Journal articles on the topic 'Structured World Model'
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Santos, José I., David J. Poza, José M. Galán, and Adolfo López-Paredes. "Evolution of Equity Norms in Small-World Networks." Discrete Dynamics in Nature and Society 2012 (2012): 1–18. http://dx.doi.org/10.1155/2012/482481.
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The topology of interactions has been proved very influential in the results of models based on learning and evolutionary game theory. This paper is aimed at investigating the effect of structures ranging from regular ring lattices to random networks, including small-world networks, in a model focused on property distribution norms. The model considers a fixed and finite population of agents who play the Nash bargaining game repeatedly. Our results show that regular networks promote the emergence of the equity norm, while less-structured networks make possible the appearance of fractious regimes. Additionally, our analysis reveals that the speed of adoption can also be affected by the network structure.
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Hewlett, Daniel, Shane Hoversten, Wesley Kerr, Paul Cohen, and Yu-Han Chang. "Wubble World." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 3, no. 1 (September 29, 2021): 20–24. http://dx.doi.org/10.1609/aiide.v3i1.18776.
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We introduce Wubble World, a virtual environment for learning situated language. In Wubble World children create avatars, called wubbles, which can interact with other other children's avatars through free-form spontaneous play or structured language games. Wubbles can also learn language from direct interaction with children, since the system uses principles from developmental psychology to restrict the complexity of this learning task: a shared attention model that includes deictic pointing, and a concept acquisition system that allows for rapid learning of new words from a limited number of exposures.Since we have complete knowledge of the state and structure of the virtual environment, we are able to track correspondences between utterances and the scene in which they are uttered. This sentence/scene corpus will be a valuable resource as we attempt to tackle more sophisticated language learning tasks, such as the acquisition of syntax and verb semantics from world dynamics.
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Morettin, Paolo, Samuel Kolb, Stefano Teso, and Andrea Passerini. "Learning Weighted Model Integration Distributions." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5224–31. http://dx.doi.org/10.1609/aaai.v34i04.5967.
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Weighted model integration (WMI) is a framework for probabilistic inference over distributions with discrete and continuous variables and structured supports. Despite the growing popularity of WMI, existing density estimators ignore the problem of learning a structured support, and thus fail to handle unfeasible configurations and piecewise-linear relations between continuous variables. We propose lariat, a novel method to tackle this challenging problem. In a first step, our approach induces an SMT(ℒℛA) formula representing the support of the structured distribution. Next, it combines the latter with a density learned using a state-of-the-art estimation method. The overall model automatically accounts for the discontinuous nature of the underlying structured distribution. Our experimental results with synthetic and real-world data highlight the promise of the approach.
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Dinneen, Michael J., Yun-Bum Kim, and Radu Nicolescu. "Towards Structured Modelling with Hyperdag P Systems." International Journal of Computers Communications & Control 5, no. 2 (June 1, 2010): 224. http://dx.doi.org/10.15837/ijccc.2010.2.2477.
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Although P systems are computationally complete, many real world models, such as socio-economic systems, databases, operating systems and distributed systems, seem to require more expressive power than provided by tree structures. Many such systems have a primary tree-like structure augmented with shared or secondary communication channels. Modelling these as tree-based systems, while theoretically possible, is not very appealing, because it typically needs artificial extensions that introduce additional complexities, inexistent in the originals. In this paper, we propose and define a new model called hyperdag P systems, in short, hP systems, which extend the definition of conventional P systems, by allowing dags, interpreted as hypergraphs, instead of trees, as models for the membrane structure. We investigate the relation between our hP systems and neural P systems. Despite using an apparently restricted structure, i.e., a dag instead of a general graph, we argue that hP systems have essentially the same computational power as tissue and neural P systems. We argue that hP systems offer a structured approach to membranebased modelling that is often closer to the behavior and underlying structure of the modelled objects.
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Trovati, Marcello. "Reduced Topologically Real-World Networks." International Journal of Distributed Systems and Technologies 6, no. 2 (April 2015): 13–27. http://dx.doi.org/10.4018/ijdst.2015040102.
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The topological and dynamical properties of real-world networks have attracted extensive research from a variety of multi-disciplinary fields. They, in fact, model typically big datasets which pose interesting challenges, due to their intrinsic size and complex interactions, as well as the dependencies between their different sub-parts. Therefore, defining networks based on such properties, is unlikely to produce usable information due to their complexity and the data inconsistencies which they typically contain. In this paper, the authors discuss the evaluation of a method as part of ongoing research which aims to mine data to assess whether their associated networks exhibit properties comparable to well-known structures, namely scale-free, small world and random networks. For this, they will use a large dataset containing information on the seismologic activity recorded by the European-Mediterranean Seismological Centre. The authors will show that it provides an accurate, agile, and scalable tool to extract useful information. This further motivates their effort to produce a big data analytics tool which will focus on obtaining in-depth intelligence from both structured and unstructured big datasets. This will ultimately lead to a better understanding and prediction of the properties of the system(s) they model.
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Scheu, S., and B. Drossel. "Sexual reproduction prevails in a world of structured resources in short supply." Proceedings of the Royal Society B: Biological Sciences 274, no. 1614 (February 27, 2007): 1225–31. http://dx.doi.org/10.1098/rspb.2007.0040.
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We present a model for the maintenance of sexual reproduction based on the availability of resources, which is the strongest factor determining the growth of populations. The model compares completely asexual species to species that switch between asexual and sexual reproduction (sexual species). Key features of the model are that sexual reproduction sets in when resources become scarce, and that at a given place only a few genotypes can be present at the same time. We show that under a wide range of conditions the sexual species outcompete the asexual ones. The asexual species win only when survival conditions are harsh and death rates are high, or when resources are so little structured or consumer genotypes are so manifold that all resources are exploited to the same extent. These conditions, largely represent the conditions in which sexuals predominate over asexuals in the field.
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Menon, Balakrishnan. "Structured Equation Modelling on Consumer Purchase Behaviour of Passenger Cars." Vision: The Journal of Business Perspective 22, no. 2 (April 19, 2018): 144–52. http://dx.doi.org/10.1177/0972262918767040.
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With increased globalization, many prominent leaders in car manufacturing industry across the world have built their manufacturing units in India, through the application of direct investment collaboration route with Indian companies, by using the liberalized foreign direct investment (FDI) policy of the Indian Government. With multiple selective choices to consumers, the car segments have undergone a metamorphosis in India. With many makes, brands and models, customers have many choices currently. The predominant purpose of this article is to conceptualize and design a purchase decision model, which would strongly provide an orientation of determinants, which influenced the consumer buyer behaviour of passenger car owners. The study results conclusively validated that eight major variables strongly influenced the consumer purchase behaviour of car owners. The major findings of the study would facilitate practical application, specifically, in the highly segmented passenger car markets in India.
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Mostafaei, Hossein, Yagub Alipouri, and Manouchehr Zadahmad. "A mathematical model for scheduling of real-world tree-structured multi-product pipeline system." Mathematical Methods of Operations Research 81, no. 1 (December 17, 2014): 53–81. http://dx.doi.org/10.1007/s00186-014-0486-5.
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Valese, M., F. Noardo, and A. Pereira Roders. "WORLD HERITAGE MAPPING IN A STANDARD-BASED STRUCTURED GEOGRAPHICAL INFORMATION SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 24, 2020): 81–88. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-81-2020.
