Search results for: data interpolating empirical orthogonal function

Authors: Duhania O. Mahara, Santi W. Purnami, Aulia N. Fitria, Merissa N. Z. Wirontono, Revina Musfiroh, Shofi Andari, Sagiran Sagiran, Estiana Khoirunnisa, Wahyudi Widada

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For assessing interventions in numerous disease areas, the use of multiple time-to-event outcomes is common. An individual might experience two different events called bivariate time-to-event data, the events may be correlated because it come from the same subject and also influenced by individual characteristics. The bivariate time-to-event case can be applied by copula-based bivariate Cox survival model, using the Clayton and Frank copulas to analyze the dependence structure of each event and also the covariates effect. By applying this method to modeling the recurrent event infection of hemodialysis insertion on chronic kidney disease (CKD) patients, from the AIC and BIC values we find that the Clayton copula model was the best model with Kendall’s Tau is (τ=0,02).

Keywords: bivariate cox, bivariate event, copula function, survival copula

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Authors: So-Ra Jeon, Chan Byon

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This study investigates the heat transfer characteristics of aluminum foam heat sink and pin fin heat sink subjected to an impinging air jet under a fixed pumping power condition as well as fixed flow rate condition. The effects of dimensionless pumping power or the Reynolds number and the impinging distance ratio on the Nusselt number are considered. The result shows that the effect of the impinging distance on the Nusselt number is negligible under a fixed pumping power condition, while the Nusselt number increases with decreasing the impinging distance under a fixed pumping power condition. A correlation for the pressure drop is obtained as a function of the flow rate and the impinging distance ratio. And correlations for the stagnation Nusselt number of the impinging jet are developed as a function of the pumping power. The aluminum foam heat sinks did not show higher thermal performance compared to a conventional pin fin heat sink under a fixed pumping power condition.

Keywords: aluminum foam, heat sinks, impinging jet, pumping power

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Authors: Hasik Lee, Seung-Young Kho

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Nowadays, automatic fare collection (AFC) system is widely used in many countries. However, smart card data from many of cities does not contain alighting information which is necessary to build OD matrices. Therefore, in order to utilize smart card data, destinations of passengers should be estimated. In this paper, kernel density estimation was used to forecast probabilities of alighting stations of bus passengers and applied to smart card data in Seoul, Korea which contains boarding and alighting information. This method was also validated with actual data. In some cases, stochastic method was more accurate than deterministic method. Therefore, it is sufficiently accurate to be used to build OD matrices.

Keywords: destination estimation, Kernel density estimation, smart card data, validation

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Authors: Jae Won Shin

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We develop an evaluated nuclear data based photonuclear reaction model of GEANT4 for a more accurate simulation of photon-induced neutron production. The evaluated photonuclear data libraries from the ENDF/B-VII.1 are taken as input. Incident photon energies up to 140 MeV which is the threshold energy for the pion production are considered. For checking the validity of the use of the data-based model, we calculate the photoneutron production cross-sections and yields and compared them with experimental data. The results obtained from the developed model are found to be in good agreement with the experimental data for (γ,xn) reactions.

Keywords: ENDF/B-VII.1, GEANT4, photoneutron, photonuclear reaction

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Authors: Rashi Bhalla, Russel Pears, M. Asif Naeem

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In this 'Information Explosion Era' analyzing data 'a critical commodity' and mining knowledge from vertically distributed data stream incurs huge communication cost. However, an effort to decrease the communication in the distributed environment has an adverse influence on the classification accuracy; therefore, a research challenge lies in maintaining a balance between transmission cost and accuracy. This paper proposes a method based on Bayesian inference to reduce the communication volume in a heterogeneous distributed environment while retaining prediction accuracy. Our experimental evaluation reveals that a significant reduction in communication can be achieved across a diverse range of dataset types.

Keywords: big data, bayesian inference, distributed data stream mining, heterogeneous-distributed data

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Authors: Chyi Jaw

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Prior literature on innovation diffusion or acceptance has almost exclusively concentrated on consumers’ positive attitudes and behaviors for new products/services. Consumers’ negative attitudes or behaviors to innovations have received relatively little marketing attention, but it happens frequently in practice. This study discusses consumer psychological factors when they try to learn or use new technologies. According to recent research, technological innovation acceptance has been considered as a dynamic or mediated process. This research argues that consumers can experience inertia and emotions in the initial use of new technologies. However, given such consumer psychology, the argument can be made as to whether the inclusion of consumer inertia (routine seeking and cognitive rigidity) and emotions increases the predictive power of new technology acceptance model. As data from the empirical study find, the process is potentially consumer emotion changing (independent of performance benefits) because of technology complexity and consumer inertia, and impact innovative technology use significantly. Finally, the study presents the superior predictability of the hypothesized model, which let managers can better predict and influence the successful diffusion of complex technological innovations.

Keywords: cognitive rigidity, consumer emotions, new technology acceptance, routine seeking, technology complexity

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Authors: Pooja Rani, G. S. Mahapatra, S. K. Pandey

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In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability.

