Search results for: regression models drone

Authors: H. A. Jawan

Abstract:

This review describes the models of environmental-related crack propagation of aluminum alloys (7xxx) during the last few decades. Acknowledge on effects of different factors on the susceptibility to SCC permits to propose valuable mechanisms on crack advancement. The reliable mechanism of cracking give a possibility to propose the optimum chemical composition and thermal treatment conditions resulting in microstructure the most suitable for real environmental condition and stress state.

Keywords: microstructure, environmental, propagation, mechanism

Procedia PDF Downloads 418

Authors: Aditya Patil, Arun Patil, Vaishali Patil, Sudhir Chitnis, Anjum Patel

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Rapid growth has given e-commerce platforms a lot of client behavior and spending data. To maximize their strategy, businesses must understand how customers utilize online shopping platforms and what influences their purchases. Our research focuses on e-commerce user behavior and purchasing trends. This extensive study examines spending and user behavior. Regression and grouping disclose relevant data from the dataset. We can understand user spending trends via multilevel regression. We can analyze how pricing, user demographics, and product categories affect customer purchase decisions with this technique. Clustering groups consumers by spending. Important information was found. Purchase habits vary by user group. Our analysis illuminates the complex world of e-commerce consumer behavior and purchase trends. Understanding user behavior helps create effective e-commerce marketing strategies. This market can benefit from K-means clustering. This study focuses on tailoring strategies to user groups and improving product and price effectiveness. Customer buying behaviors across categories were shown via K-means clusters. Average spending is highest in Cluster 4 and lowest in Cluster 3. Clothing is less popular than gadgets and appliances around the holidays. Cluster spending distribution is examined using average variables. Our research enhances e-commerce analytics. Companies can improve customer service and decision-making with this data.

Keywords: e-commerce, regression, clustering, k-means

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Authors: Samvel H. Sargsyan

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Together with the study of electron-optical properties of nanostructures and proceeding from experiment-based data, the study of the mechanical properties of nanostructures has become quite actual. For the study of the mechanical properties of fullerene, carbon nanotubes, graphene and other nanostructures one of the crucial issues is the construction of their adequate mathematical models. Among all mathematical models of graphene or carbon nano-tubes, this so-called discrete-continuous model is specifically important. It substitutes the interactions between atoms by elastic beams or springs. The present paper demonstrates the construction of the discrete-continual beam model for carbon nanotubes or graphene, where the micropolar beam model based on the theory of moment elasticity is accepted. With the account of the energy balance principle, the elastic moment constants for the beam model, expressed by the physical and geometrical parameters of carbon nanotube or graphene, are determined. By switching from discrete-continual beam model to the continual, the models of micropolar elastic cylindrical shell and micropolar elastic plate are confirmed as continual models for carbon nanotube and graphene respectively.

Keywords: carbon nanotube, discrete-continual, elastic, graphene, micropolar, plate, shell

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Authors: Salah Alrabeei, Mohammad Yousuf

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The goal of option pricing theory is to help the investors to manage their money, enhance returns and control their financial future by theoretically valuing their options. Modeling option pricing by Black-School models with jumps guarantees to consider the market movement. However, only numerical methods can solve this model. Furthermore, not all the numerical methods are efficient to solve these models because they have nonsmoothing payoffs or discontinuous derivatives at the exercise price. In this paper, the exponential time differencing (ETD) method is applied for solving partial integrodifferential equations arising in pricing European options under Merton’s and Kou’s jump-diffusion models. Fast Fourier Transform (FFT) algorithm is used as a matrix-vector multiplication solver, which reduces the complexity from O(M2) into O(M logM). A partial fraction form of Pad`e schemes is used to overcome the complexity of inverting polynomial of matrices. These two tools guarantee to get efficient and accurate numerical solutions. We construct a parallel and easy to implement a version of the numerical scheme. Numerical experiments are given to show how fast and accurate is our scheme.

