Search results for: vehicle following models

Authors: Bikis Muhammed, Sehra Sedigh Sarvestani, Ali R. Hurson, Lasanthi Gamage

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Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA.

Keywords: deep learning, real time prediction, GAT, Bi-LSTM, attention

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Authors: John Sweeney, Martin Leeming, Raj Thaker, Cristina L. Tuinea-Bobe

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Shrink wrapping is widely used as a method for secondary packaging to assemble individual items, such as cans or other consumer products, into single packages. This method involves conveying the packages into heated tunnels and so has the disadvantages that it is energy-intensive, and, in the case of aerosol products, potentially hazardous. We are developing an automated packaging system that uses stretch wrapping to address both these problems, by using a mechanical rather than a thermal process. In this study, we present a comparative study of shrink wrapping and stretch wrapping materials to assess the relative capability of candidate stretch wrap polymer film in terms of mechanical response. The stretch wrap materials are of oriented polymer and therefore elastically anisotropic. We are developing material constitutive models that include both anisotropy and nonlinearity. These material models are to be incorporated into computer simulations of the automated stretch wrapping system. We present results showing the validity of these models and the feasibility of applying them in the simulations.

Keywords: constitutive model, polymer, mechanical testing, wrapping system

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Authors: Chia-Yi Liu, Bo-Bin Xiao, Wen-Bin Lin, Hsiang-Ning Wu, Liang-Hsun Huang

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In order to reduce the risk of radiation damage that personnel may suffer during operations in the radiation environment, the use of automated guided vehicles to assist or replace on-site personnel in the radiation environment has become a key technology and has become an important trend. In this paper, we demonstrate our proof of concept for autonomous self-learning radiation source searcher in an unknown environment without a map. The research uses GPU version of Asynchronous Advantage Actor-Critic network (GA3C) of deep reinforcement learning to search for radiation sources. The searcher network, based on GA3C architecture, has self-directed learned and improved how search the anomalous radiation source by training 1 million episodes under three simulation environments. In each episode of training, the radiation source position, the radiation source intensity, starting position, are all set randomly in one simulation environment. The input for searcher network is the fused data from a 2D laser scanner and a RGB-D camera as well as the value of the radiation detector. The output actions are the linear and angular velocities. The searcher network is trained in a simulation environment to accelerate the learning process. The well-performance searcher network is deployed to the real unmanned vehicle, Dashgo E2, which mounts LIDAR of YDLIDAR G4, RGB-D camera of Intel D455, and radiation detector made by Institute of Nuclear Energy Research. In the field experiment, the unmanned vehicle is enable to search out the radiation source of the 18.5MBq Na-22 by itself and avoid obstacles simultaneously without human interference.

Keywords: deep reinforcement learning, GA3C, source searching, source detection

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Authors: G. González, J. Ramírez, A. Rubiano

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This article describes different patented systems for safe use in vehicles based on GPS technology, speed sensors, gyroscopes, maps, communication systems, and monitors, that inform the driver about traffic jam, obstruction in the road, speed limits, among others. Once the information is analyzed and contrasted to final propose new technical needs to be solved.

Keywords: GPS, information technology, telecommunications, communication networks, gyroscope, environmental pollution

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Authors: Hami Ashraf, Farid Kosari

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Graft-versus-host disease (GvHD) is a common complication of allogeneic hematopoietic stem cell transplantation that can lead to significant morbidity and mortality. Histopathological examination of affected tissues is an essential tool for diagnosing and grading GvHD in animal models, which are used to study disease mechanisms and evaluate new therapies. In this systematic review, we identified and analyzed original research articles in PubMed, Scopus, Web of Science, and Google Scholar that described grading systems for GvHD in animal models based on histopathological features. We found that several grading systems have been developed, which vary in the tissues and criteria they assess, the severity scoring scales they use, and the level of detail they provide. Skin, liver, and gut are the most commonly evaluated tissues, but lung and thymus are also included in some systems. Our analysis highlights the need for standardized criteria and consistent use of grading systems to enable comparisons between studies and facilitate the translation of preclinical findings to clinical practice.

Keywords: graft-versus-host disease, GvHD, animal model, histopathology, grading system

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Authors: Aleksandra Dudkowska, Gabriela Gic-Grusza