Keywords: software reliability, flexible logistic growth curve model, software cumulative failure prediction, neural network, particle swarm optimization

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Authors: Aster Anderson Rugamba

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Context: Live music in Burundi is currently facing neglect and a decline in popularity, resulting in artists struggling to generate income from this field. Additionally, live music from Burundi has not been able to gain traction in the international market. It is essential to establish various structures and organizations to promote cultural events and support artistic endeavors in music and performing arts. Research Aim: The aim of this research is to seek new knowledge and understanding in the field of live music and its content in Burundi. Furthermore, it aims to connect with other professionals in the industry, make new discoveries, and explore potential collaborations and investments. Methodology: The research will utilize both quantitative and qualitative research methodologies. The quantitative approach will involve a sample size of 57 musician artists in Burundi. It will employ closed-ended questions and gather quantitative data to ensure a large sample size and high external validity. The qualitative approach will provide deeper insights and understanding through open-ended questions and in-depth interviews with selected participants. Findings: The research expects to find new theories, methodologies, empirical findings, and applications of existing knowledge that can contribute to the development of live music in Burundi. By exploring the challenges faced by artists and identifying potential solutions, the study aims to establish live music as a catalyst for development and generate a positive impact on both the Burundian and international community. Theoretical Importance: Theoretical contributions of this research will expand the current understanding of the live music industry in Burundi. It will propose new theories and models to address the issues faced by artists and highlight the potential of live music as a lucrative and influential industry. By bridging the gap between theory and practice, the research aims to provide valuable insights for academics, professionals, and policymakers. Data Collection and Analysis Procedures: Data will be collected through surveys, interviews, and archival research. Surveys will be administered to the sample of 57 musician artists, while interviews will be conducted to gain in-depth insights from selected participants. The collected data will be analyzed using both quantitative and qualitative methods, including statistical analysis and thematic analysis, respectively. This mixed-method approach will ensure a comprehensive and rigorous examination of the research questions addressed.

Keywords: business music in burundi, music in burundi, promotion of art, burundi music culture

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Authors: Nazish Iftikhar, Adil Jhangeer, Tayyaba Naz

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The universe is expanding at an accelerated rate. Symmetries are useful in understanding universe’s behavior. Emmy Noether reported the relation between symmetries and conservation laws. These symmetries are known as Noether symmetries which correspond to a conserved quantity. In differential equations, conservation laws play an important role. Noether symmetries are helpful in modified theories of gravity. Time independent plane symmetric spacetime was classified by Noether`s theorem. By using Noether`s theorem, set of linear partial differential equations was obtained having A(r), B(r) and F(r) as unknown radial functions. The Lagrangian corresponding to considered spacetime in the Noether equation was used to get Noether operators. Different possibilities of radial functions were considered. Firstly, all functions were same. All the functions were considered as non-zero constant, linear, reciprocal and exponential respectively. Secondly, two functions were proportional to each other keeping third function different. Second case has four subcases in which four different relationships between A(r), B(r) and F(r) were discussed. In all cases, we obtained nontrivial Noether operators including gauge term. Conserved quantities for each Noether operators were also presented.

Keywords: Noether gauge symmetries, radial function, Noether operator, conserved quantities

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Authors: Jong Han Joo, Jung Hoon Lee, Young Sun Kim, Jae Young Kang, Seung Ho Choi

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In this study, we propose a novel technique for acoustic echo suppression (AES) during speech recognition under barge-in conditions. Conventional AES methods based on spectral subtraction apply fixed weights to the estimated echo path transfer function (EPTF) at the current signal segment and to the EPTF estimated until the previous time interval. We propose a new approach that adaptively updates weight parameters in response to abrupt changes in the acoustic environment due to background noises or double-talk. Furthermore, we devised a voice activity detector and an initial time-delay estimator for barge-in speech recognition in communication networks. The initial time delay is estimated using log-spectral distance measure, as well as cross-correlation coefficients. The experimental results show that the developed techniques can be successfully applied in barge-in speech recognition systems.

Keywords: acoustic echo suppression, barge-in, speech recognition, echo path transfer function, initial delay estimator, voice activity detector

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Authors: BenedictI Ita, Etido P. Inyang

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In this work, the resolutions of the N-dimensional Schrödinger equation with the screened modified Kratzerplus inversely quadratic Yukawa potential (SMKIQYP) have been obtained with the Greene-Aldrich approximation scheme using the Nikiforov-Uvarov method. The eigenvalues and the normalized eigenfunctions are obtained. We then apply the energy spectrum to study four (HCl, N₂, NO, and CO) diatomic molecules. The results show that the energy spectra of these diatomic molecules increase as quantum numbers increase. The energy equation was also used to calculate the partition function and other thermodynamic properties. We predicted the partition function of CO and NO. To check the accuracy of our work, the special case (Modified Kratzer and screened Modified Kratzer potentials) of the collective potential energy eigenvalues agrees excellently with the existing literature.