Keywords: Integral differential equations, , L-stable methods, pricing European options, Jump–diffusion model

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Authors: Hussaini Bala

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There are different opinions in the literature on the relationship between Audit Committee characteristics and earnings management. The mix of opinions makes the direction of their relationship ambiguous. This study investigated the relationship between Audit Committee characteristics and earnings management of listed food and beverages Firms in Nigeria. The study covered the period of six years from 2007 to 2012. Data for the study were extracted from the Firms’ annual reports and accounts. After running the OLS regression, a robustness test was conducted for the validity of statistical inferences. The dependent variable was generated using two steps regression in order to determine the discretionary accrual of the sample Firms. Multiple regression was employed to run the data of the study using Random Model. The results from the analysis revealed a significant association between audit committee characteristics and earnings management of the Firms. While audit committee size and committees’ financial expertise showed an inverse relationship with earnings management, committee’s independence, and frequency of meetings are positively and significantly related to earnings management. In line with the findings, the study recommended among others that listed food and beverages Firms in Nigeria should strictly comply with the provision of Companies and Allied Matters Act (CAMA) and SEC Code of Corporate Governance on the issues regarding Audit Committees. Regulators such as SEC should increase the minimum number of Audit Committee members with financial expertise and also have a statutory position on the maximum number of Audit Committees meetings, which should not be greater than four meetings in a year as SEC code of corporate governance is silent on this.

Keywords: audit committee, earnings management, listed Food and beverages size, leverage, Nigeria

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Authors: Sai AKhila Budaraju

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Heritage sites are an integral part of cities and carry a sense of identity to the cities/ towns, but the process of urbanization is a carrying potential threat for the loss of these heritage sites/monuments. Both Central and State Governments listed the historic Golconda fort as National Important Monument and the Heritage precinct with eight heritage-listed buildings and two historical sites respectively, for conservation and preservation, due to the presence of IT Corridor 6kms away accommodating more people in the precinct is under constant pressure. The heritage precinct possesses high property values, being a prime location connecting the IT corridor and CBD (central business district )areas. The primary objective of the study was to assess and identify the factors that are affecting the heritage precinct through Mapping and documentation, Identifying and assessing the factors through empirical analysis, Ordinal regression analysis and Hedonic Pricing Model. Ordinal regression analysis was used to identify the factors that contribute to the changes in the precinct due to urbanization. Hedonic Pricing Model was used to understand and establish a relation whether the presence of historical monuments is also a contributing factor to the property value and to what extent this influence can contribute. The above methods and field visit indicates the Physical, socio-economic factors and the neighborhood characteristics of the precinct contributing to the property values. The outturns and the potential elements derived from the analysis of the Development Control Rules were derived as recommendations to Integrate both Old and newly built environments.

Keywords: heritage planning, heritage conservation, hedonic pricing model, ordinal regression analysis

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Authors: Jamuna Konda, Umamaheswara Reddy Karumuri, Sriramya Muthugi, Varun Pishati, Ravi Shakya,

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This paper investigates the challenges involved in mathematical modeling of plant simulation models ensuring the performance of the plant models much closer to the real time physical model. The paper includes the analysis performed and investigation on different methods of modeling, design and development for plant model. Issues which impact the design time, model accuracy as real time model, tool dependence are analyzed. The real time hardware plant would be a combination of multiple physical models. It is more challenging to test the complete system with all possible test scenarios. There are possibilities of failure or damage of the system due to any unwanted test execution on real time.

Keywords: model based design (MBD), MATLAB, Simulink, stateflow, plant model, real time model, real-time workshop (RTW), target language compiler (TLC)

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Authors: Eric Dede, Marcus Tindall, John W. Cherrie, Steve Hankin, Christopher Collins

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Current exposure models used in contaminated land risk assessment are highly conservative. Use of these models may lead to over-estimation of actual exposures, possibly resulting in negative financial implications due to un-necessary remediation. Thus, we are carrying out a study seeking to improve our understanding of human exposure to selected toxic elements in soil: arsenic (As), cadmium (Cd), chromium (Cr), nickel (Ni), and lead (Pb) resulting from allotment land-use. The study employs biomonitoring and physiologically-based pharmacokinetic (PBPK) modelling to quantify human exposure to these elements. We recruited 37 allotment users (adults > 18 years old) in Scotland, UK, to participate in the study. Concentrations of the elements (and their bioaccessibility) were measured in allotment samples (soil and allotment produce). Amount of produce consumed by the participants and participants’ biological samples (urine and blood) were collected for up to 12 consecutive months. Ethical approval was granted by the University of Reading Research Ethics Committee. PBPK models (coded in MATLAB) were used to estimate the distribution and accumulation of the elements in key body compartments, thus indicating the internal body burden. Simulating low element intake (based on estimated ‘doses’ from produce consumption records), predictive models suggested that detection of these elements in urine and blood was possible within a given period of time following exposure. This information was used in planning biomonitoring, and is currently being used in the interpretation of test results from biological samples. Evaluation of the models is being carried out using biomonitoring data, by comparing model predicted concentrations and measured biomarker concentrations. The PBPK models will be used to generate bioavailability values, which could be incorporated in contaminated land exposure models. Thus, the findings from this study will promote a more sustainable approach to contaminated land management.

