Search results for: conditional random fields
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 4531

Search results for: conditional random fields

3871 Challenges for IoT Adoption in India: A Study Based on Foresight Analysis for 2025

Authors: Shruti Chopra, Vikas Rao Vadi

Abstract:

In the era of the digital world, the Internet of Things (IoT) has been receiving significant attention. Its ubiquitous connectivity between humans, machines to machines (M2M) and machines to humans provides it a potential to transform the society and establish an ecosystem to serve new dimensions to the economy of the country. Thereby, this study has attempted to identify the challenges that seem prevalent in IoT adoption in India through the literature survey. Further, the data has been collected by taking the opinions of experts to conduct the foresight analysis and it has been analyzed with the help of scenario planning process – Micmac, Mactor, Multipol, and Smic-Prob. As a methodology, the study has identified the relationship between variables through variable analysis using Micmac and actor analysis using Mactor, this paper has attempted to generate the entire field of possibilities in terms of hypotheses and construct various scenarios through Multipol. And lastly, the findings of the study include final scenarios that are selected using Smic-Prob by assigning the probability to all the scenarios (including the conditional probability). This study may help the practitioners and policymakers to remove the obstacles to successfully implement the IoT in India.

Keywords: Internet of Thing (IoT), foresight analysis, scenario planning, challenges, policymaking

Procedia PDF Downloads 148
3870 The Impact of Government Expenditure on Economic Growth: A Study of Asian Countries

Authors: K. P. K. S. Lahirushan, W. G. V. Gunasekara

Abstract:

Main purpose of this study is to identifying the impact of government expenditure on economic growth in Asian Countries. Consequently, Fist, objective is to analyze whether government expenditure causes economic growth in Asian countries vice versa and then scrutinizing long-run equilibrium relationship exists between them. The study completely based on secondary data. The methodology being quantitative that includes econometrical techniques of cointegration, panel fixed effects model and granger causality in the context of panel data of Asian countries; Singapore, Malaysia, Thailand, South Korea, Japan, China, Sri Lanka, India and Bhutan with 44 observations in each country, totaling to 396 observations from 1970 to 2013. The model used is the random effects panel OLS model. As with the above methodology, the study found the fascinating outcome. At first, empirical findings exhibit a momentous positive impact of government expenditure on Gross Domestic Production in Asian region. Secondly, government expenditure and economic growth indicate a long-run relationship in Asian countries. In conclusion, there is a unidirectional causality from economic growth to government expenditure and government expenditure to economic growth in Asian countries. Hence the study is validated that it is in line with the Keynesian theory and Wagner’s law as well. Consequently, it can be concluded that role of government would play a vital role in economic growth of Asian Countries .However; if government expenditure did not figure out with the economy’s needs it might be considerably inspiration the economy in a negative way so that society bears the costs.

Keywords: Asian countries, government expenditure, Keynesian theory, Wagner’s theory, random effects panel ols model

Procedia PDF Downloads 353
3869 Assessing the Competence of Junior Pediatric Doctors in Managing Pediatric Diabetic Ketoacidosis: An Exploration Across Pediatric Care Units

Authors: Mai Ali

Abstract:

Advancing beyond the junior stage of a paediatrician’s career is a crucial step where they accumulate essential skills and knowledge. This process prepares them for the challenges they'll encounter throughout their profession, particularly in dealing with paediatric emergencies. This can be especially demanding for trainees specializing in fields like endocrinology, particularly in the management of Diabetic Ketoacidosis (DKA) in the UK. In different societal contexts, junior doctors, whether specializing in pediatrics or other medical fields, are generally expected to possess a fundamental level of knowledge and skills necessary for managing diabetic ketoacidosis (DKA) emergencies. These physicians consistently concurred in recognizing prevalent problems in the healthcare facilities they examined. Such issues include the lack of established guidelines for DKA treatment and the inadequate availability of comprehensive training opportunities. The abstract underscores the critical importance of junior paediatricians acquiring expertise in managing paediatric emergencies, with a specific focus on DKA. Commonly, issues like the lack of standardized protocols and training deficiencies are recurring themes across healthcare facilities. This research proposal aims to conduct a thematic analysis of the proficiency of paediatric trainees in the United Kingdom when handling DKA in various clinical contexts. The primary goal is to assess their competency and suggest effective strategies for comprehensive DKA training improvement.

Keywords: junior pediatrician, DKA, standardized protocols, level of competence

Procedia PDF Downloads 85
3868 Waste Burial to the Pressure Deficit Areas in the Eastern Siberia

Authors: L. Abukova, O. Abramova, A. Goreva, Y. Yakovlev

Abstract:

Important executive decisions on oil and gas production stimulation in Eastern Siberia have been recently taken. There are unique and large fields of oil, gas, and gas-condensate in Eastern Siberia. The Talakan, Koyumbinskoye, Yurubcheno-Tahomskoye, Kovykta, Chayadinskoye fields are supposed to be developed first. It will result in an abrupt increase in environmental load on the nature of Eastern Siberia. In Eastern Siberia, the introduction of ecological imperatives in hydrocarbon production is still realistic. Underground water movement is the one of the most important factors of the ecosystems condition management. Oil and gas production is associated with the forced displacement of huge water masses, mixing waters of different composition, and origin that determines the extent of anthropogenic impact on water drive systems and their protective reaction. An extensive hydrogeological system of the depression type is identified in the pre-salt deposits here. Pressure relieve here is steady up to the basement. The decrease of the hydrodynamic potential towards the basement with such a gradient resulted in reformation of the fields in process of historical (geological) development of the Nepsko-Botuobinskaya anteclise. The depression hydrodynamic systems are characterized by extremely high isolation and can only exist under such closed conditions. A steady nature of water movement due to a strictly negative gradient of reservoir pressure makes it quite possible to use environmentally-harmful liquid substances instead of water. Disposal of the most hazardous wastes is the most expedient in the deposits of the crystalline basement in certain structures distant from oil and gas fields. The time period for storage of environmentally-harmful liquid substances may be calculated by means of the geological time scales ensuring their complete prevention from releasing into environment or air even during strong earthquakes. Disposal of wastes of chemical and nuclear industries is a matter of special consideration. The existing methods of storage and disposal of wastes are very expensive. The methods applied at the moment for storage of nuclear wastes at the depth of several meters, even in the most durable containers, constitute a potential danger. The enormous size of the depression system of the Nepsko-Botuobinskaya anteclise makes it possible to easily identify such objects at the depth below 1500 m where nuclear wastes will be stored indefinitely without any environmental impact. Thus, the water drive system of the Nepsko-Botuobinskaya anteclise is the ideal object for large-volume injection of environmentally harmful liquid substances even if there are large oil and gas accumulations in the subsurface. Specific geological and hydrodynamic conditions of the system allow the production of hydrocarbons from the subsurface simultaneously with the disposal of industrial wastes of oil and gas, mining, chemical, and nuclear industries without any environmental impact.

Keywords: Eastern Siberia, formation pressure, underground water, waste burial

Procedia PDF Downloads 259
3867 The Use of Layered Neural Networks for Classifying Hierarchical Scientific Fields of Study

Authors: Colin Smith, Linsey S Passarella

Abstract:

Due to the proliferation and decentralized nature of academic publication, no widely accepted scheme exists for organizing papers by their scientific field of study (FoS) to the author’s best knowledge. While many academic journals require author provided keywords for papers, these keywords range wildly in scope and are not consistent across papers, journals, or field domains, necessitating alternative approaches to paper classification. Past attempts to perform field-of-study (FoS) classification on scientific texts have largely used a-hierarchical FoS schemas or ignored the schema’s inherently hierarchical structure, e.g. by compressing the structure into a single layer for multi-label classification. In this paper, we introduce an application of a Layered Neural Network (LNN) to the problem of performing supervised hierarchical classification of scientific fields of study (FoS) on research papers. In this approach, paper embeddings from a pretrained language model are fed into a top-down LNN. Beginning with a single neural network (NN) for the highest layer of the class hierarchy, each node uses a separate local NN to classify the subsequent subfield child node(s) for an input embedding of concatenated paper titles and abstracts. We compare our LNN-FOS method to other recent machine learning methods using the Microsoft Academic Graph (MAG) FoS hierarchy and find that the LNN-FOS offers increased classification accuracy at each FoS hierarchical level.

Keywords: hierarchical classification, layer neural network, scientific field of study, scientific taxonomy

Procedia PDF Downloads 136
3866 Analysis of the Predictive Performance of Value at Risk Estimations in Times of Financial Crisis

Authors: Alexander Marx

Abstract:

Measuring and mitigating market risk is essential for the stability of enterprises, especially for major banking corporations and investment bank firms. To employ these risk measurement and mitigation processes, the Value at Risk (VaR) is the most commonly used risk metric by practitioners. In the past years, we have seen significant weaknesses in the predictive performance of the VaR in times of financial market crisis. To address this issue, the purpose of this study is to investigate the value-at-risk (VaR) estimation models and their predictive performance by applying a series of backtesting methods on the stock market indices of the G7 countries (Canada, France, Germany, Italy, Japan, UK, US, Europe). The study employs parametric, non-parametric, and semi-parametric VaR estimation models and is conducted during three different periods which cover the most recent financial market crisis: the overall period (2006–2022), the global financial crisis period (2008–2009), and COVID-19 period (2020–2022). Since the regulatory authorities have introduced and mandated the Conditional Value at Risk (Expected Shortfall) as an additional regulatory risk management metric, the study will analyze and compare both risk metrics on their predictive performance.

Keywords: value at risk, financial market risk, banking, quantitative risk management

Procedia PDF Downloads 95
3865 Computer Simulation to Investigate Magnetic and Wave-Absorbing Properties of Iron Nanoparticles

Authors: Chuan-Wen Liu, Min-Hsien Liu, Chung-Chieh Tai, Bing-Cheng Kuo, Cheng-Lung Chen, Huazhen Shen

Abstract:

A recent surge in research on magnetic radar absorbing materials (RAMs) has presented researchers with new opportunities and challenges. This study was performed to gain a better understanding of the wave-absorbing phenomenon of magnetic RAMs. First, we hypothesized that the absorbing phenomenon is dependent on the particle shape. Using the Material Studio program and the micro-dot magnetic dipoles (MDMD) method, we obtained results from magnetic RAMs to support this hypothesis. The total MDMD energy of disk-like iron particles was greater than that of spherical iron particles. In addition, the particulate aggregation phenomenon decreases the wave-absorbance, according to both experiments and computational data. To conclude, this study may be of importance in terms of explaining the wave- absorbing characteristic of magnetic RAMs. Combining molecular dynamics simulation results and the theory of magnetization of magnetic dots, we investigated the magnetic properties of iron materials with different particle shapes and degrees of aggregation under external magnetic fields. The MDMD of the materials under magnetic fields of various strengths were simulated. Our results suggested that disk-like iron particles had a better magnetization than spherical iron particles. This result could be correlated with the magnetic wave- absorbing property of iron material.