Keywords: Schrödinger equation, Nikiforov-Uvarov method, modified screened Kratzer, inversely quadratic Yukawa potential, diatomic molecules

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Authors: L. R. Ferreira, R. Biscaro, C. C. Danziger, C. M. Galhardi, L. C. Biscaro, R. C. Biscaro, I. S. Vasconcelos, L. C. R. Ferreira, R. Reis, L. H. Oliveira

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Introduction: There is a worldwide trend for the employer to be aware of investing in health promotion that goes beyond occupational hygiene approaches with the implementation of a comprehensive program with integration between occupational health and safety, and social/psychosocial responsibility in the workplace. Work accommodation is a necessity in most companies as it allows the worker to return to its function respecting its physical limitations. This study had the objective to verify if the integration of health and safety in the companies, with the inclusion of the concept of TWH promoted by an occupational health service has impacted in the management of absenteeism of workers in work accommodation. Method: A retrospective and paired cohort study was used, in which the impact of the implementation of the Program for the Health/Safety Integration and Total Worker Health Concept (PHSITWHC) was evaluated using the indices of absenteeism, health attestations, days and hours of sick leave of workers that underwent job accommodation/rehabilitation. This was a cohort study and the data were collected from January to September of 2017, prior to the initiation of the integration program, and compared with the data obtained from January to September of 2018, after the implementation of the program. For the statistical analysis, the student's t-test was used, with statistically significant differences being made at p < 0.05. Results: The results showed a 35% reduction in the number of absenteeism rate in 2018 compared to the same period in 2017. There was also a significant reduction in the total numbers of days of attestations/absences (mean of 2,8) as well as days of attestations, absence and sick leaves (mean of 5,2) in 2018 data after the implementation of PHSITWHC compared to 2017 data, means of 4,3 and 25,1, respectively, prior to the program. Conclusion: It can be concluded that the inclusion of the PHSITWHC was associated with a reduction in the rate of absenteeism of workers that underwent job accommodation. It was observed that, once health and safety were approached and integrated with the inclusion of the TWH concept, it was possible to reduce absenteeism, and improve worker’s quality of life and wellness, and work accommodation management.

Keywords: absenteeism, health/safety integration, work accommodation management, total worker health

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Authors: Huang Bingling, Jia Yuhong, Liu Yanhui

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As an important part of aircraft, landing gears are responsible for taking-off and landing function. In recent years, big airplane's structural quality increases a lot. As a result, landing gears have stricter technical requirements than ever before such as structure strength and etc. If the structural strength of the landing gear is enhanced through traditional methods like increasing structural quality, the negative impacts on the landing gear's function would be very serious and even counteract the positive effects. Thus, in order to solve this problem, the impact of pre-rotating of landing gears on performance of landing gears is studied from the theoretical and experimental verification in this paper. By increasing the pre-rotating speed of the wheel, it can improve the performance of the landing gear and reduce the structural quality, the force of joint parts and other properties. In addition, the pre-rotating of the wheels also has other advantages, such as reduce the friction between wheels and ground and extend the life of the wheel. In this paper, the impact of the pre-rotating speed on landing gears and the connecting between landing gears performance and pre-rotating speed would be researched in detail. This paper is divided into three parts. In the first part, large airplane landing gear model is built by CATIA and LMS. As most general landing gear type in big plane, four-wheel landing gear is picked as model. The second part is to simulate the process of landing in LMS motion, and study the impact of pre-rotating of wheels on the aircraft`s properties, including the buffer stroke, efficiency, power; friction, displacement and relative speed between piston and sleeve; force and load distribution of tires. The simulation results show that the characteristics of the different pre-rotation speed are understood. The third part is conclusion. Through the data of the previous simulation and the relationship between the pre-rotation speed of the aircraft wheels and the performance of the aircraft, recommended speed interval is proposed. This paper is of great theoretical value to improve the performance of large airplane. It is a very effective method to improve the performance of aircraft by setting wheel pre-rotating speed. Do not need to increase the structural quality too much, eliminating the negative effects of traditional methods.

Keywords: large airplane, landing gear, pre-rotating, simulation

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Authors: Benny Sand, Yotam Lurie, Shlomo Mark

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Medical Internet of Things (MIoT), AI, and data privacy are linked forever in a gordian knot. This paper explores the conflicts of interests between the stakeholders regarding data privacy in the MIoT arena. While patients are at home during healthcare hospitalization, MIoT can play a significant role in improving the health of large parts of the population by providing medical teams with tools for collecting data, monitoring patients’ health parameters, and even enabling remote treatment. While the amount of data handled by MIoT devices grows exponentially, different stakeholders have conflicting understandings and concerns regarding this data. The findings of the research indicate that medical teams are not concerned by the violation of data privacy rights of the patients' in-home healthcare, while patients are more troubled and, in many cases, are unaware that their data is being used without their consent. MIoT technology is in its early phases, and hence a mixed qualitative and quantitative research approach will be used, which will include case studies and questionnaires in order to explore this issue and provide alternative solutions.

