Search results for: March-May 2013 rainfall season

Authors: Akiode Ayobami, Akiode Akinsewa, Odeku Mojisola, Salako Busola, Odutolu Omobola, Nuhu Khadija

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Purpose: Evidence exists that family planning use can contribute to reduction in infant and maternal mortality in any country. Despite these benefits, contraceptive use in Nigeria still remains very low, only 10% among married women. Understanding factors that predict contraceptive use is very important in order to improve the situation. In this paper, we analysed data from the 2013 Nigerian Demographic and Health Survey (NDHS) to better understand predictors of contraceptive use in Nigeria. The use of logistics regression and other traditional models in this type of situation is not appropriate as they do not account for social structure influence brought about by the hierarchical nature of the data on response variable. We therefore used multilevel modelling to explore the determinants of contraceptive use in order to account for the significant variation in modern contraceptive use by socio-demographic, and other proximate variables across the different Nigerian states. Method: This data has a two-level hierarchical structure. We considered the data of 26, 403 married women of reproductive age at level 1 and nested them within the 36 states and the Federal Capital Territory, Abuja at level 2. We modelled use of modern contraceptive against demographic variables, being told about FP at health facility, heard of FP on TV, Magazine or radio, husband desire for more children nested within the state. Results: Our results showed that the independent variables in the model were significant predictors of modern contraceptive use. The estimated variance component for the null model, random intercept, and random slope models were significant (p=0.00), indicating that the variation in contraceptive use across the Nigerian states is significant, and needs to be accounted for in order to accurately determine the predictors of contraceptive use, hence the data is best fitted by the multilevel model. Only being told about family planning at the health facility and religion have a significant random effect, implying that their predictability of contraceptive use varies across the states. Conclusion and Recommendation: Results showed that providing FP information at the health facility and religion needs to be considered when programming to improve contraceptive use at the state levels.

Keywords: multilevel modelling, family planning, predictors, Nigeria

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Authors: Arjita Biswas, Sandeep Potnis

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Plastic roads (bituminous roads using waste plastic in the wearing course ) have now become familiar in the Road Construction Sector in India. With the Indian Road Congress Code (IRC SP: 98 -2013), many agencies are coming forward to implement Plastic Roads in India. This paper discuss and compare about the various properties of bituminous mix with 8% waste plastic and normal bituminous mix. This paper also signifies the performance of both the types of roads after 4 months of age under loading conditions. Experiments were carried out to evaluate its performance. The result shows improved performance of plastic roads.

Keywords: bituminous roads, experiments, performance, plastic roads

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Authors: Woldegerima G. Medhin, Tadele Araya

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

Keywords: rabies, Virus, transmision, prevalence

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Authors: V. V. Singh

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We have studied the reliability measures of a system, which consists of two subsystems i.e. subsystem-1 and subsystem-2 in series configuration under different types of failure. The subsystem-1 has three identical units in parallel configuration and operating under 2-out-of-3: G policy and connected to subsystem-2 in series configuration. Each subsystem has different types of failure and repair rates. An important cause for failure of system is unsuitability of the environmental conditions, like overheating, weather conditions, heavy rainfall, storm etc. The environmental failure is taken into account in the proposed repairable system. Supplementary variable technique is used to study of system and some traditional measures such as; availability, reliability, MTTF and profit function are obtained for different values of parameters. In the proposed model, some particular cases of failure rates are explicitly studied.

Keywords: environmental failure, human failure, availability, MTTF, reliability, profit analysis, Gumbel-Hougaard family copula

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Authors: Mehdi Fuladipanah, Mehdi Jorabloo

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Water flow management is one of the most important parts of river engineering. Non-uniformity distribution of rainfall and various flow demand with unreasonable flow management will be caused destroyed of the river ecosystem. Then, it is severe to determine ecosystem flow requirement. In this paper, Flow duration curve indices method which has hydrological based was used to evaluate environmental flow in Gharasou River, Ardabil, Iran. Using flow duration curve, Q90 and Q95 for different return periods were calculated. Their magnitude was determined as 1-day, 3-day, 7-day, and 30 days. According to the second method, hydraulic alteration indices often had low and medium range. To maintain river at an acceptable ecological condition, minimum daily discharge of index Q95 is 0.7 m^3.s^-1.