Keywords: biomonitoring, exposure, PBPK modelling, toxic elements

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Authors: Armin Rahimi

Abstract:

The Gravity Recovery and Climate Experiment (GRACE) has been a very successful project in determining math redistribution within the Earth system. Large deformations caused by earthquakes are in the high frequency band. Unfortunately, GRACE is only capable to provide reliable estimate at the low-to-medium frequency band for the gravitational changes. In this study, we computed the gravity changes after the 2012 Mw8.6 Indian Ocean earthquake off-Sumatra using the GRACE Level-2 monthly spherical harmonic (SH) solutions released by the University of Texas Center for Space Research (UTCSR). Moreover, we calculated gravity changes using different fault models derived from teleseismic data. The model predictions showed non-negligible discrepancies in gravity changes. However, after removing high-frequency signals, using Gaussian filtering 350 km commensurable GRACE spatial resolution, the discrepancies vanished, and the spatial patterns of total gravity changes predicted from all slip models became similar at the spatial resolution attainable by GRACE observations, and predicted-gravity changes were consistent with the GRACE-detected gravity changes. Nevertheless, the fault models, in which give different slip amplitudes, proportionally lead to different amplitude in the predicted gravity changes.

Keywords: undersea earthquake, GRACE observation, gravity change, dislocation model, slip distribution

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Authors: Sahar Elsaleh, Ghada Farhat, Shaikha Al-Derham, Fasih Alam

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Background: Cesarean section (CS) delivery is one of the major concerns both in developing and developed countries. The rate of CS deliveries are on the rise globally, and especially in Qatar. Many socio-economic, demographic, clinical and institutional factors play an important role for cesarean sections. This study aims to investigate factors affecting the prevalence of CS among women in Qatar using the UNICEF’s Multiple Indicator Cluster Survey (MICS) 2012 database. Methods: The study has focused on the women’s questionnaire of the MICS, which was successfully distributed to 5699 participants. Following study inclusion and exclusion criteria, a final sample of 761 women aged 19- 49 years who had at least one delivery of giving birth in their lifetime before the survey were included. A number of socio-economic, demographic, clinical and institutional factors, identified through literature review and available in the data, were considered for the analyses. Bivariate and multivariate logistic regression models, along with a multi-level modeling to investigate clustering effect, were undertaken to identify the factors that affect CS prevalence in Qatar. Results: From the bivariate analyses the study has shown that, a number of categorical factors are statistically significantly associated with the dependent variable (CS). When identifying the factors from a multivariate logistic regression, the study found that only three categorical factors -‘age of women’, ‘place at delivery’ and ‘baby weight’ appeared to be significantly affecting the CS among women in Qatar. Although the MICS dataset is based on a cluster survey, an exploratory multi-level analysis did not show any clustering effect, i.e. no significant variation in results at higher level (households), suggesting that all analyses at lower level (individual respondent) are valid without any significant bias in results. Conclusion: The study found a statistically significant association between the dependent variable (CS delivery) and age of women, frequency of TV watching, assistance at birth and place of birth. These results need to be interpreted cautiously; however, it can be used as evidence-base for further research on cesarean section delivery in Qatar.

Keywords: cesarean section, factors, multiple indicator cluster survey, MICS database, Qatar

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Authors: Ryan A. Black, Stacey A. McCaffrey