Keywords: MIoT, data privacy, stakeholders, home healthcare, information privacy, AI

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Authors: Naushad Mamode Khan

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The Com-Poisson (CMP) model is one of the most popular discrete generalized linear models (GLMS) that handles both equi-, over- and under-dispersed data. In longitudinal context, an integer-valued autoregressive (INAR(1)) process that incorporates covariate specification has been developed to model longitudinal CMP counts. However, the joint likelihood CMP function is difficult to specify and thus restricts the likelihood based estimating methodology. The joint generalized quasilikelihood approach (GQL-I) was instead considered but is rather computationally intensive and may not even estimate the regression effects due to a complex and frequently ill conditioned covariance structure. This paper proposes a new GQL approach for estimating the regression parameters (GQLIII) that are based on a single score vector representation. The performance of GQL-III is compared with GQL-I and separate marginal GQLs (GQL-II) through some simulation experiments and is proved to yield equally efficient estimates as GQL-I and is far more computationally stable.

Keywords: longitudinal, com-Poisson, ill-conditioned, INAR(1), GLMS, GQL

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Authors: E. Eroglu, S. Semsit, E. Sener, U.S. Sener

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In Electromagnetics, there are three canonical boundary value problem with given initial conditions for the electromagnetic field sought, namely: Cavity Problem, Waveguide Problem, and External Problem. The Cavity Problem and Waveguide Problem were rigorously studied and new results were arised at original works in the past decades. In based on studies of an analytical time domain method Evolutionary Approach to Electromagnetics (EAE), electromagnetic field strength vectors produced by a time dependent source function are sought. The fields are took place in L2 Hilbert space. The source function that performs signal transferring, energy and surplus of energy has been demonstrated with all clarity. Depth of the method and ease of applications are emerged needs of gathering obtained results. Main discussion is about perfect electric conductor and hollow waveguide. Even if well studied time-domain modes problems are mentioned, specifically, the modes which have a hollow (i.e., medium-free) cross-section domain are considered.

Keywords: evolutionary approach to electromagnetics, time-domain waveguide mode, Neumann problem, Dirichlet boundary value problem, Klein-Gordon

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Authors: Inten Meutia, Emylia Yuniarti

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The objective of this study is to analyze the effects of interest rate, the rate of return of Islamic banks on the amount of mudarabah deposits in Islamic banks. In analyzing the effect of rate of return in the Islamic banks and interest rate risk in the conventional banks, the 1-month Islamic deposit rate of return and 1 month fixed deposit interest rate of a total Islamic deposit are considered. Using data covering the period from January 2010 to Sepember 2013, the study applies the regression analysis to analyze the effect between variable and independence t-test to analyze the mean difference between rate of return and rate of interest. Regression analysis shows that rate of return have significantly negative influence on mudarabah deposits, while interest rate have negative influence but not significant. The result of independent t test shows that the interest rate is not different from the rate of return in Islamic Bank. It supports the hyphotesis that rate of return in Islamic banking mimic rate of interest in conventional bank. The results of the study have important implications on the risk management practices of the Islamic banks in Indonesia.

Keywords: conventional bank, interest rate, Islamic bank, rate of return

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Authors: Deepak Singh, Rail Kuliev

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The abstract highlights the critical importance of optimizing data integration and management strategies in the upstream oil and gas industry. With its complex and dynamic nature generating vast volumes of data, efficient data integration and management are essential for informed decision-making, cost reduction, and maximizing operational performance. Challenges such as data silos, heterogeneity, real-time data management, and data quality issues are addressed, prompting the proposal of several strategies. These strategies include implementing a centralized data repository, adopting industry-wide data standards, employing master data management (MDM), utilizing real-time data integration technologies, and ensuring data quality assurance. Training and developing the workforce, “reskilling and upskilling” the employees and establishing robust Data Management training programs play an essential role and integral part in this strategy. The article also emphasizes the significance of data governance and best practices, as well as the role of technological advancements such as big data analytics, cloud computing, Internet of Things (IoT), and artificial intelligence (AI) and machine learning (ML). To illustrate the practicality of these strategies, real-world case studies are presented, showcasing successful implementations that improve operational efficiency and decision-making. In present study, by embracing the proposed optimization strategies, leveraging technological advancements, and adhering to best practices, upstream oil and gas companies can harness the full potential of data-driven decision-making, ultimately achieving increased profitability and a competitive edge in the ever-evolving industry.

