Search results for: analytical modeling

Authors: Zahid Ullah, Atlas Khan

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The rapid advancements in data collection, storage, and processing capabilities have led to an explosion of data in various domains. In this era of big data, mathematical sciences play a crucial role in uncovering valuable insights and driving informed decision-making through data analytics. The purpose of this abstract is to present the latest advances in mathematical sciences and their application in harnessing the power of data analytics. This abstract highlights the interdisciplinary nature of data analytics, showcasing how mathematics intersects with statistics, computer science, and other related fields to develop cutting-edge methodologies. It explores key mathematical techniques such as optimization, mathematical modeling, network analysis, and computational algorithms that underpin effective data analysis and interpretation. The abstract emphasizes the role of mathematical sciences in addressing real-world challenges across different sectors, including finance, healthcare, engineering, social sciences, and beyond. It showcases how mathematical models and statistical methods extract meaningful insights from complex datasets, facilitating evidence-based decision-making and driving innovation. Furthermore, the abstract emphasizes the importance of collaboration and knowledge exchange among researchers, practitioners, and industry professionals. It recognizes the value of interdisciplinary collaborations and the need to bridge the gap between academia and industry to ensure the practical application of mathematical advancements in data analytics. The abstract highlights the significance of ongoing research in mathematical sciences and its impact on data analytics. It emphasizes the need for continued exploration and innovation in mathematical methodologies to tackle emerging challenges in the era of big data and digital transformation. In summary, this abstract sheds light on the advances in mathematical sciences and their pivotal role in unveiling the power of data analytics. It calls for interdisciplinary collaboration, knowledge exchange, and ongoing research to further unlock the potential of mathematical methodologies in addressing complex problems and driving data-driven decision-making in various domains.

Keywords: mathematical sciences, data analytics, advances, unveiling

Procedia PDF Downloads 93

Authors: Levicatus Mugenyi, Joaniter Nankabirwa, Emmanuel Arinaitwe, John Rek, Niel Hens, Moses Kamya, Grant Dorsey

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Background: Indoor residual spraying (IRS) reduces vector densities and malaria transmission, however, the most effective spraying intervals for IRS have not been well established. We aim to estimate the optimal timing interval for IRS using a modeling approach. Methods: We use a generalized additive model to estimate the optimal timing interval for IRS using the predicted malaria incidence. The model is applied to post IRS cohort clinical data from children aged 0.5–10 years in selected households in Tororo, historically a high malaria transmission setting in Uganda. Six rounds of IRS were implemented in Tororo during the study period (3 rounds with bendiocarb: December 2014 to December 2015, and 3 rounds with actellic: June 2016 to July 2018). Results: Monthly incidence of malaria from October 2014 to February 2019 decreased from 3.25 to 0.0 per person-years in the children under 5 years, and 1.57 to 0.0 for 5-10 year-olds. The optimal time interval for IRS differed between bendiocarb and actellic and by IRS round. It was estimated to be 17 and 40 weeks after the first round of bendiocarb and actellic, respectively. After the third round of actellic, 36 weeks was estimated to be optimal. However, we could not estimate from the data the optimal time after the second and third rounds of bendiocarb and after the second round of actellic. Conclusion: We conclude that to sustain the effect of IRS in a high-medium transmission setting, the second rounds of bendiocarb need to be applied roughly 17 weeks and actellic 40 weeks after the first round, and the timing differs for subsequent rounds. The amount of rainfall did not influence the trend in malaria incidence after IRS, as well as the IRS timing intervals. Our results suggest that shorter intervals for the IRS application can be more effective compared to the current practice, which is about 24 weeks for bendiocarb and 48 weeks for actellic. However, when considering our findings, one should account for the cost and drug resistance associated with IRS. We also recommend that the timing and incidence should be monitored in the future to improve these estimates.

Keywords: incidence, indoor residual spraying, generalized additive model, malaria

Procedia PDF Downloads 121

Authors: Elena Vinogradova, Aleksei Pleshakov, Aleksei Yakovlev

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During the flight of a supersonic aircraft under various conditions (altitude, Mach, etc.), it becomes necessary to coordinate the operating modes of the air intake and engine. On the supersonic aircraft, it’s been done by changing various control factors (the angle of rotation of the wedge panels and etc.). This paper investigates the possibility of using modern optimization methods to determine the optimal position of the supersonic air intake wedge panels in order to maximize the total pressure recovery coefficient. Modern software allows us to conduct auto-optimization, which determines the optimal position of the control elements of the investigated product to achieve its maximum efficiency. In this work, the flow in the supersonic aircraft inlet has investigated and optimized the operation of the flaps of the supersonic inlet in an aircraft in a 2-D setting. This work has done using ANSYS CFX software. The supersonic aircraft inlet is a flat adjustable external compression inlet. The braking surface is made in the form of a three-stage wedge. The IOSO NM software package was chosen for optimization. Change in the position of the panels of the input device is carried out by changing the angle between the first and second steps of the three-stage wedge. The position of the rest of the panels is changed automatically. Within the framework of the presented work, the position of the moving air intake panel was optimized under fixed flight conditions of the aircraft under a certain engine operating mode. As a result of the numerical modeling, the distribution of total pressure losses was obtained for various cases of the engine operation, depending on the incoming flow velocity and the flight altitude of the aircraft. The results make it possible to obtain the maximum total pressure recovery coefficient under given conditions. Also, the initial geometry was set with a certain angle between the first and second wedge panels. Having performed all the calculations, as well as the subsequent optimization of the aircraft input device, it can be concluded that the initial angle was set sufficiently close to the optimal angle.

Keywords: optimal angle, optimization, supersonic air intake, total pressure recovery coefficient

Procedia PDF Downloads 242

Authors: Trevor Toy, Josef Langerman

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Around a quarter of the world’s data is generated by financial with an estimated 708.5 billion global non-cash transactions reached between 2018 and. And with Open Banking still a rapidly developing concept within the financial industry, there is an opportunity to create a secure mechanism for connecting its stakeholders to openly, legitimately and consensually share the data required to enable it. Integration and data sharing of anonymised transactional data are still operated in silos and centralised between the large corporate entities in the ecosystem that have the resources to do so. Smaller fintechs generating data and businesses looking to consume data are largely excluded from the process. Therefore there is a growing demand for accessible transactional data for analytical purposes and also to support the rapid global adoption of Open Banking. The following research has provided a solution framework that aims to provide a secure decentralised marketplace for 1.) data providers to list their transactional data, 2.) data consumers to find and access that data, and 3.) data subjects (the individuals making the transactions that generate the data) to manage and sell the data that relates to themselves. The platform also provides an integrated system for downstream transactional-related data from merchants, enriching the data product available to build a comprehensive view of a data subject’s spending habits. A robust and sustainable data market can be developed by providing a more accessible mechanism for data producers to monetise their data investments and encouraging data subjects to share their data through the same financial incentives. At the centre of the platform is the market mechanism that connects the data providers and their data subjects to the data consumers. This core component of the platform is developed on a decentralised blockchain contract with a market layer that manages transaction, user, pricing, payment, tagging, contract, control, and lineage features that pertain to the user interactions on the platform. One of the platform’s key features is enabling the participation and management of personal data by the individuals from whom the data is being generated. This framework developed a proof-of-concept on the Etheruem blockchain base where an individual can securely manage access to their own personal data and that individual’s identifiable relationship to the card-based transaction data provided by financial institutions. This gives data consumers access to a complete view of transactional spending behaviour in correlation to key demographic information. This platform solution can ultimately support the growth, prosperity, and development of economies, businesses, communities, and individuals by providing accessible and relevant transactional data for big data analytics and open banking.