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Abstract. This research aims at the study of the (dynamic) relationship between the World Heritage sites and the related human settlements development. Geographical Information Systems (GIS) can be useful to represent the involved information and to analyze such relationship. However, an effective harmonized structure and unique storage of possibly heterogeneous datasets is necessary to enable it. This initial step is the focus of this paper. First, the description of the structure of the related datasets and the assessment of the availability, quality, and consistency of the available information about the Heritage sites and properties is presented. Among those requirements, the quality of the associated spatial information is critical (e.g. kind of shape, accuracy, georeferencing). Second, considering the structure of the available datasets concerning the world heritage, together with the HERILAND research requirements, a global world heritage GIS is designed. The classification and data model to manage the WH list falls within the wider issue of structuring the cultural heritage documentation, involving both the definition of the semantic content and the geometric representation. In order to comply with the important requirement of data interoperability in science and to strengthen the outcomes of the research, some standardized data models and classifications are considered.
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Chaaban, Youmen, and Rania Sawalhi. "Promoting reading in the Arab world: The book club model." IFLA Journal 44, no. 4 (October 26, 2018): 269–80. http://dx.doi.org/10.1177/0340035218806541.
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Despite their proliferation in many parts of the world, book clubs have received little attention from academic researchers. This study seeks to understand the status of book clubs particularly in the Arab world. An in-depth illustration of the status of Arab book clubs was provided by 18 moderators from six Arab countries. The findings indicated that Arab book clubs operate in several distinctive ways; ranging from formal, highly structured meetings, to informal, friendly gatherings. The findings also revealed the motivations for establishing the book clubs in the Arab world. In this respect, participants set high expectations for personal growth, interpersonal relationships, and social responsibility. However, Arab books clubs faced several challenges as perceived by the participants in this study. Several suggestions were made for the sustainability of Arab book clubs. The implications of the findings for librarians, educators, and the general public are also emphasized to create momentum for their continuity.
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Dale, Janet L. "Reflective Judgment: Seminarians' Epistemology in A World of Relativism." Journal of Psychology and Theology 33, no. 1 (March 2005): 56–64. http://dx.doi.org/10.1177/009164710503300105.
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In a culture marked by pluralism and relativism are evangelical Christian leaders with their faith-based conclusions intellectually strong? How do their assumptions about revealed knowledge affect their ability to solve important ill-structured (ambiguous and controversial) issues of life and ministry? The reflective judgment model and its semi-structured interview (RJI), based on 20 years of research, were used to assess the problem-solving ability of students preparing for ministry. The 38 male students were enrolled in an accredited seminary with an epistemology of revealed knowledge. Differences between entering and graduating students' RJI mean scores were not statistically significant, nor were their mean scores significant between religious and secular dilemmas. Further, students' scores did not decrease significantly as their references to faith increased. Recommendations for higher education are offered to help seminarians develop reflective judgment so they can adequately respond to ill-structured problems.
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Pandey, Biraj, Marius Pachitariu, Bingni W. Brunton, and Kameron Decker Harris. "Structured random receptive fields enable informative sensory encodings." PLOS Computational Biology 18, no. 10 (October 10, 2022): e1010484. http://dx.doi.org/10.1371/journal.pcbi.1010484.
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Brains must represent the outside world so that animals survive and thrive. In early sensory systems, neural populations have diverse receptive fields structured to detect important features in inputs, yet significant variability has been ignored in classical models of sensory neurons. We model neuronal receptive fields as random, variable samples from parameterized distributions and demonstrate this model in two sensory modalities using data from insect mechanosensors and mammalian primary visual cortex. Our approach leads to a significant theoretical connection between the foundational concepts of receptive fields and random features, a leading theory for understanding artificial neural networks. The modeled neurons perform a randomized wavelet transform on inputs, which removes high frequency noise and boosts the signal. Further, these random feature neurons enable learning from fewer training samples and with smaller networks in artificial tasks. This structured random model of receptive fields provides a unifying, mathematically tractable framework to understand sensory encodings across both spatial and temporal domains.
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Khvorostyna, Olha. "Structural Models of Transmedia Projects in Journalism." Scientific notes of the Institute of Journalism, no. 2 (75) (2019): 70–84. http://dx.doi.org/10.17721/2522-1272.2019.75.5.
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The transmedia projects for quality planning and implementation require well-designed, structured and interrelated content. The relevance of the study is the importance of organizing the structure of transmedia projects in journalism at the planning stage for their successful implementation. The objective of the study is to identify the qualitative differences in the structural models of transmedia projects for news content dissemination. In the article we analyze the foreign transmedia projects that cover the events and themes of the real world, and determine the defining criteria for the structure of transmedia projects in journalism. Using the modeling method (within a systematic approach), the structural models of transmedia projects were identified and a model of the complex sequential transmedia project in journalism was designed. The application models characteristics were gathered through the analysis of specific transmedia projects. The theoretical model of a simple sequential project was formed using the method of analogies: the sequential transmedia project characteristics, which cover the fictional world picture, were transferred to the sphere of journalism. The theoretical model of a complex sequential transmedia project was also designed on the basis of criteria for determining structural models of transmedia projects in journalism. The general and specific characteristics of the simple and complex models were determined using the comparison method. The generalization method was used to draw up the conclusions about the characteristics and peculiarities of using of the models of simple and complex transmedia projects in journalism. As a result of the study, the criteria for determining the structural models of transmedia projects in journalism (the number of transmedia project levels; the sequence parallel of transmedia narrative components) were revealed. Based on these criteria and modeling method, four application and two theoretical structural models of transmedia projects in journalism were obtained. The models characteristics and special aspects of their use were found out. The significance of the study is to provide the practical guidance and recommendations for the newsroom workers about the important factors and the sequence of their consideration in selecting a model for transmedia project implementation in journalism.
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BERS, JOHN A., and JOHN P. DISMUKES. "GUERRILLA INNOVATION — THE ACCELERATED RADICAL INNOVATION MODEL MEETS THE REAL WORLD." International Journal of Innovation and Technology Management 09, no. 01 (February 2012): 1250002. http://dx.doi.org/10.1142/s0219877012500022.
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The Accelerated Radical Innovation (ARI) methodology, an integrated approach to shepherding radical innovation from initial concept through commercialization, was compared to the approach used by an investor-funded seed-stage innovation incubation firm. Similarities include traversal of the same major stages of innovation, emphasis on front-end analysis before escalating commitments, and using an extended "probe-and-learn" process. Key differences were in emphasis. The ARI model relies on analysis and intelligence gleaned from external sources, while ConduIT views each innovation project as unique, requiring tailored responses that will not be found elsewhere. And while the ARI model relies on a structured process, ConduIT favors a more intuitive, people-centered approach. The message for ARI is to become more flexible and adaptable to each innovation's uniquenesses. For ConduIT the challenges are increasing portfolio turnover and scaling up, which will require a more repeatable, teachable, standardized process such as the ARI model.
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Han, Eric, Ishank Arora, and Jonathan Scarlett. "High-Dimensional Bayesian Optimization via Tree-Structured Additive Models." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 7630–38. http://dx.doi.org/10.1609/aaai.v35i9.16933.
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Bayesian Optimization (BO) has shown significant success in tackling expensive low-dimensional black-box optimization problems. Many optimization problems of interest are high-dimensional, and scaling BO to such settings remains an important challenge. In this paper, we consider generalized additive models in which low-dimensional functions with overlapping subsets of variables are composed to model a high-dimensional target function. Our goal is to lower the computational resources required and facilitate faster model learning by reducing the model complexity while retaining the sample-efficiency of existing methods. Specifically, we constrain the underlying dependency graphs to tree structures in order to facilitate both the structure learning and optimization of the acquisition function. For the former, we propose a hybrid graph learning algorithm based on Gibbs sampling and mutation. In addition, we propose a novel zooming-based algorithm that permits generalized additive models to be employed more efficiently in the case of continuous domains. We demonstrate and discuss the efficacy of our approach via a range of experiments on synthetic functions and real-world datasets.