Keywords: master data management, IoT, AI&ML, cloud Computing, data optimization

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

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Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: local nonlinear estimation, LWPR algorithm, online training method, locally weighted projection regression method

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Authors: Chang Liu, Rui Xing, Liyan Tang, Guohong Wang

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Entrepreneurial actions are based on entrepreneurial decisions. The quality of decisions influences entrepreneurial activities and subsequent new venture performance. Uncertainty of surroundings put heightened demands on the team as a whole, and each team member. Diverse team composition provides rich information, which a team can draw when making complex decisions. However, team heterogeneity may cause emotional conflicts, which is adverse to team outcomes. Thus, the effects of team heterogeneity on team outcomes are complex. Although team heterogeneity is an essential factor influencing entrepreneurial decision-making, there is a lack of empirical analysis on under what conditions team heterogeneity plays a positive role in promoting decision-making quality. Entrepreneurial teams always struggle with complex tasks. How a team shapes its teamwork is key in resolving constant issues. As a collective regulatory process, team reflexivity is characterized by continuous joint evaluation and discussion of team goals, strategies, and processes, and adapt them to current or anticipated circumstances. It enables diversified information to be shared and overtly discussed. Instead of hostile interpretation of opposite opinions team members take them as useful insights from different perspectives. Team reflexivity leads to better integration of expertise to avoid the interference of negative emotions and conflict. Therefore, we propose that team reflexivity is a conditional factor that influences the impact of team heterogeneity on high-quality entrepreneurial decisions. In this study, we identify team heterogeneity as a crucial determinant of entrepreneurial decision quality. Integrating the literature on decision-making and team heterogeneity, we investigate the relationship between team heterogeneity and entrepreneurial decision-making quality, treating team reflexivity as a moderator. We tested our hypotheses using the hierarchical regression method and the data gathered from 63 teams and 205 individual members from 45 new firms in China's first-tier cities such as Beijing, Shanghai, and Shenzhen. This research found that both teams' education heterogeneity and teams' functional background heterogeneity were significantly positively related to entrepreneurial decision-making quality, and the positive relation was stronger in teams with a high level of team reflexivity. While teams' specialization of education heterogeneity was negatively related to decision-making quality, and the negative relationship was weaker in teams with a high level of team reflexivity. We offer two contributions to decision-making and entrepreneurial team literatures. Firstly, our study enriches the understanding of the role of entrepreneurial team heterogeneity in entrepreneurial decision-making quality. Different from previous entrepreneurial decision-making literatures, which focus more on decision-making modes of entrepreneurs and the top management team, this study is a significant attempt to highlight that entrepreneurial team heterogeneity makes a unique contribution to generating high-quality entrepreneurial decisions. Secondly, this study introduced team reflexivity as the moderating variable, to explore the boundary conditions under which the entrepreneurial team heterogeneity play their roles.

Keywords: decision-making quality, entrepreneurial teams, education heterogeneity, functional background heterogeneity, specialization of education heterogeneity

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Authors: Lidia Sanchis-Marco, Antonio Rubia, Pedro Serrano

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This paper studies the informational content of pricing errors in the term structure of sovereign CDS spreads. The residuals from a non-arbitrage model are employed to construct a Price discrepancy estimate, or noise measure. The noise estimate is understood as an indicator of market distress and reflects frictions such as illiquidity. Empirically, the noise measure is computed for an extensive panel of CDS spreads. Our results reveal an important fraction of systematic risk is not priced in default swap contracts. When projecting the noise measure onto a set of financial variables, the panel-data estimates show that greater price discrepancies are systematically related to a higher level of offsetting transactions of CDS contracts. This evidence suggests that arbitrage capital flows exit the marketplace during time of distress, and this consistent with a market segmentation among investors and arbitrageurs where professional arbitrageurs are particularly ineffective at bringing prices to their fundamental values during turbulent periods. Our empirical findings are robust for the most common CDS pricing models employed in the industry.

Keywords: credit default swaps, noise measure, illiquidity, capital arbitrage

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Authors: Parisa Mansour

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Understanding how vision works in both health and disease involves understanding the anatomy and physiology of the eye as well as the neural pathways involved in visual perception. The development of imaging techniques for the visual system is essential for understanding the neural foundation of visual function or impairment. MRI provides a way to examine neural circuit structure and function without invasive procedures, allowing for the detection of brain tissue abnormalities in real time. One of the advanced MRI methods is manganese-enhanced MRI (MEMRI), which utilizes active manganese contrast agents to enhance brain tissue signals in T1-weighted imaging, showcasing connectivity and activity levels. The way manganese ions build up in the eye, and visual pathways can be due to their spread throughout the body or by moving locally along axons in a forward direction and entering neurons through calcium channels that are voltage-gated. The paramagnetic manganese contrast is utilized in MRI for various applications in the visual system, such as imaging neurodevelopment and evaluating neurodegeneration, neuroplasticity, neuroprotection, and neuroregeneration. In this assessment, we outline four key areas of scientific research where MEMRI can play a crucial role - understanding brain structure, mapping nerve pathways, monitoring nerve cell function, and distinguishing between different types of glial cell activity. We discuss various studies that have utilized MEMRI to investigate the visual system, including delivery methods, spatiotemporal features, and biophysical analysis. Based on this literature, we have pinpointed key issues in the field related to toxicity, as well as sensitivity and specificity of manganese enhancement. We will also examine the drawbacks and other options to MEMRI that could offer new possibilities for future exploration.