Keywords: big data markets, open banking, blockchain, personal data management

Procedia PDF Downloads 73

Authors: Hafsa Kamran, Asma Afreen, Zaheer Ahmed

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Internet addiction, an emerging mental health disorder from last two decades, is manifested by the inability in the controlled use of internet leading to academics, social, physiological and/or psychological difficulties. The present study aimed to assess the levels of internet addiction among university students in Lahore and to explore the effects of internet addiction on their dietary behavior and lifestyle. It was an analytical cross-sectional study. Data was collected from October to December 2016 from students of four universities selected through two-stage sampling method. The numbers of participants were 500 and 13 questionnaires were rejected due to incomplete information. Levels of Internet Addiction (IA) were calculated using Young Internet Addiction Test (YIAT). Data was also collected on students’ demographics, lifestyle factors and dietary behavior using self-reported questionnaire. Data was analyzed using SPSS (version 21). Chi-square test was applied to evaluate the relationship between variables. Results of the study revealed that 10% of the population had severe internet addiction while moderate Internet Addiction was present in 42%. High prevalence was found among males (11% vs. 8%), private sector university students (p = 0.008) and engineering students (p = 0.000). The lifestyle habits of internet addicts were significantly of poorer quality than normal users (p = 0.05). Internet addiction was found associated with lesser physically activity (p = 0.025), had shorter duration of physical activity (p = 0.016), had more disorganized sleep pattern (p = 0.023), had less duration of sleep (p = 0.019), reported being more tired and sleepy in class (p = 0.033) and spending more time on internet as compared to normal users. Severe and moderate internet addicts also found to be more overweight and obese than normal users (p = 0.000). The dietary behavior of internet addicts was significantly poorer than normal users. Internet addicts were found to skip breakfast more than a normal user (p = 0.039). Common reasons for meal skipping were lack of time and snacking between meals (p = 0.000). They also had increased meal size (p = 0.05) and habit of snacking while using the internet (p = 0.027). Fast food (p = 0.016) and fried items (p = 0.05) were most consumed snacks, while carbonated beverages (p = 0.019) were most consumed beverages among internet addicts. Internet Addicts were found to consume less than recommended daily servings of dairy (p = 0.008) and fruits (p = 0.000) and more servings of meat group (p = 0.025) than their no internet addict counterparts. In conclusion, in this study, it was demonstrated that internet addicts have unhealthy dietary behavior and inappropriate lifestyle habits. University students should be educated regarding the importance of balanced diet and healthy lifestyle, which are critical for effectual primary prevention of numerous chronic degenerative diseases. Furthermore, it is necessary to raise awareness concerning adverse effects of internet addiction among youth and their parents.

Keywords: dietary behavior, internet addiction, lifestyle, university students

Procedia PDF Downloads 201

Authors: Dmitry Aron

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Major radiation accidents carry not only the potential risks of negative consequences for public health due to exposure but also because of large-scale emergency measures were taken by authorities to protect the population, which can lead to unreasonable social and economic damage. It is technically difficult, as a rule, to assess the possible costs and damages from decisions on evacuation or resettlement of residents in the shortest possible time, since it requires specially prepared information systems containing relevant information on demographic, economic parameters and incoming data on radiation conditions. Foreign observers also face the difficulties in assessing the consequences of an accident in a foreign territory, since they usually do not have official and detailed statistical data on the territory of foreign state beforehand. Also, they can suppose the application of unofficial data from open Internet sources is an unreliable and overly labor-consuming procedure. This paper describes an approach to prompt creation of relational database that contains detailed actual data on economics, demographics and radiation situation at the Fukushima Prefecture during the Fukushima Daiichi NPP accident, received by the author from open Internet sources. This database was developed and used to assess the number of evacuated population, radiation doses, expected financial losses and other parameters of the affected areas. The costs for the areas with temporarily evacuated and long-term resettled population were investigated, and the radiological and economic effectiveness of the measures taken to protect the population was estimated. Some of the results are presented in the article. The study showed that such a tool for analyzing the consequences of radiation accidents can be prepared in a short space of time for the entire territory of Japan, and it can serve for the modeling of social and economic consequences for hypothetical accidents for any nuclear power plant in its territory.

Keywords: Fukushima, radiation accident, emergency measures, database

Procedia PDF Downloads 191

Authors: Afsaneh Mohammadnejad, Marianne Nygaard, Jan Baumbach, Shuxia Li, Weilong Li, Jesper Lund, Jacob v. B. Hjelmborg, Lene Christensen, Qihua Tan

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Cognitive impairment in the elderly is a key issue affecting the quality of life. Despite a strong genetic background in cognition, only a limited number of single nucleotide polymorphisms (SNPs) have been found. These explain a small proportion of the genetic component of cognitive function, thus leaving a large proportion unaccounted for. We hypothesize that one reason for this missing heritability is the misspecified modeling in data analysis concerning phenotype distribution as well as the relationship between SNP dosage and the phenotype of interest. In an attempt to overcome these issues, we introduced a model-free method based on the generalized correlation coefficient (GCC) in a genome-wide association study (GWAS) of cognitive function in twin samples and compared its performance with two popular linear regression models. The GCC-based GWAS identified two genome-wide significant (P-value < 5e-8) SNPs; rs2904650 near ZDHHC2 on chromosome 8 and rs111256489 near CD6 on chromosome 11. The kinship model also detected two genome-wide significant SNPs, rs112169253 on chromosome 4 and rs17417920 on chromosome 7, whereas no genome-wide significant SNPs were found by the linear mixed model (LME). Compared to the linear models, more meaningful biological pathways like GABA receptor activation, ion channel transport, neuroactive ligand-receptor interaction, and the renin-angiotensin system were found to be enriched by SNPs from GCC. The GCC model outperformed the linear regression models by identifying more genome-wide significant genetic variants and more meaningful biological pathways related to cognitive function. Moreover, GCC-based GWAS was robust in handling genetically related twin samples, which is an important feature in handling genetic confounding in association studies.

