Search results for: tree growth

Authors: Renata Konadu

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In Ghana, electricity has become the main form of energy which all sectors of the economy rely on for their businesses. Therefore, as the economy grows, the demand and consumption of electricity also grow alongside due to the heavy dependence on it. However, since the supply of electricity has not increased to match the demand, there has been frequent power outages and load shedding affecting business performances. To solve this problem and advance policies to secure electricity in Ghana, it is imperative that those factors that cause consumption to increase be analysed by considering the three classes of consumers; residential, industrial and non-residential. The main argument, however, is that, export of electricity to other neighbouring countries should be included in the electricity consumption model and considered as one of the significant factors which can decrease or increase consumption. The author made use of multivariate time series data from 1980-2010 and econometric models such as Ordinary Least Squares (OLS) and Vector Error Correction Model. Findings show that GDP growth, urban population growth, electricity exports and industry value added to GDP were cointegrated. The results also showed that there is unidirectional causality from electricity export and GDP growth and Industry value added to GDP to electricity consumption in the long run. However, in the short run, there was found to be a directional causality among all the variables and electricity consumption. The results have useful implication for energy policy makers especially with regards to electricity consumption, demand, and supply.

Keywords: electricity consumption, energy policy, GDP growth, vector error correction model

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Authors: Q. Rzina, M. Lahrouni, S. Rida, N. Saadaoui, Y. Almossaid, K. Oufdou, K. Fares

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Many organic solid wastes are produced in the world. Poultry manure (PM), municipal organic wastes (MOW) and sugar beet lime sludge (LS) are produced in large quantities in Morocco. The co-composting of these organic wastes was investigated. The recycling and the valorization of such wastes is environmentally and economically beneficial especially for PM which is known source of bacterial pathogens. The aerobic biodegradation process was carried out by using three windrows of variable compositions: C1 prepared without LS (only MOW were composted with PM), C2 prepared from MOW plus PM and10% LS; and the last one C3 from MOW plus PM and 20% LS. The main process physico-chemical parameters (temperature, pH, humidity and C/N) and microbiological populations (mesophilic and thermophilic flora, total coliform, fecal coliform, Streptococci, Staphylococcus aureus and mesophilic fungi) were monitored over three months to ascertain the compost maturity and to ensure the compost hygienic aspect. The final products were characterized by their relatively high organic matter content, and low C/N ratio of 10.6-10.9. The organic matter degradation was reached approximately 59% for C2 and C3. In addition, the monitoring of the microbial population showed that the produced composts are mature and hygienic. The agronomic valorization of the final composts was tested on radish plant with tree level of composts and poultry manure without composting. The primary results of field trial showed a growth of radish plant biomass and root development without any phytotoxicity detected which reflects the quality of the composts produced. As for poultry manure it allowed to have a better results than other composts because of its readily available nitrogen.

Keywords: compost, municipal organic wastes, poultry manure, radish crop, sugar beet lime sludge

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Authors: G. K. Madhu, S. Bhaskar, M. S. Dinesh, R. Manii, C. A. Srinivasamurthy

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A field experiment was conducted in the premises of M/s Sri Chamundeshwari Sugars Ltd., Bharathinagar, Mandya District Pvt. Ltd., during 2014 to study the effect of sugar mill effluent (SME) on growth, yield and soil properties of ratoon cane with eight treatments replicated thrice using RCBD design. Significantly higher growth parameters like cane height (249.77 cm) and number of tillers per clump (12.22) were recorded in treatment which received cycle of 3 irrigations with freshwater + 1 irrigation with sugar mill effluent + RDF as compared to other treatments. Significantly lower growth attributes were recorded in treatment which received irrigation with sugar mill effluent alone. Significantly higher cane yield (104. 93 t -1) was recorded in treatment which received cycle of 3 irrigations with freshwater + 1 irrigation with sugar mill effluent + RDF as compared to other treatments. Significantly lower cane yield (87.40 t ha-1) was observed in treatment which received irrigation with sugar mill effluent alone. Soil properties like pH (7.84) was higher in treatment receiving Alternate irrigation with freshwater and sugar mill effluent + RDF. But EC was significantly higher in treatment which received Cycle of1 irrigation with freshwater + 2 irrigations with sugar mill effluent + RDF as compared to other treatments.

Keywords: sugar mill effluent, sugarcane, irrigation, cane yield

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Authors: Mohamed Hamed

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Population studies, essentially deals with the size, growth, and distribution of the population in a given area. Size, growth, and distribution are determined by three major factors, which are reproduction, mortality, and migration. Of these factors, reproduction is a potent socio-demographic force in vital process of population growth. It is a major component of population growth, and has crucial role in population dynamic, because it measures the rate at which a population increased. In fact the most striking feature of human reproduction is its variation. Its levels are vary widely among nations, countries, geographic regions, ethnic. The variations of reproductive behaviour among married women have been empirically documented in a large numbers of countries. For instance, many researchers in developing and developed countries investigated the differential of reproductive behaviour among married women. Most of these studies found that reproductive behaviour is strongly influenced by the socioeconomic and biological factors.Such as education, income, employment of women, marriage pattern, age at marriage, contraceptive use, education, and employment. However, the above socioeconomic and biological factors are determined by cultural factors surrounded by married women. So, this study is going to find out the effect of culture on reproductive behaviour among married women in Sudan, a case of Shendi town.