Keywords: glial activity, manganese-enhanced magnetic resonance imaging, neuroarchitecture, neuronal activity, neuronal tract tracing, visual pathway, eye

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Authors: Christine P. Gibson, Ramona Alex, Michael Farney, Johana Vallejo-Elias, Mitra Esfandiarei

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Aortic aneurysm is the leading cause of death in Marfan syndrome (MFS), a connective tissue disorder caused by mutations in fibrillin-1 gene (FBN1). MFS aneurysm is characterized by weakening of the aortic wall due to elastin fibers fragmentation and disorganization. The above-average height and distinct physical features make young adults with MFS desirable candidates for competitive sports; but little is known about the exercise limit at which they will be at risk for aortic rupture. On the other hand, aerobic cardiovascular exercise has been shown to have protective effects on the heart and aorta. We have previously reported that mild aerobic exercise can delay the formation of aortic aneurysm in a mouse model of MFS. In this study, we aimed to investigate the effects of various levels of exercise intensity on the progression of aortic aneurysm in the mouse model. Starting at 4 weeks of age, we subjected control and MFS mice to different levels of exercise intensity (8m/min, 10m/min, 15m/min, and 20m/min, corresponding to 55%, 65%, 75%, and 85% of VO2 max, respectively) on a treadmill for 30 minutes per day, five days a week for the duration of the study. At 24 weeks of age, aortic tissue were isolated and subjected to structural and functional studies using histology and wire myography in order to evaluate the effects of different exercise routines on elastin fragmentation and organization and aortic wall elasticity/stiffness. Our data shows that exercise training at the intensity levels between 55%-75% significantly reduces elastin fragmentation and disorganization, with less recovery observed in 85% MFS group. The reversibility of elasticity was also significantly restored in MFS mice subjected to 55%-75% intensity; however, the recovery was less pronounced in MFS mice subjected to 85% intensity. Furthermore, our data shows that smooth muscle cells (SMCs) contractilion in response to vasoconstrictor agent phenylephrine (100nM) is significantly reduced in MFS aorta (54.84 ± 1.63 mN/mm2) as compared to control (95.85 ± 3.04 mN/mm2). At 55% of intensity, exercise did not rescue SMCs contraction (63.45 ± 1.70 mN/mm2), while at higher intensity levels, SMCs contraction in response to phenylephrine was restored to levels similar to control aorta [65% (81.88 ± 4.57 mN/mm2), 75% (86.22 ± 3.84 mN/mm2), and 85% (83.91 ± 5.42 mN/mm2)]. This study provides the first time evidence that high intensity exercise (e.g. 85%) may not provide the most beneficial effects on aortic function (vasoconstriction) and structure (elastin fragmentation, aortic wall elasticity) during the progression of aortic aneurysm in MFS mice. On the other hand, based on our observations, medium intensity exercise (e.g. 65%) seems to provide the utmost protective effects on aortic structure and function in MFS mice. These findings provide new insights into the potential capacity, in which MFS patients could participate in various aerobic exercise routines, especially in young adults affected by cardiovascular complications particularly aortic aneurysm. This work was funded by Midwestern University Research Fund.

Keywords: aerobic exercise, aortic aneurysm, aortic wall elasticity, elastin fragmentation, Marfan syndrome

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Authors: F. Amin, S. Feizi

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Big data has the potential to improve the quality of services; enable infrastructure that businesses depend on to adapt continually and efficiently; improve the performance of employees; help organizations better understand customers; and reduce liability risks. Analytics and marketing models of fixed and mobile operators are falling short in combating churn and declining revenue per user. Big Data presents new method to reverse the way and improve profitability. The benefits of Big Data and next-generation network, however, are more exorbitant than improved customer relationship management. Next generation of networks are in a prime position to monetize rich supplies of customer information—while being mindful of legal and privacy issues. As data assets are transformed into new revenue streams will become integral to high performance.

Keywords: big data, next generation networks, network transformation, strategy

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Authors: Binder Hans

Abstract:

Cancer is no longer seen as solely a genetic disease where genetic defects such as mutations and copy number variations affect gene regulation and eventually lead to aberrant cell functioning which can be monitored by transcriptome analysis. It has become obvious that epigenetic alterations represent a further important layer of (de-)regulation of gene activity. For example, aberrant DNA methylation is a hallmark of many cancer types, and methylation patterns were successfully used to subtype cancer heterogeneity. Hence, unraveling the interplay between different omics levels such as genome, transcriptome and epigenome is inevitable for a mechanistic understanding of molecular deregulation causing complex diseases such as cancer. This objective requires powerful downstream integrative bioinformatics methods as an essential prerequisite to discover the whole genome mutational, transcriptome and epigenome landscapes of cancer specimen and to discover cancer genesis, progression and heterogeneity. Basic challenges and tasks arise ‘beyond sequencing’ because of the big size of the data, their complexity, the need to search for hidden structures in the data, for knowledge mining to discover biological function and also systems biology conceptual models to deduce developmental interrelations between different cancer states. These tasks are tightly related to cancer biology as an (epi-)genetic disease giving rise to aberrant genomic regulation under micro-environmental control and clonal evolution which leads to heterogeneous cellular states. Machine learning algorithms such as self organizing maps (SOM) represent one interesting option to tackle these bioinformatics tasks. The SOMmethod enables recognizing complex patterns in large-scale data generated by highthroughput omics technologies. It portrays molecular phenotypes by generating individualized, easy to interpret images of the data landscape in combination with comprehensive analysis options. Our image-based, reductionist machine learning methods provide one interesting perspective how to deal with massive data in the discovery of complex diseases, gliomas, melanomas and colon cancer on molecular level. As an important new challenge, we address the combined portrayal of different omics data such as genome-wide genomic, transcriptomic and methylomic ones. The integrative-omics portrayal approach is based on the joint training of the data and it provides separate personalized data portraits for each patient and data type which can be analyzed by visual inspection as one option. The new method enables an integrative genome-wide view on the omics data types and the underlying regulatory modes. It is applied to high and low-grade gliomas and to melanomas where it disentangles transversal and longitudinal molecular heterogeneity in terms of distinct molecular subtypes and progression paths with prognostic impact.

Keywords: integrative bioinformatics, machine learning, molecular mechanisms of cancer, gliomas and melanomas

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Authors: Abdelrhman Elagez, Rolla Monib

Abstract:

This empirical study investigates the observed risks associated with adopting Artificial Intelligence (AI) technologies in the construction industry and proposes potential mitigation strategies. While AI has transformed several industries, the construction industry is slowly adopting advanced technologies like AI, introducing new risks that lack critical analysis in the current literature. A comprehensive literature review identified a research gap, highlighting the lack of critical analysis of risks and the need for a framework to measure and mitigate the risks of AI implementation in the construction industry. Consequently, an online survey was conducted with 24 project managers and construction professionals, possessing experience ranging from 1 to 30 years (with an average of 6.38 years), to gather industry perspectives and concerns relating to AI integration. The survey results yielded several significant findings. Firstly, respondents exhibited a moderate level of familiarity (66.67%) with AI technologies, while the industry's readiness for AI deployment and current usage rates remained low at 2.72 out of 5. Secondly, the top-ranked barriers to AI adoption were identified as lack of awareness, insufficient knowledge and skills, data quality concerns, high implementation costs, absence of prior case studies, and the uncertainty of outcomes. Thirdly, the most significant risks associated with AI use in construction were perceived to be a lack of human control (decision-making), accountability, algorithm bias, data security/privacy, and lack of legislation and regulations. Additionally, the participants acknowledged the value of factors such as education, training, organizational support, and communication in facilitating AI integration within the industry. These findings emphasize the necessity for tailored risk assessment frameworks, guidelines, and governance principles to address the identified risks and promote the responsible adoption of AI technologies in the construction sector.

Keywords: risk management, construction, artificial intelligence, technology

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Authors: Badriah Alayidi, Emad Alyahya

Abstract:

Background: Most people with Multiple Sclerosis (MS) report worsening balance as the condition progresses. Poor balance control is also well known to be a significant risk factor for both falling and fear of falling. The increased risk of falls with disease progression thus makes balance control an essential target of gait rehabilitation amongst people with MS. Intervention programs have developed various methods to improve balance control, and accumulating evidence suggests that exercise programs may help people with MS improve their balance. Among these methods, virtual reality (VR) is growing in popularity as a balance-training technique owing to its potential benefits, including better compliance and greater user happiness. However, it is not clear if a VR environment will induce different balance control mechanisms in MS as compared to healthy individuals or traditional environments. Therefore, this study aims to examine how individuals with MS control their balance in a VR setting. Methodology: The proposed study takes an empirical approach to estimate and determine the role of balance response in persons with MS using a VR environment. It will use primary data collected through patient observations, physiological and biomechanical evaluation of balance, and data analysis. Results: The preliminary systematic review and meta-analysis indicated that there was variability in terms of the outcome assessing balance response in people with MS. The preliminary results of these assessments have the potential to provide essential indicators of the progression of MS and contribute to the individualization of treatment and evaluation of the interventions’ effectiveness. The literature describes patients who have had the opportunity to experiment in VR settings and then used what they have learned in the real world, suggesting that this VR setting could be more appealing than conditional settings. The findings of the proposed study will be beneficial in estimating and determining the effect of VR on balance control in persons with MS. In previous studies, VR was shown to be an interesting approach to neurological rehabilitation, but more data are needed to support this approach in MS. Conclusions: The proposed study enables an assessment of balance and evaluations of a variety of physiological implications related to neural activity as well as biomechanical implications related to movement analysis.