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Wind wave-induced sediment transport is an important multidimensional and multiscale dynamic process affecting coastal seabed changes and coastline evolution. The knowledge about sediment transport rate is important to solve many environmental and geotechnical issues. There are many types of sediment transport models but none of them is widely accepted. It is bacause the process is not fully defined. Another problem is a lack of sufficient measurment data to verify proposed hypothesis. There are different types of models for longshore sediment transport (LST, which is discussed in this work) and cross-shore transport which is related to different time and space scales of the processes. There are models describing bed-load transport (discussed in this work), suspended and total sediment transport. LST models use among the others the information about (i) the flow velocity near the bottom, which in case of wave-currents interaction in coastal zone is a separate problem (ii) critical bed shear stress that strongly depends on the type of sediment and complicates in the case of heterogeneous sediment. Moreover, LST rate is strongly dependant on the local environmental conditions. To organize existing knowledge a series of sediment transport models intercomparisons was carried out as a part of the project “Development of a predictive model of morphodynamic changes in the coastal zone”. Four classical one-grid-point models were studied and intercompared over wide range of bottom shear stress conditions, corresponding with wind-waves conditions appropriate for coastal zone in polish marine areas. The set of models comprises classical theories that assume simplified influence of turbulence on the sediment transport (Du Boys, Meyer-Peter & Muller, Ribberink, Engelund & Hansen). It turned out that the values of estimated longshore instantaneous mass sediment transport are in general in agreement with earlier studies and measurements conducted in the area of interest. However, none of the formulas really stands out from the rest as being particularly suitable for the test location over the whole analyzed flow velocity range. Therefore, based on the models discussed a new unified formula for longshore sediment transport rate estimation is introduced, which constitutes the main original result of this study. Sediment transport rate is calculated based on the bed shear stress and critical bed shear stress. The dependence of environmental conditions is expressed by one coefficient (in a form of constant or function) thus the model presented can be quite easily adjusted to the local conditions. The discussion of the importance of each model parameter for specific velocity ranges is carried out. Moreover, it is shown that the value of near-bottom flow velocity is the main determinant of longshore bed-load in storm conditions. Thus, the accuracy of the results depends less on the sediment transport model itself and more on the appropriate modeling of the near-bottom velocities.

Keywords: bedload transport, longshore sediment transport, sediment transport models, coastal zone

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Authors: H. Namazi, H. T. N. Kuan

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It is known that in humans, the adaptation to a given odor occurs within a quite short span of time (typically one minute) after the odor is presented to the brain. Different models of human olfaction have been developed by scientists but none of these models consider the diffusion phenomenon in olfaction. A novel microscopic model of the human olfaction is presented in this paper. We develop this model by incorporating the transient diffusivity. In fact, the mathematical model is written based on diffusion of the odorant within the mucus layer. By the use of the model developed in this paper, it becomes possible to provide quantification of the objective strength of odor.

Keywords: diffusion, microscopic model, mucus layer, olfaction

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Authors: Vemula Amalakrishna, S. Pushpavanam

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Change and motion characterize and persistently reshape the world around us, on scales from molecular to global. The subtle interplay between change (Reaction) and motion (Diffusion) gives rise to an astonishing intricate spatial or temporal pattern. These pattern formation in nature has been intellectually appealing for many scientists since antiquity. Periodic precipitation patterns, also known as Liesegang patterns (LP), are one of the stimulating examples of such self-assembling reaction-diffusion (RD) systems. LP formation has a great potential in micro and nanotechnology. So far, the research on LPs has been concentrated mostly on how these patterns are forming, retrieving information to build a universal mathematical model for them. Researchers have developed various theoretical models to comprehensively construct the geometrical diversity of LPs. To the best of our knowledge, simulation studies of LPs assume an arbitrary value of RD parameters to explain experimental observation qualitatively. In this work, existing models were studied to understand the mechanism behind this phenomenon and challenges pertaining to models were understood and explained. These models are not computationally effective due to the presence of discontinuous precipitation rate in RD equations. To overcome the computational challenges, smoothened Heaviside functions have been introduced, which downsizes the computational time as well. Experiments were performed using a conventional LP system (AgNO₃-K₂Cr₂O₇) to understand the effects of different gels and temperatures on formed LPs. The model is extended for real parameter values to compare the simulated results with experimental data for both 1-D (Cartesian test tubes) and 2-D(cylindrical and Petri dish).

Keywords: reaction-diffusion, spatio-temporal patterns, nucleation and growth, supersaturation

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Authors: A. J. Al-Graitti, G. A. Khalid, P. Berthelson, A. Mason-Jones, R. Prabhu, M. D. Jones