Keywords: fertilty pattern, sudan, shendi town, factors affecting reproductive behaviour, married women

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Authors: Ulemu Maseko

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In Malawi, empowering youth-led cooperatives is a pivotal strategy for fostering sustainable development and nurturing inclusive growth. The study delves into the pressing need to understand the impact and potential of youth-led cooperatives in driving socio-economic progress within the country. Through a mixed-methods approach encompassing qualitative interviews, surveys, and case studies, this research investigates the role of these cooperatives in promoting entrepreneurship, community engagement, and sustainable practices among young individuals. The results underscore the significant contribution of youth-led cooperatives towards economic empowerment and social inclusion in Malawi, highlighting their capacity to address local challenges effectively. The study concludes that supporting and investing in youth-led cooperatives is imperative for advancing sustainable development goals and achieving inclusive growth. Recommendations include tailored support mechanisms, capacity-building programs, and strategic partnerships to enhance the resilience and scalability of youth-led cooperative initiatives in Malawi.

Keywords: youth-led cooperatives, sustainable development, Malawi, empowerment

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Authors: Kh Jalali Dehkordi, A. Jalali Dehkordi, A. Tofighi

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Background: The purpose of this study was to investigate the effects of 8 weeks of aerobic, resistance, and concurrent training on secretion of growth hormone (GH) and insulin-like growth factor-1 (IGF-1) in elderly women. Methods: A total number of 60 elderly women were randomly allocated to four groups of aerobic training (n = 15), resistance training (n = 15), concurrent training (n = 15), and control (n = 15). Blood samples were taken before and 4 weeks after the initiation of exercise training and also at the end of the 8-week course of training. Maximal oxygen consumption (VO2Peak) was measured after 48 hours using Rockport walk test. Inferential analysis of the collected data was performed by repeated measures analysis of variance (ANOVA). Significant differences were further evaluated by the least significant difference (LSD) test. The relation between VO2Peak and secretion of GH and IGF-1 was assessed by Pearson's correlation coefficient. The significance level was considered as P ≤ 0.05 in all tests. Findings: The results showed that 8 weeks of regular exercise significantly increased levels of GH and IGF-1. A significant increase was also observed in VO2Peak values after 8 weeks of regular exercise (P < 0.05). VO2Peak was directly correlated with GH and IGF (P < 0.001, r = 0.72). Conclusion: In conclusion, regular exercise significantly increased levels of anabolic hormones. Moreover, the combined-exercise training better enhanced GH and IGF-1. VO2Peak increased with increases in GH and IGF-1 levels.

Keywords: women, training, GH, IGF-1

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Authors: Arjun K., Arumugam Sankaran, Sanjay Kumar, Mousumi Das

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The paper analyses the existence of endogenous growth scenario emanating from the demographic dividend in India during the liberalization period starting from 1980. Demographic dividend creates a fertile ground for the cultivation of human and knowledge capitals contributing to technological progress which can be measured using total factor productivity. The relationship among total factor productivity, human and knowledge capitals are examined in an open endogenous framework for the period 1980-2016. The control variables such as foreign direct investment, trade openness, energy consumption are also employed. The data are sourced from Reserve Bank of India, World Bank, International Energy Agency and The National Science and Technology Management Information System. To understand the dynamic association among variables, ARDL bounds approach to cointegration followed by Toda-Yamamoto causality test are used. The results reveal a short run and long run relationship among the variables supported by the existence of causality. This calls for an integrated policy to build and augment human capital and research and development activities to sustain and pace up growth and development in the nation.

Keywords: demographic dividend, young population, open endogenous growth models, human and knowledge capital

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Authors: Liang Jiansheng, Zhu Wei

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Light and brassinosteroids are essential external and internal cues for plant survival. Although the coordination of light with phytohormone signals is crucial for plant growth and development, the molecular connection between light and brassinosteroid signaling during root soil penetration remains elusive. Here, we reveal that light-stabilized RACK1 couples a brassinosteroid signaling cascade to drive cell division in root meristems. RACK1 family scaffold proteins positively regulate light-induced the promotion of root elongation during soil penetration. Under the light condition, RACK1A interacts with both phyB and SPA1, then reinforces the phyB-SPA1 association to accumulate its abundance in roots. In response to brassinosteroid signals, RACK1A competes with BKI1 to attenuate the BRI1-BKI1 interaction, thereby leading to activating BRI1 actions in root development. Furthermore, RACK1A binds to BES1 to repress its DNA binding activity toward the target gene CYCD3;1. This ultimately allows to release the inhibition of CYCD3;1 transcription, and promotes cell division during root growth. Our study illustrates a new mechanistic model of how plants engage scaffold proteins in transducing light information to facilitate brassinosteroid signaling for root growth in the soil.