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Ristovski, Kosta, Vladan Radosavljevic, Slobodan Vucetic, and Zoran Obradovic. "Continuous Conditional Random Fields for Efficient Regression in Large Fully Connected Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 30, 2013): 840–46. http://dx.doi.org/10.1609/aaai.v27i1.8685.
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When used for structured regression, powerful Conditional Random Fields (CRFs) are typically restricted to modeling effects of interactions among examples in local neighborhoods. Using more expressive representation would result in dense graphs, making these methods impractical for large-scale applications. To address this issue, we propose an effective CRF model with linear scale-up properties regarding approximate learning and inference for structured regression on large, fully connected graphs. The proposed method is validated on real-world large-scale problems of image de-noising and remote sensing. In conducted experiments, we demonstrated that dense connectivity provides an improvement in prediction accuracy. Inference time of less than ten seconds on graphs with millions of nodes and trillions of edges makes the proposed model an attractive tool for large-scale, structured regression problems.
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Shintani, Hideyuki, Tomomi Aoyama, and Ichiro Koshijima. "Study on High Resilient Structures for IoT Systems to Detect Accidents." Journal of Disaster Research 12, no. 5 (September 27, 2017): 1073–80. http://dx.doi.org/10.20965/jdr.2017.p1073.
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In order to operate the Internet of Things (IoT) or Cyber Physical Systems (CPS) in the real world, the system needs to be structured to have people in the real world incorporated as a part of its process: Human-in-the-Loop CPS (HITLCPS). With people in the real world incorporated as a part of its process, the system must have a secure structure to be able to continue operating normally. With sensors, actuators and other devices connected in a network, it becomes vulnerable to cyberattacks; hence, its framework must be resilient and secure in order to ensure its safety in the face of any disturbances. In this paper, we describe a safety-based secure system structure, using a STAMP model and a covariance structure.
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Hutter, Marcus. "Feature Reinforcement Learning: Part II. Structured MDPs." Journal of Artificial General Intelligence 12, no. 1 (January 1, 2021): 71–86. http://dx.doi.org/10.2478/jagi-2021-0003.
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Abstract The Feature Markov Decision Processes ( MDPs) model developed in Part I (Hutter, 2009b) is well-suited for learning agents in general environments. Nevertheless, unstructured (Φ)MDPs are limited to relatively simple environments. Structured MDPs like Dynamic Bayesian Networks (DBNs) are used for large-scale real-world problems. In this article I extend ΦMDP to ΦDBN. The primary contribution is to derive a cost criterion that allows to automatically extract the most relevant features from the environment, leading to the “best” DBN representation. I discuss all building blocks required for a complete general learning algorithm, and compare the novel ΦDBN model to the prevalent POMDP approach.
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Sun, Xin, Zenghui Song, Junyu Dong, Yongbo Yu, Claudia Plant, and Christian Böhm. "Network Structure and Transfer Behaviors Embedding via Deep Prediction Model." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5041–48. http://dx.doi.org/10.1609/aaai.v33i01.33015041.
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Network-structured data is becoming increasingly popular in many applications. However, these data present great challenges to feature engineering due to its high non-linearity and sparsity. The issue on how to transfer the link-connected nodes of the huge network into feature representations is critical. As basic properties of the real-world networks, the local and global structure can be reflected by dynamical transfer behaviors from node to node. In this work, we propose a deep embedding framework to preserve the transfer possibilities among the network nodes. We first suggest a degree-weight biased random walk model to capture the transfer behaviors of the network. Then a deep embedding framework is introduced to preserve the transfer possibilities among the nodes. A network structure embedding layer is added into the conventional Long Short-Term Memory Network to utilize its sequence prediction ability. To keep the local network neighborhood, we further perform a Laplacian supervised space optimization on the embedding feature representations. Experimental studies are conducted on various real-world datasets including social networks and citation networks. The results show that the learned representations can be effectively used as features in a variety of tasks, such as clustering, visualization and classification, and achieve promising performance compared with state-of-the-art models.
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Codeço, Cláudia Torres, and Paula Mendes Luz. "Is pertussis actually reemerging? Insights from an individual-based model." Cadernos de Saúde Pública 17, no. 3 (June 2001): 491–500. http://dx.doi.org/10.1590/s0102-311x2001000300005.
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In this paper, we introduce a spatially explicit, individual-based model developed to simulate the dynamics of pertussis in a small population. With this simulation approach, complex epidemic systems can be built using information on parasite population structure (strain diversity, virulence diversity, etc.), human population structure (individual risk, age structure, interaction matrices, immune response, etc.), as well as mechanisms of evolution and learning. We parameterized our model to describe pertussis in an age-structured community. Pertussis or whooping cough is an acute infection of the respiratory tract caused by Bordetella pertussis. Despite wide-scale vaccination in many countries, this disease is reemerging throughout the world in both adults and children. Emergence has been explained by many factors: wane of vaccine and natural immunity, increase of asymptomatic carriers, and/or natural selection of non-vaccine strains. Here, we model these hypotheses and analyze their potential impact on the observed increase of pertussis notification.
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C, Siva, Maheshwari K.G, Nalinipriya G, and Priscilla Mary J. "Dynamic Analytics and Forecasting Model for Covid-19 Using Machine Learning Algorithms." Webology 18, no. 05 (October 29, 2021): 1212–25. http://dx.doi.org/10.14704/web/v18si05/web18302.
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In our day to day life, the availability of correctly labelled data as well as handling of categorical data are mostly acknowledged as two main challenges in dynamic analysis. Therefore, clustering techniques are applied on unlabelled data to group them in accordance with the homogeneity. There are many prediction methods that are being popularly used in handling forecasting problems in real time environment. The outbreak of coronavirus disease (COVID19)-2019 creates the need for a medical emergency of worldwide concern with a rapidly high danger of open out and strike the entire world. Recently, the ML prediction models were used in many real time applications which necessitate the identification and categorization for real time environment. In medical field Prediction models are vital role to obtain observations of spread and significances of infectious diseases. Machine learning related forecasting mechanisms have showed their importance to develop the decision making on the upcoming course of actions. The K-means algorithm and hierarchy were applied directly on the renewed dataset using R programming language to create the covid patient cluster. Confirmed Covid patients count are passed to Prophet package, then the prophet model has been created. This forecasts model predicts the future covid count, which is essential for the clinical and healthcare leaders to make the appropriate measures in advance. The results of the experiments indicate that the quality of Hierarchical clustering outperforms than the K-Means clustering algorithm in the structured structured dataset. Thus, the prediction model also used to support model predictions help for the officials to take timely actions and make decisions to contain the COVID-19 dilemma. This work concludes Hierarchical clustering algorithm is the best model for clustering the covid data set obtained from world health organization (WHO).
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Li, Ruiyun, C. Jessica E. Metcalf, Nils Chr Stenseth, and Ottar N. Bjørnstad. "A general model for the demographic signatures of the transition from pandemic emergence to endemicity." Science Advances 7, no. 33 (August 2021): eabf9040. http://dx.doi.org/10.1126/sciadv.abf9040.
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Anticipating the medium- and long-term trajectory of pathogen emergence has acquired new urgency given the ongoing COVID-19 pandemic. For many human pathogens, the burden of disease depends on age and previous exposure. Understanding the intersection between human population demography and transmission dynamics is therefore critical. Here, we develop a realistic age-structured mathematical model that integrates demography, social mixing, and immunity to establish a plausible range for future age incidence and mortality. With respect to COVID-19, we identify a plausible transition in the age structure of risks once the disease reaches seasonal endemism across a range of immunity durations and relative severity of primary versus subsequent reinfections. We train the model using diverse real-world demographies and age-structured mixing to bound expectations for changing age incidence and disease burden. The mathematical framework is flexible and can help tailor mitigation strategies in countries worldwide with varying demographies and social mixing patterns.
