depletion. During the 2nd year, higher organic reserve and water storage occurred despite the increasing of C and N loss with an effective contribution from soil surface compaction parameters. The overall estimation during the two years indicated clear differences among soils under vines, olive, cork and pines, suggesting on the one hand, that current cultivation practices are inappropriate and that reforestation with pines may delay the achievement of better soil conditions. On the other hand, the natural succession of vegetation under Cistus, pasture and Erica suggests the recovery of good soil conditions.

Keywords: land abandonment, land use, nutrient's depletion, soil erosion

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Authors: Yuguang Gao, Mingtao Deng

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The integration of independently operating hospitals into a unified healthcare service system has become a strategic imperative in the pursuit of hospitals’ high-quality development. Central to the concept of group governance over such transformation, exemplified by a collaborative medical alliance, is the delineation of shared value, vision, and goals. Given the inherent disparity in capabilities among hospitals within the alliance, particularly in the treatment of different diseases characterized by Disease Related Groups (DRG) in terms of effectiveness, efficiency and resource utilization, this study aims to address the centralized decision-making of intra-hospital patient referral within the medical alliance to enhance the overall production and quality of service provided. We first introduce the notion of production utility, where a higher production utility for a hospital implies better performance in treating patients diagnosed with that specific DRG group of diseases. Then, a Discrete-Event Simulation (DES) framework is established for patient referral among hospitals, where patient flow modeling incorporates a queueing system with fixed capacities for each hospital. The simulation study begins with a two-member alliance. The pivotal strategy examined is a "whether-to-refer" decision triggered when the bed usage rate surpasses a predefined threshold for either hospital. Then, the decision encompasses referring patients to the other hospital based on DRG groups’ production utility differentials as well as bed availability. The objective is to maximize the total production utility of the alliance while minimizing patients’ average length of stay and turnover rate. Thus the parameter under scrutiny is the bed usage rate threshold, influencing the efficacy of the referral strategy. Extending the study to a three-member alliance, which could readily be generalized to multi-member alliances, we maintain the core setup while introducing an additional “which-to-refer" decision that involves referring patients with specific DRG groups to the member hospital according to their respective production utility rankings. The overarching goal remains consistent, for which the bed usage rate threshold is once again a focal point for analysis. For the two-member alliance scenario, our simulation results indicate that the optimal bed usage rate threshold hinges on the discrepancy in the number of beds between member hospitals, the distribution of DRG groups among incoming patients, and variations in production utilities across hospitals. Transitioning to the three-member alliance, we observe similar dependencies on these parameters. Additionally, it becomes evident that an imbalanced distribution of DRG diagnoses and further disparity in production utilities among member hospitals may lead to an increase in the turnover rate. In general, it was found that the intra-hospital referral mechanism enhances the overall production utility of the medical alliance compared to individual hospitals without partnership. Patients’ average length of stay is also reduced, showcasing the positive impact of the collaborative approach. However, the turnover rate exhibits variability based on parameter setups, particularly when patients are redirected within the alliance. In conclusion, the re-structuring of diagnostic disease groups within the medical alliance proves instrumental in improving overall healthcare service outcomes, providing a compelling rationale for the government's promotion of patient referrals within collaborative medical alliances.

Keywords: collaborative medical alliance, disease related group, patient referral, simulation

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Authors: Md. Abu Sufian, Robiqul Islam, Imam Hossain Shajid, Mahesh Hanumanthu, Jarasree Varadarajan, Md. Sipon Miah, Mingbo Niu

