Search results for: mode choice models

Authors: Iftikhar Tayubi, Tabrej Khan, Rayan Alsulmi, Abdulrahman Labban

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Spider produces a special kind of substance. This special kind of substance is called a toxin. The toxin is composed of many types of protein, which differs from species to species. Spider toxin consists of several proteins and non-proteins that include various categories of toxins like myotoxin, neurotoxin, cardiotoxin, dendrotoxin, haemorrhagins, and fibrinolytic enzyme. Protein Sequence information with references of toxins was derived from literature and public databases. From the previous findings, the Spider toxin would be the best choice to treat different types of tumors and cancer. There are many therapeutic regimes, which causes more side effects than treatment hence a different approach must be adopted for the treatment of cancer. The combinations of drugs are being encouraged, and dramatic outcomes are reported. Spider toxin is one of the natural cytotoxic compounds. Hence, it is being used to treat different types of tumors; especially its positive effect on breast cancer is being reported during the last few decades. The efficacy of this database is that it can provide a user-friendly interface for users to retrieve the information about Spiders, toxin and toxin protein of different Spiders species. SPIDTOXD provides a single source information about spider toxins, which will be useful for pharmacologists, neuroscientists, toxicologists, medicinal chemists. The well-ordered and accessible web interface allows users to explore the detail information of Spider and toxin proteins. It includes common name, scientific name, entry id, entry name, protein name and length of the protein sequence. The utility of this database is that it can provide a user-friendly interface for users to retrieve the information about Spider, toxin and toxin protein of different Spider species. The database interfaces will satisfy the demands of the scientific community by providing in-depth knowledge about Spider and its toxin. We have adopted the methodology by using A MySQL and PHP and for designing, we used the Smart Draw. The users can thus navigate from one section to another, depending on the field of interest of the user. This database contains a wealth of information on species, toxins, and clinical data, etc. This database will be useful for the scientific community, basic researchers and those interested in potential pharmaceutical Industry.

Keywords: siptoxd, php, mysql, toxin

Procedia PDF Downloads 172

Authors: Leal Leonardo, Pires Carlos Augusto de Moraes, Casiraghi Magela

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In this work were realized the modeling and simulation of the catalytic reformer process, of ample form, considering all the equipment that influence the operation performance. Considered it a semi-regenerative reformer, with four reactors in series intercalated with four furnaces, two heat exchanges, one product separator and one recycle compressor. A simplified reactional system was considered, involving only ten chemical compounds related through five reactions. The considered process was the applied to aromatics production (benzene, toluene, and xylene). The models developed to diverse equipment were interconnecting in a simulator that consists of a computer program elaborate in FORTRAN 77. The simulation of the global model representative of reformer unity achieved results that are compatibles with the literature ones. It was then possible to study the effects of operational variables in the products concentration and in the performance of the unity equipment.

Keywords: catalytic reforming, modeling, simulation, petrochemical engineering

Procedia PDF Downloads 507

Authors: Laura Kahn, Paul Rodrigues, Onur Savas, Shannon Hahn

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An empirical understanding of an individual's personality expression can have a profound impact on organizations seeking to strengthen team performance and improve employee retention. A team's personality composition can impact overall performance. Creating a tiered information framework that leverages proxies for a user's social context and lexical and linguistic content provides insight into location-specific personality expression. We leverage the layered framework to examine domain-specific, psychological, and lexical cues within social media posts. We apply DistilBERT natural language transfer learning models with real world data to examine the relationship between Big Five personality expressions of people in Science, Technology, Engineering and Math (STEM) fields.

Keywords: big five, personality expression, social media analysis, workforce development

Procedia PDF Downloads 134

Authors: Shazalina Mat Zin, Ahmad Abd. Majid, Ahmad Izani Md. Ismail, Muhammad Abbas

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The generalized wave equation models various problems in sciences and engineering. In this paper, a new three-time level implicit approach based on cubic trigonometric B-spline for the approximate solution of wave equation is developed. The usual finite difference approach is used to discretize the time derivative while cubic trigonometric B-spline is applied as an interpolating function in the space dimension. Von Neumann stability analysis is used to analyze the proposed method. Two problems are discussed to exhibit the feasibility and capability of the method. The absolute errors and maximum error are computed to assess the performance of the proposed method. The results were found to be in good agreement with known solutions and with existing schemes in literature.