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YUSOFF, NURAINI, HARUN BUDIN, and SALEMAH ISMAIL. "STAGE-STRUCTURED POPULATION DYNAMICS OF AEDES AEGYPTI." International Journal of Modern Physics: Conference Series 09 (January 2012): 364–72. http://dx.doi.org/10.1142/s2010194512005430.
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Aedes aegypti is the main vector in the transmission of dengue fever, a vector-borne disease affecting world population living in tropical and sub-tropical countries. Better understanding of the dynamics of its population growth will help in the efforts of controlling the spread of this disease. In looking at the population dynamics of Aedes aegypti, this paper explored the stage-structured modeling of the population growth of the mosquito using the matrix population model. The life cycle of the mosquito was divided into five stages: eggs, larvae, pupae, adult1 and adult2. Developmental rates were obtained for the average Malaysian temperature and these were used in constructing the transition matrix for the matrix model. The model, which was based only on temperature, projected that the population of Aedes aegypti will blow up with time, which is not realistic. For further work, other factors need to be taken into account to obtain a more realistic result.
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Agu, Agu Godswill, Okwuagwu Okuu Kalu, Chidadi Obinna Esi-Ubani, and Paul Chinedu Agu. "Drivers of sustainable entrepreneurial intentions among university students: an integrated model from a developing world context." International Journal of Sustainability in Higher Education 22, no. 3 (February 3, 2021): 659–80. http://dx.doi.org/10.1108/ijshe-07-2020-0277.
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Purpose The purpose of this study is to integrate and extend two models of entrepreneurial intention to investigate the drivers of sustainable entrepreneurial intention among intermediate undergraduate university students in Nigeria. Specifically, this paper aims to introduce education for sustainable entrepreneurship into the integrated model, thereby fitting the model into the context of sustainable entrepreneurship. Design/methodology/approach Data was gathered with the help of a structured questionnaire from 435 students of a university in Nigeria. The students passed through a special entrepreneurship training in which they were educated on the concept and practice of sustainable entrepreneurship. SmartPLS was used to test the proposed structural model. Findings The findings revealed that education for sustainable entrepreneurship significantly influences all variables of the integrated model, but has nonsignificant direct influence on sustainable entrepreneurial intention. Sustainable entrepreneurial intention is significantly driven by attitude and propensity to act. Therefore, the inclusion of education for sustainable entrepreneurship into the regression equation adds to its explanatory power. Originality/value This study contributes toward understanding of sustainable entrepreneurial intention of intermediate university students in a developing world context – Nigeria. Above all, it is among the few studies that shed light on the strength of education for sustainable entrepreneurship in the formation of sustainable entrepreneurial intention among students. This study proposes integration and extension (by adding education for sustainable entrepreneurship) of the theory of planned behavior and entrepreneurial event model in learning about students’ intentions to engage in sustainable entrepreneurship.
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Cornejo, Ivan, and Robert E. Hayes. "A Review of the Critical Aspects in the Multi-Scale Modelling of Structured Catalytic Reactors." Catalysts 11, no. 1 (January 11, 2021): 89. http://dx.doi.org/10.3390/catal11010089.
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Structured catalytic reactors are enjoying an increasingly important role in the reaction engineering world. At the same time, there are large and growing efforts to use advanced computational models to describe such reactors. The structured reactor represents a multi-scale problem that is typically modelled at the largest scale only, with sub-models being used to improve the model granularity. Rather than a literature review, this paper provides an overview of the key factors that must be considered when choosing these sub-models (or scale bridges). The example structured reactor selected for illustration purposes is the washcoated honeycomb monolith design. The sub-models reviewed include those for pressure drop, inter- and intra-phase mass and heat transfer, and effective thermal conductivity.
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Cornejo, Ivan, and Robert E. Hayes. "A Review of the Critical Aspects in the Multi-Scale Modelling of Structured Catalytic Reactors." Catalysts 11, no. 1 (January 11, 2021): 89. http://dx.doi.org/10.3390/catal11010089.
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Structured catalytic reactors are enjoying an increasingly important role in the reaction engineering world. At the same time, there are large and growing efforts to use advanced computational models to describe such reactors. The structured reactor represents a multi-scale problem that is typically modelled at the largest scale only, with sub-models being used to improve the model granularity. Rather than a literature review, this paper provides an overview of the key factors that must be considered when choosing these sub-models (or scale bridges). The example structured reactor selected for illustration purposes is the washcoated honeycomb monolith design. The sub-models reviewed include those for pressure drop, inter- and intra-phase mass and heat transfer, and effective thermal conductivity.
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Diekmann, Odo, Mats Gyllenberg, and Johan A. J. Metz. "Finite dimensional state representation of physiologically structured populations." Journal of Mathematical Biology 80, no. 1-2 (December 21, 2019): 205–73. http://dx.doi.org/10.1007/s00285-019-01454-0.
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AbstractIn a physiologically structured population model (PSPM) individuals are characterised by continuous variables, like age and size, collectively called their i-state. The world in which these individuals live is characterised by another set of variables, collectively called the environmental condition. The model consists of submodels for (i) the dynamics of the i-state, e.g. growth and maturation, (ii) survival, (iii) reproduction, with the relevant rates described as a function of (i-state, environmental condition), (iv) functions of (i-state, environmental condition), like biomass or feeding rate, that integrated over the i-state distribution together produce the output of the population model. When the environmental condition is treated as a given function of time (input), the population model becomes linear in the state. Density dependence and interaction with other populations is captured by feedback via a shared environment, i.e., by letting the environmental condition be influenced by the populations’ outputs. This yields a systematic methodology for formulating community models by coupling nonlinear input–output relations defined by state-linear population models. For some combinations of submodels an (infinite dimensional) PSPM can without loss of relevant information be replaced by a finite dimensional ODE. We then call the model ODE-reducible. The present paper provides (a) a test for checking whether a PSPM is ODE reducible, and (b) a catalogue of all possible ODE-reducible models given certain restrictions, to wit: (i) the i-state dynamics is deterministic, (ii) the i-state space is one-dimensional, (iii) the birth rate can be written as a finite sum of environment-dependent distributions over the birth states weighted by environment independent ‘population outputs’. So under these restrictions our conditions for ODE-reducibility are not only sufficient but in fact necessary. Restriction (iii) has the desirable effect that it guarantees that the population trajectories are after a while fully determined by the solution of the ODE so that the latter gives a complete picture of the dynamics of the population and not just of its outputs.
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COMBADÃO, JAIME, PAULO R. A. CAMPOS, FRANCISCO DIONISIO, and ISABEL GORDO. "Small-world networks decrease the speed of Muller's ratchet." Genetical Research 89, no. 1 (February 2007): 7–18. http://dx.doi.org/10.1017/s0016672307008658.
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Muller's ratchet is an evolutionary process that has been implicated in the extinction of asexual species, the evolution of non-recombining genomes, such as the mitochondria, the degeneration of the Y chromosome, and the evolution of sex and recombination. Here we study the speed of Muller's ratchet in a spatially structured population which is subdivided into many small populations (demes) connected by migration, and distributed on a graph. We studied different types of networks: regular networks (similar to the stepping-stone model), small-world networks and completely random graphs. We show that at the onset of the small-world network – which is characterized by high local connectivity among the demes but low average path length – the speed of the ratchet starts to decrease dramatically. This result is independent of the number of demes considered, but is more pronounced the larger the network and the stronger the deleterious effect of mutations. Furthermore, although the ratchet slows down with increasing migration between demes, the observed decrease in speed is smaller in the stepping-stone model than in small-world networks. As migration rate increases, the structured populations approach, but never reach, the result in the corresponding panmictic population with the same number of individuals. Since small-world networks have been shown to describe well the real contact networks among people, we discuss our results in the light of the evolution of microbes and disease epidemics.