Keywords: root growth, cell division, light signaling, brassinosteroid signaling, soil penetration, scaffold protein, RACK1

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Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Purba Biswas, Priyanka Dey

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Worldwide rapid urbanisation has accelerated city expansion in both developed and developing nations. This unprecedented urbanisation trend due to the increasing population and economic growth has caused challenges for the decision-makers in city planning and urban management. Metropolitan cities, class-I towns, and major urban centres undergo a continuous process of evolution due to interaction between socio-cultural and economic attributes. This constant evolution leads to urban expansion in all directions. Understanding the patterns and dynamics of urban built-up growth is crucial for policymakers, urban planners, and researchers, as it aids in resource management, decision-making, and the development of sustainable strategies to address the complexities associated with rapid urbanisation. Identifying spatio-temporal patterns of urban growth has emerged as a crucial challenge in monitoring and assessing present and future trends in urban development. Analysing urban growth patterns and tracking changes in land use is an important aspect of urban studies. This study analyses spatio-temporal urban transformations and land-use and land cover changes using remote sensing and GIS techniques. Built-up growth analysis has been done for the city of Surat as a case example, using the GIS tools of NDBI and GIS models of the Built-up Urban Density Index and Shannon Entropy Index to identify trends and the geographical direction of transformation from 2005 to 2020. Surat is one of the fastest-growing urban centres in both the state and the nation, ranking as the 4th fastest-growing city globally. This study analyses the dynamics of urban built-up area transformations both zone-wise and geographical direction-wise, in which their trend, rate, and magnitude were calculated for the period of 15 years. This study also highlights the need for analysing and monitoring the urban growth pattern of class-I cities in India using spatio-temporal and quantitative techniques like GIS for improved urban management.

Keywords: urban expansion, built-up, geographic information system, remote sensing, Shannon’s entropy

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Authors: Mohammed Hmimou, Khalid Amediaz, Imane Sebari, Nabil Bounajma

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Annual crop production is one of the most important macroeconomic indicators for the majority of countries around the world. This information is valuable, especially for exporting countries which need a yield estimation before harvest in order to correctly plan the supply chain. When it comes to estimating agricultural yield, especially for arboriculture, conventional methods are mostly applied. In the case of the citrus industry, the sale before harvest is largely practiced, which requires an estimation of the production when the fruit is on the tree. However, conventional method based on the sampling surveys of some trees within the field is always used to perform yield estimation, and the success of this process mainly depends on the expertise of the ‘estimator agent’. The present study aims to propose a methodology based on the combination of unmanned aerial vehicle (UAV) images and terrestrial laser scanner (TLS) point cloud to estimate citrus production. During data acquisition, a fixed wing and rotatory drones, as well as a terrestrial laser scanner, were tested. After that, a pre-processing step was performed in order to generate point cloud and digital surface model. At the processing stage, a machine vision workflow was implemented to extract points corresponding to fruits from the whole tree point cloud, cluster them into fruits, and model them geometrically in a 3D space. By linking the resulting geometric properties to the fruit weight, the yield can be estimated, and the statistical distribution of fruits size can be generated. This later property, which is information required by importing countries of citrus, cannot be estimated before harvest using the conventional method. Since terrestrial laser scanner is static, data gathering using this technology can be performed over only some trees. So, integration of drone data was thought in order to estimate the yield over a whole orchard. To achieve that, features derived from drone digital surface model were linked to yield estimation by laser scanner of some trees to build a regression model that predicts the yield of a tree given its features. Several missions were carried out to collect drone and laser scanner data within citrus orchards of different varieties by testing several data acquisition parameters (fly height, images overlap, fly mission plan). The accuracy of the obtained results by the proposed methodology in comparison to the yield estimation results by the conventional method varies from 65% to 94% depending mainly on the phenological stage of the studied citrus variety during the data acquisition mission. The proposed approach demonstrates its strong potential for early estimation of citrus production and the possibility of its extension to other fruit trees.

Keywords: citrus, digital surface model, point cloud, terrestrial laser scanner, UAV, yield estimation, 3D modeling

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Authors: M. T. Alodat

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In this paper, we propose a mechanism for skewing any symmetric distribution. The new distribution is called the deflation-inflation distribution (DID). We discuss some statistical properties of the DID such moments, stochastic representation, log-concavity. Also we fit the distribution to real data and we compare it to normal distribution and Azzlaini's skew normal distribution. Numerical results show that the DID fits the the tree ring data better than the other two distributions.

Keywords: normal distribution, moments, Fisher information, symmetric distributions

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Authors: Mostafa M. Rady, Majed M. Howladar, Saad M. Howladar

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A field trial was conducted during two successive seasons (2013 and 2014) in Southeast Fayoum, Egypt (29º 17'N; 30º 53'E) to investigate the improving effect of ascorbic acid (Vit C) foliar spray at the rates of 0, 1, 2 or 3 mM on the growth, seed and oil yields, and some chemical constituents of sunflower plants grown on a reclaimed saline soil (EC = 7.98–7.83). Vit C application at all rates (1, 2 and 3 mM) was significantly increased growth traits, seed and oil yields, and the concentrations of endogenous Vit C, leaf photosynthetic pigments, total soluble sugars, free proline and nutrient elements as well as K/Na ratio. In contrast, Na concentration was significantly reduced with the application of all Vit C levels. Vit C foliar spray at the rate of 2 mM was found to be the best treatment, alleviating the inhibitory effects of salinity on sunflower plants grown on a reclaimed saline soil.