Keywords: cognition, generalized correlation coefficient, GWAS, twins

Procedia PDF Downloads 124

Authors: Carolina Mayen Huerta, Clara Ivanescu, Paloma Marcos

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Due to their unique geographic challenges and the imperative to combat climate change, Small Island Developing States (SIDS) are experiencing rapid growth in the renewable energy (RE) sector. However, women's representation in formal employment within this burgeoning field remains significantly lower than their male counterparts. Conventional methodologies often overlook critical geographic data that influence women's job prospects. To address this gap, this paper introduces a Multicriteria Evaluation (MCE) framework designed to identify spatially enabling environments and restrictions affecting women's access to formal employment and business opportunities in the SIDS' RE sector. The proposed MCE framework comprises 24 key factors categorized into four dimensions: Individual, Contextual, Accessibility, and Place Characterization. "Individual factors" encompass personal attributes influencing women's career development, including caregiving responsibilities, exposure to domestic violence, and disparities in education. "Contextual factors" pertain to the legal and policy environment, influencing workplace gender discrimination, financial autonomy, and overall gender empowerment. "Accessibility factors" evaluate women's day-to-day mobility, considering travel patterns, access to public transport, educational facilities, RE job opportunities, healthcare facilities, and financial services. Finally, "Place Characterization factors" enclose attributes of geographical locations or environments. This dimension includes walkability, public transport availability, safety, electricity access, digital inclusion, fragility, conflict, violence, water and sanitation, and climatic factors in specific regions. The analytical framework proposed in this paper incorporates a spatial methodology to visualize regions within countries where conducive environments for women to access RE jobs exist. In areas where these environments are absent, the methodology serves as a decision-making tool to reinforce critical factors, such as transportation, education, and internet access, which currently hinder access to employment opportunities. This approach is designed to equip policymakers and institutions with data-driven insights, enabling them to make evidence-based decisions that consider the geographic dimensions of disparity. These insights, in turn, can help ensure the efficient allocation of resources to achieve gender equity objectives.

Keywords: gender, women, spatial analysis, renewable energy, access

Procedia PDF Downloads 69

Authors: Carolina Mayen Huerta, Clara Ivanescu, Paloma Marcos

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Due to their unique geographic challenges and the imperative to combat climate change, Small Island Developing States (SIDS) are experiencing rapid growth in the renewable energy (RE) sector. However, women's representation in formal employment within this burgeoning field remains significantly lower than their male counterparts. Conventional methodologies often overlook critical geographic data that influence women's job prospects. To address this gap, this paper introduces a Multicriteria Evaluation (MCE) framework designed to identify spatially enabling environments and restrictions affecting women's access to formal employment and business opportunities in the SIDS' RE sector. The proposed MCE framework comprises 24 key factors categorized into four dimensions: Individual, Contextual, Accessibility, and Place Characterization. "Individual factors" encompass personal attributes influencing women's career development, including caregiving responsibilities, exposure to domestic violence, and disparities in education. "Contextual factors" pertain to the legal and policy environment, influencing workplace gender discrimination, financial autonomy, and overall gender empowerment. "Accessibility factors" evaluate women's day-to-day mobility, considering travel patterns, access to public transport, educational facilities, RE job opportunities, healthcare facilities, and financial services. Finally, "Place Characterization factors" enclose attributes of geographical locations or environments. This dimension includes walkability, public transport availability, safety, electricity access, digital inclusion, fragility, conflict, violence, water and sanitation, and climatic factors in specific regions. The analytical framework proposed in this paper incorporates a spatial methodology to visualize regions within countries where conducive environments for women to access RE jobs exist. In areas where these environments are absent, the methodology serves as a decision-making tool to reinforce critical factors, such as transportation, education, and internet access, which currently hinder access to employment opportunities. This approach is designed to equip policymakers and institutions with data-driven insights, enabling them to make evidence-based decisions that consider the geographic dimensions of disparity. These insights, in turn, can help ensure the efficient allocation of resources to achieve gender equity objectives.

Keywords: gender, women, spatial analysis, renewable energy, access

Procedia PDF Downloads 83

Authors: Oubelkacem Yacine, El Bast Hassan, El Bakkali Abdelmajid, Lamhasni Taibi, Ettakni Mahmoud, Ait Lyazidi Saadia, Haddad Mustapha, Ben-Ncer Abdelouahed, El Ferrane Mohammed, Boufarra Abdelkrim

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The present work relates to an on-site and completely non-invasive investigation of one of the most famous west Mediterranean Torah Scroll housed at the National Library of the Kingdom of Morocco. The scroll is 26 m long and consists of 143 parchment sheets of 59 cm x 19 cm, exhibiting only black writings; it is of unknown age. The artifact has been restored by the curator staff of the library. The investigation exploring separately the parchment support and the writing black ink aims at: i) the examination of the parchment conservation/degradation state, ii) the identification of the black ink and iii) the identification of the parchment handcrafting materials. For this purpose, the analyses have been based on combining all of elemental XRF and structural Raman, ATR-FT Infrared Red and Fiber Optical Reflectance spectroscopies, in addition to chroma-metric and pH measurements. pH measurements showing values around 6.5 are in concordance with the absence of any visual corrosion related to the parchment acidity. However, on the basis of the relative intensities and frequency shift of amid I (AI) and amid II (AII) vibrational bands of the collagen, ATR-FTIR spectra revealed diffuse hydrolysis and gelatinization of the parchment writing support; diffuse and non-homogeny degradation by gelatinization has been also confirmed by the IG gelatinization index deduced from the NIR bands on the FOR spectra. This IG index, defined as the ratio I (6860 cm-1) / I (6685 cm-1), ranges in the interval 0.98 – 1 and highlights collagen degradation at the molecular level. Sequentially Shifted Excitation Raman measurements (SSERS) crossed to X-ray fluorescence (XRF) ones on the black writings revealed that the black ink used is an iron-copper gall one, while FOR spectra are typical of pure metal gall inks. These later reflectance measurements exclude, thus, any intentional addition of carbon black to the ink recipe. Moreover, no lead white had been used while pre-drawing the writing lines. On another side, ATR-FTIR measurements highlighted the presence of oxalates as ink degradation products. Considering the parchment handcrafting, the combination of XRF and ATR-FTIR measurements led to the assumption that this writing support had been prepared according to ancient Middle East practices; the parchment infrared fingerprint seems identical to that of the Dead Sea scroll. The present multi-technical analyses are the first ones performed on an ancient Judaic written parchment of Morocco; it is under furthering. The investigation will be extended to other parchments belonging to the Jewish Cultural Heritage Museum of Morocco in Casablanca.

Keywords: torah scroll, parchment, black ink, non-invasive analyses, XRF/ATR-FTIR/RAMAN/FORS

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Authors: Nuri Onat, Omer Tatari, Gokhan Egilmez

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The U.S. buildings consume significant amount of energy and natural resources and they are responsible for approximately 40 % of the greenhouse gases emitted in the United States. Awareness of these environmental impacts paved the way for the adoption of green building movement. The green building movement is a rapidly increasing trend. Green Construction market has generated $173 billion dollars in GDP, supported over 2.4 million jobs, and provided $123 billion dollars in labor earnings. The number of LEED certified buildings is projected to be almost half of the all new, nonresidential buildings by 2015. National Science and Technology Council (NSTC) aims to increase number of net-zero energy buildings (NZB). The ultimate goal is to have all commercial NZB by 2050 in the US (NSTC 2008). Green Building Initiative (GBI) became the first green building organization that is accredited by American National Standards Institute (ANSI), which will also boost number of green buildings certified by Green Globes. However, there is much less focus on greening the residential buildings, although the environmental impacts of existing residential buildings are more than that of commercial buildings. In this regard, current research aims to model the residential green building movement with a dynamic model approach and assess the possible strategies to stabilize the carbon footprint of the U.S. residential building stock. Three aspects of sustainable development are considered in policy making, namely: high performance green building (HPGB) construction, NZB construction and building retrofitting. 19 different policy options are proposed and analyzed. Results of this study explored that increasing the construction rate of HPGBs or NZBs is not a sufficient policy to stabilize the carbon footprint of the residential buildings. Energy efficient building retrofitting options are found to be more effective strategies then increasing HPGBs and NZBs construction. Also, significance of shifting to renewable energy sources for electricity generation is stressed.