Keywords: collocation method, cubic trigonometric B-spline, finite difference, wave equation

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Authors: Sajana Suwal, Ganesh R. Nhemafuki

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Evaluation of ground response under earthquake shaking is crucial in geotechnical earthquake engineering. Damage due to seismic excitation is mainly correlated to local geological and geotechnical conditions. It is evident from the past earthquakes (e.g. 1906 San Francisco, USA, 1923 Kanto, Japan) that the local geology has strong influence on amplitude and duration of ground motions. Since then significant studies has been conducted on ground motion amplification revealing the importance of influence of local geology on ground. Observations from the damaging earthquakes (e.g. Nigata and San Francisco, 1964; Irpinia, 1980; Mexico, 1985; Kobe, 1995; L’Aquila, 2009) divulged that non-uniform damage pattern, particularly in soft fluvio-lacustrine deposit is due to the local amplification of seismic ground motion. Non-uniform damage patterns are also observed in Kathmandu Valley during 1934 Bihar Nepal earthquake and recent 2015 Gorkha earthquake seemingly due to the modification of earthquake ground motion parameters. In this study, site effects resulting from amplification of soft soil in Kathmandu are presented. A large amount of subsoil data was collected and used for defining the appropriate subsoil model for the Kathamandu valley. A comparative study of one-dimensional total-stress equivalent linear and non-linear site response is performed using four strong ground motions for six sites of Kathmandu valley. In general, one-dimensional (1D) site-response analysis involves the excitation of a soil profile using the horizontal component and calculating the response at individual soil layers. In the present study, both equivalent linear and non-linear site response analyses were conducted using the computer program DEEPSOIL. The results show that there is no significant deviation between equivalent linear and non-linear site response models until the maximum strain reaches to 0.06-0.1%. Overall, it is clearly observed from the results that non-linear site response model perform better as compared to equivalent linear model. However, the significant deviation between two models is resulted from other influencing factors such as assumptions made in 1D site response, lack of accurate values of shear wave velocity and nonlinear properties of the soil deposit. The results are also presented in terms of amplification factors which are predicted to be around four times more in case of non-linear analysis as compared to equivalent linear analysis. Hence, the nonlinear behavior of soil prevails the urgent need of study of dynamic characteristics of the soft soil deposit that can specifically represent the site-specific design spectra for the Kathmandu valley for building resilient structures from future damaging earthquakes.

Keywords: deep soil, equivalent linear analysis, non-linear analysis, site response

Procedia PDF Downloads 287

Authors: Ilia Zharikov, Philipp Nikitin, Ilia Vasiliev, Vladimir Dokholyan

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With the increasing popularity of document analysis and recognition systems, text detection (TD) and optical character recognition (OCR) in document images become challenging tasks. However, according to our best knowledge, no publicly available datasets for these particular problems exist. In this paper, we introduce a Distorted Document Images dataset (DDI-100) and provide a detailed analysis of the DDI-100 in its current state. To create the dataset we collected 7000 unique document pages, and extend it by applying different types of distortions and geometric transformations. In total, DDI-100 contains more than 100,000 document images together with binary text masks, text and character locations in terms of bounding boxes. We also present an analysis of several state-of-the-art TD and OCR approaches on the presented dataset. Lastly, we demonstrate the usefulness of DDI-100 to improve accuracy and stability of the considered TD and OCR models.

Keywords: document analysis, open dataset, optical character recognition, text detection

Procedia PDF Downloads 164

Authors: Anne M. Denton, Rahul Gomes, David W. Franzen

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High-resolution elevation data are becoming increasingly available, but typical approaches for computing topographic features, like slope and curvature, still assume small sliding windows, for example, of size 3x3. That means that the digital elevation model (DEM) has to be resampled to the scale of the landform features that are of interest. Any higher resolution is lost in this resampling. When the topographic features are computed through regression that is performed at the resolution of the original data, the accuracy can be much higher, and the reported result can be adjusted to the length scale that is relevant locally. Slope and variance are calculated for overlapping windows, meaning that one regression result is computed per raster point. The number of window centers per area is the same for the output as for the original DEM. Slope and variance are computed by performing regression on the points in the surrounding window. Such an approach is computationally feasible because of the additive nature of regression parameters and variance. Any doubling of window size in each direction only takes a single pass over the data, corresponding to a logarithmic scaling of the resulting algorithm as a function of the window size. Slope and variance are stored for each aggregation step, allowing the reported slope to be selected to minimize variance. The approach thereby adjusts the effective window size to the landform features that are characteristic to the area within the DEM. Starting with a window size of 2x2, each iteration aggregates 2x2 non-overlapping windows from the previous iteration. Regression results are stored for each iteration, and the slope at minimal variance is reported in the final result. As such, the reported slope is adjusted to the length scale that is characteristic of the landform locally. The length scale itself and the variance at that length scale are also visualized to aid in interpreting the results for slope. The relevant length scale is taken to be half of the window size of the window over which the minimum variance was achieved. The resulting process was evaluated for 1-meter DEM data and for artificial data that was constructed to have defined length scales and added noise. A comparison with ESRI ArcMap was performed and showed the potential of the proposed algorithm. The resolution of the resulting output is much higher and the slope and aspect much less affected by noise. Additionally, the algorithm adjusts to the scale of interest within the region of the image. These benefits are gained without additional computational cost in comparison with resampling the DEM and computing the slope over 3x3 images in ESRI ArcMap for each resolution. In summary, the proposed approach extracts slope and aspect of DEMs at the lengths scales that are characteristic locally. The result is of higher resolution and less affected by noise than existing techniques.