Keywords: Helianthus annuus L., Vit C, salinity, growth, seed and oil yields, osmoprotectants

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Authors: Remy Jonkam Oben

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Using a time series approach, this study investigates how natural resources impact financial development from a global perspective over the 1980-2019 period. Some important determinants of financial development (economic growth, trade openness, population growth, and investment) have been added to the model as control variables. Unit root tests have revealed that all the variables are integrated into order one. Johansen's cointegration test has shown that the variables are in a long-run equilibrium relationship. The vector error correction model (VECM) has estimated the coefficient of the error correction term (ECT), which suggests that the short-run values of natural resources, economic growth, trade openness, population growth, and investment contribute to financial development converging to its long-run equilibrium level by a 23.63% annual speed of adjustment. The estimated coefficients suggest that global natural resource rent has a statistically-significant negative impact on global financial development in the long-run (thereby validating the financial resource curse) but not in the short-run. Causality test results imply that neither global natural resource rent nor global financial development Granger-causes each other.

Keywords: financial development, natural resources, resource curse hypothesis, time series analysis, Granger causality, global perspective

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Authors: Maria Manzoor, Iram Gul, Muhammad Arshad, Jean Kallerhoff

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The potential of fungal isolates to be used in phytoremediation of widespread lead contaminated soil has been evaluated in this study. Five different fungal isolates (Trichoderma harzianum, Penicillium simplicissimum, Aspergillus flavus, Aspergillus niger and Mucor spp.) were obtained and tested for their tolerance to increasing concentration of lead (Pb) i.e. 100, 200, 300, 400 and 500 mgL-1 on PDA and PDB culture experiment. All strains were tolerant up to 500 mgL-1 following sequence; A. flavus > A. niger > Mucor spp. > P. simplicissimum > T. harzianum. Further the isolates were then monitored for possible effect on Pb solubility/mobility through soil incubation experiments and characterized for essays including pathogenicity, germination and root elongation and plant growth promoting activities including IAA (indole acetic acid), phosphorus solubilization and gibberellic acid (GA3) production. Results revealed that fungal isolates have positive effect on Pb mobility in soil and plant biomass production. Pb solubility was significantly (P> 0.05) increased in soil upon application of Mucor spp. P. simplicissimum and T. harzianum. when compared to control. Among different strains three isolates (Mucor spp., P. simplicissimum and T. harzianum) were nonpathogenic because no inhibitory effect of fungus was observed to plant growth when exposed to these strains in root shoot elongation essay. Particularly T. harzianum and P. simplicissimum showed great ability to increase root length by 1.1 and 1.3 folds and shoot length by 1.47 and 1.5 folds respectively under Pb stress (500 mgL-1). Significantly high production of IAA was observed in A. niger (26.7 μg/ml), Phosphorus solubilization was observed in T. harzianum (9.15 μg/ml) and GA3 production was observed in P. simplicissimum (11.02 μg/ml). From results it is concluded that Mucor spp., P. simplicissimum and T. harzianum have potential to increase Pb mobility and improving plant growth under highy Pb contamination, therefore can be used in microbially assisted phytoremediation of Pb contaminated soil.

Keywords: Pb tolerant fungus, Pb mobility, plant growth promoting activities, indole acetic acid (IAA)

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Authors: Mei-Ying Huang, Huei-Jen Ju, Liang-Wei Tseng, Chin-Jung Hsu

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The aim of this study was to investigate a probiotic, Leuconostoc mesenteroides B4, and its products, isomaltooligosaccharide and dextran, on growth performance, digestive enzymes, immune responses, and pathogen resistance of spotted grouper Epinephelus coioides. The grouper were fed control and diets supplemented with L. mesenteroides B4 (107 CFU/g), isomaltooligosaccharide (0.15%), isomaltooligosaccharide (0.15%) + L. mesenteroides B4 (107 CFU/g) (I + B4), and dextran (0.15%) + L. mesenteroides B4 (107 CFU/g) (D + B4) for 8 weeks. The result showed that final weights and percent weight gains of the grouper fed diets supplemented with L. mesenteroides B4 and I + B4 were significantly higher than that of the control group (p < 0.05). The activities of digestive enzymes in the grouper fed with I + B4 were significantly higher than the control group (p < 0.05), too. After challenge with Vibrio harveyi, the enzyme activities of antiprotease and lysozyme as well as of respiratory burst of the fish fed with I + B4 and D + B4 were significantly higher than that of the control group (p < 0.05). The grouper fed with the both diets also had higher survival rates than that of the control group after the challenge. Overall, the study indicated that feeding diets supplemented with L. mesenteroides B4, and its products, isomaltooligosaccharide, and dextran could be an effective method for enhancing the growth performance and disease resistance in orange-spotted grouper.

Keywords: orange-spotted grouper, probiotic Leuconostoc mesenteroides B4, isomaltooligosaccharide, dextran, growth performance, pathogen resistance

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Authors: Ronit Chakraborty, Sugata Banerji

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There has been a marked increase in the reported prevalence of Autism Spectrum Disorder (ASD) among children in the US over the past two decades. This research has analyzed the growth in state-level ASD prevalence against 45 different potentially explanatory factors, including socio-economic, demographic, healthcare, public policy, and political factors. The goal was to understand if these factors have adequate predictive power in modeling the differential growth in ASD prevalence across various states and if they do, which factors are the most influential. The key findings of this study include (1) the confirmation that the chosen feature set has considerable power in predicting the growth in ASD prevalence, (2) the identification of the most influential predictive factors, (3) given the nature of the most influential predictive variables, an indication that a considerable portion of the reported ASD prevalence differentials across states could be attributable to over and under diagnosis, and (4) identification of Florida as a key outlier state pointing to a potential under-diagnosis of ASD there.