Keywords: Gharasou River, water flow management, non-uniformity distribution, ecosystem flow requirement, hydraulic alteration

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Authors: Ahmad Saadiq, Neeraj Sahu

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Sediment, which comprises of solid particles of mineral and organic material are transported by water. In river systems, the amount of sediment transported is controlled by both the transport capacity of the flow and the supply of sediment. The transport of sediment in rivers is important with respect to pollution, channel navigability, reservoir ageing, hydroelectric equipment longevity, fish habitat, river aesthetics and scientific interests. The sediment load transported in a river is a very complex hydrological phenomenon. Hence, sediment transport has attracted the attention of engineers from various aspects, and different methods have been used for its estimation. So, several experimental equations have been submitted by experts. Though the results of these methods have considerable differences with each other and with experimental observations, because the sediment measures have some limits, these equations can be used in estimating sediment load. In this present study, two black box models namely, an SRC (Sediment Rating Curve) and ANN (Artificial Neural Network) are used in the simulation of the suspended sediment load. The study is carried out for Seonath subbasin. Seonath is the biggest tributary of Mahanadi river, and it carries a vast amount of sediment. The data is collected for Jondhra hydrological observation station from India-WRIS (Water Resources Information System) and IMD (Indian Meteorological Department). These data include the discharge, sediment concentration and rainfall for 10 years. In this study, sediment load is estimated from the input parameters (discharge, rainfall, and past sediment) in various combination of simulations. A sediment rating curve used the water discharge to estimate the sediment concentration. This estimated sediment concentration is converted to sediment load. Likewise, for the application of these data in ANN, they are normalised first and then fed in various combinations to yield the sediment load. RMSE (root mean square error) and R² (coefficient of determination) between the observed load and the estimated load are used as evaluating criteria. For an ideal model, RMSE is zero and R² is 1. However, as the models used in this study are black box models, they don’t carry the exact representation of the factors which causes sedimentation. Hence, a model which gives the lowest RMSE and highest R² is the best model in this study. The lowest values of RMSE (based on normalised data) for sediment rating curve, feed forward back propagation, cascade forward back propagation and neural network fitting are 0.043425, 0.00679781, 0.0050089 and 0.0043727 respectively. The corresponding values of R² are 0.8258, 0.9941, 0.9968 and 0.9976. This implies that a neural network fitting model is superior to the other models used in this study. However, a drawback of neural network fitting is that it produces few negative estimates, which is not at all tolerable in the field of estimation of sediment load, and hence this model can’t be crowned as the best model among others, based on this study. A cascade forward back propagation produces results much closer to a neural network model and hence this model is the best model based on the present study.

Keywords: artificial neural network, Root mean squared error, sediment, sediment rating curve

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Authors: Huma Naeem, Saif-Ur-Rehman Kashif, Muhammad Nawaz Chaudhry

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Milk plays a significant role in the dietary requirements of human beings as it is a single source that provides various essential nutrients. A study was conducted to evaluate the heavy metal concentration in the raw milk marketed in Data Gunj Baksh Town of Lahore. A total of 180 samples of raw milk were collected in pre-monsoon, monsoon and post-monsoon season from five colonies of Data Gunj Baksh Town, Lahore. The milk samples were subjected to heavy metal analysis (Cr, Cu) by atomic absorption spectrophotometer. Results indicated high levels of Cr and Cu in post-monsoon seasons. Heavy metals were detected in milk in all samples under study and exceeded the standards given by FAO.

Keywords: atomic absorption spectrophotometer, chromium, copper, heavy metal

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Authors: Suzana Ramli, Wardah Tahir

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Estimation of surface runoff depth is a vital part in any rainfall-runoff modeling. It leads to stream flow calculation and later predicts flood occurrences. GIS (Geographic Information System) is an advanced and opposite tool used in simulating hydrological model due to its realistic application on topography. The paper discusses on calculation of surface runoff depth for two selected events by using GIS with Curve Number method for Upper Klang River basin. GIS enables maps intersection between soil type and land use that later produces curve number map. The results show good correlation between simulated and observed values with more than 0.7 of R2. Acceptable performance of statistical measurements namely mean error, absolute mean error, RMSE, and bias are also deduced in the paper.

Keywords: surface runoff, geographic information system, curve number method, environment

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Authors: Sulaiman Almutairy, Trevor Davies, Yota Dimitriadi

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This paper presents study investigating how to understand better the psychological readiness for mobile learning (m-learning) among Saudi students, while also evaluating m-learning in Saudi Arabia-a topic that has not yet received adequate attention from researchers. Data was acquired through a questionnaire administered to 131 Saudi students at UK universities, in July 2013. The study confirmed that students are confident using mobile devices in their daily lives and that they would welcome more opportunities for mobile learning. The findings indicated that Saudi higher education students are highly familiar with, and are psychologically ready for, m-learning.