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Jones, Margaret. "Policy Innovation and Policy Pathways: Tuberculosis Control in Sri Lanka, 1948–1990." Medical History 60, no. 4 (September 15, 2016): 514–33. http://dx.doi.org/10.1017/mdh.2016.58.
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This paper, based on World Health Organization and Sri Lankan sources, examines the attempts to control tuberculosis in Sri Lanka from independence in 1948. It focuses particularly on the attempt in 1966 to implement a World Health Organization model of community-orientated tuberculosis control that sought to establish a horizontally structured programme through the integration of control into the general health services. The objective was to create a cost- effective method of control that relied on a simple bacteriological test for case finding and for treatment at the nearest health facility that would take case detection and treatment to the rural periphery where specialist services were lacking. In the late 1940s and early 1950s, Sri Lanka had already established a specialist control programme composed of chest clinics, mass X-ray, inpatient and domiciliary treatment, and social assistance for sufferers. This programme had both reduced mortality and enhanced awareness of the disease. This paper exposes the obstacles presented in trying to impose the World Health Organization’s internationally devised model onto the existing structure of tuberculosis control already operating in Sri Lanka. One significant hindrance to the WHO approach was lack of resources but, equally important, was the existing medical culture that militated against its acceptance.
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Silveira, Leonardo José, Plácido Rogério Pinheiro, and Leopoldo Soares de Melo Junior. "A Novel Model Structured on Predictive Churn Methods in a Banking Organization." Journal of Risk and Financial Management 14, no. 10 (October 12, 2021): 481. http://dx.doi.org/10.3390/jrfm14100481.
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A constant in the business world is the frequent movement of customers joining or abandoning companies’ services and products. The customer is one of the company’s most important assets. Reducing the customer abandonment rate has become a matter of survival and, at the same time, the most efficient way to maintain the customer base, since the replacement of dropouts by new customers costs, on average, 40% more. Aiming to mitigate the churn (customer evasion) phenomenon, this study compared predictive models to discover the most efficient method to identify customers who tend to drop out in the context of a banking organization. A literature review of related works on the subject found the neural network, decision tree, random forest and logistic regression models were the most cited, and thus the models were chosen for this work. Quantitative analyses were carried out on a sample of 200,000 credit operations, with 497 explanatory variables. The statistical treatment of the data and the developments of predictive models of churn were performed using the Orange data mining software. The most expressive results were achieved using the random forest model, with an accuracy of 82%.
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Shergadwala, Murtuza N., Himabindu Lakkaraju, and Krishnaram Kenthapadi. "A Human-Centric Perspective on Model Monitoring." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 10, no. 1 (October 14, 2022): 173–83. http://dx.doi.org/10.1609/hcomp.v10i1.21997.
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Predictive models are increasingly used to make various consequential decisions in high-stakes domains such as healthcare, finance, and policy. It becomes critical to ensure that these models make accurate predictions, are robust to shifts in the data, do not rely on spurious features, and do not unduly discriminate against minority groups. To this end, several approaches spanning various areas such as explainability, fairness, and robustness have been proposed in recent literature. Such approaches need to be human-centered as they cater to the understanding of the models to their users. However, there is little to no research on understanding the needs and challenges in monitoring deployed machine learning (ML) models from a human-centric perspective. To address this gap, we conducted semi-structured interviews with 13 practitioners who are experienced with deploying ML models and engaging with customers spanning domains such as financial services, healthcare, hiring, online retail, computational advertising, and conversational assistants. We identified various human-centric challenges and requirements for model monitoring in real-world applications. Specifically, we found that relevant stakeholders would want model monitoring systems to provide clear, unambiguous, and easy-to-understand insights that are readily actionable. Furthermore, our study also revealed that stakeholders desire customization of model monitoring systems to cater to domain-specific use cases.
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Bradley, John, Dauvit Broun, Alice Rio, and Matthew Hammond. "Exploring a Model for the Semantics of Medieval Legal Charters." International Journal of Humanities and Arts Computing 13, no. 1-2 (October 2019): 136–54. http://dx.doi.org/10.3366/ijhac.2017.0184.
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This paper describes several aspects of a formal digital semantic model that expresses some issues presented by medieval charters. Surprisingly, perhaps, this model does not deal directly with a charter's text and is not mark-up based. Instead, it draws on the authors’ experience with the construction of three highly structured factoid-oriented prosopographical databases that drew heavily on charter sources, and that also did not explicitly contain a digital representation of the charter texts. The paper explains the way in which the structured data model thus derived differs from text-oriented approaches such as TEI/CEI work that has been done so far on charters. It presents a view on why this factoid-based model seems to capture more readily some of the complexity in the apparent meanings of the charters, and suggests that this is because it is also more likely to relate to a richer conception of the broader medieval world in which these charters were created than text-oriented work does. Finally, drawing on recent work on the ChartEx project, it explores how a combined approach, that takes the best of both text-markup and structured data modelling techniques, could evolve in the future.
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Duan, Ying, Xiuwen Fu, Wenfeng Li, Yu Zhang, and Giancarlo Fortino. "Evolution of Scale-Free Wireless Sensor Networks with Feature of Small-World Networks." Complexity 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/2516742.
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Scale-free network and small-world network are the most impacting discoveries in the complex networks theories and have already been successfully proved to be highly effective in improving topology structures of wireless sensor networks. However, currently both theories are not jointly applied to have further improvements in the generation of WSN topologies. Therefore, this paper proposes a cluster-structured evolution model of WSNs considering the characteristics of both networks. With introduction of energy sensitivity and maximum limitation of degrees that a cluster head could have, the performance of our model can be ensured. In order to give an overall assessment of lifting effects of shortcuts, four placement schemes of shortcuts are analyzed. The characteristics of small-world network and scale-free network of our model are proved via theoretical derivation and simulations. Besides, we find that, by introducing shortcuts into scale-free wireless sensor network, the performance of the network can be improved concerning energy-saving and invulnerability, and we discover that the schemes constructing shortcuts between cluster heads and the sink node have better promoted effects than the scheme building shortcuts between pairs of cluster heads, and the schemes based on the preferential principle are superior to the schemes based on the random principle.
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Johanson, Colden, Hu T. Huang, Danny Idryo, Ronda Broome, Matthew J. Rioth, and Rayna K. Matsuno. "Machine learning application to find patients with lower-risk myelodysplastic syndrome from real-world data." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): 1555. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.1555.