Keywords: high resolution digital elevation models, multi-scale analysis, slope calculation, window-based regression

Procedia PDF Downloads 122

Authors: Muhammet Baldan, Emel Timuçin

Abstract:

Generally, absorption and bioavailability increase if solubility increases; therefore, it is crucial to predict them in drug discovery applications. Molecular descriptors and Molecular properties are traditionally used for the prediction of water solubility. There are various key descriptors that are used for this purpose, namely Drogan Descriptors, Morgan Descriptors, Maccs keys, etc., and each has different prediction capabilities with differentiating successes between different data sets. Another source for the prediction of solubility is structural features; they are commonly used for the prediction of solubility. However, there are little to no studies that combine three or more properties or descriptors for prediction to produce a more powerful prediction model. Unlike available models, we used a combination of those features in a random forest machine learning model for improved solubility prediction to better predict and, therefore, contribute to drug discovery systems.

Keywords: solubility, random forest, molecular descriptors, maccs keys

Procedia PDF Downloads 33

Authors: Hanan. Al Chaghouri, Mohammad Azad Malik, P. John Thomas, Paul O’Brien

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The process of assembling metal nanoparticles at the interface of two liquids has received a great interest over the past few years due to a wide range of important applications and their unusual properties compared to bulk materials. We present a low cost, simple and cheap synthesis of metal nanoparticles, core/shell structures and semiconductors followed by assembly of these particles between immiscible liquids. The aim of this talk is divided to three parts: firstly, to describe the achievement of a closed loop recycling for producing cadmium sulphide as powders and/or nanostructured thin films for solar cells or other optoelectronic devices applications by using a different chain length of commercially available secondary amines of dithiocarbamato complexes. The approach can be extended to other metal sulphides such as those of Zn, Pb, Cu, or Fe and many transition metals and oxides. Secondly, to synthesis significantly cheaper magnetic particles suited for the mass market. Ni/NiO nanoparticles with ferromagnetic properties at room temperature were among the smallest and strongest magnets (5 nm) were made in solution. The applications of this work can be applied to produce viable storage devices and the other possibility is to disperse these nanocrystals in solution and use it to make ferro-fluids which have a number of mature applications. The third part is about preparing and assembling of submicron silver, cobalt and nickel particles by using polyol methods and liquid/liquid interface, respectively. Noble metal like gold, copper and silver are suitable for plasmonic thin film solar cells because of their low resistivity and strong interactions with visible light waves. Silver is the best choice for solar cell application since it has low absorption losses and high radiative efficiency compared to gold and copper. Assembled cobalt and nickel as films are promising for spintronic, magnetic and magneto-electronic and biomedics.

Keywords: assembling nanoparticles, liquid/liquid interface, thin film, core/shell, solar cells, recording media

Procedia PDF Downloads 295

Authors: Abd Latif Abdul Rahman, Zuraidah Arif, Muhammad Faizal Iylia, Mohd Ghazali, Asmadi Mohammed Ghazali

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Mobile information is a significant access point for human information activities. Theories and models of human information behavior have developed over several decades but have not yet considered the role of the user’s computing device in digital information interactions. This paper reviews the literature that leads to developing a conceptual framework of a study on the managers mobile information behavior. Based on the literature review, dimensions of mobile information behavior are identified, namely, dimension information needs, dimension information access, information retrieval and dimension of information use. The study is significant to understand the nature of librarians’ behavior in searching, retrieving and using information via the mobile device. Secondly, the study would provide suggestions about various kinds of mobile applications which organization can provide for their staff to improve their services.

Keywords: mobile information behavior, information behavior, mobile information, mobile devices

Procedia PDF Downloads 341

Authors: Tarek Litim, Ouahiba Taamallah

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The present paper is an investigation of the effect of burnishing on the surface integrity of a component made of 15NiCr6 steel. This work shows a statistical study based on regression, and Taguchi's design has allowed the development of mathematical models to predict the output responses as a function of the technological parameters studied. The response surface methodology (RSM) showed a simultaneous influence of the burnishing parameters and observe the optimal processing parameters. ANOVA analysis of the results resulted in the validation of the prediction model with a determination coefficient R=90.60% and 92.41% for roughness and hardness, respectively. Furthermore, a multi-objective optimization allowed to identify a regime characterized by P=10kgf, i=3passes, and f=0.074mm/rev, which favours minimum roughness and maximum hardness. The result was validated by the desirability of D= (0.99 and 0.95) for roughness and hardness, respectively.