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Over the past few decades, great strides have been made towards improving the science in the measurement of psychological constructs. Item Response Theory (IRT) has been the foundation upon which statistical models have been derived to increase both precision and accuracy in psychological measurement. These models are now being used widely to develop and refine tests intended to measure an individual's level of academic achievement, aptitude, and intelligence. Recently, the field of clinical psychology has adopted IRT models to measure psychopathological phenomena such as depression, anxiety, and addiction. Because advances in IRT measurement models are being made so rapidly across various fields, it has become quite challenging for psychologists and other behavioral scientists to keep abreast of the most recent developments, much less learn how to employ and decide which models are the most appropriate to use in their line of work. In the same vein, IRT measurement models vary greatly in complexity in several interrelated ways including but not limited to the number of item-specific parameters estimated in a given model, the function which links the expected response and the predictor, response option formats, as well as dimensionality. As a result, inferior methods (a.k.a. Classical Test Theory methods) continue to be employed in efforts to measure psychological constructs, despite evidence showing that IRT methods yield more precise and accurate measurement. To increase the use of IRT methods, this study endeavors to provide a comprehensive overview of binary IRT models; that is, measurement models employed on test data consisting of binary response options (e.g., correct/incorrect, true/false, agree/disagree). Specifically, this study will cover the most basic binary IRT model, known as the 1-parameter logistic (1-PL) model dating back to over 50 years ago, up until the most recent complex, 4-parameter logistic (4-PL) model. Binary IRT models will be defined mathematically and the interpretation of each parameter will be provided. Next, all four binary IRT models will be employed on two sets of data: 1. Simulated data of N=500,000 subjects who responded to four dichotomous items and 2. A pilot analysis of real-world data collected from a sample of approximately 770 subjects who responded to four self-report dichotomous items pertaining to emotional consequences to alcohol use. Real-world data were based on responses collected on items administered to subjects as part of a scale-development study (NIDA Grant No. R44 DA023322). IRT analyses conducted on both the simulated data and analyses of real-world pilot will provide a clear demonstration of how to construct, evaluate, and compare binary IRT measurement models. All analyses will be performed using the new IRT procedure in SAS 9.4. SAS code to generate simulated data and analyses will be available upon request to allow for replication of results.

Keywords: instrument development, item response theory, latent trait theory, psychometrics

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Authors: S. A. N. Danushka, T. A. Weerasinghe

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The diverse necessities of instruction could be addressed effectively with the support of new dimensions of ICT integrated learning such as blended learning –which is a combination of face-to-face and online instruction which ensures greater flexibility in student learning and congruity of course delivery. As blended learning has been the ‘new normality' in education, many experimental and quasi-experimental research studies provide ample of evidence on its successful implementation in many fields of studies, but it is hard to justify whether blended learning could work similarly in the delivery of technology-teacher development programmes (TTDPs). The present study is bound with the particular research uncertainty, and having considered existing research approaches, the study methodology was set to decide the efficient instructional strategies for flipped classroom learning in TTDPs. In a quasi-experimental pre-test and post-test design with a mix-method research approach, the major study objective was tested with two heterogeneous samples (N=135) identified in a virtual learning environment in a Sri Lankan university. Non-randomized informal ‘before-and-after without control group’ design was employed, and two data collection methods, identical pre-test and post-test and Likert-scale questionnaires were used in the study. Selected two instructional strategies, self-directed learning (SDL) and self-regulated learning (SRL), were tested in an appropriate instructional framework with two heterogeneous samples (pre-service and in-service teachers). Data were statistically analyzed, and an efficient instructional strategy was decided via t-test, ANOVA, ANCOVA. The effectiveness of the two instructional strategy implementation models was decided via multiple linear regression analysis. ANOVA (p < 0.05) shows that age, prior-educational qualifications, gender, and work-experiences do not impact on learning achievements of the two diverse groups of learners through the instructional strategy is changed. ANCOVA (p < 0.05) analysis shows that SDL is efficient for two diverse groups of technology-teachers than SRL. Multiple linear regression (p < 0.05) analysis shows that the staged self-directed learning (SSDL) model and four-phased model of motivated self-regulated learning (COPES Model) are efficient in the delivery of course content in flipped classroom learning.

Keywords: COPES model, flipped classroom learning, self-directed learning, self-regulated learning, SSDL model

Procedia PDF Downloads 197

Authors: Ye Xue, Zhenhua Deng

Abstract:

Objectives: Facial soft tissue thickness (FSTT) data could be obtained from CT scans by measuring the face-to-skull distances at sparsely distributed anatomical landmarks by manually located on face and skull. However, automated measurement using 3D facial and skull models by dense points using open-source software has become a viable option due to the development of computed assisted imaging technologies. By utilizing dense FSTT information, it becomes feasible to generate plausible automated facial approximations. Therefore, establishing a comprehensive and detailed, densely calculated FSTT database is crucial in enhancing the accuracy of facial approximation. Materials and methods: This study utilized head CT scans from 250 Chinese adults of Han ethnicity, with 170 participants originally born and residing in northern China and 80 participants in southern China. The age of the participants ranged from 14 to 82 years, and all samples were divided into five non-overlapping age groups. Additionally, samples were also divided into three categories based on BMI information. The 3D Slicer software was utilized to segment bone and soft tissue based on different Hounsfield Unit (HU) thresholds, and surface models of the face and skull were reconstructed for all samples from CT data. Following procedures were performed unsing MeshLab, including converting the face models into hollowed cropped surface models amd automatically measuring the Hausdorff Distance (referred to as FSTT) between the skull and face models. Hausdorff point clouds were colorized based on depth value and exported as PLY files. A histogram of the depth distributions could be view and subdivided into smaller increments. All PLY files were visualized of Hausdorff distance value of each vertex. Basic descriptive statistics (i.e., mean, maximum, minimum and standard deviation etc.) and distribution of FSTT were analysis considering the sex, age, BMI and birthplace. Statistical methods employed included Multiple Regression Analysis, ANOVA, principal component analysis (PCA). Results: The distribution of FSTT is mainly influenced by BMI and sex, as further supported by the results of the PCA analysis. Additionally, FSTT values exceeding 30mm were found to be more sensitive to sex. Birthplace-related differences were observed in regions such as the forehead, orbital, mandibular, and zygoma. Specifically, there are distribution variances in the depth range of 20-30mm, particularly in the mandibular region. Northern males exhibit thinner FSTT in the frontal region of the forehead compared to southern males, while females shows fewer distribution differences between the northern and southern, except for the zygoma region. The observed distribution variance in the orbital region could be attributed to differences in orbital size and shape. Discussion: This study provides a database of Chinese individuals distribution of FSTT and suggested opening source tool shows fine function for FSTT measurement. By incorporating birthplace as an influential factor in the distribution of FSTT, a greater level of detail can be achieved in facial approximation.

Keywords: forensic anthropology, forensic imaging, cranial facial reconstruction, facial soft tissue thickness, CT, open-source tool

Procedia PDF Downloads 58

Authors: M. T. Bouziane, A. Benkhaled, B. Achour

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In this study, data from 30 catchments in the Chott Melghir basin in the semiarid region of southern East Algeria were analyzed to investigate changes in annual discharge, annual precipitation over the 1965-2005 period. These data were analyzed with the aid of Kendall test trend and regression analysis. The results indicate that the major variations in all catchments discharge in Chott Melghir correspond well to the precipitation. Changes in total annual discharge of Chott Melghir were lower than changes in annual precipitation. Annual precipitation decreased by 66 percent and annual discharge decreased by 4 percent. No significant trend is detected for annual discharge and precipitation at major catchments up to 95% confidence level. The decreasing trend in Chott Melghir discharge is mainly attributed to the decrease of precipitation.

Keywords: trends, climate change, precipitation, discharge, Kendall test, regression analysis, Chott Melghir catchments

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Authors: Xitong Liu, Yutong Huang, Shu-Ching Yang

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The phenomena of hypergamous and hypogamous marriages have become popular due to the imbalanced sex ratio caused by Chinese social preference for sons. Our research explores the spillover effect of husbands' lifestyles on their wives' marital satisfaction in China. Both personal and spouse lifestyle elements are utilized to develop regression models to study husbands' spillover effects on women's marital satisfaction. With data from China Family Panel Study and Stata for analysis, we tested our hypothesis that both smoking and substance use by a spouse will negatively impact women's marital satisfaction. Our empirical findings suggest that substance use has negative implications on marriage satisfaction. In particular, husbands' substance use is more critical to wives' marriage satisfaction than wives' behaviours. Conversely, another behavior indicating bad habits, the number of times the spouse drank alcohol, had no significant effect on the wife's marital satisfaction. We concluded our investigation and provided future implications for scholars in the family economics field.

Keywords: Asian/Pacific Islander families, family economics, housework/division of labor, spillover

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Authors: Waqas Ali, Nadeem Majeed

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Requirement engineering is an essence of software development life cycle. The more time we spend on requirement engineering, higher the probability of success. Effective requirement engineering ensures and predicts successful software product. This paper presents the adaptation of requirement engineering practices in small and medium size companies of Pakistan. The study is conducted by questionnaires to show how much of requirement engineering models and practices are followed in Pakistan.