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1555 Background: It is a challenge to identify patients with myelodysplastic syndrome (MDS) using structured data from electronic health records (EHRs). Current claims-based algorithms incorporating diagnosis codes, clinical labs, and procedures have not been validated against an expert reference standard. A machine learning-based approach was investigated to identify erythropoietin-stimulating agent (ESA)-treated, lower-risk (LR)-MDS patients from structured EHR data. Methods: A sample of 1,549 patients from the Syapse Learning Health Network (SLHN) was identified as potential ESA-treated LR-MDS patients by a team of clinicians and epidemiologists based on diagnosis and medication data from multiple health systems’ EHRs and cancer registries. Of these, 404 (25%) were confirmed as ESA-treated LR-MDS patients through a review of patient records by certified cancer registrars (CTRs). The sample was divided into training and validation sets at a ratio of 80/20, stratified by the outcome. Age, sex, diagnosis codes corresponding to MDS and chronic kidney disease, medication (ESA, luspatercept, lenalidomide), clinical lab tests (hemoglobin, absolute neutrophils, platelet, blast percentage), and evidence of bone marrow biopsy were included as the predictive variables for the models. Gradient boosting machines with a nested cross-validation scheme were adopted to build the optimal model on the training set. Model acceptance was evaluated based on precision and recall on the validation set. The optimal model was then applied to the remaining unscreened SLHN patient population. Results: The optimal model identified an additional cohort of 157 patients based on the predicted likelihood. Among these, 69 (44%) were CTR-confirmed ESA-treated LR-MDS patients, all of whom were previously missed by the initial expert-determined selection criteria, as shown in the table. Conclusions: The application of machine learning methods increased the rate of ESA-treated MDS patient identification even after the expertly-determined population was depleted. This suggests the application of machine learning models using EHR data may improve the efficiency of MDS patient identification and screening efforts for research, quality improvement, and clinical care. [Table: see text]
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Ackerman, Farrell, Robert Malouf, and James P. Blevins. "Patterns and discriminability in language analysis." Word Structure 9, no. 2 (October 2016): 132–55. http://dx.doi.org/10.3366/word.2016.0091.
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Recent developments in the Word and Pattern approach to complex morphology have argued that words and the patterned relations between words are primary objects of morphological analysis. The primacy of words has two part/whole dimensions: the nature of their internal structure and the nature of their external relations to one another. Words consist of constitutive parts and words themselves are parts of larger patterns of systemic relatedness. We argue that internal structure is essentially discriminative, rather than morphemic, i.e., what is crucial for morphological organization is the ability to discriminate (patterns of) words from one another and all types of internal distinctions suffice to facilitate the necessary discriminability to establish patterns of words. The value and the operation of a discriminative perspective on the internal structure of words is also evident in the analysis of an entirely different phenomenon. Greenberg's (1963) Universal 34 states that “No language has a trial number unless it has a dual. No language has a dual unless it has a plural.” We present an associative model of the acquisition of grammatical number based on the Rescorla-Wagner learning theory Rescorla & Wagner (1972) that predicts this generalization. Number as a real-world category is inherently structured: higher numerosity sets are mentioned less frequently than lower numerosity sets, and higher numerosity sets always contain lower numerosity sets. Using simulations, we demonstrate that these facts, along with general principles of probabilistic learning, lead to the emergence of Greenberg's Number Hierarchy. The value of a discriminative perspective for language analysis ( Ramscar & Yarlett 2007 , Ramscar et al. 2010 , 2013 ) becomes clear in both word-based morphology and its explanatory role addressing a typological conundrum.
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Scire, Jérémie, Joëlle Barido-Sottani, Denise Kühnert, Timothy G. Vaughan, and Tanja Stadler. "Robust Phylodynamic Analysis of Genetic Sequencing Data from Structured Populations." Viruses 14, no. 8 (July 27, 2022): 1648. http://dx.doi.org/10.3390/v14081648.
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The multi-type birth–death model with sampling is a phylodynamic model which enables the quantification of past population dynamics in structured populations based on phylogenetic trees. The BEAST 2 package bdmm implements an algorithm for numerically computing the probability density of a phylogenetic tree given the population dynamic parameters under this model. In the initial release of bdmm, analyses were computationally limited to trees consisting of up to approximately 250 genetic samples. We implemented important algorithmic changes to bdmm which dramatically increased the number of genetic samples that could be analyzed and which improved the numerical robustness and efficiency of the calculations. Including more samples led to the improved precision of parameter estimates, particularly for structured models with a high number of inferred parameters. Furthermore, we report on several model extensions to bdmm, inspired by properties common to empirical datasets. We applied this improved algorithm to two partly overlapping datasets of the Influenza A virus HA sequences sampled around the world—one with 500 samples and the other with only 175—for comparison. We report and compare the global migration patterns and seasonal dynamics inferred from each dataset. In this way, we show the information that is gained by analyzing the bigger dataset, which became possible with the presented algorithmic changes to bdmm. In summary, bdmm allows for the robust, faster, and more general phylodynamic inference of larger datasets.
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Zhao, Qibin, Liqing Zhang, and Andrzej Cichocki. "A Tensor-Variate Gaussian Process for Classification of Multidimensional Structured Data." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 30, 2013): 1041–47. http://dx.doi.org/10.1609/aaai.v27i1.8568.
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As tensors provide a natural and efficient representation of multidimensional structured data, in this paper, we consider probabilistic multinomial probit classification for tensor-variate inputs with Gaussian processes (GP) priors placed over the latent function. In order to take into account the underlying multimodes structure information within the model, we propose a framework of probabilistic product kernels for tensorial data based on a generative model assumption. More specifically, it can be interpreted as mapping tensors to probability density function space and measuring similarity by an information divergence. Since tensor kernels enable us to model input tensor observations, the proposed tensor-variate GP is considered as both a generative and discriminative model. Furthermore, a fully variational Bayesian treatment for multiclass GP classification with multinomial probit likelihood is employed to estimate the hyperparameters and infer the predictive distributions. Simulation results on both synthetic data and a real world application of human action recognition in videos demonstrate the effectiveness and advantages of the proposed approach for classification of multiway tensor data, especially in the case that the underlying structure information among multimodes is discriminative for the classification task.
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Zhao, Fuqiang, Manita Kusi, Yun Chen, Wei Hu, Fawad Ahmed, and Dinesh Sukamani. "Influencing Mechanism of Green Human Resource Management and Corporate Social Responsibility on Organizational Sustainable Performance." Sustainability 13, no. 16 (August 9, 2021): 8875. http://dx.doi.org/10.3390/su13168875.
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Sustainable organizational achievement of firms is the dominant approach in today’s world, with an inclination on improving profitability, social condition, and a healthy environment. Based on ability, motivation, opportunity (AMO), and stakeholder theories, this study explored the green human resource management (GHRM) and corporate social responsibility (CSR) mechanism towards sustainable performance achievement. A structured questionnaire survey from 305 samples was appropriate for the quantitative study on this research. Moreover, widely used structural equation modeling and analysis of moment structures (AMOS) was used to analyze the proposed structural model of the study. Findings from the study added significance to all posited hypotheses and validated the study model. The current study added literature to the body of knowledge on green human resource management practices and corporate social responsibility in the pursuit of improved performance towards society, the environment, and sustainability of construction firms. For future studies, the inclusion of industries such as transportation-based industries and manufacturing companies that contribute to development should be considered for broad learning.
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Liu, Zhao-ge, Xiang-yang Li, and Xiao-han Zhu. "A full-view scenario model for urban waterlogging response in a big data environment." Open Geosciences 13, no. 1 (January 1, 2021): 1432–47. http://dx.doi.org/10.1515/geo-2020-0317.
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Abstract The emergence of big data is breaking the spatial and time limitations of urban waterlogging scenario description. The scenario data of different dimensions (e.g., administrative levels, sectors, granularities, and time) have become highly integrated. Accordingly, a structural and systematic model is needed to represent waterlogging scenarios for more efficient waterlogging response decision-making. In this article, a full-view urban waterlogging scenario is first defined and described from four dimensions. Next a structured representation of scenario element is given based on knowledge unit method. The full-view scenario model is then constructed by extracting the scenario correlation structures between different dimensions (called scenario nesting), i.e., inheritance nesting, feedback nesting, aggregation nesting, and selection nesting. Finally, a real-world case study in Wuhan East Lake High-tech Development Zone, China is evaluated to verify the reasonability of the full-view model. The results show that the proposed model effectively integrates scenario data from different dimensions, which helps generate the complete key scenario information for urban waterlogging decision-making. The full-view scenario model is expected to be applicable for other disasters under big data environment.