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Motor vehicle related pedestrian road traffic collisions are a major road safety challenge, since they are a leading cause of death and serious injury worldwide, contributing to a third of the global disease burden. The auto rickshaw, which is a common form of urban transport in many developing countries, plays a major transport role, both as a vehicle for hire and for private use. The most common auto rickshaws are quite unlike ‘typical’ four-wheel motor vehicle, being typically characterised by three wheels, a non-tilting sheet-metal body or open frame construction, a canvas roof and side curtains, a small drivers’ cabin, handlebar controls and a passenger space at the rear. Given the propensity, in developing countries, for auto rickshaws to be used in mixed cityscapes, where pedestrians and vehicles share the roadway, the potential for auto rickshaw impacts with pedestrians is relatively high. Whilst auto rickshaws are used in some Western countries, their limited number and spatial separation from pedestrian walkways, as a result of city planning, has not resulted in significant accident statistics. Thus, auto rickshaws have not been subject to the vehicle impact related pedestrian crash kinematic analyses and/or injury mechanics assessment, typically associated with motor vehicle development in Western Europe, North America and Japan. This study presents a parametric analysis of auto rickshaw related pedestrian impacts by computational simulation, using a Finite Element model of an auto rickshaw and an LS-DYNA 50^th percentile male Hybrid III Anthropometric Test Device (dummy). Parametric variables include auto rickshaw impact velocity, auto rickshaw impact region (front, centre or offset) and relative pedestrian impact position (front, side and rear). The output data of each impact simulation was correlated against reported injury metrics, Head Injury Criterion (front, side and rear), Neck injury Criterion (front, side and rear), Abbreviated Injury Scale and reported risk level and adds greater understanding to the issue of auto rickshaw related pedestrian injury risk. The parametric analyses suggest that pedestrians are subject to a relatively high risk of injury during impacts with an auto rickshaw at velocities of 20 km/h or greater, which during some of the impact simulations may even risk fatalities. The present study provides valuable evidence for informing a series of recommendations and guidelines for making the auto rickshaw safer during collisions with pedestrians. Whilst it is acknowledged that the present research findings are based in the field of safety engineering and may over represent injury risk, compared to “Real World” accidents, many of the simulated interactions produced injury response values significantly greater than current threshold curves and thus, justify their inclusion in the study. To reduce the injury risk level and increase the safety of the auto rickshaw, there should be a reduction in the velocity of the auto rickshaw and, or, consideration of engineering solutions, such as retro fitting injury mitigation technologies to those auto rickshaw contact regions which are the subject of the greatest risk of producing pedestrian injury.

Keywords: auto rickshaw, finite element analysis, injury risk level, LS-DYNA, pedestrian impact

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Authors: Mohamed Amine Ben Henni, Taher Hassaine Daouadji, Boussad Abbes, Yu Ming Li, Fazilay Abbes

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The reinforcement and repair of concrete structures by bonding composite materials have become relatively common operations. Different types of composite materials can be used: carbon fiber reinforced polymer (CFRP), glass fiber reinforced polymer (GFRP) as well as functionally graded material (FGM). The development of analytical and numerical models describing the mechanical behavior of structures in civil engineering reinforced by composite materials is necessary. These models will enable engineers to select, design, and size adequate reinforcements for the various types of damaged structures. This study focuses on the free vibration behavior of orthotropic laminated composite plates using a refined shear deformation theory. In these models, the distribution of transverse shear stresses is considered as parabolic satisfying the zero-shear stress condition on the top and bottom surfaces of the plates without using shear correction factors. In this analysis, the equation of motion for simply supported thick laminated rectangular plates is obtained by using the Hamilton’s principle. The accuracy of the developed model is demonstrated by comparing our results with solutions derived from other higher order models and with data found in the literature. Besides, a finite-element analysis is used to calculate the natural frequencies of laminated composite plates and is compared with those obtained by the analytical approach.

Keywords: composites materials, laminated composite plate, finite-element analysis, free vibration

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Authors: Promise Ekpo Osaine, Daniel Melesse

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Image captioning is an active area of research in the multi-modal artificial intelligence (AI) community as it connects vision and language understanding, especially in settings where it is required that a model understands the content shown in an image and generates semantically and grammatically correct descriptions. In this project, we followed a standard approach to a deep learning-based image captioning model, injecting architecture for the encoder-decoder setup, where the encoder extracts image features, and the decoder generates a sequence of words that represents the image content. As such, we investigated image encoders, which are ResNet101, InceptionResNetV2, EfficientNetB7, EfficientNetV2M, and CLIP. As a caption generation structure, we explored long short-term memory (LSTM). The CLIP-LSTM model demonstrated superior performance compared to the encoder-decoder models, achieving a BLEU-1 score of 0.904 and a BLEU-4 score of 0.640. Additionally, among the CNN-LSTM models, EfficientNetV2M-LSTM exhibited the highest performance with a BLEU-1 score of 0.896 and a BLEU-4 score of 0.586 while using a single-layer LSTM.

Keywords: multi-modal AI systems, image captioning, encoder, decoder, BLUE score

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Authors: Adetunji Abiola Olaoye

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The study examined the students’ self-concepts and mathematics achievement viz-a-viz the existing three theoretical models: Humanist self-concept (M1), Contemporary self-concept (M2) and Skills development self-concept (M3). As a qualitative research study, it comprised of one research question, which was transformed into hypothesis viz-a-viz the existing theoretical models. Sample to the study comprised of twelve public secondary schools from which twenty-five mathematics teachers, twelve counselling officers and one thousand students of Upper Basic II were selected based on intact class as school administrations and system did not allow for randomization. Two instruments namely 10 items ‘Achievement test in Mathematics’ (r1=0.81) and 10 items Student’s self-concept questionnaire (r2=0.75) were adapted, validated and used for the study. Data were analysed through descriptive, one way ANOVA, t-test and correlation statistics at 5% level of significance. Finding revealed mean and standard deviation of pre-achievement test scores of (51.322, 16.10), (54.461, 17.85) and (56.451, 18.22) for the Humanist Self-Concept, Contemporary Self-Concept and Skill Development Self-Concept respectively. Apart from that study showed that there was significant different in the academic performance of students along the existing models (F-cal>F-value, df = (2,997); P<0.05). Furthermore, study revealed students’ achievement in mathematics and self-concept questionnaire with the mean and standard deviation of (57.4, 11.35) and (81.6, 16.49) respectively. Result confirmed an affirmative relationship with the Contemporary Self-Concept model that expressed an individual subject and specific self-concept as the primary determinants of higher academic achievement in the subject as there is a statistical correlation between students’ self-concept and mathematics achievement viz-a-viz the existing three theoretical models of Contemporary (M2) with -Z_cal<-Z_val, df=998: P<0.05*. The implication of the study was discussed with recommendations and suggestion for further studies proffered.