Keywords: green building movement, residential buildings, carbon footprint, system dynamics

Procedia PDF Downloads 428

Authors: Ben Khayut, Lina Fabri, Maya Avikhana

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The models of the modern Artificial Narrow Intelligence (ANI) cannot: a) independently and continuously function without of human intelligence, used for retraining and reprogramming the ANI’s models, and b) think, understand, be conscious, cognize, infer, and more in state of Uncertainty, and changes in situations, and environmental objects. To eliminate these shortcomings and build a new generation of Artificial Intelligence systems, the paper proposes a Conception, Model, and Method of Computational Psycholinguistic Cognitive Situational-Fuzzy Self-Controlled Brain and Mind System (CPCSFSCBMSUU) using a neural network as its computational memory, operating under uncertainty, and activating its functions by perception, identification of real objects, fuzzy situational control, forming images of these objects, modeling their psychological, linguistic, cognitive, and neural values of properties and features, the meanings of which are identified, interpreted, generated, and formed taking into account the identified subject area, using the data, information, knowledge, and images, accumulated in the Memory. The functioning of the CPCSFSCBMSUU is carried out by its subsystems of the: fuzzy situational control of all processes, computational perception, identifying of reactions and actions, Psycholinguistic Cognitive Fuzzy Logical Inference, Decision making, Reasoning, Systems Thinking, Planning, Awareness, Consciousness, Cognition, Intuition, Wisdom, analysis and processing of the psycholinguistic, subject, visual, signal, sound and other objects, accumulation and using the data, information and knowledge in the Memory, communication, and interaction with other computing systems, robots and humans in order of solving the joint tasks. To investigate the functional processes of the proposed system, the principles of Situational Control, Fuzzy Logic, Psycholinguistics, Informatics, and modern possibilities of Data Science were applied. The proposed self-controlled System of Brain and Mind is oriented on use as a plug-in in multilingual subject Applications.

Keywords: computational brain, mind, psycholinguistic, system, under uncertainty

Procedia PDF Downloads 177

Authors: Siswoyo Haryono

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Purpose – The fundamental goal of this research is to see if the integrative organizational behavior model can be used effectively in Islamic boarding schools. This paper also seeks to assess the effect of Islamic organizational culture, leadership, and spiritual intelligence on teachers' organizational commitment to Islamic Boarding schools. The goal of the mediation analysis is to see if the Islamic work ethic has a more significant effect on the instructors' organizational commitment than the direct effects of Islamic organizational culture, leadership, and Islamic spiritual intelligence. Design/methodology/approach – A questionnaire survey was used to obtain data from teachers at Islamic Boarding Schools. This study used the AMOS technique for structural equation modeling to evaluate the expected direct effect. To test the hypothesized indirect effect, employed Sobel test. Findings – Islamic organizational culture, Islamic leadership, and Islamic spiritual intelligence significantly affect Islamic work ethic. When it comes to Islamic corporate culture, Islamic leadership, Islamic spiritual intelligence, and Islamic work ethics have a significant impact. The findings of the mediation study reveal that Islamic organizational culture, leadership, and spiritual intelligence influences organizational commitment through Islamic work ethic. The total effect analysis shows that the most effective path to increasing teachers’ organizational commitment is Islamic leadership - Islamic work ethic – organizational commitment. Originality/value – This study evaluates the Integrative Model of Organizational Behavior by Colquitt (2016) applied in Islamic Boarding School. The model consists of contemporary leadership and individual characteristic as the antecedent. The mediating variables of the model consist of individual mechanisms such as trust, justice, and ethic. Individual performance and organizational commitment are the model's outcomes. These variables, on the other hand, do not represent the Islamic viewpoint as a whole. As a result, this study aims to assess the role of Islamic principles in the model. The study employs reliability and validity tests to get reliable and valid measures. The findings revealed that the evaluation model is proven to improve organizational commitment at Islamic Boarding School.

Keywords: Islamic leadership, Islamic spiritual intelligence, Islamic work ethic, organizational commitment, Islamic boarding school

Procedia PDF Downloads 161

Authors: Shivdayal Patel, Suhail Ahmad

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Safety assurance and failure prediction of composite material component of an offshore structure due to low velocity impact is essential for associated risk assessment. It is important to incorporate uncertainties associated with material properties and load due to an impact. Likelihood of this hazard causing a chain of failure events plays an important role in risk assessment. The material properties of composites mostly exhibit a scatter due to their in-homogeneity and anisotropic characteristics, brittleness of the matrix and fiber and manufacturing defects. In fact, the probability of occurrence of such a scenario is due to large uncertainties arising in the system. Probabilistic finite element analysis of composite plates due to low-velocity impact is carried out considering uncertainties of material properties and initial impact velocity. Impact-induced damage of composite plate is a probabilistic phenomenon due to a wide range of uncertainties arising in material and loading behavior. A typical failure crack initiates and propagates further into the interface causing de-lamination between dissimilar plies. Since individual crack in the ply is difficult to track. The progressive damage model is implemented in the FE code by a user-defined material subroutine (VUMAT) to overcome these problems. The limit state function is accordingly established while the stresses in the lamina are such that the limit state function (g(x)>0). The Gaussian process response surface method is presently adopted to determine the probability of failure. A comparative study is also carried out for different combination of impactor masses and velocities. The sensitivity based probabilistic design optimization procedure is investigated to achieve better strength and lighter weight of composite structures. Chain of failure events due to different modes of failure is considered to estimate the consequences of failure scenario. Frequencies of occurrence of specific impact hazards yield the expected risk due to economic loss.

Keywords: composites, damage propagation, low velocity impact, probability of failure, uncertainty modeling

Procedia PDF Downloads 279

Authors: Dheyaa W. Abbood, Rafa H. AL-Suhaili, May S. Saleh

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A model using the artificial neural networks and genetic algorithm technique is developed for obtaining optimum dimensions of the foundation length and protections of small hydraulic structures. The procedure involves optimizing an objective function comprising a weighted summation of the state variables. The decision variables considered in the optimization are the upstream and downstream cutoffs length sand their angles of inclination, the foundation length, and the length of the downstream soil protection. These were obtained for a given maximum difference in head, depth of impervious layer and degree of anisotropy.The optimization carried out subjected to constraints that ensure a safe structure against the uplift pressure force and sufficient protection length at the downstream side of the structure to overcome an excessive exit gradient. The Geo-studios oft ware, was used to analyze 1200 different cases. For each case the length of protection and volume of structure required to satisfy the safety factors mentioned previously were estimated. An ANN model was developed and verified using these cases input-output sets as its data base. A MatLAB code was written to perform a genetic algorithm optimization modeling coupled with this ANN model using a formulated optimization model. A sensitivity analysis was done for selecting the cross-over probability, the mutation probability and level ,the number of population, the position of the crossover and the weights distribution for all the terms of the objective function. Results indicate that the most factor that affects the optimum solution is the number of population required. The minimum value that gives stable global optimum solution of this parameters is (30000) while other variables have little effect on the optimum solution.