Keywords: wave-absorbing property, magnetic material, micro-dot magnetic dipole, particulate aggregation

Procedia PDF Downloads 492
3864 Adjustment with Changed Lifestyle at Old Age Homes: A Perspective of Elderly in India

Authors: Priyanka V. Janbandhu, Santosh B. Phad, Dhananjay W. Bansod

Abstract:

The current changing scenario of the family is a compelling aged group not only to be alone in a nuclear family but also to join the old age institutions. The consequences of it are feeling of neglected or left alone by the children, adding a touch of helpless in the absence of lack of expected care and support. The accretion of all these feelings and unpleasant events ignite a question in their mind that – who is there for me? The efforts have taken to highlight the issues of the elderly after joining the old age home and their perception about the current life as an institutional inmate. This attempt to cover up the condition, adjustment, changed lifestyle and perspective in the association with several issues of the elderly, which have an essential effect on their well-being. The present research piece has collected the information about institutionalized elderly with the help of a semi-structured questionnaire. This study interviewed 500 respondents from 22 old age homes of Pune city of Maharashtra State, India. This data collection methodology consists of Multi-stage random sampling. In which the stratified random sampling adopted for the selection of old age homes and sample size determination, sample selection probability proportional to the size and simple random sampling techniques implemented. The study provides that around five percent of the elderly shifted to old age home along with their spouse, whereas ten percent of the elderly are staying away from their spouse. More than 71 percent of the elderly have children, and they are an involuntary inmate of the old age institution, even less than one-third of the elderly consulted to the institution before the joining it. More than sixty percent of the elderly have children, but they joined institution due to the unpleasant response of their children only. Around half of the elderly responded that there are issues while adjusting to this environment, many of them are still persistent. At least one elderly out of ten is there who is suffering from the feeling of loneliness and left out by children and other family members. In contrast, around 97 percent of the elderly are very happy or satisfied with the institutional facilities. It illustrates that the issues are associated with their children and other family members, even though they left their home before a year or more. When enquired about this loneliness feeling few of them are suffering from it before leaving their homes, it was due to lack of interaction with children, as they are too busy to have time for the aged parents. Additionally, the conflicts or fights within the family due to the presence of old persons in the family contributed to establishing another feeling of insignificance among the elderly parents. According to these elderly, have more than 70 percent of the share, the children are ready to spend money indirectly for us through these institutions, but not prepared to provide some time and very few amounts of all this expenditure directly for us.

Keywords: elderly, old age homes, life style changes and adjustment, India

Procedia PDF Downloads 134
3863 Using Cyclic Structure to Improve Inference on Network Community Structure

Authors: Behnaz Moradijamei, Michael Higgins

Abstract:

Identifying community structure is a critical task in analyzing social media data sets often modeled by networks. Statistical models such as the stochastic block model have proven to explain the structure of communities in real-world network data. In this work, we develop a goodness-of-fit test to examine community structure's existence by using a distinguishing property in networks: cyclic structures are more prevalent within communities than across them. To better understand how communities are shaped by the cyclic structure of the network rather than just the number of edges, we introduce a novel method for deciding on the existence of communities. We utilize these structures by using renewal non-backtracking random walk (RNBRW) to the existing goodness-of-fit test. RNBRW is an important variant of random walk in which the walk is prohibited from returning back to a node in exactly two steps and terminates and restarts once it completes a cycle. We investigate the use of RNBRW to improve the performance of existing goodness-of-fit tests for community detection algorithms based on the spectral properties of the adjacency matrix. Our proposed test on community structure is based on the probability distribution of eigenvalues of the normalized retracing probability matrix derived by RNBRW. We attempt to make the best use of asymptotic results on such a distribution when there is no community structure, i.e., asymptotic distribution under the null hypothesis. Moreover, we provide a theoretical foundation for our statistic by obtaining the true mean and a tight lower bound for RNBRW edge weights variance.

Keywords: hypothesis testing, RNBRW, network inference, community structure

Procedia PDF Downloads 152
3862 Bayesian Using Markov Chain Monte Carlo and Lindley's Approximation Based on Type-I Censored Data

Authors: Al Omari Moahmmed Ahmed

Abstract:

These papers describe the Bayesian Estimator using Markov Chain Monte Carlo and Lindley’s approximation and the maximum likelihood estimation of the Weibull distribution with Type-I censored data. The maximum likelihood method can’t estimate the shape parameter in closed forms, although it can be solved by numerical methods. Moreover, the Bayesian estimates of the parameters, the survival and hazard functions cannot be solved analytically. Hence Markov Chain Monte Carlo method and Lindley’s approximation are used, where the full conditional distribution for the parameters of Weibull distribution are obtained via Gibbs sampling and Metropolis-Hastings algorithm (HM) followed by estimate the survival and hazard functions. The methods are compared to Maximum Likelihood counterparts and the comparisons are made with respect to the Mean Square Error (MSE) and absolute bias to determine the better method in scale and shape parameters, the survival and hazard functions.

Keywords: weibull distribution, bayesian method, markov chain mote carlo, survival and hazard functions

Procedia PDF Downloads 479
3861 A Machine Learning Approach for Performance Prediction Based on User Behavioral Factors in E-Learning Environments

Authors: Naduni Ranasinghe

Abstract:

E-learning environments are getting more popular than any other due to the impact of COVID19. Even though e-learning is one of the best solutions for the teaching-learning process in the academic process, it’s not without major challenges. Nowadays, machine learning approaches are utilized in the analysis of how behavioral factors lead to better adoption and how they related to better performance of the students in eLearning environments. During the pandemic, we realized the academic process in the eLearning approach had a major issue, especially for the performance of the students. Therefore, an approach that investigates student behaviors in eLearning environments using a data-intensive machine learning approach is appreciated. A hybrid approach was used to understand how each previously told variables are related to the other. A more quantitative approach was used referred to literature to understand the weights of each factor for adoption and in terms of performance. The data set was collected from previously done research to help the training and testing process in ML. Special attention was made to incorporating different dimensionality of the data to understand the dependency levels of each. Five independent variables out of twelve variables were chosen based on their impact on the dependent variable, and by considering the descriptive statistics, out of three models developed (Random Forest classifier, SVM, and Decision tree classifier), random forest Classifier (Accuracy – 0.8542) gave the highest value for accuracy. Overall, this work met its goals of improving student performance by identifying students who are at-risk and dropout, emphasizing the necessity of using both static and dynamic data.