Keywords: m-learning, mobile technologies, psychological readiness, higher education

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Authors: Adamu Usman, Grace Stanley Balami

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Pearl millet (Pennisetum glaucum L. R. Br.) is a cereal cultivated in arid and semi-arid areas of the world. It supports more than 100 million people around the world. Parasitic weed (Striga hermonthica Del. Benth) is a major constraint to its production. Estimated yield losses are put at 10 - 95% depending on variety, ecology and cultural practices. Potentials in selection of traits in pearl millets for grain yield have been reported and it depends on genotypic variability and heritability among landraces. Variability and heritability among cultivars could offer opportunities for improvement. The study was conducted to determine the genetic variability among cultivars and estimate broad sense heritability among grain yield and related traits. F1 breeding populations were generated with 9 parental cultivars, viz; Ex-Gubio, Ex-Monguno, Ex-Baga as males and PEO 5984, Super-SOSAT, SOSAT-C88, Ex-Borno and LCIC9702 as females through Line × Tester mating during 2017 dry season at Lushi Irrigation Station, Bauchi Metropolitan in Bauchi State, Nigeria. The F1 population and the parents were evaluated during cropping season of 2018 at Bauchi and Maiduguri. Data collected were subjected to analysis of variance. Results showed significant difference among cultivars and among traits indicating variability. Number of plants at emergence, days to 50% flowering, days to 100% flowering, plant height, panicle length, number of plants at harvest, Striga count at 90 days after sowing, panicle weight and grain yield were significantly different. Significant variability offer opportunity for improvement as superior individuals can be isolated. Genotypic variance estimates of traits were largely greater than environmental variances except in plant height and 1000 seed weight. Environmental variances were low and in some cases negligible. The phenotypic variances of all traits were higher than genotypic variances. Similarly phenotypic coefficient of variation (PCV) was higher than genotypic coefficient of variation (GCV). High heritability was found in days to 50% flowering (90.27%), Striga count at 90 days after sowing (90.07%), number of plants at harvest (87.97%), days to 100% flowering (83.89%), number of plants at emergence (82.19%) and plant height (73.18%). Greater heritability estimates could be due to presence of additive gene. The result revealed wider variability among genotypes and traits. Traits having high heritability could easily respond to selection. High value of GCV, PCV and heritability estimates indicate that selection for these traits are possible and could be effective.

Keywords: variability, heritability, phenotypic, genotypic, striga

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Authors: Veruska C. Dutra, Mary L. G. S. Senna, Felipe S. Spindler

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The State Park Jalapao - PEJ proved to be one of the protected areas that has attracted tourists from all over the world with its unique scenic landscapes. Although the region already has a considerable tourist flow, to our knowledge there is a lack of continuity of studies in the region capable of drawing a plan of activities, such as the profile of the tourist and analysis of their experiences in the region, carried out from 2006-2007. Therefore, this study was proposed to know the profile and experiences of tourists visiting the park today, making a connection with the earlier study, in order to generate subsidies to trace improvement actions. We conducted interviews with tourists in the main tourism season 2015. The results show that after eight years of carrying out the first study, there were no changes, highlighting the lack of a tourism plan for the park.

Keywords: Jalapao, profile tourist, level, satisfaction

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Authors: Htet Khaing Lin

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This study explores the relationship between a diverse set of variables, encompassing both environmental and socio-economic factors, and poverty levels in the Philippines for the years 2012, 2015, and 2018. Employing Ordinary Least Squares (OLS), Spatial Lag Models (SLM), and Spatial Error Models (SEM), this study delves into the dynamics of key indicators, including daytime and nighttime land surface temperature, cropland surface, urban land surface, rainfall, population size, normalized difference water, vegetation, and drought indices. The findings reveal consistent patterns and unexpected correlations, highlighting the need for nuanced policies that address the multifaceted challenges arising from the interplay of environmental and socio-economic factors.