Keywords: requirement engineering, Pakistan, models, practices, organizations

Procedia PDF Downloads 719

Authors: Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Diego Santibañez Oyarce, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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Streptococcus pyogenes is a gram-positive bacteria involved in a wide range of diseases and is a major-human-specific bacterial pathogen. In Chile, this year the 'Ministerio de Salud' declared an alert due to the increase in strains throughout the year. This increase can be attributed to the multitude of factors including antimicrobial resistance (AMR) and Virulence Factors (VF). Understanding these VF and AMR is crucial for developing effective strategies and improving public health responses. Moreover, experimental identification and characterization of these pathogenic mechanisms are labor-intensive and time-consuming. Therefore, new computational methods are required to provide robust techniques for accelerating this identification. Advances in Machine Learning (ML) algorithms represent the opportunity to refine and accelerate the discovery of VF associated with Streptococcus pyogenes. In this work, we evaluate the accuracy of various machine learning models in predicting the virulence factors and antimicrobial resistance of Streptococcus pyogenes, with the objective of providing new methods for identifying the pathogenic mechanisms of this organism.Our comprehensive approach involved the download of 32,798 genbank files of S. pyogenes from NCBI dataset, coupled with the incorporation of data from Virulence Factor Database (VFDB) and Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. These datasets provided labeled examples of both virulent and non-virulent genes, enabling a robust foundation for feature extraction and model training. We employed preprocessing, characterization and feature extraction techniques on primary nucleotide/amino acid sequences and selected the optimal more for model training. The feature set was constructed using sequence-based descriptors (e.g., k-mers and One-hot encoding), and functional annotations based on database prediction. The ML models compared are logistic regression, decision trees, support vector machines, neural networks among others. The results of this work show some differences in accuracy between the algorithms, these differences allow us to identify different aspects that represent unique opportunities for a more precise and efficient characterization and identification of VF and AMR. This comparative analysis underscores the value of integrating machine learning techniques in predicting S. pyogenes virulence and AMR, offering potential pathways for more effective diagnostic and therapeutic strategies. Future work will focus on incorporating additional omics data, such as transcriptomics, and exploring advanced deep learning models to further enhance predictive capabilities.

Keywords: antibiotic resistance, streptococcus pyogenes, virulence factors., machine learning

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Authors: Natália Botica, Luís Luís, Paulo Bernardes

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The Côa Valley, listed World Heritage since 1998, presents more than 1300 open-air engraved rock panels. The Archaeological Park of the Côa Valley recorded the rock art motifs, testing various techniques based on direct tracing processes on the rock, using natural and artificial lighting. In this work, integrated in the "Open Access Rock Art Repository" (RARAA) project, we present the methodology adopted for the vectorial drawing of the rock art motifs based on orthophotos taken from the photogrammetric survey and 3D models of the rocks. We also present the information system designed to integrate the vector drawing and the characterization data of the motifs, as well as the open access sharing, in order to promote their reuse in multiple areas. The 3D models themselves constitute a very detailed record, ensuring the digital preservation of the rock and iconography. Thus, even if a rock or motif disappears, it can continue to be studied and even recreated.

Keywords: rock art, archaeology, iron age, 3D models

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Authors: H. Jawan

Abstract:

This review describes the models of environmental-related crack propagation of aluminum alloys (7xxx) during the last few decades. Acknowledge on effects of different factors on the susceptibility to SCC permits to propose valuable mechanisms on crack advancement. The reliable mechanism of cracking give a possibility to propose the optimum chemical composition and thermal treatment conditions resulting in microstructure the most suitable for real environmental condition and stress state.

Keywords: microstructure, environmental, propagation, mechanism

Procedia PDF Downloads 390

Authors: Yihua Wang, Yunru Lai

Abstract:

Statistical process control has been successfully applied in a variety of industries. In some applications, the quality of a process or product is better characterized and summarized by a functional relationship between a response variable and one or more explanatory variables. A collection of this type of data is called a profile. Profile monitoring is used to understand and check the stability of this relationship or curve over time. The independent assumption for the error term is commonly used in the existing profile monitoring studies. However, in many applications, the profile data show correlations over time. Therefore, we focus on a general linear regression model with a first-order autocorrelation between profiles in this study. We propose an exponentially weighted moving average charting scheme to monitor this type of profile. The simulation study shows that our proposed methods outperform the existing schemes based on the average run length criterion.