Keywords: inclined cutoff, optimization, genetic algorithm, artificial neural networks, geo-studio, uplift pressure, exit gradient, factor of safety

Procedia PDF Downloads 324

Authors: Samuel Abebe Ebebo

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Scale precipitation causes a major issue for geothermal power plants because it reduces the production rate of geothermal energy. Each geothermal power plant's different chemical and physical conditions can cause the scale to precipitate under a particular set of fluid-rock interactions. Depending on the mineral, it is possible to have scale in the production well, steam separators, heat exchangers, reinjection wells, and everywhere in between. The scale consists mainly of smectite and trace amounts of chlorite, magnetite, quartz, hematite, dolomite, aragonite, and amorphous silica. The smectite scale is one of the difficult scales at injection wells in geothermal power plants. X-ray diffraction and chemical composition identify this smectite as Stevensite. The characteristics and the scale of each injection well line are different depending on the fluid chemistry. The smectite scale has been widely distributed in pipelines and surface plants. Mineral water equilibrium showed that the main factors controlling the saturation indices of smectite increased pH and dissolved Mg concentration due to the precipitate on the equipment surface. This study aims to characterize the scales and geothermal fluids collected from the Onuma geothermal power plant in Akita Prefecture, Japan. Field tests were conducted on October 30–November 3, 2021, at Onuma to determine the pH control methods for preventing magnesium silicate scaling, and as exemplified, the formation of magnesium silicate hydrates (M-S-H) with MgO to SiO2 ratios of 1.0 and pH values of 10 for one day has been studied at 25 °C. As a result, M-S-H scale formation could be suppressed, and stevensite formation could also be suppressed when we can decrease the pH of the fluid by less than 8.1, 7.4, and 8 (at 97 °C) in the fluid from O-3Rb and O-6Rb, O-10Rg, and O-12R, respectively. In this context, the scales and fluids collected from injection wells at a geothermal power plant in Japan were analyzed and characterized to understand the formation conditions of Mg-silicate scales with on-site synthesis experiments. From the results of the characterizations and on-site synthesis experiments, the inhibition method of their scale formation is discussed based on geochemical modeling in this study.

Keywords: magnesium silicate, scaling, inhibitor, geothermal power plant

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Authors: İlkay Özer Erselcan, Abdi Kükner, Gökhan Ceylan

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The power generated by eight point absorber type wave energy converters each having a different buoy are calculated in order to investigate the performances of buoys in this study. The calculations are carried out by modeling three different sea states observed in two different locations in the Black Sea. The floats analyzed in this study have two basic geometries and four different draft/radius (d/r) ratios. The buoys possess the shapes of a semi-ellipsoid and a semi-elliptic paraboloid. Additionally, the draft/radius ratios range from 0.25 to 1 by an increment of 0.25. The radiation forces acting on the buoys due to the oscillatory motions of these bodies are evaluated by employing a 3D panel method along with a distribution of 3D pulsating sources in frequency domain. On the other hand, the wave forces acting on the buoys which are taken as the sum of Froude-Krylov forces and diffraction forces are calculated by using linear wave theory. Furthermore, the wave energy converters are assumed to be taut-moored to the seabed so that the secondary body which houses a power take-off system oscillates with much smaller amplitudes compared to the buoy. As a result, it is assumed that there is not any significant contribution to the power generation from the motions of the housing body and the only contribution to power generation comes from the buoy. The power take-off systems of the wave energy converters are high pressure oil hydraulic systems which are identical in terms of their characteristic parameters. The results show that the power generated by wave energy converters which have semi-ellipsoid floats is higher than that of those which have semi elliptic paraboloid floats in both locations and in all sea states. It is also determined that the power generated by the wave energy converters follow an unsteady pattern such that they do not decrease or increase with changing draft/radius ratios of the floats. Although the highest power level is obtained with a semi-ellipsoid float which has a draft/radius ratio equal to 1, other floats of which the draft/radius ratio is 0.25 delivered higher power that the floats with a draft/radius ratio equal to 1 in some cases.

Keywords: Black Sea, buoys, hydraulic power take-off system, wave energy converters

Procedia PDF Downloads 351

Authors: Fidelia A. Orji, Julita Vassileva

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This research aims to develop machine learning models for students' academic performance and study strategy prediction, which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) used in building the models are chosen based on prior studies, which revealed that the attributes are essential in students’ learning process. Previous studies revealed the individual effects of each of these attributes on students’ learning progress. However, few studies have investigated the combined effect of the attributes in predicting student study strategy and academic performance to reduce the dropout rate. To bridge this gap, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Keywords: classification models, learning strategy, predictive modeling, regression models, student academic performance, student motivation, supervised machine learning

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Authors: Xiao Yang, Ji Zhang, Yingli Song, Hui Huang, Jing Zhang, Yan Wang, Rongrong Han, Zhixuan Xiang, Lu Chen, Lingling Gao

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Aim and Objectives: To examine physical activity self-efficacy, identify its predictors, and further explore the mechanism of action among the predictors in mainland Chinese pregnant women with high risk for gestational diabetes mellitus (GDM). Background: Physical activity could protect pregnant women from developing GDM. Physical activity self-efficacy was the key predictor of physical activity. Design: A cross-sectional study was conducted from October 2021 to May 2022 in Zhengzhou, China. Methods: 252 eligible pregnant women completed the Pregnancy Physical Activity Self-efficacy Scale, the Social Support for Physical Activity Scale, the Knowledge on Physical Activity Questionnaire, the 7-item Generalized Anxiety Disorder scale, the Edinburgh Postnatal Depression Scale, and a socio-demographic data sheet. Multiple linear regression was applied to explore the predictors of physical activity self-efficacy. Structural equation modeling was used to explore the mechanism of action among the predictors. Results: Chinese pregnant women with a high risk for GDM reported a moderate level of physical activity self-efficacy. The best-fit regression analysis revealed four variables explained 17.5% of the variance in physical activity self-efficacy. Social support for physical activity was the strongest predictor, followed by knowledge of the physical activity, intention to do physical activity, and anxiety symptoms. The model analysis indicated that knowledge of physical activity could release anxiety and depressive symptoms and then increase physical activity self-efficacy. Conclusion: The present study revealed a moderate level of physical activity self-efficacy. Interventions targeting pregnant women with high risk for GDM need to include the predictors of physical activity self-efficacy. Relevance to clinical practice: To facilitate pregnant women with high risk for GDM to engage in physical activity, healthcare professionals may find assess physical activity self-efficacy and intervene as soon as possible on their first antenatal visit. Physical activity intervention programs focused on self-efficacy may be conducted in further research.