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Coronary Heart Disease (CHD) remains a principal cause of global morbidity and mortality, characterized by atherosclerosis—the build-up of fatty deposits inside the arteries. The study introduces an innovative methodology that leverages cloud-based platforms like AWS Live Streaming and Artificial Intelligence (AI) to early detect and prevent CHD symptoms in web applications. By employing novel simulation processes and AI algorithms, this research aims to significantly mitigate the health and societal impacts of CHD. Methodology: This study introduces a novel simulation process alongside a multi-phased model development strategy. Initially, health-related data, including heart rate variability, blood pressure, lipid profiles, and ECG readings, were collected through user interactions with web-based applications as well as API Integration. The novel simulation process involved creating synthetic datasets that mimic early-stage CHD symptoms, allowing for the refinement and training of AI algorithms under controlled conditions without compromising patient privacy. AWS Live Streaming was utilized to capture real-time health data, which was then processed and analysed using advanced AI techniques. The novel aspect of our methodology lies in the simulation of CHD symptom progression, which provides a dynamic training environment for our AI models enhancing their predictive accuracy and robustness. Model Development: it developed a machine learning model trained on both real and simulated datasets. Incorporating a variety of algorithms including neural networks and ensemble learning model to identify early signs of CHD. The model's continuous learning mechanism allows it to evolve adapting to new data inputs and improving its predictive performance over time. Results and Findings: The deployment of our model yielded promising results. In the validation phase, it achieved an accuracy of 92% in predicting early CHD symptoms surpassing existing models. The precision and recall metrics stood at 89% and 91% respectively, indicating a high level of reliability in identifying at-risk individuals. These results underscore the effectiveness of combining live data streaming with AI in the early detection of CHD. Societal Implications: The implementation of cloud-based AI for CHD symptom detection represents a significant step forward in preventive healthcare. By facilitating early intervention, this approach has the potential to reduce the incidence of CHD-related complications, decrease healthcare costs, and improve patient outcomes. Moreover, the accessibility and scalability of cloud-based solutions democratize advanced health monitoring, making it available to a broader population. This study illustrates the transformative potential of integrating technology and healthcare, setting a new standard for the early detection and management of chronic diseases.

Keywords: coronary heart disease, cloud-based ai, machine learning, novel simulation techniques, early detection, preventive healthcare

Procedia PDF Downloads 22

Authors: Mandeep Singh Azad.Dibyendu Chakraborty, Vikas Vohra

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Introduction: Poonch is one of the remotest districts of the Jammu and Kashmir (UT) and situated on international borders. This native poultry population in these areas is quite hardy and thrives well in adverse climatic conditions. Till date, no local breed from this area (Jammu Province) has been characterized thus present study was undertaken with the main objectives of molecular characterization of ChB6 gene in local native chicken of Poonch region located at international borders between India and Pakistan. The chicken B-cell marker (ChB6) gene has been proposed as a candidate gene in regulating B-cell development. Material and Method: RNA was isolated by Blood RNA Purification Kit (HiPura) and Trizol method from whole blood samples. Positive PCR products with size 1110 bp were selected for further purification, sequencing and analysis. The amplified PCR product was sequenced by Sangers dideoxy chain termination method. The obtained sequence of ChB6 gene of Poonchi chicken were compared by MEGAX software. BioEdit software was used to construct phylogenic tree, and Neighbor Joining method was used to infer evolutionary history. In order to compute evolutionary distance Maximum Composite Likelihood method was used. Results: The positively amplified samples of ChB6 genes were then subjected to Sanger sequencing with “Primer Walking. The sequences were then analyzed using MEGA X and BioEdit software. The sequence results were compared with other reported sequence from different breed of chicken and with other species obtained from the NCBI (National Center for Biotechnology Information). ClustalW method using MEGA X software was used for multiple sequence alignment. The sequence results of ChB6 gene of Poonchi chicken was compared with Centrocercus urophasianus, G. gallus mRNA for B6.1 protein, G. gallus mRNA for B6.2, G. gallus mRNA for B6.3, Gallus gallus B6.1, Halichoeres bivittatus, Miniopterus fuliginosus Ferringtonia patagonica, Tympanuchus phasianellus. The genetic distances were 0.2720, 0.0000, 0.0245, 0.0212, 0.0147, 1.6461, 2.2394, 2.0070 and 0.2363 for ChB6 gene of Poonchi chicken sequence with other sequences in the present study respectively. Sequencing results showed variations between different species. It was observed that AT content were higher then GC content for ChB6 gene. The lower AT content suggests less thermostable. It was observed that there was no sequence difference within the Poonchi population for ChB6 gene. The high homology within chicken population indicates the conservation of ChB6 gene. The maximum difference was observed with Miniopterus fuliginosus (Eastern bent-wing bat) followed by Ferringtonia patagonica and Halichoeres bivittatus. Conclusion: Genetic variation is the essential component for genetic improvement. The results of immune related gene Chb6 shows between population genetic variability. Therefore, further association studies of this gene with some prevalent diseases in large population would be helpful to identify disease resistant/ susceptible genotypes in the indigenous chicken population.