Keywords: autocorrelation, EWMA control chart, general linear regression model, profile monitoring

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Authors: Jiwan Kumar, Pooja, Sandeep Negi, Anjum Rouf, Amit Kumar, Naveen Lakra

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In modern times where, health issues are increasing day by day, breast cancer is also one of them, which is very crucial and really important to find in the early stages. Doctors can use this model in order to tell their patients whether a cancer is not harmful (benign) or harmful (malignant). We have used the knowledge of machine learning in order to produce the model. we have used algorithms like Logistic Regression, Random forest, support Vector Classifier, Bayesian Network and Radial Basis Function. We tried to use the data of crucial parts and show them the results in pictures in order to make it easier for doctors. By doing this, we're making ML better at finding breast cancer, which can lead to saving more lives and better health care.

Keywords: Bayesian network, radial basis function, ensemble learning, understandable, data making better, random forest, logistic regression, breast cancer

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Authors: Adel Almasarwah

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This study investigates the nature of association between profit warnings and stock price informativeness in the context of Jordan as an emerging country. The analysis is based on the response of stock price synchronicity to profit warnings percentages that have been published in Jordanian firms throughout the period spanning 2005–2016 in the Amman Stock Exchange. The standard of profit warnings indicators have related negatively to stock price synchronicity in Jordanian firms, meaning that firms with a high portion of profit warnings integrate with more firm-specific information into stock price. Robust regression was used rather than OLS as a parametric test to overcome the variances inflation factor (VIF) and heteroscedasticity issues recognised as having occurred during running the OLS regression; this enabled us to obtained stronger results that fall in line with our prediction that higher profit warning encourages firm investors to collect and process more firm-specific information than common market information.

Keywords: Profit Warnings, Jordanian Firms, Stock Price Informativeness, Synchronicity

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Authors: Saghar Heidari

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In this paper, we study the valuation problem of European continuous-installment options under Markov-modulated models with a partial differential equation approach. Due to the opportunity for continuing or stopping to pay installments, the valuation problem under regime-switching models can be formulated as coupled partial differential equations (CPDE) with free boundary features. To value the installment options, we express the truncated CPDE as a linear complementarity problem (LCP), then a finite element method is proposed to solve the resulted variational inequality. Under some appropriate assumptions, we establish the stability of the method and illustrate some numerical results to examine the rate of convergence and accuracy of the proposed method for the pricing problem under the regime-switching model.

Keywords: continuous-installment option, European option, regime-switching model, finite element method

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Authors: Patrick Ho

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Bloodstain Pattern Analysis (BPA) forensic evidence can be complex, requiring effective courtroom presentation to ensure clear and comprehensive understanding of the analyst’s findings. BPA witness statements can often involve reference to spatial information (such as location of rooms, objects, walls) which, when coupled with classified blood patterns, may illustrate the reconstructed movements of suspects and injured parties. However, it may be difficult to communicate this information through photography alone, despite this remaining the UK’s established method for presenting BPA evidence. Through an academic-police partnership between the University of Warwick and West Midlands Police (WMP), an integrated 3D scanning and HDR photography workflow for BPA was developed. Homicide scenes were laser scanned and, after processing, the 3D models were utilised in the BPA peer-review process. The same 3D models were made available for court but were not always utilised. This workflow has improved the ease of presentation for analysts and provided 3D scene models that assist with the investigation. However, the effects of incorporating 3D scene models in judicial processes may need to be studied before they are adopted more widely. 3D models from a simulated crime scene and West Midlands Police cases approved for conference disclosure are presented. We describe how the workflow was developed and integrated into established practices at WMP, including peer-review processes and witness statement delivery in court, and explain the impact the work has had on the Criminal Justice System in the West Midlands.

Keywords: bloodstain pattern analysis, forensic science, criminal justice, 3D scanning

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Authors: Junjie Zhong, Kai Hong, Lei Wang

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Passage Retrieval (PR) plays an important role in many Natural Language Processing (NLP) tasks. Traditional efficient retrieval models relying on exact term-matching, such as TF-IDF or BM25, have nowadays been exceeded by pre-trained language models which match by semantics. Though they gain effectiveness, deep language models often require large memory as well as time cost. To tackle the trade-off between efficiency and effectiveness in PR, this paper proposes Graph Passage Retriever (GraphPR), a graph-based model inspired by the development of graph learning techniques. Different from existing works, GraphPR is end-to-end and integrates both term-matching information and semantics. GraphPR constructs a passage-level graph from BM25 retrieval results and trains a GCN-like model on the graph with graph-based objectives. Passages were regarded as nodes in the constructed graph and were embedded in dense vectors. PR can then be implemented using embeddings and a fast vector-similarity search. Experiments on a variety of real-world retrieval datasets show that the proposed model outperforms related models in several evaluation metrics (e.g., mean reciprocal rank, accuracy, F1-scores) while maintaining a relatively low query latency and memory usage.