Keywords: autism spectrum disorder, clustering, machine learning, predictive modeling

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Authors: Athanasia Eirini Zarkadi, Despoina Balli, Olga Elpis Kolokitha

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Objective: Impacted maxillary canines are a frequent condition and a common reason for patients seeking orthodontic treatment. Their simultaneous presence with dental anomalies raises a question about their possible connection. The aim of this study was to investigate the association of maxillary impacted canines with dental anomalies. Materials and Methods: Files of 874 patients from an orthodontic private practice in Greece were evaluated for the presence of maxillary impacted canines. From this sample, a group of 97 patients (39 males and 58 females) with at least one impacted maxillary canine were selected and consisted of the study group (canine impaction group) of this study. This group was compared to a control group of 97 patients (42 males and 55 females) that was created by random selection from the initial sample without maxillary canine impaction. The impaction diagnosis was made from the panoramic radiographs and confirmed from the surgery. The association between maxillary canine impaction and dental anomalies was examined with the chi-square test. A classification tree was created to further investigate the relations between impaction and dental anomalies. The reproducibility of diagnoses was assessed by re-examining the records of 25 patients two weeks after the first examination. Results: The found associated anomalies were cone-shaped upper lateral incisors and infraocclusion of deciduous molars. There is a significant increase in the prevalence of 12,4% of distal displacement of the unerupted mandibular second premolar in the canine impaction group compared to the control group that was 7,2%. The classification tree showed that the presence of a cone-shaped maxillary lateral incisor gave rise to the probability of an impacted canine to 83,3%. Conclusions: The presence of cone-shaped maxillary lateral incisors and infraocclusion of deciduous molars can be considered valuable early risk indicators for maxillary canine impaction.

Keywords: cone-shaped maxillary lateral incisors, dental anomalies, impacted canines, infraoccluded deciduous molars

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Authors: Akhil Teja Kambhampati, Mark A. Hoffman

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In the realm of infectious disease research, airborne viral transmission stands as a paramount concern due to its pivotal role in propagating pathogens within densely populated regions. However, amidst this landscape, the phenomenon of hygroscopic growth within respiratory bioaerosols remains relatively underexplored. Unlike pure water aerosols, the unique composition of respiratory bioaerosols leads to varied evaporation rates and hygroscopic growth patterns, influenced by factors such as ambient humidity, temperature, and airflow. This study addresses this gap by focusing on the behaviors of single respiratory bioaerosol utilizing salinity to induce saliva-like hygroscopic behavior. By employing mass, momentum, and energy equations, the study unveils the intricate interplay between evaporation and hygroscopic growth over time. The numerical model enables temporal analysis of bioaerosol characteristics, including size, temperature, and trajectory. The analysis reveals that due to evaporation, there is a reduction in initial size, which shortens the lifetime and distance traveled. However, when hygroscopic growth begins to influence the bioaerosol size, the rate of size reduction slows significantly. The interplay between evaporation and hygroscopic growth results in bioaerosol size within the inhalation range of humans and prolongs the traveling distance. Findings procured from the analysis are crucial for understanding the spread of infectious diseases, especially in high-risk environments such as healthcare facilities and public transportation systems. By elucidating the nuanced behaviors of respiratory bioaerosols, this study seeks to inform the development of more effective preventative strategies against pathogens propagation in the air, thereby contributing to public health efforts on a global scale.

Keywords: airborne viral transmission, high-risk environments, hygroscopic growth, evaporation, numerical modeling, pathogen propagation, preventative strategies, public health, respiratory bioaerosols

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Authors: Abdullah Al Farwan, Ya Zhang

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In today’s educational arena, it is critical to understand educational data and be able to evaluate important aspects, particularly data on student achievement. Educational Data Mining (EDM) is a research area that focusing on uncovering patterns and information in data from educational institutions. Teachers, if they are able to predict their students' class performance, can use this information to improve their teaching abilities. It has evolved into valuable knowledge that can be used for a wide range of objectives; for example, a strategic plan can be used to generate high-quality education. Based on previous data, this paper recommends employing data mining techniques to forecast students' final grades. In this study, five data mining methods, Decision Tree, JRip, Naive Bayes, Multi-layer Perceptron, and Random Forest with wrapper feature selection, were used on two datasets relating to Portuguese language and mathematics classes lessons. The results showed the effectiveness of using data mining learning methodologies in predicting student academic success. The classification accuracy achieved with selected algorithms lies in the range of 80-94%. Among all the selected classification algorithms, the lowest accuracy is achieved by the Multi-layer Perceptron algorithm, which is close to 70.45%, and the highest accuracy is achieved by the Random Forest algorithm, which is close to 94.10%. This proposed work can assist educational administrators to identify poor performing students at an early stage and perhaps implement motivational interventions to improve their academic success and prevent educational dropout.