Keywords: multiple sclerosis, virtual reality, postural control, balance

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Authors: Percy Galvez, Anatoly Petukhin, Paul Somerville, Ken Miyakoshi, Kojiro Irikura, Daniel Peter

Abstract:

Physics-based dynamic rupture modelling is necessary for estimating parameters such as rupture velocity and slip rate function that are important for ground motion simulation, but poorly resolved by observations, e.g. by seismic source inversion. In order to generate a large number of physically self-consistent rupture models, whose rupture process is consistent with the spatio-temporal heterogeneity of past earthquakes, we use multicycle simulations under the heterogeneous rate-and-state (RS) friction law for a 45deg dip-slip fault. We performed a parametrization study by fully dynamic rupture modeling, and then, a set of spontaneous source models was generated in a large magnitude range (Mw > 7.0). In order to validate rupture models, we compare the source scaling relations vs. seismic moment Mo for the modeled rupture area S, as well as average slip Dave and the slip asperity area Sa, with similar scaling relations from the source inversions. Ground motions were also computed from our models. Their peak ground velocities (PGV) agree well with the GMPE values. We obtained good agreement of the permanent surface offset values with empirical relations. From the heterogeneous rupture models, we analyzed parameters, which are critical for ground motion simulations, i.e. distributions of slip, slip rate, rupture initiation points, rupture velocities, and source time functions. We studied cross-correlations between them and with the friction weakening distance Dc value, the only initial heterogeneity parameter in our modeling. The main findings are: (1) high slip-rate areas coincide with or are located on an outer edge of the large slip areas, (2) ruptures have a tendency to initiate in small Dc areas, and (3) high slip-rate areas correlate with areas of small Dc, large rupture velocity and short rise-time.

Keywords: earthquake dynamics, strong ground motion prediction, seismic engineering, source characterization

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Authors: Apkar Salatian

Abstract:

To relieve the burden of reasoning on a point to point basis, in many domains there is a need to reduce large and noisy data sets into trends for qualitative reasoning. In this paper we propose and describe a new architectural design pattern called REDUCER for reducing large and noisy data sets that can be tailored for particular situations. REDUCER consists of 2 consecutive processes: Filter which takes the original data and removes outliers, inconsistencies or noise; and Compression which takes the filtered data and derives trends in the data. In this seminal article, we also show how REDUCER has successfully been applied to 3 different case studies.

Keywords: design pattern, filtering, compression, architectural design

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Authors: David Edgar

Abstract:

To understand how the brain’s subconscious and conscious functions, we must conquer the physics of Unity, which leads to duality’s algorithm. Where the subconscious (bottom-up) and conscious (top-down) processes function together to produce and consume intelligence, we use terms like ‘time is relative,’ but we really do understand the meaning. In the brain, there are different processes and, therefore, different observers. These different processes experience time at different rates. A sensory system such as the eyes cycles measurement around 33 milliseconds, the conscious process of the frontal lobe cycles at 300 milliseconds, and the subconscious process of the thalamus cycle at 5 milliseconds. Three different observers experience time differently. To bridge observers, the thalamus, which is the fastest of the processes, maintains a synchronous state and entangles the different components of the brain’s physical process. The entanglements form a synchronous cohesion between the brain components allowing them to share the same state and execute in the same measurement cycle. The thalamus uses the shared state to control the firing sequence of the brain’s linear subconscious process. Sharing state also allows the brain to cheat on the amount of sensory data that must be exchanged between components. Only unpredictable motion is transferred through the synchronous state because predictable motion already exists in the shared framework. The brain’s synchronous subconscious process is entirely based on energy conservation, where prediction regulates energy usage. So, the eyes every 33 milliseconds dump their sensory data into the thalamus every day. The thalamus is going to perform a motion measurement to identify the unpredictable motion in the sensory data. Here is the trick. The thalamus conducts its measurement based on the original observation time of the sensory system (33 ms), not its own process time (5 ms). This creates a data payload of synchronous motion that preserves the original sensory observation. Basically, a frozen moment in time (Flat 4D). The single moment in time can then be processed through the single state maintained by the synchronous process. Other processes, such as consciousness (300 ms), can interface with the synchronous state to generate awareness of that moment. Now, synchronous data traveling through a separate faster synchronous process creates a theoretical time tunnel where observation time is tunneled through the synchronous process and is reproduced on the other side in the original time-relativity. The synchronous process eliminates time dilation by simply removing itself from the equation so that its own process time does not alter the experience. To the original observer, the measurement appears to be instantaneous, but in the thalamus, a linear subconscious process generating sensory perception and thought production is being executed. It is all just occurring in the time available because other observation times are slower than thalamic measurement time. For life to exist in the physical universe requires a linear measurement process, it just hides by operating at a faster time relativity. What’s interesting is time dilation is not the problem; it’s the solution. Einstein said there was no universal time.

Keywords: neural synchronization, natural intelligence, 99.95% IoT data transmission savings, artificial subconscious intelligence (ASI)

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