Keywords: poverty analysis, OLS, spatial lag models, spatial error models, Philippines, google earth engine, satellite data, environmental dynamics, socio-economic factors

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Authors: Maryam Khalilzadeh Poshtegal, Seyed Ahmad Mirbagheri, Mojtaba Noury

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In this paper, by application of the input yearly data of rainfall, temperature and flow to the Urmia Lake, the simulation of water level fluctuation were applied by means of three models. According to the climate change investigation the fluctuation of lakes water level are of high interest. This study investigate data-driven models, support vector machines (SVM), SVM method which is a new regression procedure in water resources are applied to the yearly level data of Lake Urmia that is the biggest and the hyper saline lake in Iran. The evaluated lake levels are found to be in good correlation with the observed values. The results of SVM simulation show better accuracy and implementation. The mean square errors, mean absolute relative errors and determination coefficient statistics are used as comparison criteria.

Keywords: simulation, water level fluctuation, urmia lake, support vector machine

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Authors: Renu Saini, Deepak Agrawal

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Background: Patients with head and spinal cord injury frequently have deranged coagulation profiles and require blood products transfusion perioperatively. Thromboelastography (TEG) is a ‘bedside’ global test of coagulation which may have role in deciding the need of transfusion in such patients. Aim: To assess the usefulness of TEG in department of neurosurgery in decreasing transfusion rates and coagulation-related mortality in traumatic head and spinal cord injury. Method and Methodology: A retrospective comparative study was carried out in the department of neurosurgery over a period of 1 year. There are two groups in this study. ‘Control’ group constitutes the patients in whom data was collected over 6 months (1/6/2009-31/12/2009) prior to installation of TEG machine. ‘Test’ group includes patients in whom data was collected over 6months (1/1/2013-30/6/2013) post TEG installation. Total no. of platelet, FFP, and cryoprecipitate transfusions were noted in both groups along with in hospital mortality and length of stay. Result: Both groups were matched in age and sex of patients, number of head and spinal cord injury cases, number of patients with thrombocytopenia and number of patients who underwent operation. Total 178 patients (135 head injury and 43 spinal cord injury patents) were admitted in neurosurgery department during time period June 2009 to December 2009 i.e. prior to TEG installation and after TEG installation a total of 243 patients(197 head injury and 46 spinal cord injury patents) were admitted. After TEG introduction platelet transfusion significantly reduced (p=0.000) compare to control group (67 units to 34 units). Mortality rate was found significantly reduced after installation (77 patients to 57 patients, P=0.000). Length of stay was reduced significantly (Prior installation 1-211days and after installation 1-115days, p=0.02). Conclusion: Bedside TEG can dramatically reduce platelet transfusion components requirement in department of neurosurgery. TEG also lead to a drastic decrease in mortality rate and length of stay in patients with traumatic head and spinal cord injuries. We recommend its use as a standard of care in the patients with traumatic head and spinal cord injuries.

Keywords: blood component transfusion, mortality, neurosurgery ICU, thromboelastography

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Authors: Dimitri Bacco, Arianna Trentini, Fabiana Scotto, Flavio Rovere, Daniele Foscoli, Cinzia Para, Paolo Veronesi, Silvia Sandrini, Claudia Zigola, Michela Comandini, Marilena Montalti, Marco Zamagni, Vanes Poluzzi

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The Po Valley, in the north of Italy, is one of the most polluted areas in Europe. The air quality of the area is linked not only to anthropic activities but also to its geographical characteristics and stagnant weather conditions with frequent inversions, especially in the cold season. Even the coastal areas present high values of particulate matter (PM10 and PM2.5) because the area closed between the Adriatic Sea and the Apennines does not favor the dispersion of air pollutants. The aim of the present work was to identify the main sources of particulate matter in Rimini, a tourist city in northern Italy. Two sampling campaigns were carried out in 2018, one in winter (60 days) and one in summer (30 days), in 4 sites: an urban background, a city hotspot, a suburban background, and a rural background. The samples are characterized by the concentration of the ionic composition of the particulates and of the main a hydro-sugars, in particular levoglucosan, a marker of the biomass burning, because one of the most important anthropogenic sources in the area, both in the winter and surprisingly even in the summer, is the biomass burning. Furthermore, three sampling points were chosen in order to maximize the contribution of a specific biomass source: a point in a residential area (domestic cooking and domestic heating), a point in the agricultural area (weed fires), and a point in the tourist area (restaurant cooking). In these sites, the analyzes were enriched with the quantification of the carbonaceous component (organic and elemental carbon) and with measurement of the particle number concentration and aerosol size distribution (6 - 600 nm). The results showed a very significant impact of the combustion of biomass due to domestic heating in the winter period, even though many intense peaks were found attributable to episodic wood fires. In the summer season, however, an appreciable signal was measured linked to the combustion of biomass, although much less intense than in winter, attributable to domestic cooking activities. Further interesting results were the verification of the total absence of sea salt's contribution in the particulate with the lower diameter (PM2.5), and while in the PM10, the contribution becomes appreciable only in particular wind conditions (high wind from north, north-east). Finally, it is interesting to note that in a small town, like Rimini, in summer, the traffic source seems to be even more relevant than that measured in a much larger city (Bologna) due to tourism.