Keywords: academic performance prediction, e learning, learning analytics, machine learning, predictive model

Procedia PDF Downloads 157
3860 Understanding the Cultural Landscape of Kuttanad: Life within the Constraints of Nature

Authors: K. Nikilsha, Lakshmi Manohar, Debayan Chatterjee

Abstract:

Landscape is a setting that informs the way of life of a set of people, and the repository of intangible values and human meanings that nurture our very existence. Along with the linkage that it forms with our lives, it can be argued that landscape and memory cannot be separated, as landscape is the nucleus of our memories. In this context, this paper studies landscape evolution of a region with unique geographic setting, where the dependency of the inhabitants on its resources, led to the formation of certain peculiar beliefs and taboos that formed the basis of a set of unwritten rules and guidelines which they still follow as a part of their lifestyle. One such example is Kuttanad, a low lying region in Kerala which is a complex mosaic of fragmented agricultural landscape incorporating coastal backwaters, rivers, marshes, paddy fields and water channels. The more the physical involvement with the resources, the more was the inhabitants attachment towards it. This attachment of the inhabitants to the place is very strong because the creation of this land was the result of the toil of the low caste labourers who strived day and night to create Kuttanad, which was reclaimed from water with the help of the finance supplied by their landlords. However, the greatest challenge faced by them is posed by the forces of water in the form of floods. As this land is fed by five rivers, even the slight variation in rainfall in its watershed area can cause a large imbalance in the water level causing the reclaimed land to be inundated. The effects of climate change including increase in rainfall, rise in sea level and change of seasons can act as a catalyst to this damage. Hasty urbanization has led to the conversion of paddy fields to housing plots and coconut/plantain fields giving no regard to the traditional systems which had once respected nature and combated floods and draughts through the various cultural practices and taboos practiced by the people. Thus it is essential to look back at the landscape evolution of Kuttanad and to recognise methods used traditionally in the region to establish a cultural landscape, and to understand how climate change and urbanisation shall pose a challenge to the existing landscape and lifestyle. This research also explores the possibilities of alternative and sustainable approaches for resilient urban development learned from Kuttanad as a case study.

Keywords: ecological conservation, landscape and ecological engineering, landscape evolution, man-made landscapes

Procedia PDF Downloads 266
3859 Prime Graphs of Polynomials and Power Series Over Non-Commutative Rings

Authors: Walaa Obaidallah Alqarafi, Wafaa Mohammed Fakieh, Alaa Abdallah Altassan

Abstract:

Algebraic graph theory is defined as a bridge between algebraic structures and graphs. It has several uses in many fields, including chemistry, physics, and computer science. The prime graph is a type of graph associated with a ring R, where the vertex set is the whole ring R, and two vertices x and y are adjacent if either xRy=0 or yRx=0. However, the investigation of the prime graph over rings remains relatively limited. The behavior of this graph in extended rings, like R[x] and R[[x]], where R is a non-commutative ring, deserves more attention because of the wider applicability in algebra and other mathematical fields. To study the prime graphs over polynomials and power series rings, we used a combination of ring-theoretic and graph-theoretic techniques. This paper focuses on two invariants: the diameter and the girth of these graphs. Furthermore, the work discusses how the graph structures change when passing from R to R[x] and R[[x]]. In our study, we found that the set of strong zero-divisors of ring R represents the set of vertices in prime graphs. Based on this discovery, we redefined the vertices of prime graphs using the definition of strong zero divisors. Additionally, our results show that although the prime graphs of R[x] and R[[x]] are comparable to the graph of R, they have different combinatorial characteristics since these extensions contain new strong zero-divisors. In particular, we find conditions in which the diameter and girth of the graphs, as they expand from R to R[x] and R[[x]], do not change or do change. In conclusion, this study shows how extending a non-commutative ring R to R[x] and R[[x]] affects the structure of their prime graphs, particularly in terms of diameter and girth. These findings enhance the understanding of the relationship between ring extensions and graph properties.

Keywords: prime graph, diameter, girth, polynomial ring, power series ring

Procedia PDF Downloads 22
3858 Compact Optical Sensors for Harsh Environments

Authors: Branislav Timotijevic, Yves Petremand, Markus Luetzelschwab, Dara Bayat, Laurent Aebi

Abstract:

Optical miniaturized sensors with remote readout are required devices for the monitoring in harsh electromagnetic environments. As an example, in turbo and hydro generators, excessively high vibrations of the end-windings can lead to dramatic damages, imposing very high, additional service costs. A significant change of the generator temperature can also be an indicator of the system failure. Continuous monitoring of vibrations, temperature, humidity, and gases is therefore mandatory. The high electromagnetic fields in the generators impose the use of non-conductive devices in order to prevent electromagnetic interferences and to electrically isolate the sensing element to the electronic readout. Metal-free sensors are good candidates for such systems since they are immune to very strong electromagnetic fields and given the fact that they are non-conductive. We have realized miniature optical accelerometer and temperature sensors for a remote sensing of the harsh environments using the common, inexpensive silicon Micro Electro-Mechanical System (MEMS) platform. Both devices show highly linear response. The accelerometer has a deviation within 1% from the linear fit when tested in a range 0 – 40 g. The temperature sensor can provide the measurement accuracy better than 1 °C in a range 20 – 150 °C. The design of other type of sensors for the environments with high electromagnetic interferences has also been discussed.