Keywords: 15NiCr6 steel, burnishing, surface integrity, Taguchi, RSM, ANOVA

Procedia PDF Downloads 187

Authors: Jessica BrodeFrank, Beka Bryer, Lacey Wilson, Sierra Van Ryck deGroot

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Museums are enjoying quite the moment in pop culture. From discussions of labor in Bob’s Burger to introducing cultural repatriation in The Black Panther, discussions of various museum issues are making their way to popular media. “Talking Back to Hollywood” analyzes the impact museums have on movies and television. The paper will highlight a series of cultural cameos and discuss what each reveals about critical themes in museums: repatriation, labor, obfuscated histories, institutional legacies, artificial intelligence, and holograms. Using a mixed methods approach to include surveys, descriptive research, thematic analysis, and context analysis, the authors of this paper will explore how we, as the museum staff, might begin to cite museums and movies together as texts. Drawing from their experience working in museums and public history, this contingent of mid-career professionals will highlight the impact museums have had on movies and television and the didactic lessons these portrayals can provide back to cultural heritage professionals. From tackling critical themes in museums such as repatriation, labor conditions/inequities, obfuscated histories, curatorial choice and control, institutional legacies, and more, this paper is grounded in the cultural zeitgeist of the 2000s and the message these media portrayals send to the public and the cultural heritage sector. In particular, the paper will examine how portrayals of AI, holograms, and more technology can be used as entry points for necessary discussions with the public on mistrust, misinformation, and emerging technologies. This paper will not only expose the legacy and cultural understanding of the museum field within popular culture but also will discuss actionable ways that public historians can use these portrayals as an entry point for discussions with the public, citing literature reviews and quantitative and qualitative analysis of survey results. As Hollywood is talking about museums, museums can use that to better connect to the audiences who feel comfortable at the cinema but are excluded from the museum.

Keywords: museums, public memory, representation, popular culture

Procedia PDF Downloads 76

Authors: Ehab AbdulRazzaq Hussein

Abstract:

Quantum cryptography (QC) is an emerging technology for secure key distribution with single-photon transmissions. In contrast to classical cryptographic schemes, the security of QC schemes is guaranteed by the fundamental laws of nature. Their security stems from the impossibility to distinguish non-orthogonal quantum states with certainty. A potential eavesdropper introduces errors in the transmissions, which can later be discovered by the legitimate participants of the communication. In this paper, the modeling approach is proposed for QC protocol BB84 using polarization coding. The single-photon system is assumed to be used in the designed models. Thus, Eve cannot use beam-splitting strategy to eavesdrop on the quantum channel transmission. The only eavesdropping strategy possible to Eve is the intercept/resend strategy. After quantum transmission of the QC protocol, the quantum bit error rate (QBER) is estimated and compared with a threshold value. If it is above this value the procedure must be stopped and performed later again.

Keywords: security, key distribution, cryptography, quantum protocols, Quantum Cryptography (QC), Quantum Key Distribution (QKD).

Procedia PDF Downloads 395

Authors: Katarzyna Palus, Jarosław Całka

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Gastrointestinal disorders, especially acid-related diseases, including peptic and duodenal ulcers, gastroesophageal reflux disease, upper GI bleeding or stress-related mucosal disease, are currently serious health issues encountered very frequently in patients worldwide. However, to date, the response of sympathetic neurons to gastric mucosal injury and local inflammation following hyperacidity is unknown. Thus, the present study was designed to determine possible changes in expression of galanin (GAL) in the CSMG neurons supplying the prepyloric area of the porcine stomach in a physiological state and following experimentally-induced hyperacidity by using combined retrograde tracing and double-labelling immunohistochemistry. The choice of the domestic pig as an experimental model in the present study is not accidental and is justified by the high degree of physiological and anatomical similarity to human digestive system functions. In this experiment ten juvenile female pigs of the Large White Polish breed were used. The animals were divided into two groups: control and animals with hydrochloric acid infusion (HCl). The neuronal retrograde marker Fast Blue (FB) was injected into the anterior prepyloric wall of the stomach of all animals. After 23 days, animals of the HCl-group were reintroduced into a state of general anesthesia and intragastrically given 5 ml/kg of body weight of 0.25 M aqueous solution of hydrochloric acid. On the 28th day, all animals were euthanized. The CSMG complexes were then collected and the CSMG cryostat sections were stained immunocytochemically for GAL and TH (tyrosine hydroxylase). Immunohistochemistry revealed that in the control group 8.40 ± 0.53 % out of 200 FB-positive CSMG neurons contained GAL. In HCl group upregulation of the GAL-IR neurons to 22.52 ± 1.18 % were observed. All GAL-IR neurons in both groups showed the simultaneously TH immunoreactivity. Increase in the expression of GAL in FB-positive neurons of the HCL group may suggest its participation in the protective mechanisms of neurons in different pathological processes, such as gastric hyperacidity.