Keywords: aerosol, biomass burning, seacoast, urban area

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Authors: Muhammad Shafiq, Huynh Trung Luong

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In this paper, we proposed a commitment based revenue sharing contract for a supply chain comprising one manufacturer and one retailer facing highly uncertain demand of a short life span fashionable product. In our model, the retailer reserves a commitment level with the manufacturer prior to the selling season. In response, the manufacturer allocates and produces a specific quantity which is the maximum available quantity for the retailer. The retailer is motivated to commit more by offering higher revenue sharing percentage for reserved capacity than non-reserved capacity. Due to asymmetric information, it is found that the manufacturer can optimize quantity allocation decision while the commitment level decision of the retailer may not be optimal.

Keywords: supply chain coordination, revenue sharing contract, commitment based revenue sharing, quantity allocation

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Authors: Chiara Cesali

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The effects of climate change are increasingly evident: increases in temperature (i.e. global warming), greater frequency of extreme weather events, i.e. storms, floods, which often affect transport infrastructures. Large-scale climatological models with long-term horizons (up to 2100) show the possibility of significant increases in precipitation in the future, according to the greenhouse gas emissions scenarios from IPCC. Consequently, the insufficiency of existing hydraulic works (i.e. bridges, culverts, drainage systems) may be more frequent, or those currently being designed may become insufficient in the future. Thus, the hydraulic design methods of transport infrastructure must begin to take into account the influence of climate change. To this purpose, criteria for applying to the hydraulic design of a railway infrastructure some of the approaches currently available for determining design rainfall intensity and/or peak discharge flow on the basis of possible climate change scenarios are defined and proposed in the paper. Some application cases are also described.

Keywords: climate change, hydraulic design, precipitation, railway

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Authors: Krishan Lal, Anish Dua

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Gobind Sagar Reservoir has been created in Himachal Pradesh, India (31° 25´ N and 76 ° 25´E) by damming River Sutlej at village Bhakra in 1963. The average water spread area of this reservoir is 10,000 hectares. Fishermen have organized themselves in the form of co-operative societies. 26 fisheries co-operative societies were working in Gobind Sagar Reservoir up till 2012. June and July months were observed as closed season, no fishing was done during this period. Proper record maintaining of fish catch was done at different levels by the state fisheries department. Different measures like minimum harvestable size, mesh size regulation and prohibition of illegal fishing etc. were taken for fish conservation. Fishermen were actively involved in the management. Gill nets were used for catching fishes from this reservoir. State fisheries department is realizing 15% royalty of the sold fish. Data used in this paper is about the fish catch during 2006-2012 and were obtained from the state fisheries department, Himachal Pradesh. Catla catla, Labeo rohita, Cirrhinus mrigala, Sperata seenghala, Cyprinus carpio, Tor putitora, Hypophthalmichthys molitrix, Labeo calbasu, Labeo dero and Ctenopharyngodon idella etc., were the fish species exploited for commercial purposes. Total number of individuals of all species caught was 3141236 weighing 5637108.9 kg during 2006-2012. H. molitrix was introduced accidently in this reservoir and was making a good share of fish catch in this reservoir. The annual catch of this species was varying between 161279.6 kg, caught in 2011 and 788030.8 kg caught in 2009. Total numbers of individuals of C. idella caught were 8966 weighing 64320.2 kg. The catch of Cyprinus carpio was varying between 144826.1 kg caught in 2006 and 214480.1 kg caught in 2010. Total catch of Tor putitora was 180263.2 kg during 2006-2012. Total catch of L. dero, S. seenghala and Catla catla remained 100637.4 kg, 75297.8 kg and 561802.9 kg, respectively, during 2006-2012. Maximum fish catch was observed during the months of August (after observing Closed Season). Maximum catch of exotic carps was from Bhakra area of the reservoir which has fewer fluctuations in water levels. The reservoir has been divided into eight beats for administrative purpose, to avoid conflicts between operating fisheries co-operative societies for area of operation. Fish catch was more by co-operative societies operating in the area of reservoir having fewer fluctuations in water level and catch was less by co-operative societies operating in the area of more fluctuations in water level. Species-wise fish catch by different co-operative societies from their allotted area was studied. This reservoir is one of most scientifically managed reservoirs.