Keywords: classification algorithms, decision tree, feature selection, multi-layer perceptron, Naïve Bayes, random forest, students’ academic performance

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Authors: Zohreh Bang Tavakoli, Shuktika Chatterjee

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This study investigates the fiscal policy impact on countries’ economic growth in developing countries with a different external debt level. The fiscal policy effectiveness has been re-emphasized in the global financial crisis of 2008 with the external debt as its new contemporary driver (Ruščáková and Semančíková, 2016). According to Bouakez, (2014 ) different theories have proposed the economic consequence of fiscal policy, specifically for developing countries. However, fiscal policy literature is lacking research regarding the fiscal policy’s effectiveness with the external debt’s contributions through comprehensive study (Canh, 2018). Also, according to scholars, high levels of external debt will influence economic growth. First, through foreign resources and channel of investment in which high level of debt decreases the amount of foreign investment in the developing countries. Second, through the deterioration of foreign investors and fiscal policies related to a high level of debt (Cordella, et.al., 2010). Therefore, this study proposed that only countries with a low external debt level and appropriate fiscal policies and good quality institutions can gain the proper quantity and quality of foreign investors, which will help the economic growth. For this, this research is examining the impact of fiscal policy on developing countries' economic growth in the situation of different external debt levels.

Keywords: fiscal policy, external debt, gross domestic product, developing countries

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Authors: Tamar Khardziani, Violeta Berikashvili, Mariam Rusitashvili, Eva Kachlishvili, Vladimir Elisashvili, Mikheil Asatiani

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Last years, the search for natural sources of novel and effective antifungal substances became a scientific and technological challenge. In the present research, thirty basidiomycetes isolated from various ecological niches of Georgia and belonging to different taxonomic groups were screened for their antifungal activities against pathogenic fungi such as Aspergillus, Fusarium, and Guignardia bidwellii. Among mushroom tested, several potential producers of antifungal substances have been revealed, such as Schizophyllum commune, Lentinula edodes, Ganoderma abietinum, Fomes fomentarius, Hericium erinaceus, and Trametes versicolor. For mushroom cultivation and expression of antifungal potential, submerged and solid-state fermentations of different plant raw materials were performed and various approaches and strategies have been exploited. Sch. commune appeared as a most promising producer of antifungal compounds. It was established that among different agro-industrial wastes, the presence of mandarin juice production waste in a nutrient medium, causing the significant increase of antifungal activity Sch. commune (growth inhibition: Aspergillus – 59 %, Fusarium – 55 %, G. bidwellii – 78 %, after 3, 2 and 4 days of cultivation, respectively). Besides this, Sch. commune demonstrate similar antifungal activities in the presence of glucose, glycerol, maltose, mannitol, and xylose, and growth inhibition of Fusarium ranged in 41 % - 49 % during 6 days of cultivation. Inhibition of Aspergillus growth inhibition varied in 27 % - 36 %, and inhibition of G. bidwellii was in the range 49 % - 61 %, respectively. Sch. commune under solid-state fermentation of mandarin peels at 13 days of cultivation demonstrates powerful growth inhibition of pathogenic fungi (growth inhibition: Aspergillus – 50 %, Fusarium – 61 %, G. bidwellii – 68 %, after 3, 4, and 4 days of cultivation, respectively) as well as at 20 days old mushroom (growth inhibition: Aspergillus – 41 %, Fusarium – 54 %, G. bidwellii – 66 %, after 3 days of cultivation). It was established that Sch. commune was effective as a producer of antifungal compounds in submerged as well as in solid-state fermentation. Finally, performed study confirms that the higher basidiomycetes possess antifungal potential, which strongly depends on the physiological factors of growth. Acknowledgments: The work was implemented with the financial support of fundamental science project FR-19-3719 by the Shota Rustaveli National Science Foundation of Georgia.

Keywords: antifungal potential, higher basidiomycetes, pathogenic fungi, submerged and solid-state fermentation

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Authors: Chayakarn Bamrungbutr, James Sillitoe

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The term ‘shadow port’ is used to describe a port whose markets are dominated by an adjacent port that has a more competitive capability. Recently, researchers have put effort into studying the mechanisms of how a regional port, in the shadow of a nearby predominant port which is a capital city port, can compete and grow. However, such mechanism is still unclear. This study thus focuses on understanding the growth of shadow port and the type of shadow port by using the two capital city ports of Thailand; Bangkok port (the former main port) and Laem Chabang port (the current main port), as the case study. By developing an understanding of the mechanisms of shadow, port could ultimately lead to an increase in the competitiveness. In this study, a framework of opportunity capture (introduced by Magala, 2004) will be used to create a framework for the study of the growth of the selected shadow port. In the process of building this framework, five groups of port development experts, consisting of government, council, academia, logistics provider and industry, will be interviewed. To facilitate this work, the Noticing, Collecting and Thinking model which was developed by Seidel (1998) will be used in an analysis of the dataset. The resulting analysis will be used to classify the type of shadow port. The type of these ports will be a significant factor for developing a feasible strategic guideline for the future management planning of ports, particularly, shadow ports, and then to increase the competitiveness of a nation’s maritime transport industry, and eventually lead to a boost in the national economy.