Keywords: physical activity, gestational diabetes, self-efficacy, predictors

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Authors: Ansoné Human, Nantes Kirsten, Tanja Verster, Willem D. Schutte

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Every year the telecommunications industry suffers huge losses due to fraud. Mobile fraud, or generally, telecommunications fraud is the utilisation of telecommunication products or services to acquire money illegally from or failing to pay a telecommunication company. A South African telecommunication operator developed two internal fraud scorecards to mitigate future risks of application fraud events. The scorecards aim to predict the likelihood of an application being fraudulent and surprise fraudsters before they surprise the telecommunication operator by identifying fraud at the time of application. The scorecards are utilised in the vetting process to evaluate the applicant in terms of the fraud risk the applicant would present to the telecommunication operator. Telecommunication providers can utilise these scorecards to profile customers, as well as isolate fraudulent and/or high-risk applicants. We provide the complete methodology utilised in the development of the scorecards. Furthermore, a Determination and Discrimination (DD) ratio is provided in the methodology to select the most influential variables from a group of related variables. Throughout the development of these scorecards, the following was revealed regarding fraudulent cases and fraudster behaviour within the telecommunications industry: Fraudsters typically target high-value handsets. Furthermore, debit order dates scheduled for the end of the month have the highest fraud probability. The fraudsters target specific stores. Applicants who acquire an expensive package and receive a medium-income, as well as applicants who obtain an expensive package and receive a high income, have higher fraud percentages. If one month prior to application, the status of an account is already in arrears (two months or more), the applicant has a high probability of fraud. The applicants with the highest average spend on calls have a higher probability of fraud. If the amount collected changes from month to month, the likelihood of fraud is higher. Lastly, young and middle-aged applicants have an increased probability of being targeted by fraudsters than other ages.

Keywords: application fraud scorecard, predictive modeling, regression, telecommunications

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Authors: Adam Gushgari

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The SARS-CoV-2 pandemic has catalyzed the rapid adoption of wastewater-based epidemiology (WBE) methodologies both domestically and internationally. To support the rapid scale-up of pandemic-response wastewater surveillance systems, multiple federal agencies (i.e. US CDC), non-government organizations (i.e. Water Environment Federation), and private charities (i.e. Bill and Melinda Gates Foundation) have funded over $220 million USD supporting development and expanding equitable access of surveillance methods. Funds were primarily distributed directly to municipalities under the CARES Act (90.6%), followed by academic projects (7.6%), and initiatives developed by private companies (1.8%). In addition to federal funding for wastewater monitoring primarily conducted at wastewater treatment plants, state/local governments and private companies have leveraged wastewater sampling to obtain health and lifestyle data on student, prison inmate, and employee populations. We explore the viable paths for expansion of the WBE m1ethodology across a variety of analytical methods; the development of WBE-specific samplers and real-time wastewater sensors; and their application to various governments and private sector industries. Considerable investment in, and public acceptance of WBE suggests the methodology will be applied to other future notifiable diseases and health risks. Early research suggests that WBE methods can be applied to a host of additional “biological insults” including communicable diseases and pathogens, such as influenza, Cryptosporidium, Giardia, mycotoxin exposure, hepatitis, dengue, West Nile, Zika, and yellow fever. Interest in chemical insults is also likely, providing community health and lifestyle data on narcotics consumption, use of pharmaceutical and personal care products (PPCP), PFAS and hazardous chemical exposure, and microplastic exposure. Successful application of WBE to monitor analytes correlated with carcinogen exposure, community stress prevalence, and dietary indicators has also been shown. Additionally, technology developments of in situ wastewater sensors, WBE-specific wastewater samplers, and integration of artificial intelligence will drastically change the landscape of WBE through the development of “smart sewer” networks. The rapid expansion of the WBE field is creating significant business opportunities for professionals across the scientific, engineering, and technology industries ultimately focused on community health improvement.

Keywords: wastewater surveillance, wastewater-based epidemiology, smart cities, public health, pandemic management, substance abuse

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Authors: Yasmeen Cheema, Parvinder Singh

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The evolution of Islamic State (acronym IS) has an ultimate goal of restoring the caliphate. IS threat to the global security is main concern of international community but has also raised a factual concern for India about the regular radicalization of IS ideology among Indian youth. The incident of joining Arif Ejaz Majeed, an Indian as ‘jihadist’ in IS has set strident alarm in law & enforcement agencies. On 07.03.2017, many people were injured in an Improvised Explosive Device (IED) blast on-board of Bhopal Ujjain Express. One perpetrator of this incident was killed in encounter with police. But, the biggest shock is that the conspiracy was pre-planned and the assailants who carried out the blast were influenced by the ideology perpetrated by the Islamic State. This is the first time name of IS has cropped up in a terror attack in India. It is a red indicator of violent presence of IS in India, which is spreading through social media. The IS have the capacity to influence the younger Muslim generation in India through its brutal and aggressive propaganda videos, social media apps and hatred speeches. It is a well known fact that India is on the radar of IS, as well on its ‘Caliphate Map’. IS uses Twitter, Facebook and other social media platforms constantly. Islamic State has used enticing videos, graphics, and articles on social media and try to influence persons from India & globally that their jihad is worthy. According to arrested perpetrator of IS in different cases in India, the most of Indian youths are victims to the daydreams which are fondly shown by IS. The dreams that the Muslim empire as it was before 1920 can come back with all its power and also that the Caliph and its caliphate can be re-established are shown by the IS. Indian Muslim Youth gets attracted towards these euphemistic ideologies. Islamic State has used social media for disseminating its poisonous ideology, recruitment, operational activities and for future direction of attacks. IS through social media inspired its recruits & lone wolfs to continue to rely on local networks to identify targets and access weaponry and explosives. Recently, a pro-IS media group on its Telegram platform shows Taj Mahal as the target and suggested mode of attack as a Vehicle Born Improvised Explosive Attack (VBIED). Islamic State definitely has the potential to destroy the Indian national security & peace, if timely steps are not taken. No doubt, IS has used social media as a critical mechanism for recruitment, planning and executing of terror attacks. This paper will therefore examine the specific characteristics of social media that have made it such a successful weapon for Islamic State. The rise of IS in India should be viewed as a national crisis and handled at the central level with efficient use of modern technology.

Keywords: ideology, India, Islamic State, national security, recruitment, social media, terror attack

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Authors: Zahid Ullah, Atlas Khan

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In today's data-driven world, the role of mathematics in bridging the gap between theory and applications is becoming increasingly vital. This abstract highlights the significance of mathematics as a powerful tool for analyzing, interpreting, and extracting meaningful insights from vast amounts of data. By integrating mathematical principles with real-world applications, researchers can unlock the full potential of data-driven decision-making processes. This abstract delves into the various ways mathematics acts as a bridge connecting theoretical frameworks to practical applications. It explores the utilization of mathematical models, algorithms, and statistical techniques to uncover hidden patterns, trends, and correlations within complex datasets. Furthermore, it investigates the role of mathematics in enhancing predictive modeling, optimization, and risk assessment methodologies for improved decision-making in diverse fields such as finance, healthcare, engineering, and social sciences. The abstract also emphasizes the need for interdisciplinary collaboration between mathematicians, statisticians, computer scientists, and domain experts to tackle the challenges posed by the data-driven landscape. By fostering synergies between these disciplines, novel approaches can be developed to address complex problems and make data-driven insights accessible and actionable. Moreover, this abstract underscores the importance of robust mathematical foundations for ensuring the reliability and validity of data analysis. Rigorous mathematical frameworks not only provide a solid basis for understanding and interpreting results but also contribute to the development of innovative methodologies and techniques. In summary, this abstract advocates for the pivotal role of mathematics in bridging theory and applications in a data-driven world. By harnessing mathematical principles, researchers can unlock the transformative potential of data analysis, paving the way for evidence-based decision-making, optimized processes, and innovative solutions to the challenges of our rapidly evolving society.