Keywords: co-operative societies, fish catch, fish species, reservoir

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Authors: Ma Chu Jui

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Culture plays a pivotal role in shaping societal responses to change and fostering adaptability. In the realm of healthcare provision, hospitals serve as dynamic settings molded by the cultural consciousness of healthcare professionals. This intricate interplay extends to their expectations of leadership, communication styles, and attitudes towards patient care. Recognizing the cultural inclinations of healthcare professionals becomes imperative in navigating this complex landscape. This study will utilize Hofstede's Value Survey Module 2013 (VSM 2013) as a comprehensive analytical tool. The targeted participants for this research are in-service nursing professionals with a tenure of at least three months, specifically employed in the nursing department of an Eastern hospital. This quantitative approach seeks to quantify diverse cultural tendencies among the targeted nursing professionals, elucidating not only abstract cultural concepts but also revealing their cultural inclinations across different dimensions. The study anticipates gathering between 400 to 500 responses, ensuring a robust dataset for a comprehensive analysis. The focused approach on nursing professionals within the Eastern hospital setting enhances the relevance and specificity of the cultural insights obtained. The research aims to contribute valuable knowledge to the understanding of cultural tendencies among in-service nursing personnel in the nursing department of this specific Eastern hospital. The VSM 2013 will be initially distributed to this specific group to collect responses, aiming to calculate scores on each of Hofstede's six cultural dimensions—Power Distance Index (PDI), Individualism vs. Collectivism (IDV), Uncertainty Avoidance Index (UAI), Masculinity vs. Femininity (MAS), Long-Term Orientation vs. Short-Term Normative Orientation (LTO), and Indulgence vs. Restraint (IVR). the study unveils a significant correlation between different cultural dimensions and healthcare professionals' tendencies in understanding leadership expectations through PDI, grasping behavioral patterns via IDV, acknowledging risk acceptance through UAI, and understanding their long-term and short-term behaviors through LTO. These tendencies extend to communication styles and attitudes towards patient care. These findings provide valuable insights into the nuanced interconnections between cultural factors and healthcare practices. Through a detailed analysis of the varying levels of these cultural dimensions, we gain a comprehensive understanding of the predominant inclinations among the majority of healthcare professionals. This nuanced perspective adds depth to our comprehension of how cultural values shape their approach to leadership, communication, and patient care, contributing to a more holistic understanding of the healthcare landscape. A profound comprehension of the cultural paradigms embraced by healthcare professionals holds transformative potential. Beyond a mere understanding, it acts as a catalyst for elevating the caliber of healthcare services. This heightened awareness fosters cohesive collaboration among healthcare teams, paving the way for the establishment of a unified healthcare ethos. By cultivating shared values, our study envisions a healthcare environment characterized by enhanced quality, improved teamwork, and ultimately, a more favorable and patient-centric healthcare landscape. In essence, our research underscores the critical role of cultural awareness in shaping the future of healthcare delivery.

Keywords: hofstede's cultural, cultural dimensions, cultural values in healthcare, cultural awareness in nursing

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Authors: Ahmed Ali Hassan

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The fall armyworm (FAW) was first identified in 2016 in Africa. FAW is widely distributed in Somalia and severely damages the maize crop. The effect of three different pesticides used to control the autumn armyworm, Spodoptera frugiperda (Noctuidae: Lepidoptera), on maize productivity was investigated in this study. During the 2020–2021 growing season, three insecticides (Malathion 57 EC, Ampligo150 ZC, and Carbryle 85 WP) were evaluated at field demonstration plots. Our result showed that significant mortality of S. frugiperda was observed on the treatment plot treated with Amplico. After spraying, Ampligo resulted in (92.200%) larval death. Compared to Carbaryl, which was less active and only caused 36.367% mortality after application, Malathion had a moderate mortality rate of 53.733%. Consequently, our current finding shows that the three selected insecticides reduced the damage and infestation level of S. frugiperda in the maize field conditions, and the most effective treatment was Amplico.