Keywords: coeliac-superior mesenteric ganglion complex, gastric innervation, hyperacidity, immunohistochemistry

Procedia PDF Downloads 240

Authors: Mir Jalil Razavi, Tianming Liu, Xianqiao Wang

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Cortical folding, characterized by convex gyri and concave sulci, has an intrinsic relationship to the brain’s functional organization. Understanding the mechanism of the brain’s convoluted patterns can provide useful clues into normal and pathological brain function. During the development, the cerebral cortex experiences a noticeable expansion in volume and surface area accompanied by tremendous tissue folding which may be attributed to many possible factors. Despite decades of endeavors, the fundamental mechanism and key regulators of this crucial process remain incompletely understood. Therefore, to taking even a small role in unraveling of brain folding mystery, we present a mechanical model to find mechanism of 3-hinges formation in a growing brain that it has not been addressed before. A 3-hinge is defined as a gyral region where three gyral crests (hinge-lines) join. The reasons that how and why brain prefers to develop 3-hinges have not been answered very well. Therefore, we offer a theoretical and computational explanation to mechanism of 3-hinges formation in a growing brain and validate it by experimental observations. In theoretical approach, the dynamic behavior of brain tissue is examined and described with the aid of a large strain and nonlinear constitutive model. Derived constitute model is used in the computational model to define material behavior. Since the theoretical approach cannot predict the evolution of cortical complex convolution after instability, non-linear finite element models are employed to study the 3-hinges formation and secondary morphological folds of the developing brain. Three-dimensional (3D) finite element analyses on a multi-layer soft tissue model which mimics a small piece of the brain are performed to investigate the fundamental mechanism of consistent hinge formation in the cortical folding. Results show that after certain amount growth of cortex, mechanical model starts to be unstable and then by formation of creases enters to a new configuration with lower strain energy. By further growth of the model, formed shallow creases start to form convoluted patterns and then develop 3-hinge patterns. Simulation results related to 3-hinges in models show good agreement with experimental observations from macaque, chimpanzee and human brain images. These results have great potential to reveal fundamental principles of brain architecture and to produce a unified theoretical framework that convincingly explains the intrinsic relationship between cortical folding and 3-hinges formation. This achieved fundamental understanding of the intrinsic relationship between cortical folding and 3-hinges formation would potentially shed new insights into the diagnosis of many brain disorders such as schizophrenia, autism, lissencephaly and polymicrogyria.

Keywords: brain, cortical folding, finite element, three hinge

Procedia PDF Downloads 231

Authors: Frank Kuebler, Rolf Steinhilper

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Artificial neural networks (ANN) as well as Design of Experiments (DOE) based regression analysis (RA) are mainly used for modeling of complex systems. Both methodologies are commonly applied in process and quality control of manufacturing processes. Due to the fact that resource efficiency has become a critical concern for manufacturing companies, these models needs to be extended to predict resource-consumption of manufacturing processes. This paper describes an approach to use neural networks as well as DOE based regression analysis for predicting resource consumption of manufacturing processes and gives a comparison of the achievable results based on an industrial case study of a turning process.

Keywords: artificial neural network, design of experiments, regression analysis, resource efficiency, manufacturing process

Procedia PDF Downloads 516

Authors: Kamel Al-Khaled

Abstract:

Reaction-diffusion equations are commonly used in population biology to model the spread of biological species. In this paper, we propose a fractional reaction-diffusion equation, where the classical second derivative diffusion term is replaced by a fractional derivative of order less than two. Based on the symbolic computation system Mathematica, Adomian decomposition method, developed for fractional differential equations, is directly extended to derive explicit and numerical solutions of space fractional reaction-diffusion equations. The fractional derivative is described in the Caputo sense. Finally, the recent appearance of fractional reaction-diffusion equations as models in some fields such as cell biology, chemistry, physics, and finance, makes it necessary to apply the results reported here to some numerical examples.

Keywords: fractional partial differential equations, reaction-diffusion equations, adomian decomposition, biological species

Procedia PDF Downloads 364

Authors: Hans Tjio, Wee Meng Seng

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The interaction of contract and property has always been a concern in corporate and commercial law, where there are internal structures created that may not match the externally perceived image generated by the labels attached to those structures. We will focus, in particular, on the priority structures created by affirmative asset partitioning, which have increasingly come under challenge by those attempting to negotiate around them. The most prominent has been the AT1 bonds issued by Credit Suisse which were wiped out before its equity when the troubled bank was acquired by UBS. However, this should not have come as a surprise to those whose “bonds” had similarly been “redeemed” upon the occurrence of certain reference events in countries like Singapore, Hong Kong and Taiwan during their Minibond crisis linked to US sub-prime defaults. These were derivatives classified as debentures and sold as such. At the same time, we are again witnessing “liabilities” seemingly ranking higher up the balance sheet ladder, finding themselves lowered in events of default. We will examine the mechanisms holders of perpetual securities or preference shares have tried to use to protect themselves. This is happening against a backdrop that sees a rise in the strength of private credit and inter-creditor conflicts. The restructuring regime of the hybrid scheme in Singapore now, while adopting the absolute priority rule in Chapter 11 as the quid pro quo for creditor cramdown, does not apply to shareholders and so exempts them from cramdown. Complicating the picture further, shareholders are not exempted from cramdown in the Dutch scheme, but it adopts a relative priority rule. At the same time, the important UK Supreme Court decision in BTI 2014 LLC v Sequana [2022] UKSC 25 has held that directors’ duties to take account of creditor interests are activated only when a company is almost insolvent. All this has been complicated by digital assets created by businesses. Investors are quite happy to have them classified as property (like a thing) when it comes to their transferability, but then when the issuer defaults to have them seen as a claim on the business (as a choice in action), that puts them at the level of a creditor. But these hidden interests will not show themselves on an issuer’s balance sheet until it is too late to be considered and yet if accepted, may also prevent any meaningful restructuring.