Keywords: mathematics, bridging theory and applications, data-driven world, mathematical models

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Authors: Tewele Gerlase Haile

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Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs. The global development landscape underwent a transformative shift in 2015 as the international community pivoted from the MDGs to the more ambitious and comprehensive SDGs. The NDGs were a set of eight international development goals established by the United Nations in 2000, with the aim of improving the lives of people around the world by 2015. SDGs are a continuation of the MDGs. Unlike on the other development goals, progress on eradication of extreme hunger and poverty (MDG 1) has been slow at a continental level. The implementation of the MDGs was uneven: some countries have already achieved many of them, while the others have not started any of them yet. With its Poverty Reduction Strategic Papers (PRSPs), Ethiopia has been given special attention to the first MDG since 1993. The Ethiopian government was actively engaged in anti-poverty political campaign leaving other agendas as secondary issues. Poverty in Ethiopia progressively reduced over the years; it was 44.2% in 2000, 38.7% in 2007, 29.6 % in 2011, and it is projected to further reduce to 16.7% by the end of 2020. The long-term impact of war on the sustainability and effectiveness of SDG-related initiatives in post-conflict regions, particularly in how local governance and community resilience are affected. This could involve exploring how war interrupts progress, which specific SDGs are most vulnerable, and what strategies might mitigate these impacts. Reviewing a transitional period enables policy makers to align global or national development goals into local development goals with an uninterrupted policy continuity. The existing literature on development economics often neglects the importance of reviewing the transitional period of consecutive global development goals in a local or regional perspective. Reviewing a transitional period enables policy makers to align global or national development goals into local development goals with an uninterrupted policy continuity. Using a Policy Coherence for Development (PCD) approach as analytical tool, this paper is intended to retrospectively review what happened to the local economy of Tigrai Regional State during the transitional period from MDGs (2000-2015) to SDGs (2015-2030). Taking a retrospective facts and observations into account, policy discontinuity is witnessed in Tigrai following the dissolution of the EPRDF that followed with a terrible war that claimed about a million human lives and worth of over a hundred Billion US dollars economic costs. The unhealthy political reform caused not only a terrible war but also breaks the promising SDGs. Unlike other regional states, Tigrai left unprivileged to translate the ambitious SDGs into its local development policies.

Keywords: local development, political reform, war, MDGs, SDGs, Ethiopia, tigrai

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Authors: Sergio V Davalos, Alison L. Watkins

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User generated content (UGC) such as website post has data associated with it: time of the post, gender, location, type of device, and number of words. The text entered in user generated content (UGC) can provide a valuable dimension for analysis. In this research, each user post is treated as a collection of terms (words). In addition to the number of words per post, the frequency of each term is determined by post and by the sum of occurrences in all posts. This research focuses on one specific aspect of UGC: sentiment. Sentiment analysis (SA) was applied to the content (user posts) of two sets of political forums related to the US presidential elections for 2012 and 2016. Sentiment analysis results in deriving data from the text. This enables the subsequent application of data analytic methods. The SASA (SAIL/SAI Sentiment Analyzer) model was used for sentiment analysis. The application of SASA resulted with a sentiment score for each post. Based on the sentiment scores for the posts there are significant differences between the content and sentiment of the two sets for the 2012 and 2016 presidential election forums. In the 2012 forums, 38% of the forums started with positive sentiment and 16% with negative sentiment. In the 2016 forums, 29% started with positive sentiment and 15% with negative sentiment. There also were changes in sentiment over time. For both elections as the election got closer, the cumulative sentiment score became negative. The candidate who won each election was in the more posts than the losing candidates. In the case of Trump, there were more negative posts than Clinton’s highest number of posts which were positive. KNIME topic modeling was used to derive topics from the posts. There were also changes in topics and keyword emphasis over time. Initially, the political parties were the most referenced and as the election got closer the emphasis changed to the candidates. The performance of the SASA method proved to predict sentiment better than four other methods in Sentibench. The research resulted in deriving sentiment data from text. In combination with other data, the sentiment data provided insight and discovery about user sentiment in the US presidential elections for 2012 and 2016.

Keywords: sentiment analysis, text mining, user generated content, US presidential elections

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Authors: Zhang Qingzhong

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Though there exists an increasing number of research studies on the headquarters-subsidiary relationship, and within this context, there is a focus on subsidiaries' contributory role to multinational corporations (MNC), subsidiary autonomy, and the conditions under which autonomy exerts an effect on subsidiary performance still constitute a subject of debate in the literature. The objective of this research is to study the MNC subsidiary autonomy and performance relationship and the effect of subsidiary entrepreneurial orientation and trust on subsidiary autonomy in the China environment, a phenomenon that has not yet been studied. The research addresses the following three questions: (i) Is subsidiary autonomy associated with MNC subsidiary performance in the China environment? (ii) How do subsidiary entrepreneurship and its trust in headquarters affect the level of subsidiary autonomy and its relationship with subsidiary performance? (iii) Does subsidiary autonomy have a mediating effect on subsidiary performance with subsidiary’s entrepreneurship and trust in headquarters? In the present study, we have reviewed literature and conducted semi-structured interviews with multinational corporation (MNC) subsidiary senior executives in China. Building on our insights from the interviews and taking perspectives from four theories, namely the resource-based view (RBV), resource dependency theory, integration-responsiveness framework, and social exchange theory, as well as the extant articles on subsidiary autonomy, entrepreneurial orientation, trust, and subsidiary performance, we have developed a model and have explored the direct and mediating effects of subsidiary autonomy on subsidiary performance within the framework of the MNC. To test the model, we collected and analyzed data based on cross-industry two waves of an online survey from 102 subsidiaries of MNCs in China. We used structural equation modeling to test measurement, direct effect model, and conceptual framework with hypotheses. Our findings confirm that (a) subsidiary autonomy is positively related to subsidiary performance; (b) subsidiary entrepreneurial orientation is positively related to subsidiary autonomy; (c) subsidiary’s trust in headquarters has a positive effect on subsidiary autonomy; (d) subsidiary autonomy mediates the relationship between entrepreneurial orientation and subsidiary performance; (e) subsidiary autonomy mediates the relationship between trust and subsidiary performance. Our study highlights the important role of subsidiary autonomy in leveraging the resource of subsidiary entrepreneurial orientation and its trust relationship with headquarters to achieve high performance. We discuss the theoretical and managerial implications of the findings and propose directions for future research.