Keywords: ChB6, sequencing, ClustalW, genetic distance, poonchi chicken, SNP

Procedia PDF Downloads 37

Authors: Stanislav Babaev, Vladimir Cuk, Sjef Cobben, Jan Desmet

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The procedures for performing harmonic studies for medium-voltage distribution feeders have become relatively mature topics since the early 1980s. The efforts of various electric power engineers and researchers were mainly focused on handling large harmonic non-linear loads connected scarcely at several buses of medium-voltage feeders. In order to assess the impact of these loads on the voltage quality of the distribution system, specific modeling and simulation strategies were proposed. These methodologies could deliver a reasonable estimation accuracy given the requirements of least computational efforts and reduced complexity. To uphold these requirements, certain analysis assumptions have been made, which became de facto standards for establishing guidelines for harmonic analysis. Among others, typical assumptions include balanced conditions of the study and the negligible impact of impedance frequency characteristics of various power system components. In latter, skin and proximity effects are usually omitted, and resistance and reactance values are modeled based on the theoretical equations. Further, the simplifications of the modelling routine have led to the commonly accepted practice of neglecting phase angle diversity effects. This is mainly associated with developed load models, which only in a handful of cases are representing the complete harmonic behavior of a certain device as well as accounting on the harmonic interaction between grid harmonic voltages and harmonic currents. While these modelling practices were proven to be reasonably effective for medium-voltage levels, similar approaches have been adopted for low-voltage distribution systems. Given modern conditions and massive increase in usage of residential electronic devices, recent and ongoing boom of electric vehicles, and large-scale installing of distributed solar power, the harmonics in current low-voltage grids are characterized by high degree of variability and demonstrate sufficient diversity leading to a certain level of cancellation effects. It is obvious, that new modelling algorithms overcoming previously made assumptions have to be accepted. In this work, a simulation approach aimed to deal with some of the typical assumptions is proposed. A practical low-voltage feeder is modeled in PowerFactory. In order to demonstrate the importance of diversity effect and harmonic interaction, previously developed measurement-based models of photovoltaic inverter and battery charger are used as loads. The Python-based script aiming to supply varying voltage background distortion profile and the associated current harmonic response of loads is used as the core of unbalanced simulation. Furthermore, the impact of uncertainty of feeder frequency-impedance characteristics on total harmonic distortion levels is shown along with scenarios involving linear resistive loads, which further alter the impedance of the system. The comparative analysis demonstrates sufficient differences with cases when all the assumptions are in place, and results indicate that new modelling and simulation procedures need to be adopted for low-voltage distribution systems with high penetration of non-linear loads and renewable generation.

Keywords: electric power system, harmonic distortion, power quality, public low-voltage network, harmonic modelling

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Authors: Irina Mizeva, Elena Potapova, Viktor Dremin, Mikhail Mezentsev, Valeri Shupletsov