Keywords: asset partitioning, creditor priority, restructuring, BTI v Sequana, digital assets

Procedia PDF Downloads 71

Authors: Aly Elshafei, Daniela Romano

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With the development of autonomous vehicles, a human-machine interaction (HMI) system is needed for a safe transition of control when a takeover request (TOR) is required. An important part of the HMI system is the ability to monitor the level of situational awareness (SA) of any driver in real-time, in different scenarios, and without any pre-calibration. Presenting state-of-the-art machine learning models used to measure SA is the purpose of this systematic review. Investigating the limitations of each type of sensor, the gaps, and the most suited sensor and computational model that can be used in driving applications. To the author’s best knowledge this is the first literature review identifying online and offline classification methods used to measure SA, explaining which measurements are subject or session-specific, and how many classifications can be done with each classification model. This information can be very useful for researchers measuring SA to identify the most suited model to measure SA for different applications.

Keywords: situational awareness, autonomous driving, gaze metrics, EEG, ECG

Procedia PDF Downloads 111

Authors: Dimitrios Binas, Marianna Konidari, Charis Bourgioti, Lia Angela Moulopoulou, Theodore Economopoulos, George Matsopoulos

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High grade ovarian epithelial cancer (OEC) is fatal gynecological cancer and the poor prognosis of this entity is closely related to considerable intratumoral genetic heterogeneity. By examining imaging data, it is possible to assess the heterogeneity of tumorous tissue. This study proposes a methodology for aligning, segmenting and finally visualizing information from various magnetic resonance imaging series in order to construct 3D models of heterogeneity maps from the same tumor in OEC patients. The proposed system may be used as an adjunct digital tool by health professionals for personalized medicine, as it allows for an easy visual assessment of the heterogeneity of the examined tumor.

Keywords: image segmentation, ovarian epithelial cancer, quantitative characteristics, image registration, tumor visualization

Procedia PDF Downloads 198

Authors: Al-Amin, Huanjun Jiang, Anayat Ali

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Reinforced concrete frames (RC), especially Ordinary RC frames, are prone to structural failures/collapse during seismic events, leading to a large proportion of debris from the structures, which obstructs adjacent areas, including streets. These blocked areas severely impede post-earthquake resilience. This study uses computational simulation (FEM) to investigate the amount of debris generated by the seismic collapse of an ordinary reinforced concrete moment frame building and its effects on the adjacent pedestrian and road network. A three-story ordinary reinforced concrete frame building, primarily designed for gravity load and earthquake resistance, was selected for analysis. Sixteen different ground motions were applied and scaled up until the total collapse of the tested building to evaluate the failure mode under various seismic events. Four types of collapse direction were identified through the analysis, namely aligned (positive and negative) and skewed (positive and negative), with aligned collapse being more predominant than skewed cases. The amount and distribution of debris around the collapsed building were assessed to investigate the interaction between collapsed buildings and adjacent street networks. An interaction was established between a building that collapsed in an aligned direction and the adjacent pedestrian walkway and narrow street located in an unplanned old city. The FEM model was validated against an existing shaking table test. The presented results can be utilized to simulate the interdependency between the debris generated from the collapse of seismic-prone buildings and the resilience of street networks. These findings provide insights for better disaster planning and resilient infrastructure development in earthquake-prone regions.

Keywords: building collapse, earthquake-induced debris, ORC moment resisting frame, street network

Procedia PDF Downloads 81

Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

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Authors: Rui Xing, Baolin Ye, Nan Zhou, Guohong Wang

Abstract:

Drawing upon a perspective of cognitive interaction, this study explores the relationship between team cognitive heterogeneity and team strategic decision-making flexibility, treating the transactive memory system as a mediator and task complexity as a moderator. The hypotheses were tested in linear regression models by using data gathered from 67 strategic decision-making teams in the new-energy vehicle industry. It is found that team cognitive heterogeneity has a positive impact on strategic decision-making flexibility through the mediation of specialization and coordination of the transactive memory system, which is positively moderated by task complexity.

Keywords: strategic decision-making flexibility, team cognitive heterogeneity, transactive memory system, task complexity

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Authors: Sayedramin Hashemianesfehani, Seyed Hossein Hosseinilargani

Abstract:

In this study, we attempt to realize that to what extent the increase in selected commodities in Iran Market is originated from the implementation of the targeted subsidies law. Hence, an econometric model based on existing theories of increasing and transferring prices in order to transferring inflation is developed. In other words, world price index and virtual variables defined for targeted subsidies has significant and positive impact on the producer price index. The obtained results indicated that the targeted subsidies act in Iran has influential long and short-term impacts on producer price indexes. Finally, world prices of dairy products and dairy price with respect to major parameters is carried out to obtain some managerial ‎results.