Keywords: contemporary, humanists, self-concepts, skill development

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Authors: Jianhua Wang

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To obtain key information quickly from long texts on small screens of mobile devices, sight-interpreters need to establish optimized summarization model for fast information retrieval. Four summarization models based on previous studies were studied including title+key words (TKW), title+topic sentences (TTS), key words+topic sentences (KWTS) and title+key words+topic sentences (TKWTS). Psychological experiments were conducted on the four models for three different genres of interpreting texts to establish the optimized summarization model for sight-interpreters. This empirical study shows that the optimized summarization model for sight-interpreters to quickly grasp the key information of the texts they interpret is title+key words (TKW) for cultural texts, title+key words+topic sentences (TKWTS) for economic texts and topic sentences+key words (TSKW) for political texts.

Keywords: different genres, mobile screens, optimized summarization models, sight-interpreters

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Authors: Furkan Ozkan, M. Selcuk Arslan, Hatice Mercan

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Electric vehicles are becoming increasingly popular asa sustainable alternative to traditional combustion engine vehicles. However, to fully realize the potential of EVs in reducing environmental impact and energy consumption, efficient control strategies are essential. This study explores the application of green wave control using model predictive control for electric vehicles, coupled with energy consumption modeling using neural networks. The use of MPC allows for real-time optimization of the vehicles’ energy consumption while considering dynamic traffic conditions. By leveraging neural networks for energy consumption modeling, the EV's performance can be further enhanced through accurate predictions and adaptive control. The integration of these advanced control and modeling techniques aims to maximize energy efficiency and range while navigating urban traffic scenarios. The findings of this research offer valuable insights into the potential of green wave control for electric vehicles and demonstrate the significance of integrating MPC and neural network modeling for optimizing energy consumption. This work contributes to the advancement of sustainable transportation systems and the widespread adoption of electric vehicles. To evaluate the effectiveness of the green wave control strategy in real-world urban environments, extensive simulations were conducted using a high-fidelity vehicle model and realistic traffic scenarios. The results indicate that the integration of model predictive control and energy consumption modeling with neural networks had a significant impact on the energy efficiency and range of electric vehicles. Through the use of MPC, the electric vehicle was able to adapt its speed and acceleration profile in realtime to optimize energy consumption while maintaining travel time objectives. The neural network-based energy consumption modeling provided accurate predictions, enabling the vehicle to anticipate and respond to variations in traffic flow, further enhancing energy efficiency and range. Furthermore, the study revealed that the green wave control strategy not only reduced energy consumption but also improved the overall driving experience by minimizing abrupt acceleration and deceleration, leading to a smoother and more comfortable ride for passengers. These results demonstrate the potential for green wave control to revolutionize urban transportation by enhancing the performance of electric vehicles and contributing to a more sustainable and efficient mobility ecosystem.

Keywords: electric vehicles, energy efficiency, green wave control, model predictive control, neural networks

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Authors: Amreth Chandrasehar

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Machine Learning (ML) Models are developed and run in production to solve various use cases that help organizations to be more efficient and help drive the business. But this comes at a massive development cost and lost business opportunities. According to the Gartner report, 85% of data science projects fail, and one of the factors impacting this is not paying attention to Model Observability. Model Observability helps the developers and operators to pinpoint the model performance issues data drift and help identify root cause of issues. This paper focuses on providing insights into incorporating model observability in model development and operationalizing it in production.

Keywords: model observability, monitoring, drift detection, ML observability platform

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Authors: Altaf H. Khan, Frank Stenger, Mohammed A. Hussein, Reaz A. Chaudhuri, Sameera Asif

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This paper discusses a novel approach to approximate quadrature integrals that arise in the estimation of likelihood parameters for the generalized linear mixed models (GLMM) as well as Bayesian methodology also requires computation of multidimensional integrals with respect to the posterior distributions in which computation are not only tedious and cumbersome rather in some situations impossible to find solutions because of singularities, irregular domains, etc. An attempt has been made in this work to apply Sinc function based quadrature rules to approximate intractable integrals, as there are several advantages of using Sinc based methods, for example: order of convergence is exponential, works very well in the neighborhood of singularities, in general quite stable and provide high accurate and double precisions estimates. The Sinc function based approach seems to be utilized first time in statistical domain to our knowledge, and it's viability and future scopes have been discussed to apply in the estimation of parameters for GLMM models as well as some other statistical areas.