Keywords: optical MEMS, temperature sensor, accelerometer, remote sensing, harsh environment

Procedia PDF Downloads 367
3857 Simulation Studies of High-Intensity, Nanosecond Pulsed Electric Fields Induced Dynamic Membrane Electroporation

Authors: Jiahui Song

Abstract:

The application of an electric field can cause poration at cell membranes. This includes the outer plasma membrane, as well as the membranes of intracellular organelles. In order to analyze and predict such electroporation effects, it becomes necessary to first evaluate the electric fields and the transmembrane voltages. This information can then be used to assess changes in the pore formation energy that finally yields the pore distributions and their radii based on the Smolchowski equation. The dynamic pore model can be achieved by including a dynamic aspect and a dependence on the pore population density into the pore formation energy equation. These changes make the pore formation energy E(r) self-adjusting in response to pore formation without causing uncontrolled growth and expansion. By using dynamic membrane tension, membrane electroporation in response to a 180kV/cm trapezoidal pulse with a 10 ns on time and 1.5 ns rise- and fall-times is discussed. Poration is predicted to occur at times beyond the peak at around 9.2 ns. Modeling also yields time-dependent distributions of the membrane pore population after multiple pulses. It shows that the pore distribution shifts to larger values of the radius with multiple pulsing. Molecular dynamics (MD) simulations are also carried out for a fixed field of 0.5 V/nm to demonstrate nanopore formation from a microscopic point of view. The result shows that the pore is predicted to be about 0.9 nm in diameter and somewhat narrower at the central point.

Keywords: high-intensity, nanosecond, dynamics, electroporation

Procedia PDF Downloads 165
3856 Leadership Development of Professional Ethiopian Women in Science, Technology, Engineering, and Mathematics: Insights Gained through an Onsite Culturally Embedded Workshop

Authors: Araceli Martinez Ortiz, Gillian Bayne, Solomon Abraham

Abstract:

This paper describes research led by faculty from three American universities and four Ethiopian universities on the delivery of professional leadership development for early-career female Ethiopian university instructors in the Science, Technology, Engineering, and Mathematics (STEM) fields. The objective was to carry out a case study focused on the impact of an innovative intervention program designed to assist in the empowerment and leadership development related to teaching effectiveness, scholarly activity participation, and professional service participation by female instructors. This research was conducted utilizing a case study methodology for the weeklong intervention and a survey to capture the voices of the leadership program participants. The data regarding insights into the challenges and opportunities for women in these fields is presented. The research effort project expands upon existing linkages between universities to support professional development and research effort in this region of the world. Findings indicate the positive reception of this kind of professional development by the participating women. Survey data also reflects the particular cultural challenges professional women in STEM education face in Ethiopia as well as the global challenges of balancing family expectations with career development.

Keywords: Ethiopian women, STEM leadership, professional development, gender equity

Procedia PDF Downloads 112
3855 Accelerating Quantum Chemistry Calculations: Machine Learning for Efficient Evaluation of Electron-Repulsion Integrals

Authors: Nishant Rodrigues, Nicole Spanedda, Chilukuri K. Mohan, Arindam Chakraborty

Abstract:

A crucial objective in quantum chemistry is the computation of the energy levels of chemical systems. This task requires electron-repulsion integrals as inputs, and the steep computational cost of evaluating these integrals poses a major numerical challenge in efficient implementation of quantum chemical software. This work presents a moment-based machine-learning approach for the efficient evaluation of electron-repulsion integrals. These integrals were approximated using linear combinations of a small number of moments. Machine learning algorithms were applied to estimate the coefficients in the linear combination. A random forest approach was used to identify promising features using a recursive feature elimination approach, which performed best for learning the sign of each coefficient but not the magnitude. A neural network with two hidden layers were then used to learn the coefficient magnitudes along with an iterative feature masking approach to perform input vector compression, identifying a small subset of orbitals whose coefficients are sufficient for the quantum state energy computation. Finally, a small ensemble of neural networks (with a median rule for decision fusion) was shown to improve results when compared to a single network.

Keywords: quantum energy calculations, atomic orbitals, electron-repulsion integrals, ensemble machine learning, random forests, neural networks, feature extraction

Procedia PDF Downloads 116
3854 Dimension of Water Accessibility in the Southern Part of Niger State, Nigeria

Authors: Kudu Dangana, Pai H. Halilu, Osesienemo R. Asiribo-Sallau, Garba Inuwa Kuta

Abstract:

The study examined the determinants of household water accessibility in Southern part of Niger State, Nigeria. Data for the study was obtained from primary and secondary sources using questionnaire, interview, personal observation and documents. 1,192 questionnaires were administered; sampling techniques adopted are combination of purposive, stratified and simple random. Purposive sampling technique was used to determine sample frame; sample unit was determined using stratified sampling method and simple random technique was used in administering questionnaires. The result was analyzed within the scope of “WHO” water accessibility indicators using descriptive statistics. Major sources of water in the area are well; hand and electric pump borehole and streams. These sources account for over 90% of household’s water. Average per capita water consumption in the area is 22 liters per day, while location efficiency of facilities revealed an average of 80 people per borehole. Household water accessibility is affected mainly by the factors of distances, time spent to obtain water, low income status of the majority of respondents to access modern water infrastructure, and to a lesser extent household size. Recommendations includes, all tiers of government to intensify efforts in providing water infrastructures and existing ones through budgetary provisions, and communities should organize fund raising bazaar, so as to raise fund to improve water infrastructures in the area.