Keywords: shadow port, Bangkok Port, Laem Chabang Port, port growth

Procedia PDF Downloads 177

Authors: Ugbor Raphael Oluchukwu

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The survey research design was adopted to examine whether liquidity and cash management practices of owner-managed firms in South East Nigeria influence their profitability, growth and survival. Four independent variables (accounting systems, working capital management, budgetary control, and managerial planning) were used in the evaluation which was restricted to eight small firms. Results indicate that one variable, working capital management alone dominate the liquidity perception of owner managers. As a result, owner managers find it difficult to meet maturing business obligations as growth sets in. The study also reveals that the four independent variables have significant impact on the profitability, growth and survival of owner managed firms. Owner managers are therefore advised to undertake regular entrepreneurship training in order to upgrade their liquidity and cash management knowledge and practices to enhance their overall performance.

Keywords: liquidity management, owner-managed firm, profitability, survival

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Authors: Serier Bouchenak NORA, Bouguerni ABDELMADJID

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Algeria is a Mediterranean country which provides an ideal habitat for a wide range of species of medicinal plants. The objective of this current work is to extract the gel contained in the leaves of Aloe vera in order to formulate an oral gel as a prebiotic and see its effects on probiotics (lactic and pseudo lactic bacteria and bifido bacterium). Aloe vera polysaccharid extract is a matrix mainly composed of non-digestible oligosaccharids or slow-fermentation polysaccharids, as this produces a lower pH. The behavior of Aloe vera during in vitro fermentation of the colon was similar to that of lactulose, indicating the possibility of using Aloe vera and its polysaccharids extracts as a prebiotic. The microbiological control of the two kinds of bacteria (bifidobacteria and staphylococci) has demonstrated the gel capacity to stimulate them by these bioactive compounds. The generation time of Bifidobacteria in fermented milk with added prebiotic Aloe vera gel is 80.408 min with a µ growth rate equal to 0.012 min -1. The doubling time is 61.459 min with a growth rate µ equal to 0.016 min -1 for the Streptococcus sp. species.

Keywords: aloe vera, probiotics, prebiotics, growth rate, bifidobacteria

Procedia PDF Downloads 69

Authors: Sami Ali Metwally, Bedour Helmy Abou-Leila, Hussien Ibrahim Abdel-Shafy

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This study was carried out at the pilot plant area in the National Research Centre during the two successive seasons, 2020 and 2022. The aim is to investigate the response of vegetative growth and chemical constituents of sunflowers plants irrigated by two types of wastewater, namely: black wastewater W1 (Bathroom) and grey wastewater W1, under irradiation conditions of helium-neon (He-Ne) laser. The examined data indicated that irrigation of W1 significantly increased the growth and flowering parameters (plant height, leaves number, leaves area, leaves fresh and dry weight, flower diameter, flower stem length, flower stem thickness, number of days to flower, and total chlorophyll). Treated sunflower plants with 0 to 10 min. recorded an increase in the fresh weight and dry weight of leaves. However, the superiority of increasing vase life and delaying flowers were recorded by prolonging exposure time by up to 10 min. Regarding the effect of interaction treatments, the data indicated that the highest values on almost growth parameters were obtained from plants treated with W1+0 laser followed by W2+10 min. laser, compared with all interaction treatments. As for flowering parameters, the interactions between W2+2 min. time exposure, W1+0 time, w1+10 min., and w1+2 min. exposures recorded the highest values on flower diameter, flower stem length, flower stem thickness, vase life, and delaying flowering.

Keywords: greywater, sunflower plant, water reuse, vegetative growth, laser radiation

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Authors: G. Singer, M. Golan

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Providing students with learning disabilities (LD) with extra time to grant them equal access to the exam is a necessary but insufficient condition to compensate for their LD; there should also be a clear indication that the additional time was actually used. For example, if students with LD use more time than students without LD and yet receive lower grades, this may indicate that a different accommodation is required. If they achieve higher grades but use the same amount of time, then the effectiveness of the accommodation has not been demonstrated. The main goal of this study is to evaluate the effect of including parameters related to LD and extended exam time, along with other commonly-used characteristics (e.g., student background and ability measures such as high-school grades), on the ability of ordinal decision-tree algorithms to predict exam performance. We use naturally-occurring data collected from hundreds of undergraduate engineering students. The sub-goals are i) to examine the improvement in prediction accuracy when the indicator of exam performance includes 'actual time used' in addition to the conventional indicator (exam grade) employed in most research; ii) to explore the effectiveness of extended exam time on exam performance for different courses and for LD students with different profiles (i.e., sets of characteristics). This is achieved by using the patterns (i.e., subgroups) generated by the algorithms to identify pairs of subgroups that differ in just one characteristic (e.g., course or type of LD) but have different outcomes in terms of exam performance (grade and time used). Since grade and time used to exhibit an ordering form, we propose a method based on ordinal decision-trees, which applies a weighted information-gain ratio (WIGR) measure for selecting the classifying attributes. Unlike other known ordinal algorithms, our method does not assume monotonicity in the data. The proposed WIGR is an extension of an information-theoretic measure, in the sense that it adjusts to the case of an ordinal target and takes into account the error severity between two different target classes. Specifically, we use ordinal C4.5, random-forest, and AdaBoost algorithms, as well as an ensemble technique composed of ordinal and non-ordinal classifiers. Firstly, we find that the inclusion of LD and extended exam-time parameters improves prediction of exam performance (compared to specifications of the algorithms that do not include these variables). Secondly, when the indicator of exam performance includes 'actual time used' together with grade (as opposed to grade only), the prediction accuracy improves. Thirdly, our subgroup analyses show clear differences in the effect of extended exam time on exam performance among different courses and different student profiles. From a methodological perspective, we find that the ordinal decision-tree based algorithms outperform their conventional, non-ordinal counterparts. Further, we demonstrate that the ensemble-based approach leverages the strengths of each type of classifier (ordinal and non-ordinal) and yields better performance than each classifier individually.