Keywords: generalized linear mixed model, likelihood parameters, qudarature, Sinc function

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Authors: Paua Ketan, Bhagate Rajkumar, Adiga Ganesh, M. Kiran

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Low gear noise is an important sound quality feature in modern passenger cars. Annoying gear noise from the gearbox is influenced by the gear design, gearbox shaft layout, manufacturing deviations in the components, assembly errors and the mounting arrangement of the complete gearbox. Geometrical deviations in the form of profile and lead errors are often present on the flanks of the inspected gears. Ghost frequencies of a gear are very challenging to identify in standard gear measurement and analysis process due to small wavelengths involved. In this paper, gear whine noise occurring at non-integral multiples of gear mesh frequency of passenger car gearbox is investigated and the root cause is identified using the displacement deviation analysis (DDA) method. DDA method is applied to identify ghost frequency excitations on the flanks of gears arising out of generation grinding. Frequency identified through DDA correlated with the frequency of vibration and noise on the end-of-line machine as well as vehicle level measurements. With the application of DDA method along with standard lead profile measurement, gears with ghost frequency geometry deviations were identified on the production line to eliminate defective parts and thereby eliminate ghost frequency noise from a vehicle. Further, displacement deviation analysis can be used in conjunction with the manufacturing process simulation to arrive at suitable countermeasures for arresting the ghost frequency.

Keywords: displacement deviation analysis, gear whine, ghost frequency, sound quality

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Authors: Pedro M. Abreu, Bruno R. Mendes

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The management of pharmacotherapy and the process of dispensing medicines is becoming critical in clinical pharmacy due to the increase of incidence and prevalence of chronic diseases, the complexity and customization of therapeutic regimens, the introduction of innovative and more expensive medicines, the unbalanced relation between expenditure and revenue as well as due to the lack of rationalization associated with medication use. For these reasons, co-payments emerged in Europe in the 70s and have been applied over the past few years in healthcare. Co-payments lead to a rationing and rationalization of user’s access under healthcare services and products, and simultaneously, to a qualification and improvement of the services and products for the end-user. This analysis, under hospital practices particularly and co-payment strategies in general, was carried out on all the European regions and identified four reference countries, that apply repeatedly this tool and with different approaches. The structure, content and adaptation of European co-payments were analyzed through 7 qualitative attributes and 19 performance indicators, and the results expressed in a scorecard, allowing to conclude that the German models (total score of 68,2% and 63,6% in both elected co-payments) can collect more compliance and effectiveness, the English models (total score of 50%) can be more accessible, and the French models (total score of 50%) can be more adequate to the socio-economic and legal framework. Other European models did not show the same quality and/or performance, so were not taken as a standard in the future design of co-payments strategies. In this sense, we can see in the co-payments a strategy not only to moderate the consumption of healthcare products and services, but especially to improve them, as well as a strategy to increment the value that the end-user assigns to these services and products, such as medicines.

Keywords: clinical pharmacy, co-payments, healthcare, medicines

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Authors: Uduak Umoh, Imo Eyoh, Emmauel Nyoho

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This paper compares fuzzy-machine learning algorithms such as Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) for the predicting cases of fire outbreak. The paper uses the fire outbreak dataset with three features (Temperature, Smoke, and Flame). The data is pre-processed using Interval Type-2 Fuzzy Logic (IT2FL) algorithm. Min-Max Normalization and Principal Component Analysis (PCA) are used to predict feature labels in the dataset, normalize the dataset, and select relevant features respectively. The output of the pre-processing is a dataset with two principal components (PC1 and PC2). The pre-processed dataset is then used in the training of the aforementioned machine learning models. K-fold (with K=10) cross-validation method is used to evaluate the performance of the models using the matrices – ROC (Receiver Operating Curve), Specificity, and Sensitivity. The model is also tested with 20% of the dataset. The validation result shows KNN is the better model for fire outbreak detection with an ROC value of 0.99878, followed by SVM with an ROC value of 0.99753.

Keywords: Machine Learning Algorithms , Interval Type-2 Fuzzy Logic, Fire Outbreak, Support Vector Machine, K-Nearest Neighbour, Principal Component Analysis

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Authors: Mohammed Nasser Al-Suqri

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Former information-seeking models are proposed more than two decades ago. These already existed models were given prior to the evolution of digital information era and Artificial Intelligence (AI) technologies. Lack of current information seeking models within Library and Information Studies resulted in fewer advancements for teaching students about information-seeking behaviors, design of library tools and services. In order to better facilitate the aforementioned concerns, this study aims to propose state-of-the-art model while focusing on the information seeking behavior of library users in the Sultanate of Oman. This study aims for the development, designing and contextualizing the real-time user-centric information seeking model capable of enhancing information needs and information usage along with incorporating critical insights for the digital library practices. Another aim is to establish far-sighted and state-of-the-art frame of reference covering Artificial Intelligence (AI) while synthesizing digital resources and information for optimizing information-seeking behavior. The proposed study is empirically designed based on a mix-method process flow, technical surveys, in-depth interviews, focus groups evaluations and stakeholder investigations. The study data pool is consist of users and specialist LIS staff at 4 public libraries and 26 academic libraries in Oman. The designed research model is expected to facilitate LIS by assisting multi-dimensional insights with AI integration for redefining the information-seeking process, and developing a technology rich model.