Keywords: accessibility, determined, stratified, scope

Procedia PDF Downloads 394
3853 A Comparative Analysis of Vocabulary Learning Strategies among EFL Freshmen and Senior Medical Sciences Students across Different Fields of Study

Authors: M. Hadavi, Z. Hashemi

Abstract:

Learning strategies play an important role in the development of language skills. Vocabulary learning strategies as the backbone of these strategies have become a major part of English language teaching. This study is a comparative analysis of Vocabulary Learning Strategies (VLS) use and preference among freshmen and senior EFL medical sciences students with different fields of study. 449 students (236 freshman and 213 seniors) participated in the study. 64.6% were female and 35.4% were male. The instrument utilized in this research was a questionnaire consisting of 41 items related to the students’ approach to vocabulary learning. The items were classified under eight sections as dictionary strategies, guessing strategies, study preferences, memory strategies, autonomy, note- taking strategies, selective attention, and social strategies. The participants were asked to answer each item with a 5-point Likert-style frequency scale as follows:1) I never or almost never do this, 2) I don’t usually do this, 3) I sometimes do this, 4) I usually do this, and 5)I always or almost always do this. The results indicated that freshmen students and particularly surgical technology students used more strategies compared to the seniors. Overall guessing and dictionary strategies were the most frequently used strategies among all the learners (p=0/000). The mean and standard deviation of using VLS in the students who had no previous history of participating in the private English language classes was less than the students who had attended these type of classes (p=0/000). Female students tended to use social and study preference strategies whereas male students used mostly guessing and dictionary strategies. It can be concluded that the senior students under instruction from the university have learned to rely on themselves and choose the autonomous strategies more, while freshmen students use more strategies that are related to the study preferences.

Keywords: vocabulary leaning strategies, medical sciences, students, linguistics

Procedia PDF Downloads 452
3852 Sequence Polymorphism and Haplogroup Distribution of Mitochondrial DNA Control Regions HVS1 and HVS2 in a Southwestern Nigerian Population

Authors: Ogbonnaya O. Iroanya, Samson T. Fakorede, Osamudiamen J. Edosa, Hadiat A. Azeez

Abstract:

The human mitochondrial DNA (mtDNA) is about 17 kbp circular DNA fragments found within the mitochondria together with smaller fragments of 1200 bp known as the control region. Knowledge of variation within populations has been employed in forensic and molecular anthropology studies. The study was aimed at investigating the polymorphic nature of the two hypervariable segments (HVS) of the mtDNA, i.e., HVS1 and HVS2, and to determine the haplogroup distribution among individuals resident in Lagos, Southwestern Nigeria. Peripheral blood samples were obtained from sixty individuals who are not related maternally, followed by DNA extraction and amplification of the extracted DNA using primers specific for the regions under investigation. DNA amplicons were sequenced, and sequenced data were aligned and compared to the revised Cambridge Reference Sequence (rCRS) GenBank Accession number: NC_012920.1) using BioEdit software. Results obtained showed 61 and 52 polymorphic nucleotide positions for HVS1 and HVS2, respectively. While a total of three indels mutation were recorded for HVS1, there were seven for HVS2. Also, transition mutations predominate nucleotide change observed in the study. Genetic diversity (GD) values for HVS1 and HVS2 were estimated to be 84.21 and 90.4%, respectively, while random match probability was 0.17% for HVS1 and 0.89% for HVS2. The study also revealed mixed haplogroups specific to the African (L1-L3) and the Eurasians (U and H) lineages. New polymorphic sites obtained from the study are promising for human identification purposes.

Keywords: hypervariable region, indels, mitochondrial DNA, polymorphism, random match probability

Procedia PDF Downloads 115
3851 Method to Find a ε-Optimal Control of Stochastic Differential Equation Driven by a Brownian Motion

Authors: Francys Souza, Alberto Ohashi, Dorival Leao

Abstract:

We present a general solution for finding the ε-optimal controls for non-Markovian stochastic systems as stochastic differential equations driven by Brownian motion, which is a problem recognized as a difficult solution. The contribution appears in the development of mathematical tools to deal with modeling and control of non-Markovian systems, whose applicability in different areas is well known. The methodology used consists to discretize the problem through a random discretization. In this way, we transform an infinite dimensional problem in a finite dimensional, thereafter we use measurable selection arguments, to find a control on an explicit form for the discretized problem. Then, we prove the control found for the discretized problem is a ε-optimal control for the original problem. Our theory provides a concrete description of a rather general class, among the principals, we can highlight financial problems such as portfolio control, hedging, super-hedging, pairs-trading and others. Therefore, our main contribution is the development of a tool to explicitly the ε-optimal control for non-Markovian stochastic systems. The pathwise analysis was made through a random discretization jointly with measurable selection arguments, has provided us with a structure to transform an infinite dimensional problem into a finite dimensional. The theory is applied to stochastic control problems based on path-dependent stochastic differential equations, where both drift and diffusion components are controlled. We are able to explicitly show optimal control with our method.

Keywords: dynamic programming equation, optimal control, stochastic control, stochastic differential equation

Procedia PDF Downloads 190
3850 The Profit Trend of Cosmetics Products Using Bootstrap Edgeworth Approximation

Authors: Edlira Donefski, Lorenc Ekonomi, Tina Donefski

Abstract:

Edgeworth approximation is one of the most important statistical methods that has a considered contribution in the reduction of the sum of standard deviation of the independent variables’ coefficients in a Quantile Regression Model. This model estimates the conditional median or other quantiles. In this paper, we have applied approximating statistical methods in an economical problem. We have created and generated a quantile regression model to see how the profit gained is connected with the realized sales of the cosmetic products in a real data, taken from a local business. The Linear Regression of the generated profit and the realized sales was not free of autocorrelation and heteroscedasticity, so this is the reason that we have used this model instead of Linear Regression. Our aim is to analyze in more details the relation between the variables taken into study: the profit and the finalized sales and how to minimize the standard errors of the independent variable involved in this study, the level of realized sales. The statistical methods that we have applied in our work are Edgeworth Approximation for Independent and Identical distributed (IID) cases, Bootstrap version of the Model and the Edgeworth approximation for Bootstrap Quantile Regression Model. The graphics and the results that we have presented here identify the best approximating model of our study.