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FREITAS, ANDRÉ, EDWARD CURRY, JOÃO GABRIEL OLIVEIRA, and SEÁN O'RIAIN. "A DISTRIBUTIONAL STRUCTURED SEMANTIC SPACE FOR QUERYING RDF GRAPH DATA." International Journal of Semantic Computing 05, no. 04 (December 2011): 433–62. http://dx.doi.org/10.1142/s1793351x1100133x.
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The vision of creating a Linked Data Web brings together the challenge of allowing queries across highly heterogeneous and distributed datasets. In order to query Linked Data on the Web today, end users need to be aware of which datasets potentially contain the data and also which data model describes these datasets. The process of allowing users to expressively query relationships in RDF while abstracting them from the underlying data model represents a fundamental problem for Web-scale Linked Data consumption. This article introduces a distributional structured semantic space which enables data model independent natural language queries over RDF data. The center of the approach relies on the use of a distributional semantic model to address the level of semantic interpretation demanded to build the data model independent approach. The article analyzes the geometric aspects of the proposed space, providing its description as a distributional structured vector space, which is built upon the Generalized Vector Space Model (GVSM). The final semantic space proved to be flexible and precise under real-world query conditions achieving mean reciprocal rank = 0.516, avg. precision = 0.482 and avg. recall = 0.491.
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Xue, Kun, Xiaoxia Han, Jinde Wu, Yadi Shen, Xinying Xu, and Gang Xie. "Community detection based on competitive walking network embedding method." Journal of Statistical Mechanics: Theory and Experiment 2022, no. 9 (September 1, 2022): 093402. http://dx.doi.org/10.1088/1742-5468/ac8807.
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Abstract Currently, much of the information of the real world is network-structured, and extracting hidden information from network-structured data helps to understand the corresponding systems, but can also be a challenging problem. In recent years, network embedding has been an effective way to extract network information, which represents nodes in complex networks as low-dimensional space vectors, while preserving the properties of the network. Community attributes are an important property of networks, and in most network embedding algorithms, the community structure is usually ignored or cannot be explicitly preserved. In this paper, we propose a new network embedding framework that explicitly considers community structure feature extraction. The framework, called competitive walking network embedding (CWNE), extracts sample sequences by competitive walking and obtains node representation vectors by skip-gram training. Competitive walking allows the extracted sample sequences to be concentrated within the same community, effectively preserving the community structure features of the network. The results of testing the proposed method on artificial and real-world networks show that our model is more effective in detecting community structure in networks. In addition, visualization experiments show that the results of CWNE show that nodes from the same community are more tightly distributed in low-dimensional space.
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Eckstein, Maria K., and Anne G. E. Collins. "Computational evidence for hierarchically structured reinforcement learning in humans." Proceedings of the National Academy of Sciences 117, no. 47 (November 23, 2020): 29381–89. http://dx.doi.org/10.1073/pnas.1912330117.
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Humans have the fascinating ability to achieve goals in a complex and constantly changing world, still surpassing modern machine-learning algorithms in terms of flexibility and learning speed. It is generally accepted that a crucial factor for this ability is the use of abstract, hierarchical representations, which employ structure in the environment to guide learning and decision making. Nevertheless, how we create and use these hierarchical representations is poorly understood. This study presents evidence that human behavior can be characterized as hierarchical reinforcement learning (RL). We designed an experiment to test specific predictions of hierarchical RL using a series of subtasks in the realm of context-based learning and observed several behavioral markers of hierarchical RL, such as asymmetric switch costs between changes in higher-level versus lower-level features, faster learning in higher-valued compared to lower-valued contexts, and preference for higher-valued compared to lower-valued contexts. We replicated these results across three independent samples. We simulated three models—a classic RL, a hierarchical RL, and a hierarchical Bayesian model—and compared their behavior to human results. While the flat RL model captured some aspects of participants’ sensitivity to outcome values, and the hierarchical Bayesian model captured some markers of transfer, only hierarchical RL accounted for all patterns observed in human behavior. This work shows that hierarchical RL, a biologically inspired and computationally simple algorithm, can capture human behavior in complex, hierarchical environments and opens the avenue for future research in this field.
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Turk, Žiga, Zhiliang Ma, and Robert Klinc. "Parsimonious process model of energy retrofit of buildings." Organization, Technology and Management in Construction: an International Journal 14, no. 1 (January 1, 2022): 2631–39. http://dx.doi.org/10.2478/otmcj-2022-0007.
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Abstract Building information modelling (BIM) technology has been focusing on the creation, sharing and use of a comprehensive digital twin of the facility under construction or renovation. It is believed that a more comprehensive, structured and complete model would be better. The aim of this paper and the underlying research is to reaffirm the view that the essence of information is to inform the user and that the main purpose of information models is that they are informative – that they contain the necessary information. Parsimonious information is information that is necessary but as simple as possible. Our hypothesis is that in many cases simpler models could be more useful, as they do not introduce technological barriers with the usually involved low- and medium-level contractors. One such case is the energy retrofitting of existing buildings. Many public and residential buildings with poor energy performance are located all over the world, especially in Eastern and Central Europe, as well as in China. They are an efficient target for a significant improvement in their energy performance by improving the building envelope and openings. The paper presents an economical process model for the modernisation of existing buildings. This model provides a basis for a parsimonious product model, which is currently under development. The broader objective of the research presented is to examine cases where a heavy information model may not be necessary and where a detailed process definition – as described in the BIM implementation plan and related documents – would lead to over-specification that would limit the free dynamics of a workflow.
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Lee, Kyeo Re, Byungjun Kim, Dongyan Nan, and Jang Hyun Kim. "Structural Topic Model Analysis of Mask-Wearing Issue Using International News Big Data." International Journal of Environmental Research and Public Health 18, no. 12 (June 14, 2021): 6432. http://dx.doi.org/10.3390/ijerph18126432.
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Media plays an important role in the acquisition of health information worldwide. This was particularly evident in the face of the COVID-19 epidemic. Relatedly, it is practical and desirable for people to wear masks for health, fashion, and religious regions. However, depending on cultural differences, people naturally accept wearing a mask, or they look upon it negatively. In 2020, the COVID-19 pandemic led to widespread mask-wearing mandates worldwide. In the case of COVID-19, wearing a mask is strongly recommended, so by analyzing the news data before and after the spread of the epidemic, it is possible to see how the direction of crisis management is being structured. In particular, by utilizing big data analysis of international news data, discourses around the world can be analyzed more deeply. This study collected and analyzed 58,061 international news items related to mask-wearing from 1 January 2019 to 31 December 2020. The collected dataset was compared before and after the World Health Organization’s pandemic declaration by applying structural topic model analysis. The results revealed that prior to the declaration, issues related to the COVID-19 outbreak were emphasized, but afterward, issues related to movement restrictions, quarantine management, and local economic impacts emerged.
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Ohlin, Christer Lars. "Information and Communication Technology in a Global World." Research in Social Sciences and Technology 4, no. 2 (October 17, 2019): 41–57. http://dx.doi.org/10.46303/ressat.04.02.4.