Keywords: artificial intelligence, information seeking, information behavior, information seeking models, libraries, Sultanate of Oman

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Authors: H. Hruschka

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This paper presents the first comparison of the performance of the restricted Boltzmann machine and the deep belief net on binary market basket data relative to binary factor analysis and the two best-known topic models, namely Dirichlet allocation and the correlated topic model. This comparison shows that the restricted Boltzmann machine and the deep belief net are superior to both binary factor analysis and topic models. Managerial implications that differ between the investigated models are treated as well. The restricted Boltzmann machine is defined as joint Boltzmann distribution of hidden variables and observed variables (purchases). It comprises one layer of observed variables and one layer of hidden variables. Note that variables of the same layer are not connected. The comparison also includes deep belief nets with three layers. The first layer is a restricted Boltzmann machine based on category purchases. Hidden variables of the first layer are used as input variables by the second-layer restricted Boltzmann machine which then generates second-layer hidden variables. Finally, in the third layer hidden variables are related to purchases. A public data set is analyzed which contains one month of real-world point-of-sale transactions in a typical local grocery outlet. It consists of 9,835 market baskets referring to 169 product categories. This data set is randomly split into two halves. One half is used for estimation, the other serves as holdout data. Each model is evaluated by the log likelihood for the holdout data. Performance of the topic models is disappointing as the holdout log likelihood of the correlated topic model – which is better than Dirichlet allocation - is lower by more than 25,000 compared to the best binary factor analysis model. On the other hand, binary factor analysis on its own is clearly surpassed by both the restricted Boltzmann machine and the deep belief net whose holdout log likelihoods are higher by more than 23,000. Overall, the deep belief net performs best. We also interpret hidden variables discovered by binary factor analysis, the restricted Boltzmann machine and the deep belief net. Hidden variables characterized by the product categories to which they are related differ strongly between these three models. To derive managerial implications we assess the effect of promoting each category on total basket size, i.e., the number of purchased product categories, due to each category's interdependence with all the other categories. The investigated models lead to very different implications as they disagree about which categories are associated with higher basket size increases due to a promotion. Of course, recommendations based on better performing models should be preferred. The impressive performance advantages of the restricted Boltzmann machine and the deep belief net suggest continuing research by appropriate extensions. To include predictors, especially marketing variables such as price, seems to be an obvious next step. It might also be feasible to take a more detailed perspective by considering purchases of brands instead of purchases of product categories.

Keywords: binary factor analysis, deep belief net, market basket analysis, restricted Boltzmann machine, topic models

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Authors: Huijuan Liu, Fukun Li, Hao Yuan

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The bolted spherical node is a common type of joint in space steel structures. The bolt-sphere joint portion almost always controls the bearing capacity of the bolted spherical node. The investigation of the bearing performance and progressive failure in service often requires high-fidelity numerical models. This paper focuses on the constitutive models of bolt steel and sphere steel used in China’s space structure construction. The elastoplastic model is determined by a standard tensile test and calibrated Voce saturated hardening rule. The ductile damage is found dominant based on the fractography analysis. Then Rice-Tracey ductile fracture rule is selected and the model parameters are calibrated based on tensile tests of notched specimens. These calibrated material models can benefit research or engineering work in similar fields.

Keywords: bolt-sphere joint, steel, constitutive model, ductile damage, model calibration

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Authors: Avinoam Rabinovich

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CO₂ geological storage is a proven technology for reducing anthropogenic carbon emissions, which is paramount for achieving the ambitious net zero emissions goal. Core flooding experiments are an important step in any CO₂ storage project, allowing us to gain information on the flow of CO₂ and brine in the porous rock extracted from the reservoir. This information is important for understanding basic mechanisms related to CO₂ geological storage as well as for reservoir modeling, which is an integral part of a field project. In this work, a different method for constructing accurate models of CO₂-brine core flooding will be presented. Results for synthetic cases and real experiments will be shown and compared with numerical models to exhibit their predictive capabilities. Furthermore, the various mechanisms which impact the CO₂ distribution and trapping in the rock samples will be discussed, and examples from models and experiments will be provided. The new method entails solving an inverse problem to obtain a three-dimensional permeability distribution which, along with the relative permeability and capillary pressure functions, constitutes a model of the flow experiments. The model is more accurate when data from a number of experiments are combined to solve the inverse problem. This model can then be used to test various other injection flow rates and fluid fractions which have not been tested in experiments. The models can also be used to bridge the gap between small-scale capillary heterogeneity effects (sub-core and core scale) and large-scale (reservoir scale) effects, known as the upscaling problem.