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The local tissue perfusion is regulated by the microvascular tone which is under the control of a number of physiological mechanisms. Laser Doppler flowmetry (LDF) together with wavelet analyses is the most commonly used technique to study the regulatory mechanisms of cutaneous microcirculation. External factors such as temperature, local pressure of the probe on the skin, etc. influence on the blood flow characteristics and are used as physiological tests to evaluate microvascular regulatory mechanisms. Local probe pressure influences on the microcirculation parameters measured by optical methods: diffuse reflectance spectroscopy, fluorescence spectroscopy, and LDF. Therefore, further study of probe pressure effects can be useful to improve the reliability of optical measurement. During pressure tests variation of the mean perfusion measured by means of LDF usually is estimated. An additional information concerning the physiological mechanisms of the vascular tone regulation system in response to local pressure can be obtained using spectral analyses of LDF samples. The aim of the present work was to develop protocol and algorithm of data processing appropriate for study physiological response to the local pressure test. Involving 6 subjects (20±2 years) and providing 5 measurements for every subject we estimated intersubject and-inter group variability of response of both averaged and oscillating parts of the LDF sample on external surface pressure. The final purpose of the work was to find special features which further can be used in wider clinic studies. The cutaneous perfusion measurements were carried out by LAKK-02 (SPE LAZMA Ltd., Russia), the skin loading was provided by the originally designed device which allows one to distribute the pressure around the LDF probe. The probe was installed on the dorsal part of the distal finger of the index figure. We collected measurements continuously for one hour and varied loading from 0 to 180mmHg stepwise with a step duration of 10 minutes. Further, we post-processed the samples using the wavelet transform and traced the energy of oscillations in five frequency bands over time. Weak loading leads to pressure-induced vasodilation, so one should take into account that the perfusion measured under pressure conditions will be overestimated. On the other hand, we revealed a decrease in endothelial associated fluctuations. Further loading (88 mmHg) induces amplification of pulsations in all frequency bands. We assume that such loading leads to a higher number of closed capillaries, higher input of arterioles in the LDF signal and as a consequence more vivid oscillations which mainly are formed in arterioles. External pressure higher than 144 mmHg leads to the decrease of oscillating components, after removing the loading very rapid restore of the tissue perfusion takes place. In this work, we have demonstrated that local skin loading influence on the microcirculation parameters measured by optic technique; this should be taken into account while developing portable electronic devices. The proposed protocol of local loading allows one to evaluate PIV as far as to trace dynamic of blood flow oscillations. This study was supported by the Russian Science Foundation under project N 18-15-00201.

Keywords: blood microcirculation, laser Doppler flowmetry, pressure-induced vasodilation, wavelet analyses blood

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Authors: Alison C. Essary, Victor Trastek

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Introduction: The current state of the health care system in the U.S. is characterized by an unprecedented number of people living with multiple chronic conditions, unsustainable rise in health care costs, inadequate access to care, and wide variation in health outcomes throughout the country. An estimated two-thirds of Americans are living with two or more chronic conditions, contributing to 75% of all health care spending. In 2013, the School for the Science of Health Care Delivery (SHCD) was charged with redesigning the health care system through education and research. Faculty in business, law, and public policy, and thought leaders in health care delivery, administration, public health and health IT created undergraduate, graduate, and executive academic programs to address this pressing need. Faculty and students work across disciplines, and with community partners and employers to improve care delivery and increase value for patients. Methods: Curricula apply content in health care administration and operations within the clinical context. Graduate modules are team-taught by faculty across academic units to model team-based practice. Seminars, team-based assignments, faculty mentoring, and applied projects are integral to student success. Cohort-driven models enhance networking and collaboration. This observational study evaluated two years of admissions data, and one year of graduate data to assess program outcomes and inform the current graduate-level curricula. Descriptive statistics includes means, percentages. Results: Fall 2013, the program received 51 applications. The mean GPA of the entering class of 37 students was 3.38. Ninety-seven percent of the fall 2013 cohort successfully completed the program (n=35). Sixty-six percent are currently employed in the health care industry (n=23). Of the remaining 12 graduates, two successfully matriculated to medical school; one works in the original field of study; four await results on the MCAT or DAT, and five were lost to follow up. Attrition of one student was attributed to non-academic reasons. Fall 2014, the program expanded to include both on-ground and online cohorts. Applications were evenly distributed between on-ground (n=70) and online (n=68). Thirty-eight students enrolled in the on-ground program. The mean GPA was 3.95. Ninety-five percent of students successfully completed the program (n=36). Thirty-six students enrolled in the online program. The mean GPA was 3.85. Graduate outcomes are pending. Discussion: Challenges include demographic variability between online and on-ground students; yet, both profiles are similar in that students intend to become change agents in the health care system. In the past two years, on-ground applications increased by 31%, persistence to graduation is > 95%, mean GPA is 3.67, graduates report admission to six U.S. medical schools, the Mayo Medical School integrates SHCD content within their curricula, and there is national interest in collaborating on industry and academic partnerships. This places SHCD at the forefront of developing innovative curricula in order to improve high-value, patient-centered care.