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This study takes its departure from ongoing debate about teachers’ (collective) “continuing professional development” (CPD). The overall aim is to highlight active teachers’ perceptions on developing a self-understanding of their complex role in daily practice by using digital tools. The following research questions guided the study: How can teachers make schools more relevant and engaging? How can students’ achievement increase? In what way can teachers provide high-quality education for all students? Three perspectives will be guiding the study: Information and Communication Technology (ICT), learning, and special education. The participants in the study are 21 staff members in preschool, primary school, and a recreation center with whom we conducted structured individual interviews and focus group discussions. Field notes were also taken during the interview and discussion sessions. The findings reveal the importance of the teachers’ deeper understanding of students’ creativity, critical thinking, communication, and collaboration. Furthermore, the most powerful thing teachers can do is to design engaging, meaningful, and authentic work and technology-enhanced learning experiences. In order to improve learning in a digital world, the teachers must be engaged and supported by professional learning opportunities to continually improve and strengthen their digital competencies and teaching practices. The theoretical standpoint is the norm model as an analytical tool to understand the teachers’ perceptions. The concept of “norm” is a collective term for the factors and structures that are regarded as a normal balance between the aspects of value/will, system conditions/possibilities, and cognition/knowledge.
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Zhang, Guangjun, Zhenzhong Wei, and Xin Li. "3D Double-Vision Inspection Based on Structured Light." Journal of Manufacturing Science and Engineering 125, no. 3 (July 23, 2003): 617–23. http://dx.doi.org/10.1115/1.1557292.
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3D double-vision inspection is very necessary. It has a larger field of view, and can solve the problem of “blind area” for 3D measurement, as proposed by 3D single-vision inspection. At the beginning of this paper, the principle of structured-light based 3D vision inspection is introduced. Then, a method of gaining calibration points for 3D double-vision inspection system is proposed in detail. In order to gain calibration points with high precision, a double-directional photoelectric aiming device is designed as well, and a method for compensating the position-setting error of the aiming device is described. The coordinates of all calibration points are precisely unified in a world coordinate system. The application of RBF (radial basis function) neural network in establishing the inspection model of structured-light based 3D vision is described in detail. Finally, with the use of the calibration points, the inspection model of 3D double-vision based on RBF neural network is successfully established. The model’s training accuracy is 0.078 mm, and the testing accuracy is 0.084 mm.
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Liu, Baolong, Ruixia Wu, and Yu Liu. "Calibration Algorithm for Error Screening Based on Line Structured Light." International Journal on Artificial Intelligence Tools 29, no. 07n08 (November 30, 2020): 2040013. http://dx.doi.org/10.1142/s0218213020400138.
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The 3D measurement system based on line-structured light uses a camera to capture laser stripes due to changing in the shape of an object, and uses the acquired pixel coordinates for 3D reconstruction. System calibration is an important step in 3D measurement. The current camera calibration algorithm research mainly focuses on improving the algorithm itself, and there is less research on the influence of external factors. This paper proposes a coplanar hybrid calibration algorithm based on the error screening model by combining the error screening model, mathematical model and neural network model. It is mainly divided into two steps. The first step is to use the radial array constraint calibration algorithm based on the error screening model to solve the camera’s internal and external parameters. The second step uses the camera internal and external parameters obtained in the first step to convert the pixel coordinates into real three-dimensional coordinates, and compares the calculated three-dimensional coordinates with the actual coordinates. Using machine learning to establish a compensation network, get a compensation function, and use the resulting 3D world coordinates to perform point cloud stitching. Experiments show that compared with the traditional calibration algorithm, the calibration algorithm has a small error and reduces the calibration error by about 6.5%.
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Tutzauer, P., D. Laupheimer, and N. Haala. "SEMANTIC URBAN MESH ENHANCEMENT UTILIZING A HYBRID MODEL." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W7 (September 16, 2019): 175–82. http://dx.doi.org/10.5194/isprs-annals-iv-2-w7-175-2019.
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<p><strong>Abstract.</strong> We propose a feature-based approach for semantic mesh segmentation in an urban scenario using real-world training data. There are only few works that deal with semantic interpretation of urban triangle meshes so far. Most 3D classifications operate on point clouds. However, we claim that point clouds are an intermediate product in the photogrammetric pipeline. For this reason, we explore the capabilities of a Convolutional Neural Network (CNN) based approach to semantically enrich textured urban triangle meshes as generated from LiDAR or Multi-View Stereo (MVS). For each face within a mesh, a feature vector is computed and fed into a multi-branch 1D CNN. Ordinarily, CNNs are an end-to-end learning approach operating on regularly structured input data. Meshes, however, are not regularly structured. By calculating feature vectors, we enable the CNN to process mesh data. By these means, we combine explicit feature calculation and feature learning (hybrid model). Our model achieves close to 80% Overall Accuracy (OA) on dedicated test meshes. Additionally, we compare our results with a default Random Forest (RF) classifier that performs slightly worse. In addition to slightly better performance, the 1D CNN trains faster and is faster at inference.</p>
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Kazemi, Nasser. "Automatic blind deconvolution with Toeplitz-structured sparse total least squares." GEOPHYSICS 83, no. 6 (November 1, 2018): V345—V357. http://dx.doi.org/10.1190/geo2018-0136.1.
Full textAbstract:
Given the noise-corrupted seismic recordings, blind deconvolution simultaneously solves for the reflectivity series and the wavelet. Blind deconvolution can be formulated as a fully perturbed linear regression model and solved by the total least-squares (TLS) algorithm. However, this algorithm performs poorly when the data matrix is a structured matrix and ill-conditioned. In blind deconvolution, the data matrix has a Toeplitz structure and is ill-conditioned. Accordingly, we develop a fully automatic single-channel blind-deconvolution algorithm to improve the performance of the TLS method. The proposed algorithm, called Toeplitz-structured sparse TLS, has no assumptions about the phase of the wavelet. However, it assumes that the reflectivity series is sparse. In addition, to reduce the model space and the number of unknowns, the algorithm benefits from the structural constraints on the data matrix. Our algorithm is an alternating minimization method and uses a generalized cross validation function to define the optimum regularization parameter automatically. Because the generalized cross validation function does not require any prior information about the noise level of the data, our approach is suitable for real-world applications. We validate the proposed technique using synthetic examples. In noise-free data, we achieve a near-optimal recovery of the wavelet and the reflectivity series. For noise-corrupted data with a moderate signal-to-noise ratio (S/N), we found that the algorithm successfully accounts for the noise in its model, resulting in a satisfactory performance. However, the results deteriorate as the S/N and the sparsity level of the data are decreased. We also successfully apply the algorithm to real data. The real-data examples come from 2D and 3D data sets of the Teapot Dome seismic survey.
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Han, Sufang, Yaqin Li, Guoxin Liu, Lianglin Xiong, and Tianwei Zhang. "Dynamics of two-species delayed competitive stage-structured model described by differential-difference equations." Open Mathematics 17, no. 1 (May 16, 2019): 385–401. http://dx.doi.org/10.1515/math-2019-0030.
Full textAbstract:
Abstract Overf the last few years, by utilizing Mawhin’s continuation theorem of coincidence degree theory and Lyapunov functional, many scholars have been concerned with the global asymptotical stability of positive periodic solutions for the non-linear ecosystems. In the real world, almost periodicity is usually more realistic and more general than periodicity, but there are scarcely any papers concerning the issue of the global asymptotical stability of positive almost periodic solutions of non-linear ecosystems. In this paper we consider a kind of delayed two-species competitive model with stage structure. By means of Mawhin’s continuation theorem of coincidence degree theory, some sufficient conditions are obtained for the existence of at least one positive almost periodic solutions for the above model with nonnegative coefficients. Furthermore, the global asymptotical stability of positive almost periodic solution of the model is also studied. The work of this paper extends and improves some results in recent years. An example and simulations are employed to illustrate the main results of this paper.