Keywords: bootstrap, edgeworth approximation, IID, quantile

Procedia PDF Downloads 160
3849 Interpretation and Prediction of Geotechnical Soil Parameters Using Ensemble Machine Learning

Authors: Goudjil kamel, Boukhatem Ghania, Jlailia Djihene

Abstract:

This paper delves into the development of a sophisticated desktop application designed to calculate soil bearing capacity and predict limit pressure. Drawing from an extensive review of existing methodologies, the study meticulously examines various approaches employed in soil bearing capacity calculations, elucidating their theoretical foundations and practical applications. Furthermore, the study explores the burgeoning intersection of artificial intelligence (AI) and geotechnical engineering, underscoring the transformative potential of AI- driven solutions in enhancing predictive accuracy and efficiency.Central to the research is the utilization of cutting-edge machine learning techniques, including Artificial Neural Networks (ANN), XGBoost, and Random Forest, for predictive modeling. Through comprehensive experimentation and rigorous analysis, the efficacy and performance of each method are rigorously evaluated, with XGBoost emerging as the preeminent algorithm, showcasing superior predictive capabilities compared to its counterparts. The study culminates in a nuanced understanding of the intricate dynamics at play in geotechnical analysis, offering valuable insights into optimizing soil bearing capacity calculations and limit pressure predictions. By harnessing the power of advanced computational techniques and AI-driven algorithms, the paper presents a paradigm shift in the realm of geotechnical engineering, promising enhanced precision and reliability in civil engineering projects.

Keywords: limit pressure of soil, xgboost, random forest, bearing capacity

Procedia PDF Downloads 26
3848 A Probabilistic Theory of the Buy-Low and Sell-High for Algorithmic Trading

Authors: Peter Shi

Abstract:

Algorithmic trading is a rapidly expanding domain within quantitative finance, constituting a substantial portion of trading volumes in the US financial market. The demand for rigorous and robust mathematical theories underpinning these trading algorithms is ever-growing. In this study, the author establishes a new stock market model that integrates the Efficient Market Hypothesis and the statistical arbitrage. The model, for the first time, finds probabilistic relations between the rational price and the market price in terms of the conditional expectation. The theory consequently leads to a mathematical justification of the old market adage: buy-low and sell-high. The thresholds for “low” and “high” are precisely derived using a max-min operation on Bayes’s error. This explicit connection harmonizes the Efficient Market Hypothesis and Statistical Arbitrage, demonstrating their compatibility in explaining market dynamics. The amalgamation represents a pioneering contribution to quantitative finance. The study culminates in comprehensive numerical tests using historical market data, affirming that the “buy-low” and “sell-high” algorithm derived from this theory significantly outperforms the general market over the long term in four out of six distinct market environments.

Keywords: efficient market hypothesis, behavioral finance, Bayes' decision, algorithmic trading, risk control, stock market

Procedia PDF Downloads 72
3847 Competition, Stability, and Economic Growth: A Causality Approach

Authors: Mahvish Anwaar

Abstract:

Research Question: In this paper, we explore the causal relationship between banking competition, banking stability, and economic growth. Research Findings: The unbalanced panel data starting from 2000 to 2018 is collected to analyze the causality among banking competition, banking stability, and economic growth. The main focus of the study is to check the direction of causality among selected variables. The results of the study support the demand following, supply leading, feedback, and neutrality hypothesis conditional to different measures of banking competition, banking stability, and economic growth. Theoretical Implication: Jayakumar, Pradhan, Dash, Maradana, and Gaurav (2018) proposed a theoretical model of the causal relationship between banking competition, banking stability, and economic growth by using different indicators. So, we empirically test the proposed indicators in our study. This study makes a contribution to the literature by showing the defined relationship between developing and developed countries. Policy Implications: The study covers various policy implications regarding investors to analyze how to properly manage their finances, and government agencies will take help from the present study to find the best and most suitable policies by examining how the economy can grow concerning its finances.

Keywords: competition, stability, economic growth, vector auto-regression, granger causality

Procedia PDF Downloads 64
3846 Stochastic Optimization of a Vendor-Managed Inventory Problem in a Two-Echelon Supply Chain

Authors: Bita Payami-Shabestari, Dariush Eslami

Abstract:

The purpose of this paper is to develop a multi-product economic production quantity model under vendor management inventory policy and restrictions including limited warehouse space, budget, and number of orders, average shortage time and maximum permissible shortage. Since the “costs” cannot be predicted with certainty, it is assumed that data behave under uncertain environment. The problem is first formulated into the framework of a bi-objective of multi-product economic production quantity model. Then, the problem is solved with three multi-objective decision-making (MODM) methods. Then following this, three methods had been compared on information on the optimal value of the two objective functions and the central processing unit (CPU) time with the statistical analysis method and the multi-attribute decision-making (MADM). The results are compared with statistical analysis method and the MADM. The results of the study demonstrate that augmented-constraint in terms of optimal value of the two objective functions and the CPU time perform better than global criteria, and goal programming. Sensitivity analysis is done to illustrate the effect of parameter variations on the optimal solution. The contribution of this research is the use of random costs data in developing a multi-product economic production quantity model under vendor management inventory policy with several constraints.