Keywords: CO₂ geological storage, residual trapping, capillary heterogeneity, core flooding, CO₂-brine flow

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Authors: Dino Carpentras, Alejandro Dinkelberg, Michael Quayle

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Opinion dynamics models use an agent-based modeling approach to model people’s opinions. Model's properties are usually explored by testing the two 'degrees of freedom': the interaction rule and the network topology. The latter defines the connection, and thus the possible interaction, among agents. The interaction rule, instead, determines how agents select each other and update their own opinion. Here we show the existence of the third degree of freedom. This can be used for turning one model into each other or to change the model’s output up to 100% of its initial value. Opinion dynamics models represent the evolution of real-world opinions parsimoniously. Thus, it is fundamental to know how real-world opinion (e.g., supporting a candidate) could be turned into a number. Specifically, we want to know if, by choosing a different opinion-to-number transformation, the model’s dynamics would be preserved. This transformation is typically not addressed in opinion dynamics literature. However, it has already been studied in psychometrics, a branch of psychology. In this field, real-world opinions are converted into numbers using abstract objects called 'scales.' These scales can be converted one into the other, in the same way as we convert meters to feet. Thus, in our work, we analyze how this scale transformation may affect opinion dynamics models. We perform our analysis both using mathematical modeling and validating it via agent-based simulations. To distinguish between scale transformation and measurement error, we first analyze the case of perfect scales (i.e., no error or noise). Here we show that a scale transformation may change the model’s dynamics up to a qualitative level. Meaning that a researcher may reach a totally different conclusion, even using the same dataset just by slightly changing the way data are pre-processed. Indeed, we quantify that this effect may alter the model’s output by 100%. By using two models from the standard literature, we show that a scale transformation can transform one model into the other. This transformation is exact, and it holds for every result. Lastly, we also test the case of using real-world data (i.e., finite precision). We perform this test using a 7-points Likert scale, showing how even a small scale change may result in different predictions or a number of opinion clusters. Because of this, we think that scale transformation should be considered as a third-degree of freedom for opinion dynamics. Indeed, its properties have a strong impact both on theoretical models and for their application to real-world data.

Keywords: degrees of freedom, empirical validation, opinion scale, opinion dynamics

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Authors: Aydin Sipahioglu, Gokhan Celik

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Helicopter routing problem (HRP) is finding good tours for helicopter so as to pick up and deliver personnel or material among specified nodes, mutually. It can be encountered in case of being lots of supply and demand points for different commodities and requiring delivering commodities with helicopter. For instance, to deliver personnel or material from shore to oil rig is a good example. In fact, HRP is a branch of vehicle routing problem with pickup and delivery (VRPPD). However, it has additional constraints such that fuel capacity, performance of helicopter in different altitude and temperature, and the number of maximum takeoff and landing allowed. This kind of pickup and delivery problems can be classified into 3 groups, basically. 1-1 (one to one), M-M (many to many) and 1-M-1 (one to many to one). 1-1 means each commodity has only one supply and one demand point. M-M means there can be more than one supply and demand points for each kind of commodity. 1-M-1 means commodities at depot are delivered to demand points and commodities at customers are delivered to depot. In this case helicopter takes off from its own base, complete its tour and return to its own base. In this study, we define 1-M-M-1 type HRP. That means helicopter takes off from its home base, deliver commodities among the nodes as well as between depot and customers and return to its home base. These problems have NP-hard nature. Therefore, obtaining a good solution in a reasonable time is not easy. In this study, a model is offered for 1-M-M-1 type HRP. It is shown on small scale test instances that the model can find the optimal solution.

Keywords: helicopter routing problem, vehicle routing with pickup and delivery, integer programming

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Authors: David Lall, Vikram Vishal, P. G. Ranjith

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Numerical modeling of gas production from hydrate-bearing reservoirs requires the solution of various thermal, hydrological, chemical, and mechanical phenomena in a coupled manner. Among the various reservoir properties that influence gas production estimates, the distribution of permeability across the domain is one of the most crucial parameters since it determines both heat transfer and mass transfer. The aspect of permeability in hydrate-bearing reservoirs is particularly complex compared to conventional reservoirs since it depends on the saturation of gas hydrates and hence, is dynamic during production. The dependence of permeability on hydrate saturation is mathematically represented using permeability-reduction models, which are specific to the expected morphology of hydrate accumulations (such as grain-coating or pore-filling hydrates). In this study, we demonstrate the impact of various permeability-reduction models, and consequently, different morphologies of hydrate deposits on the estimates of gas production using depressurization at the reservoir scale. We observe significant differences in produced water volumes and cumulative mass of produced gas between the models, thereby highlighting the uncertainty in production behavior arising from the ambiguity in the prevalent gas hydrate morphology.