Keywords: efficiency, effectiveness, graph learning, language model, passage retrieval, term-matching model

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Authors: Manan Aslam, Shafqat Rasool

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This paper attempts to examine impact of major factors affecting marketed surplus of seed cotton in district Khanewal (Punjab) using primary source of data. A representative sample of 40 cotton farmers was selected using stratified random sampling technique. The impact of major factors on marketed surplus of seed cotton growers was estimated by employing double log form of regression analysis. The value of adjusted R2 was 0.64 whereas the F-value was 10.81. The findings of analysis revealed that experience of farmers, education of farmers, area under cotton crop and distance from wholesale market were the significant variables affecting marketed surplus of cotton whereas the variables (marketing cost and sale price) showed insignificant impact. The study suggests improving prevalent marketing practices to increase volume of marketed surplus of cotton in district Khanewal.

Keywords: seed cotton, marketed surplus, double log regression analysis

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Authors: Yahia. Kourd, N. Guersi D. Lefebvre

Abstract:

In this paper we propose to study the faults diagnosis in squirrel-cage induction motor using MLP neural networks. We use neural healthy and faulty models of the behavior in order to detect and isolate some faults in machine. In the first part of this work, we have created a neural model for the healthy state using Matlab and a motor located in LGEB by acquirins data inputs and outputs of this engine. Then we detected the faults in the machine by residual generation. These residuals are not sufficient to isolate the existing faults. For this reason, we proposed additive neural networks to represent the faulty behaviors. From the analysis of these residuals and the choice of a threshold we propose a method capable of performing the detection and diagnosis of some faults in asynchronous machines with squirrel cage rotor.

Keywords: faults diagnosis, neural networks, multi-models, squirrel-cage induction motor

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Authors: Semih Sözer

Abstract:

One of the most common issues in economic systems is understanding characteristics of economic activities in cities and regions. Although there are critics to economic base models in conceptual and empirical aspects, these models are useful tools to examining the economic structure of a nation, regions or cities. This paper uses one of the methodologies of economic base models namely the location quotients model. Data for this model includes employment numbers of provinces and NUTS II regions in Turkey. Time series of data covers the years of 1990, 2000, 2003, and 2009. Aim of this study is finding which sectors are export-base and which sectors are import-base in provinces and regions. Model results show that big provinces or powerful regions (population, size etc.) mostly have basic sectors in their economic system. However, interesting facts came from different sectors in different provinces and regions in the model results.

Keywords: economic base, location quotients model, regional economics, regional development

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Authors: Ridha Salhi, Mondher Labidi, Fethi Choubani

Abstract:

Metamaterials have attracted much attention in recent years because of their electromagnetic exquisite proprieties. We will present, in this paper, the modeling of three metamaterial structures by equivalent circuit model. We begin by modeling the SRR (Split Ring Resonator), then we model the HIS (High Impedance Surfaces), and finally, we present the model of the CPW (Coplanar Wave Guide). In order to validate models, we compare the results obtained by an equivalent circuit models with numerical simulation.

Keywords: metamaterials, SRR, HIS, CPW, IDC

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Authors: Abbas Vahedian, Rijun Shrestha, Keith Crews

Abstract:

In recent years, fibre reinforced polymers as applications of strengthening materials have received significant attention by civil engineers and environmentalists because of their excellent characteristics. Currently, these composites have become a mainstream technology for strengthening of infrastructures such as steel, concrete and more recently, timber and masonry structures. However, debonding is identified as the main problem which limit the full utilisation of the FRP material. In this paper, a preliminary analysis of factors affecting bond strength of FRP-to-concrete and timber bonded interface has been conducted. A novel theoretical method through regression analysis has been established to evaluate these factors. Results of proposed model are then assessed with results of pull-out tests and satisfactory comparisons are achieved between measured failure loads (R² = 0.83, P < 0.0001) and the predicted loads (R² = 0.78, P < 0.0001).

Keywords: debonding, fibre reinforced polymers (FRP), pull-out test, stepwise regression analysis

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