Keywords: delivery science, education, health care delivery, high-value care, innovation in education, patient-centered

Procedia PDF Downloads 255

Authors: Pablo Oteiza, Ricardo Alvarez, Mehrdad Pirnia, Fuat Can

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To effectively combat climate change, many countries around the world have committed to a decarbonisation of their electricity, along with promoting a large-scale integration of renewable energy sources (RES). While this trend represents a unique opportunity to effectively combat climate change, achieving a sound and cost-efficient energy transition towards low-carbon power systems poses significant challenges for the multi-year Transmission Network Expansion Planning (TNEP) problem. The objective of the multi-year TNEP is to determine the necessary network infrastructure to supply the projected demand in a cost-efficient way, considering the evolution of the new generation mix, including the integration of RES. The rapid integration of large-scale RES increases the variability and uncertainty in the power system operation, which in turn increases short-term flexibility requirements. To meet these requirements, flexible generating technologies such as energy storage systems must be considered within the TNEP as well, along with proper models for capturing the operational challenges of future power systems. As a consequence, TNEP formulations are becoming more complex and difficult to solve, especially for its application in realistic-sized power system models. To meet these challenges, there is an increasing need for developing efficient algorithms capable of solving the TNEP problem with reasonable computational time and resources. In this regard, a promising research area is the use of artificial intelligence (AI) techniques for solving large-scale mixed-integer optimization problems, such as the TNEP. In particular, the use of AI along with mathematical optimization strategies based on decomposition has shown great potential. In this context, this paper presents an efficient algorithm for solving the multi-year TNEP problem. The algorithm combines AI techniques with Column Generation, a traditional decomposition-based mathematical optimization method. One of the challenges of using Column Generation for solving the TNEP problem is that the subproblems are of mixed-integer nature, and therefore solving them requires significant amounts of time and resources. Hence, in this proposal we solve a linearly relaxed version of the subproblems, and trained a binary classifier that determines the value of the binary variables, based on the results obtained from the linearized version. A key feature of the proposal is that we integrate the binary classifier into the optimization algorithm in such a way that the optimality of the solution can be guaranteed. The results of a study case based on the HRP 38-bus test system shows that the binary classifier has an accuracy above 97% for estimating the value of the binary variables. Since the linearly relaxed version of the subproblems can be solved with significantly less time than the integer programming counterpart, the integration of the binary classifier into the Column Generation algorithm allowed us to reduce the computational time required for solving the problem by 50%. The final version of this paper will contain a detailed description of the proposed algorithm, the AI-based binary classifier technique and its integration into the CG algorithm. To demonstrate the capabilities of the proposal, we evaluate the algorithm in case studies with different scenarios, as well as in other power system models.

Keywords: integer optimization, machine learning, mathematical decomposition, transmission planning

Procedia PDF Downloads 43

Authors: L. Abubakar, B. M. Sokoto, I. U. Mohammed, M. S. Na’allah, A. Mohammad, A. N. Garba, T. S. Bubuche