Keywords: subsidiary entrepreneurial orientation, trust, subsidiary autonomy, subsidiary performance

Procedia PDF Downloads 186

Authors: María Dolores Caro, Eugenio M. Fedriani, Ángel F. Tenorio

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The transportation of dangerous goods by lorries throughout Europe must be done by using the roads conforming the European Road Transport Network. In this network, there are several parking areas where lorry drivers can park to rest according to the regulations. According to the "European Agreement concerning the International Carriage of Dangerous Goods by Road", parking areas where lorries transporting dangerous goods can park to rest, must follow several security stipulations to keep safe the rest of road users. At this respect, these lorries must be parked in adapted areas with strict and permanent surveillance measures. Moreover, drivers must satisfy several restrictions about resting and driving time. Under these facts, one may expect that there exist enough parking areas for the transport of this type of goods in order to obey the regulations prescribed by the European Union and its member countries. However, the already-existing parking areas are not sufficient to cover all the stops required by drivers transporting dangerous goods. Our main goal is, starting from the already-existing parking areas and the loading-and-unloading location, to provide an optimal answer to the following question: how many additional parking areas must be built and where must they be located to assure that lorry drivers can transport dangerous goods following all the stipulations about security and safety for their stops? The sense of the word “optimal” is due to the fact that we give a global solution for the location of parking areas throughout the whole European Road Transport Network, adjusting the number of additional areas to be as lower as possible. To do so, we have modeled the problem using graph theory since we are working with a road network. As nodes, we have considered the locations of each already-existing parking area, each loading-and-unloading area each road bifurcation. Each road connecting two nodes is considered as an edge in the graph whose weight corresponds to the distance between both nodes in the edge. By applying a new efficient algorithm, we have found the additional nodes for the network representing the new parking areas adapted for dangerous goods, under the fact that the distance between two parking areas must be less than or equal to 400 km.

Keywords: trans-european transport network, dangerous goods, parking areas, graph-based modeling

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Authors: M. Abuziarov

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The designed complex is intended for the numerical simulation of fast dynamic processes of interaction of heterogeneous environments susceptible to the significant formability. The main challenges in solving such problems are associated with the construction of the numerical meshes. Currently, there are two basic approaches to solve this problem. One is using of Lagrangian or Lagrangian Eulerian grid associated with the boundaries of media and the second is associated with the fixed Eulerian mesh, boundary cells of which cut boundaries of the environment medium and requires the calculation of these cut volumes. Both approaches require the complex grid generators and significant time for preparing the code’s data for simulation. In this codes these problems are solved using two grids, regular fixed and mobile local Euler Lagrange - Eulerian (ALE approach) accompanying the contact and free boundaries, the surfaces of shock waves and phase transitions, and other possible features of solutions, with mutual interpolation of integrated parameters. For modeling of both liquids and gases, and deformable solids the Godunov scheme of increased accuracy is used in Lagrangian - Eulerian variables, the same for the Euler equations and for the Euler- Cauchy, describing the deformation of the solid. The increased accuracy of the scheme is achieved by using 3D spatial time dependent solution of the discontinuity problem (3D space time dependent Riemann's Problem solver). The same solution is used to calculate the interaction at the liquid-solid surface (Fluid Structure Interaction problem). The codes does not require complex 3D mesh generators, only the surfaces of the calculating objects as the STL files created by means of engineering graphics are given by the user, which greatly simplifies the preparing the task and makes it convenient to use directly by the designer at the design stage. The results of the test solutions and applications related to the generation and extension of the detonation and shock waves, loading the constructions are presented.

Keywords: fluid structure interaction, Riemann's solver, Euler variables, 3D codes

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Authors: L. Guevara, Glória L. S., Corea E. E, A. Ramírez-Zamora M., Salinas-Martinez J. A., Angeles-Hernandez J. C.

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Mathematical models are useful for identifying the characteristics of sheep lactation curves to develop and implement improved strategies. However, the accuracy of these models is influenced by factors such as the recording regime, mainly the intervals between test day records (TDR). The current study aimed to evaluate the effect of different TDR intervals on the goodness of fit of the Wood model (WM) applied to dairy sheep lactations. A total of 4,494 weekly TDRs from 156 lactations of dairy crossbred sheep were analyzed. Three new databases were generated from the original weekly TDR data (7D), comprising intervals of 14(14D), 21(21D), and 28(28D) days. The parameters of WM were estimated using the “minpack.lm” package in the R software. The shape of the lactation curve (typical and atypical) was defined based on the WM parameters. The goodness of fit was evaluated using the mean square of prediction error (MSPE), Root of MSPE (RMSPE), Akaike´s Information Criterion (AIC), Bayesian´s Information Criterion (BIC), and the coefficient of correlation (r) between the actual and estimated total milk yield (TMY). WM showed an adequate estimate of TMY regardless of the TDR interval (P=0.21) and shape of the lactation curve (P=0.42). However, we found higher values of r for typical curves compared to atypical curves (0.9vs.0.74), with the highest values for the 28D interval (r=0.95). In the same way, we observed an overestimated peak yield (0.92vs.6.6 l) and underestimated time of peak yield (21.5vs.1.46) in atypical curves. The best values of RMSPE were observed for the 28D interval in both lactation curve shapes. The significant lowest values of AIC (P=0.001) and BIC (P=0.001) were shown by the 7D interval for typical and atypical curves. These results represent the first approach to define the adequate interval to record the regime of dairy sheep in Latin America and showed a better fitting for the Wood model using a 7D interval. However, it is possible to obtain good estimates of TMY using a 28D interval, which reduces the sampling frequency and would save additional costs to dairy sheep producers.

Keywords: gamma incomplete, ewes, shape curves, modeling

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Authors: Omid Giahi, Ebrahim Darvishi, Mahdi Akbarzadeh

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Introduction: Occupational low back pain (LBP) is one of the most prevalent work-related musculoskeletal disorders in which a lot of risk factors are involved that. The present study focuses on the relation between personal, psycho-social and occupational risk factors and LBP severity in industrial workers. Materials and Methods: This research was a case-control study which was conducted in Kurdistan province. 100 workers (Mean Age ± SD of 39.9 ± 10.45) with LBP were selected as the case group, and 100 workers (Mean Age ± SD of 37.2 ± 8.5) without LBP were assigned into the control group. All participants were selected from various industrial units, and they had similar occupational conditions. The required data including demographic information (BMI, smoking, alcohol, and family history), occupational (posture, mental workload (MWL), force, vibration and repetition), and psychosocial factors (stress, occupational satisfaction and security) of the participants were collected via consultation with occupational medicine specialists, interview, and the related questionnaires and also the NASA-TLX software and REBA worksheet. Chi-square test, logistic regression and structural equation modeling (SEM) were used to analyze the data. For analysis of data, IBM Statistics SPSS 24 and Mplus6 software have been used. Results: 114 (77%) of the individuals were male and 86 were (23%) female. Mean Career length of the Case Group and Control Group were 10.90 ± 5.92, 9.22 ± 4.24, respectively. The statistical analysis of the data revealed that there was a significant correlation between the Posture, Smoking, Stress, Satisfaction, and MWL with occupational LBP. The odds ratios (95% confidence intervals) derived from a logistic regression model were 2.7 (1.27-2.24) and 2.5 (2.26-5.17) and 3.22 (2.47-3.24) for Stress, MWL, and Posture, respectively. Also, the SEM analysis of the personal, psycho-social and occupational factors with LBP revealed that there was a significant correlation. Conclusion: All three broad categories of risk factors simultaneously increase the risk of occupational LBP in the workplace. But, the risks of Posture, Stress, and MWL have a major role in LBP severity. Therefore, prevention strategies for persons in jobs with high risks for LBP are required to decrease the risk of occupational LBP.

Keywords: industrial workers occupational, low back pain, occupational risk factors, psychosocial factors

Procedia PDF Downloads 258