Keywords: economic production quantity, random cost, supply chain management, vendor-managed inventory

Procedia PDF Downloads 129
3845 Unlocking Green Hydrogen Potential: A Machine Learning-Based Assessment

Authors: Said Alshukri, Mazhar Hussain Malik

Abstract:

Green hydrogen is hydrogen produced using renewable energy sources. In the last few years, Oman aimed to reduce its dependency on fossil fuels. Recently, the hydrogen economy has become a global trend, and many countries have started to investigate the feasibility of implementing this sector. Oman created an alliance to establish the policy and rules for this sector. With motivation coming from both global and local interest in green hydrogen, this paper investigates the potential of producing hydrogen from wind and solar energies in three different locations in Oman, namely Duqm, Salalah, and Sohar. By using machine learning-based software “WEKA” and local metrological data, the project was designed to figure out which location has the highest wind and solar energy potential. First, various supervised models were tested to obtain their prediction accuracy, and it was found that the Random Forest (RF) model has the best prediction performance. The RF model was applied to 2021 metrological data for each location, and the results indicated that Duqm has the highest wind and solar energy potential. The system of one wind turbine in Duqm can produce 8335 MWh/year, which could be utilized in the water electrolysis process to produce 88847 kg of hydrogen mass, while a solar system consisting of 2820 solar cells is estimated to produce 1666.223 MWh/ year which is capable of producing 177591 kg of hydrogen mass.

Keywords: green hydrogen, machine learning, wind and solar energies, WEKA, supervised models, random forest

Procedia PDF Downloads 79
3844 Land Suitability Prediction Modelling for Agricultural Crops Using Machine Learning Approach: A Case Study of Khuzestan Province, Iran

Authors: Saba Gachpaz, Hamid Reza Heidari

Abstract:

The sharp increase in population growth leads to more pressure on agricultural areas to satisfy the food supply. To achieve this, more resources should be consumed and, besides other environmental concerns, highlight sustainable agricultural development. Land-use management is a crucial factor in obtaining optimum productivity. Machine learning is a widely used technique in the agricultural sector, from yield prediction to customer behavior. This method focuses on learning and provides patterns and correlations from our data set. In this study, nine physical control factors, namely, soil classification, electrical conductivity, normalized difference water index (NDWI), groundwater level, elevation, annual precipitation, pH of water, annual mean temperature, and slope in the alluvial plain in Khuzestan (an agricultural hotspot in Iran) are used to decide the best agricultural land use for both rainfed and irrigated agriculture for ten different crops. For this purpose, each variable was imported into Arc GIS, and a raster layer was obtained. In the next level, by using training samples, all layers were imported into the python environment. A random forest model was applied, and the weight of each variable was specified. In the final step, results were visualized using a digital elevation model, and the importance of all factors for each one of the crops was obtained. Our results show that despite 62% of the study area being allocated to agricultural purposes, only 42.9% of these areas can be defined as a suitable class for cultivation purposes.

Keywords: land suitability, machine learning, random forest, sustainable agriculture

Procedia PDF Downloads 86
3843 Ethiopian Women in Science, Technology, Engineering, and Mathematics Higher Education: Insights Gained Through an Onsite Culturally Embedded Workshop

Authors: Araceli Martinez Ortiz, Gillian U Bayne, Solomon Abraham

Abstract:

This paper describes research led by faculty from three American universities and four Ethiopian universities on the delivery of professional leadership development for early-career female Ethiopian university instructors in the Science, Technology, Engineering, and Mathematics (STEM) fields. The objective was to carry out a case study focused on the impact of an innovative intervention program designed to assist in the empowerment and leadership development related to teaching effectiveness, scholarly activity participation, and professional service participation by female instructors. This research was conducted utilizing a case study methodology for the weeklong intervention and a survey to capture the voices of the leadership program participants. The data regarding insights into the challenges and opportunities for women in these fields is presented. The research effort project expands upon existing linkages between universities to support professional development and research effort in this region of the world. Findings indicate the positive reception of this kind of professional development by the participating women. Survey data also reflects the educational technology and cultural challenges professional women in STEM education face in Ethiopia as well as the global challenges of balancing family expectations with career development.

Keywords: women, STEM education, higher education, Ethiopia

Procedia PDF Downloads 70
3842 Self-Action of Pyroelectric Spatial Soliton in Undoped Lithium Niobate Samples with Pyroelectric Mechanism of Nonlinear Response

Authors: Anton S. Perin, Vladimir M. Shandarov

Abstract:

Compensation for the nonlinear diffraction of narrow laser beams with wavelength of 532 and the formation of photonic waveguides and waveguide circuits due to the contribution of pyroelectric effect to the nonlinear response of lithium niobate crystal have been experimentally demonstrated. Complete compensation for the linear and nonlinear diffraction broadening of light beams is obtained upon uniform heating of an undoped sample from room temperature to 55 degrees Celsius. An analysis of the light-field distribution patterns and the corresponding intensity distribution profiles allowed us to estimate the spacing for the channel waveguides. The observed behavior of bright soliton beams may be caused by their coherent interaction, which manifests itself in repulsion for anti-phase light fields and in attraction for in-phase light fields. The experimental results of this study showed a fundamental possibility of forming optically complex waveguide structures in lithium niobate crystals with pyroelectric mechanism of nonlinear response. The topology of these structures is determined by the light field distribution on the input face of crystalline sample. The optical induction of channel waveguide elements by interacting spatial solitons makes it possible to design optical systems with a more complex topology and a possibility of their dynamic reconfiguration.

Keywords: self-action, soliton, lithium niobate, piroliton, photorefractive effect, pyroelectric effect

Procedia PDF Downloads 168