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Onion (Allium cepa var. cepa L.), is the most important species of the Allium group belonging to family Alliaceae and genus Allium. It can be regarded as the single important vegetable species in the world after tomatoes. Despite the similarities, which bring the species together, the genus is a strikingly diverse one, with more than five hundred species, which are perennial and mostly bulbous plants. Out of these, only seven species are in cultivation, and five are the most important species of the cultivated Allium. However, Allium cepa (onion) and Allium sativum (Garlic) are the two major cultivated species grown all over the world of which the onion crop is the most important. Heritability defined as the proportion of the observed total variability that is genetic, and its estimates from variance components give more useful information of genotypic variation from the total phenotypic differences and environmental effects on the individuals or families. It therefore guide the breeder with respect to the ease with which selection of traits can be carried out. Heritability estimates guide the breeder with respect to ease of selection of traits while correlations suggest how selection among characters can be practiced. Correlations explain relationship between characters and suggest how selection among characters can be practiced in breeding programmes. Highly significant correlations have been reported, between yield, maturity, rings/bulb and storage loss in onions. Similarly significant positive correlation exists between total bulb yield and plant height, leaf number/plant, bulb diameter and bulb yield/plant. Moderate positive correlations have been observed between maturity date and yield, dry matter content was highly correlated with soluble solids, and higher correlations were also observed between storage loss and soluble solids. The objective of the study is to determine heritability estimates and correlations for characters among onion cultivars of North Western Nigeria. This is envisaged will assist in the breeding of superior onion cultivars within the zone. Thirteen onion cultivars were collected during an expedition covering north western Nigeria and Southern part of Niger Republic during 2013, which are areas noted for onion production. The cultivars were evaluated at two locations; Sokoto, in Sokoto State and Jega in Kebbi State all in Nigeria during the 2013/14 onion season (dry season) under irrigation. Combined analysis of the results revealed fresh bulb yield is highly significantly positively correlated with bulb height and cured bulb yield, and significant positive correlation with plant height and bulb diameter. It also recorded significant negative correlation with mean No. of leaves/plant and non significant negative correlation with bolting %. Cured bulb yield (marketable yield) had highly significant positive correlation with mean bulb weight and fresh bulb yield/ha, with significant positive correlation with bulb height. It also recorded highly significant negative correlation with No. of leaves/plant and significant negative correlation with bolting % and non significant positive correlation with plant height and non significant negative correlation with bulb diameter. High broad sense heritability estimates were recorded for plant height, fresh bulb yield, number of leaves/plant, bolting % and cured bulb yield. Medium to low broad sense heritabilities were also observed for mean bulb weight, plant height and bulb diameter.

Keywords: correlation, heritability, onions, North Western Nigeria

Procedia PDF Downloads 362

Authors: Swati Bhomia, C. M. Kishtawal, Neeru Jaiswal

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Traditionally, monsoon forecasts have encountered many difficulties that stem from numerous issues such as lack of adequate upper air observations, mesoscale nature of convection, proper resolution, radiative interactions, planetary boundary layer physics, mesoscale air-sea fluxes, representation of orography, etc. Uncertainties in any of these areas lead to large systematic errors. Global circulation models (GCMs), which are developed independently at different institutes, each of which carries somewhat different representation of the above processes, can be combined to reduce the collective local biases in space, time, and for different variables from different models. This is the basic concept behind the multi-model superensemble and comprises of a training and a forecast phase. The training phase learns from the recent past performances of models and is used to determine statistical weights from a least square minimization via a simple multiple regression. These weights are then used in the forecast phase. The superensemble forecasts carry the highest skill compared to simple ensemble mean, bias corrected ensemble mean and the best model out of the participating member models. This approach is a powerful post-processing method for the estimation of weather forecast parameters reducing the direct model output errors. Although it can be applied successfully to the continuous parameters like temperature, humidity, wind speed, mean sea level pressure etc., in this paper, this approach is applied to rainfall, a parameter quite difficult to handle with standard post-processing methods, due to its high temporal and spatial variability. The present study aims at the development of advanced superensemble schemes comprising of 1-5 day daily precipitation forecasts from five state-of-the-art global circulation models (GCMs), i.e., European Centre for Medium Range Weather Forecasts (Europe), National Center for Environmental Prediction (USA), China Meteorological Administration (China), Canadian Meteorological Centre (Canada) and U.K. Meteorological Office (U.K.) obtained from THORPEX Interactive Grand Global Ensemble (TIGGE), which is one of the most complete data set available. The novel approaches include the dynamical model selection approach in which the selection of the superior models from the participating member models at each grid and for each forecast step in the training period is carried out. Multi-model superensemble based on the training using similar conditions is also discussed in the present study, which is based on the assumption that training with the similar type of conditions may provide the better forecasts in spite of the sequential training which is being used in the conventional multi-model ensemble (MME) approaches. Further, a variety of methods that incorporate a 'neighborhood' around each grid point which is available in literature to allow for spatial error or uncertainty, have also been experimented with the above mentioned approaches. The comparison of these schemes with respect to the observations verifies that the newly developed approaches provide more unified and skillful prediction of the summer monsoon (viz. June to September) rainfall compared to the conventional multi-model approach and the member models.

Keywords: multi-model superensemble, dynamical model selection, similarity criteria, neighborhood technique, rainfall prediction

Procedia PDF Downloads 111