Keywords: econometric models, targeted subsidies, consumer price index (CPI), producer price index (PPI)

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Authors: Gurjinder Singh, Ankur Gill, Amardeep Singh Kang

Abstract:

In the present work an effort has been made to study the influence of the welding parameters on tensile strength of friction stir welding of aluminum. Three process parameters tool rotation speed, welding speed, and shoulder diameter were selected for the study. Two level factorial design of eight runs was selected for conducting the experiments. The mathematical model was developed from the data obtained. The significance of coefficients and adequacy of developed models were tested by ‘t’ test and ‘F’ test respectively. The effects of process parameters on mechanical properties have been represented in the form of graphs for better understanding.

Keywords: friction stir welding, aluminium alloy, mathematical model, welding speed

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Authors: Marco Naegele, Klaus Stoppel, Thomas Dekorsy

Abstract:

Passively Q-switched microchip lasers enable the great potential for sophisticated LiDAR systems due to their compact overall system design, excellent beam quality, and scalable pulse energies. However, many near-infrared solid-state lasers show emitting wavelengths > 1000 nm, which are not compatible with state-of-the-art silicon detectors. Here we demonstrate a passively Q-switched microchip laser operating at 914 nm. The microchip laser consists of a 3 mm long Nd:YVO₄ crystal as a gain medium, while Cr⁴⁺:YAG with an initial transmission of 98% is used as a saturable absorber. Quasi-continuous pumping enables single pulse operation, and low duty cycles ensure low overall heat generation and power consumption. Thus, thermally induced instabilities are minimized, and operation without active cooling is possible while ambient temperature changes are compensated by adjustment of the pump laser current only. Single-emitter diode pumping at 808 nm leads to a compact overall system design and robust setup. Utilization of a microchip cavity approach ensures single-longitudinal mode operation with spectral bandwidths in the picometer regime and results in short laser pulses with pulse durations below 10 ns. Beam quality measurements reveal an almost diffraction-limited beam and enable conclusions concerning the thermal lens, which is essential to stabilize the plane-plane resonator. A 7% output coupler transmissivity is used to generate pulses with energies in the microjoule regime and peak powers of more than 600 W. Long-term pulse duration, pulse energy, central wavelength, and spectral bandwidth measurements emphasize the excellent system stability and facilitate the utilization of this laser in the context of a LiDAR system.

Keywords: diode-pumping, LiDAR system, microchip laser, Nd:YVO4 laser, passively Q-switched

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Authors: Sreto Boljevic

Abstract:

In the absence of considerable investment in electricity generation, transmission and distribution network (DN) capacity, the demand for electrical energy will quickly strain the capacity of the existing electrical power network. With anticipated growth and proliferation of Electric vehicles (EVs) and Heat pump (HPs) identified the likelihood that the additional load from EV changing and the HPs operation will require capital investment in the DN. While an area-wide implementation of EVs and HPs will contribute to the decarbonization of the energy system, they represent new challenges for the existing low-voltage (LV) network. Distributed energy resources (DER), operating both as part of the DN and in the off-network mode, have been offered as a means to meet growing electricity demand while maintaining and ever-improving DN reliability, resiliency and power quality. DN planning has traditionally been done by forecasting future growth in demand and estimating peak load that the network should meet. However, new problems are arising. These problems are associated with a high degree of proliferation of EVs and HPs as load imposes on DN. In addition to that, the promotion of electricity generation from renewable energy sources (RES). High distributed generation (DG) penetration and a large increase in load proliferation at low-voltage DNs may have numerous impacts on DNs that create issues that include energy losses, voltage control, fault levels, reliability, resiliency and power quality. To mitigate negative impacts and at a same time enhance positive impacts regarding the new operational state of DN, CHP system integration can be seen as best action to postpone/reduce capital investment needed to facilitate promotion and maximize benefits of EVs, HPs and RES integration in low-voltage DN. The aim of this paper is to generate an algorithm by using an analytical approach. Algorithm implementation will provide a way for optimal placement of the CHP system in the DN in order to maximize the integration of RES and increase in proliferation of EVs and HPs.