Keywords: maize, fall armyworm, insecticides, mortality

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Authors: Ashutosh Saxena, Kumar Pallav, Ramji Dwivedi

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The present study estimates the seismicity parameter 'b' and maximum possible magnitude of an earthquake (Mmax) for Gujarat region with three well-established methods viz. Kijiko parametric model (KP), Kijiko-Sellevol-Bayern (KSB) and Tapered Gutenberg-Richter (TGR), as a combined seismic source regime. The earthquake catalogue is prepared for a period of 1330 to 2013 in the region Latitudes 20o N to 250 N and Longitudinally extending from 680 to 750 E for earthquake moment magnitude (Mw) ≥4.0. The ’a’ and 'b' value estimated for the region as 4.68 and 0.58. Further, Mmax estimated as 8.54 (± 0.29), 8.69 (± 0.48), and 8.12 with KP, KSB, and TGR, respectively.

Keywords: Mmax, seismicity parameter, Gujarat, Tapered Gutenberg-Richter

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Authors: Nilda Ersoy, Sevinç Şener, Ayşe Yalçın Elidemir, Ebru Evcil, Ergün Döğen

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In this study, pesticide residues were investigated in black cumin (Nigella orientalis L.) seeds grown in Turkey. GC-MS and LC-MS/MS analytical instruments are used in high precision when determining residue limits. A total of 100 pesticide active ingredients in LC-MS/MS devices have been performed in Nigella orientalis L. seeds samples. Also for the same aim, 103 pesticide active ingredients were analyzed in GC-MS. This study was conducted in 2012 and 2013. Sample residues were not found in detectable levels for two years.

Keywords: pesticide, residue, black cumin, Nigella orientalis L.

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Authors: Parvaneh Ziviar Pardehei, Esmaeil Hossinnejad

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In this research, the tourism climate index Miczcofski (TCI) and to assess climate Trjvng Piranshahr city tourism is discussed. The index is a systematic way to evaluate the climatic conditions for tourism. To calculate the parameters of mean monthly maximum temperature, minimum relative humidity, average daily relative humidity, rainfall, sunshine and the wind speed are used. In the months of April, July, August and September of comfort there in December, January, February and March, the nerve is cold comfort factor. Baker calculation method showed that during spring and summer cooling environment, mild, pleasant, and comfortable Byvklymay there. TCI results suggest that the months of April to July are top rated and best climatic conditions in terms of comfort to the tourists. In general, indices used in this paper show that the months of April to October is the best time for tourism in the city Piranshahr.

Keywords: tourism, climate, Piranshahr city, TCI indicators and trjvng

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Authors: Neda Orak, Mostafa Zarei

Abstract:

Environmental assessment is an important session in environment management. Since various methods and techniques have been produces and implemented. Remote sensing (RS) is widely used in many scientific and research fields such as geology, cartography, geography, agriculture, forestry, land use planning, environment, etc. It can show earth surface objects cyclical changes. Also, it can show earth phenomena limits on basis of electromagnetic reflectance changes and deviations records. The research has been done on mangrove forests assessment by RS techniques. Mangrove forests quantitative analysis in Basatin and Bidkhoon estuaries was the aim of this research. It has been done by Landsat satellite images from 1975- 2013 and match to ground control points. This part of mangroves are the last distribution in northern hemisphere. It can provide a good background to improve better management on this important ecosystem. Landsat has provided valuable images to earth changes detection to researchers. This research has used MSS, TM, +ETM, OLI sensors from 1975, 1990, 2000, 2003-2013. Changes had been studied after essential corrections such as fix errors, bands combination, georeferencing on 2012 images as basic image, by maximum likelihood and IPVI Index. It was done by supervised classification. 2004 google earth image and ground points by GPS (2010-2012) was used to compare satellite images obtained changes. Results showed mangrove area in bidkhoon was 1119072 m2 by GPS and 1231200 m2 by maximum likelihood supervised classification and 1317600 m2 by IPVI in 2012. Basatin areas is respectively: 466644 m2, 88200 m2, 63000 m2. Final results show forests have been declined naturally. It is due to human activities in Basatin. The defect was offset by planting in many years. Although the trend has been declining in recent years again. So, it mentioned satellite images have high ability to estimation all environmental processes. This research showed high correlation between images and indexes such as IPVI and NDVI with ground control points.

Keywords: IPVI index, Landsat sensor, maximum likelihood supervised classification, Nayband National Park

Procedia PDF Downloads 287

Authors: Hamid Abchir, Mohammed Sabak2

Abstract:

We construct an infinite family of links which are both almost alternating and quasi-alternating from a given either almost alternating diagram representing a quasi-alternating link, or connected and reduced alternating tangle diagram. To do that we use what we call a dealternator extension which consists in replacing the dealternator by a rational tangle extending it. We note that all non-alternating and quasi-alternating Montesinos links can be obtained in that way. We check that all the obtained quasi-alternating links satisfy Conjecture 3.1 of Qazaqzeh et al. (JKTR 22 (6), 2013), that is the crossing number of a quasi-alternating link is less than or equal to its determinant. We also prove that the converse of Theorem 3.3 of Qazaqzeh et al. (JKTR 24 (1), 2015) is false.