Keywords: actual exam time usage, ensemble learning, learning disabilities, ordinal classification, time extension

Procedia PDF Downloads 100

Authors: Garry Gorman, Nigel McKelvey, James Connolly

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This study will evaluate a growth mindset intervention with Junior Cycle Coding and Senior Cycle Computer Science students in Ireland, where gamification will be used to incentivise growth mindset behaviour. An artificial intelligence (AI) driven personalised learning system will be developed to present computer programming learning tasks in a manner that is best suited to the individuals’ own learning preferences while incentivising and rewarding growth mindset behaviour of persistence, mastery response to challenge, and challenge seeking. This research endeavours to measure mindset with before and after surveys (conducted nationally) and by recording growth mindset behaviour whilst playing a digital game. This study will harness the capabilities of AI and aims to determine how a personalised learning (PL) experience can impact the mindset of a broad range of students. The focus of this study will be to determine how personalising the learning experience influences female and disadvantaged students' sense of belonging in the computer science classroom when tasks are presented in a manner that is best suited to the individual. Whole Brain Learning will underpin this research and will be used as a framework to guide the research in identifying key areas such as thinking and learning styles, cognitive potential, motivators and fears, and emotional intelligence. This research will be conducted in multiple school types over one academic year. Digital games will be played multiple times over this period, and the data gathered will be used to inform the AI algorithm. The three data sets are described as follows: (i) Before and after survey data to determine the grit scores and mindsets of the participants, (ii) The Growth Mind-Set data from the game, which will measure multiple growth mindset behaviours, such as persistence, response to challenge and use of strategy, (iii) The AI data to guide PL. This study will highlight the effectiveness of an AI-driven personalised learning experience. The data will position AI within the Irish educational landscape, with a specific focus on the teaching of CS. These findings will benefit coding and computer science teachers by providing a clear pedagogy for the effective delivery of personalised learning strategies for computer science education. This pedagogy will help prevent students from developing a fixed mindset while helping pupils to exhibit persistence of effort, use of strategy, and a mastery response to challenges.

Keywords: computer science education, artificial intelligence, growth mindset, pedagogy

Procedia PDF Downloads 87

Authors: J. Dharmaraj, C. Gunasekaran

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Invertebrates are the reliable ecological indicators of disturbance of the forest ecosystems and they respond to environment changes more quickly than other fauna. Among these the terrestrial invertebrates are vital to functioning ecosystems, contributing to processes such as decomposition, nutrient cycling and soil fertility. The natural ecosystems of the forests have been subject to various types of disturbances, which lead to decline of flora and fauna. The comparative diversity of micro-arthropods in natural forest, wattle plantation and eucalyptus plantations were studied in Nilgiris. The study area was divided in to five major sites (Emerald (Site-I), Thalaikundha (Site-II), Kodapmund (Site-III), Aravankad (Site-IV), Kattabettu (Site-V). The research was conducted during period from March 2014 to August 2014. The leaf and soil samples were collected and isolated by using Berlese funnel extraction methods. Specimens were isolated and identified according to their morphology (Balogh 1972). In the present study results clearly showed the variation in soil pH, NPK (Major Nutrients) and organic carbon among the study sites. The chemical components of the leaf litters of the plantation decreased the diversity of micro-arthropods and decomposition rate leads to low amount of carbon and other nutrients present in the soil. Moreover eucalyptus and wattle plantations decreases the availability of the ground water source to other plantations and micro-arthropods and hences affects the soil fertility. Hence, the present study suggests to minimize the growth of wattle and eucalyptus tree plantations in the natural areas which may help to reduce the decline of forests.

Keywords: micro-arthropods, assemblage, berlese funnel, morphology, NPK, nilgiris

Procedia PDF Downloads 308

Authors: Teerada Apibunyopas, Nithinant Thammakoranonta

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Many organizations bring e-Learning to use as a tool in their training and human development department. It is getting more popular because it is easy to access to get knowledge all the time and also it provides a rich content, which can develop the employees skill efficiently. This study focused on the factors that affect using e-Learning efficiently, so it will make job satisfaction increased. The questionnaires were sent to employees in large commercial banks, which use e-Learning located in Bangkok, the results from multiple linear regression analysis showed that employee’s characteristics, characteristics of e-Learning, learning and growth have influence on job satisfaction.

Keywords: e-Learning, job satisfaction, learning and growth, Bangkok

Procedia PDF Downloads 491