Keywords: combined heat & power (CHP), distribution networks, EVs, HPs, RES

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Authors: Diane Ghanem, Oscar Covarrubias, Michael Raad, Dawn LaPorte, Babar Shafiq

Abstract:

Introduction: Standardized exams have long been considered a cornerstone in measuring cognitive competency and academic achievement. Their fixed nature and predetermined scoring methods offer a consistent yardstick for gauging intellectual acumen across diverse demographics. Consequently, the performance of artificial intelligence (AI) in this context presents a rich, yet unexplored terrain for quantifying AI's understanding of complex cognitive tasks and simulating human-like problem-solving skills. Publicly available AI language models such as ChatGPT have demonstrated utility in text generation and even problem-solving when provided with clear instructions. Amidst this transformative shift, the aim of this study is to assess ChatGPT’s performance on the orthopaedic surgery in-training examination (OITE). Methods: All 213 OITE 2021 web-based questions were retrieved from the AAOS-ResStudy website. Two independent reviewers copied and pasted the questions and response options into ChatGPT Plus (version 4.0) and recorded the generated answers. All media-containing questions were flagged and carefully examined. Twelve OITE media-containing questions that relied purely on images (clinical pictures, radiographs, MRIs, CT scans) and could not be rationalized from the clinical presentation were excluded. Cohen’s Kappa coefficient was used to examine the agreement of ChatGPT-generated responses between reviewers. Descriptive statistics were used to summarize the performance (% correct) of ChatGPT Plus. The 2021 norm table was used to compare ChatGPT Plus’ performance on the OITE to national orthopaedic surgery residents in that same year. Results: A total of 201 were evaluated by ChatGPT Plus. Excellent agreement was observed between raters for the 201 ChatGPT-generated responses, with a Cohen’s Kappa coefficient of 0.947. 45.8% (92/201) were media-containing questions. ChatGPT had an average overall score of 61.2% (123/201). Its score was 64.2% (70/109) on non-media questions. When compared to the performance of all national orthopaedic surgery residents in 2021, ChatGPT Plus performed at the level of an average PGY3. Discussion: ChatGPT Plus is able to pass the OITE with a satisfactory overall score of 61.2%, ranking at the level of third-year orthopaedic surgery residents. More importantly, it provided logical reasoning and justifications that may help residents grasp evidence-based information and improve their understanding of OITE cases and general orthopaedic principles. With further improvements, AI language models, such as ChatGPT, may become valuable interactive learning tools in resident education, although further studies are still needed to examine their efficacy and impact on long-term learning and OITE/ABOS performance.

Keywords: artificial intelligence, ChatGPT, orthopaedic in-training examination, OITE, orthopedic surgery, standardized testing

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Authors: Agnese Natali, Francesco Morelli, Walter Salvatore, José Humberto Matias de Paula Filho, Patrick Pol

Abstract:

In the framework of the evaluation of the applicability of high strength steel to produce thin-walled elements to be used in Automated Rack Supported Warehouses, an experimental campaign is carried outto evaluate the structural performance of typical profile shapes adopted for such purposes and made of high strength steel. Numerical models are developed to fit the observed failure modes, stresses, and deformation patterns, and proper directions are proposed to simplify the numerical simulations to be used in further applications and to evaluate the mechanical behavior and performance of profiles.

Keywords: Steel racks, Automated Rack Supported Warehouse, thin walled cold-formed elements, high strength steel.

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Authors: Sergejs Kolesovs, Pavels Semjonovs

Abstract:

Whey and dairy wastewaters if disposed in the environment without proper treatment, cause serious environmental risks contributing to overall and particular environmental pollution and climate change. Biological treatment of wastewater is considered to be most eco-friendly approach, as compared to the chemical treatment methods. Research shows, that dairy wastewater can potentially be remediated by use of microalgae thussignificantly reducing the content of carbohydrates, P, N, K and other pollutants. Moreover, it has been shown, that use of dairy wastewaters results in higher microalgae biomass production. In recent decades microalgal biomass has entailed a big interest for its potential applications in pharmaceuticals, biomedicine, health supplementation, cosmetics, animal feed, plant protection, bioremediation and biofuels. It was shown, that lipids productivity on whey and dairy wastewater is higher as compared with standard cultivation media and occurred without the necessity of inducing specific stress conditions such as N starvation. Moreover, microalgae biomass production as usually associated with high production costs may benefit from perspective of both reasons – enhanced microalgae biomass or target substances productivity on cheap growth substrate and effective management of whey and dairy wastewaters, which issignificant for decrease of total production costs in both processes. Obviously, it became especially important when large volume and low cost industrial microalgal biomass production is anticipated for further use in agriculture of crops as plant growth stimulants, biopesticides soil fertilisers or remediating solutions. Environmental load of dairy wastewaters can be significantly decreased when microalgae are grown in coculture with other microorganisms. This enhances the utilisation of lactose, which is main C source in whey and dairy wastewaters when it is not metabolised easily by most microalgal species chosen. Our study showsthat certain microalgae strains can be used in treatment of residual sugars containing industrial wastewaters and decrease of their concentration thus approving that further extensive research on dairy wastewaters pre-treatment optionsfor effective cultivation of microalgae, carbon uptake and metabolism, strain selection and choice of coculture candidates is needed for further optimisation of the process.

Keywords: microalgae, whey, dairy wastewaters, sustainability, plant biostimulants

Procedia PDF Downloads 86