Keywords: quasi-alternating links, almost alternating links, tangles, determinants

Procedia PDF Downloads 155

Authors: S. N. Al-Bahry, S. K. Al-Musharafi, I. Y. Mahmoud

Abstract:

Recycled treated sewage effluents (TSE) is used for agriculture, Public park irrigation and industrial purposes. TSE was found to play a major role in the distribution of antibiotic resistant bacteria into the environment. Fecal coliform and enterococci counts were significantly higher during summer compared to winter seasons. Oman has low annual rainfall with annual average temperature varied between 15-45oC. The main source of potable water is from seawater desalination. Resistance of the isolates to 10 antibiotics (Amikacin, Ampicillin, chloramphenicol, gentamycine, minocylin, nalidixicacid, neomycin, streptomycin, Tetracycline, Tobramycin, and Trimethoprim) was tested. Both fecal coliforms and enterococci were multiple resistant to 2-10 antibiotics. However, temperature variation during summer and winter did not affect resistance of the isolates to antibiotics. The significance of this investigation may be indicator to the environmental TSE pollution.

Keywords: antibiotic resistance, bacteria, environment, sewage treated effluent

Procedia PDF Downloads 407

Authors: Enikő Bene

Abstract:

Our sowing date experiment took place in the Demonstration Garden of Institution of Plant Sciences, Agricultural Center of University of Debrecen, in 2012-2014. The thesis contains data of test year 2014. Our purpose, besides several other examinations, was to observe how sowing date influences leaf area index and activity of photosynthesis of maize hybrids, and how those factors affect fruiting. In the experiment we monitored the change of the leaf area index and the photosynthesis of hybrids with four different growing seasons. The results obtained confirm that not only the environmental and agricultural factors in the growing season have effect on the yield, but also other factors like the leaf area index and the photosynthesis are determinative parameters, and all those factors together, modifying effects of each other, develop average yields

Keywords: sowing date, hybrid, leaf area index, photosynthetic capacity

Procedia PDF Downloads 326

Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 120

Authors: Imessaoudene Y., Mouhouche B., Sengouga A., Kadir M.

Abstract:

The objective of this work is to determine the virtual water of main crops grown in the province of Djelfa and water use efficiency (W.U.E.), Which is essential to approach the application and better integration with the offer in the region. In the case of agricultural production, virtual water is the volume of water evapo-transpired by crops. It depends on particular on the expertise of its producers and its global production area, warm and dry climates induce higher consumption. At the scale of the province, the determination of the quantities of virtual water is done by calculating the unit water requirements related to water irrigated hectare and total rainfall over the crop using the Cropwat 8.0 F.A.O. software. Quantifying the volume of agricultural virtual water of crops practiced in the study area demonstrates the quantitative importance of these volumes of water in terms of available water resources in the province, so the advantages which can be the concept of virtual water as an analysis tool and decision support for the management and distribution of water in scarcity situation.

Keywords: virtual water, water use efficiency, water requirements, Djelfa

Procedia PDF Downloads 422

Authors: Maryam Ehsanpour, Majid Afkhami ‎

Abstract:

This study investigated the presence of hydrocarbon pollution in industrial waste water sediments found in west coast of Bandar Abass (northern part of Hormoz strait). Therefore, six transects from west of the city were selected. Each transect consists of three stations intervals 100, 600 and 1100 meter from the low tide were sampled in both the summer and winter season (July and January 2009). Physical and chemical parameters of water, concentration of total petroleum hydrocarbons (TPH) and soil tissue deposition were evaluated according to standard procedures of MOOPAM. Average results of dissolved oxygen were 6.42 mg/l, temperature 26.31°C, pH 8.55, EC 54.47 ms/cm and salinity 35.98 g/l respectively. Results indicate that minimum, maximum and average concentration of total petroleum hydrocarbons (TPH) in sediments were, 60.18, 751.83, and 229.21 µg/kg respectively which are less than comparable studies in other parts of Persian Gulf.

Keywords: oil pollution, Bandar Abbas, costal sediments, TPH ‎

Procedia PDF Downloads 712