Keywords: gas hydrate morphology, multi-scale modeling, THMC, fluid flow in porous media

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Authors: Samuel Ahamefula

Abstract:

Turbulent motion is a highly nonlinear and complex phenomenon, and its modelling is still very challenging. In this study, we developed a hybrid computational approach to accurately simulate fluid turbulence phenomenon. The focus is coupling and transitioning between Direct Numerical Simulation (DNS) and Large Eddy Simulating Wall Models (LES-WM) regions. In the framework, high-order fidelity fluid dynamical methods are utilized to simulate the unsteady compressible Navier-Stokes equations in the Eulerian format on the unstructured moving grids. The coupling and transitioning of DNS and LES-WM are conducted through the linearly staggered Dirichlet-Neumann coupling scheme. The high-fidelity framework is verified and validated based on namely, DNS ability for capture full range of turbulent scales, giving accurate results and LES-WM efficiency in simulating near-wall turbulent boundary layer by using wall models.

Keywords: computational methods, turbulence modelling, turbulence entropy, navier-stokes equations

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Authors: Fikret Yalcinkaya, Hamza Unsal

Abstract:

To understand how neurons work, it is required to combine experimental studies on neural science with numerical simulations of neuron models in a computer environment. In this regard, the simplicity and applicability of spiking neuron modeling functions have been of great interest in computational neuron science and numerical neuroscience in recent years. Spiking neuron models can be classified by exhibiting various neuronal behaviors, such as spiking and bursting. These classifications are important for researchers working on theoretical neuroscience. In this paper, three different spiking neuron models; Izhikevich, Adaptive Exponential Integrate Fire (AEIF) and Hindmarsh Rose (HR), which are based on first order differential equations, are discussed and compared. First, the physical meanings, derivatives, and differential equations of each model are provided and simulated in the Matlab environment. Then, by selecting appropriate parameters, the models were visually examined in the Matlab environment and it was aimed to demonstrate which model can simulate well-known biological neuron behaviours such as Tonic Spiking, Tonic Bursting, Mixed Mode Firing, Spike Frequency Adaptation, Resonator and Integrator. As a result, the Izhikevich model has been shown to perform Regular Spiking, Continuous Explosion, Intrinsically Bursting, Thalmo Cortical, Low-Threshold Spiking and Resonator. The Adaptive Exponential Integrate Fire model has been able to produce firing patterns such as Regular Ignition, Adaptive Ignition, Initially Explosive Ignition, Regular Explosive Ignition, Delayed Ignition, Delayed Regular Explosive Ignition, Temporary Ignition and Irregular Ignition. The Hindmarsh Rose model showed three different dynamic neuron behaviours; Spike, Burst and Chaotic. From these results, the Izhikevich cell model may be preferred due to its ability to reflect the true behavior of the nerve cell, the ability to produce different types of spikes, and the suitability for use in larger scale brain models. The most important reason for choosing the Adaptive Exponential Integrate Fire model is that it can create rich ignition patterns with fewer parameters. The chaotic behaviours of the Hindmarsh Rose neuron model, like some chaotic systems, is thought to be used in many scientific and engineering applications such as physics, secure communication and signal processing.

Keywords: Izhikevich, adaptive exponential integrate fire, Hindmarsh Rose, biological neuron behaviours, spiking neuron models

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Authors: Ignatio Madanhire, Charles Mbohwa

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This study looks at the best model of aggregate planning activity in an industrial entity and uses the trial and error method on spreadsheets to solve aggregate production planning problems. Also linear programming model is introduced to optimize the aggregate production planning problem. Application of the models in a furniture production firm is evaluated to demonstrate that practical and beneficial solutions can be obtained from the models. Finally some benchmarking of other furniture manufacturing industries was undertaken to assess relevance and level of use in other furniture firms

Keywords: aggregate production planning, trial and error, linear programming, furniture industry

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Authors: Farid Khosravikia, Patricia Clayton

Abstract:

The main objective of this study is to evaluate the advantages and disadvantages of various machine learning techniques in forecasting ground-motion intensity measures given source characteristics, source-to-site distance, and local site condition. Intensity measures such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Estimating these variables for future earthquake events is a key step in seismic hazard assessment and potentially subsequent risk assessment of different types of structures. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as a statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The algorithms are adjusted to quantify event-to-event and site-to-site variability of the ground motions by implementing them as random effects in the proposed models to reduce the aleatory uncertainty. All the algorithms are trained using a selected database of 4,528 ground-motions, including 376 seismic events with magnitude 3 to 5.8, recorded over the hypocentral distance range of 4 to 500 km in Oklahoma, Kansas, and Texas since 2005. The main reason of the considered database stems from the recent increase in the seismicity rate of these states attributed to petroleum production and wastewater disposal activities, which necessities further investigation in the ground motion models developed for these states. Accuracy of the models in predicting intensity measures, generalization capability of the models for future data, as well as usability of the models are discussed in the evaluation process. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available.

Keywords: artificial neural network, ground-motion models, machine learning, random forest, support vector machine

Procedia PDF Downloads 122