Academic literature on the topic 'Topic model methods'
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Journal articles on the topic "Topic model methods"
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BOYKO, N., and O. PETROVSKYI. "METHODS OF CLASSIFICATION OF MACHINE LEARNING FOR CONSTRUCTION OF MATHEMATICAL MODELS ON MULTIMODAL DATA." Herald of Khmelnytskyi National University. Technical sciences 307, no. 2 (May 2, 2022): 25–32. http://dx.doi.org/10.31891/2307-5732-2022-307-2-25-32.
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This article is dedicated to topic modeling as an unsupervised machine learning technique. It is analyzed how it seems possible to determine the topics of documents in order to categorize them further with the help of topic modeling methods. Such methods as latent semantic analysis, probabilistic latent semantic analysis and latent Dirichlet allocation are considered. An approach that allows the construction of effective topic models of text document collections in Ukrainian and other synthetic languages based on peculiarities of this linguistic language type is proposed, and its main stages are described. The proposed approach consists of a custom input data preprocessing pipeline, which covers file loading, text extraction, removal of improper symbols, tokenization, removal of stop-words, stemming of each token and a newly introduced model pruning stage, which makes any of the modern topic modeling methods applicable for synthetic language topic modeling. The approach was implemented in Python programming language and used to obtain the topic model of the collection of Ukrainian-language scientific publications on civic identity and related topics. An expert in political psychology, who studies the phenomenon of civic identity, was involved in the research for the topic model quality evaluation. As a result of expert evaluation of the topics singled out during the modeling, it was proposed to clarify the formulation of cluster names based on the semantics of the sets of words that form them. In general, according to the expert, the topics singled out represent the concept of the civic identity of an individual and will allow researchers to simplify the work with literature sources on this issue when used to categorize documents. This demonstrates the efficiency of the proposed approach.
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Jankowski, Maciej. "Ensemble Methods for Improving Classification of Data Produced by Latent Dirichlet Allocation." Computer Science and Mathematical Modelling, no. 8/2018 (March 25, 2019): 17–28. http://dx.doi.org/10.5604/01.3001.0013.1458.
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Topic models are very popular methods of text analysis. The most popular algorithm for topic modelling is LDA (Latent Dirichlet Allocation). Recently, many new methods were proposed, that enable the usage of this model in large scale processing. One of the problem is, that a data scientist has to choose the number of topics manually. This step, requires some previous analysis. A few methods were proposed to automatize this step, but none of them works very well if LDA is used as a preprocessing for further classification. In this paper, we propose an ensemble approach which allows us to use more than one model at prediction phase, at the same time, reducing the need of finding a single best number of topics. We have also analyzed a few methods of estimating topic number.
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Xie, Qianqian, Yutao Zhu, Jimin Huang, Pan Du, and Jian-Yun Nie. "Graph Neural Collaborative Topic Model for Citation Recommendation." ACM Transactions on Information Systems 40, no. 3 (July 31, 2022): 1–30. http://dx.doi.org/10.1145/3473973.
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Due to the overload of published scientific articles, citation recommendation has long been a critical research problem for automatically recommending the most relevant citations of given articles. Relational topic models (RTMs) have shown promise on citation prediction via joint modeling of document contents and citations. However, existing RTMs can only capture pairwise or direct (first-order) citation relationships among documents. The indirect (high-order) citation links have been explored in graph neural network–based methods, but these methods suffer from the well-known explainability problem. In this article, we propose a model called Graph Neural Collaborative Topic Model that takes advantage of both relational topic models and graph neural networks to capture high-order citation relationships and to have higher explainability due to the latent topic semantic structure. Experiments on three real-world citation datasets show that our model outperforms several competitive baseline methods on citation recommendation. In addition, we show that our approach can learn better topics than the existing approaches. The recommendation results can be well explained by the underlying topics.
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Gou, Zhinan, Yan Li, and Zheng Huo. "A Method for Constructing Supervised Time Topic Model Based on Variational Autoencoder." Scientific Programming 2021 (February 8, 2021): 1–11. http://dx.doi.org/10.1155/2021/6623689.
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Topic modeling is a probabilistic generation model to find the representative topic of a document and has been successfully applied to various document-related tasks in recent years. Especially in the supervised topic model and time topic model, many methods have achieved some success. The supervised topic model can learn topics from documents annotated with multiple labels and the time topic model can learn topics that evolve over time in a sequentially organized corpus. However, there are some documents with multiple labels and time-stamped in reality, which need to construct a supervised time topic model to achieve document-related tasks. There are few research papers on the supervised time topic model. To solve this problem, we propose a method for constructing a supervised time topic model. By analysing the generative process of the supervised topic model and time topic model, respectively, we introduce the construction process of the supervised time topic model based on variational autoencoder in detail and conduct preliminary experiments. Experimental results demonstrate that the supervised time topic model outperforms several state-of-the-art topic models.
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Zhu, Lixing, Yulan He, and Deyu Zhou. "A Neural Generative Model for Joint Learning Topics and Topic-Specific Word Embeddings." Transactions of the Association for Computational Linguistics 8 (August 2020): 471–85. http://dx.doi.org/10.1162/tacl_a_00326.
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We propose a novel generative model to explore both local and global context for joint learning topics and topic-specific word embeddings. In particular, we assume that global latent topics are shared across documents, a word is generated by a hidden semantic vector encoding its contextual semantic meaning, and its context words are generated conditional on both the hidden semantic vector and global latent topics. Topics are trained jointly with the word embeddings. The trained model maps words to topic-dependent embeddings, which naturally addresses the issue of word polysemy. Experimental results show that the proposed model outperforms the word-level embedding methods in both word similarity evaluation and word sense disambiguation. Furthermore, the model also extracts more coherent topics compared with existing neural topic models or other models for joint learning of topics and word embeddings. Finally, the model can be easily integrated with existing deep contextualized word embedding learning methods to further improve the performance of downstream tasks such as sentiment classification.
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Mylläri, Sanna, Suoma Eeva Saarni, Ville Ritola, Grigori Joffe, Jan-Henry Stenberg, Ole André Solbakken, Nikolai Olavi Czajkowski, and Tom Rosenström. "Text Topics and Treatment Response in Internet-Delivered Cognitive Behavioral Therapy for Generalized Anxiety Disorder: Text Mining Study." Journal of Medical Internet Research 24, no. 11 (November 9, 2022): e38911. http://dx.doi.org/10.2196/38911.
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Background Text mining methods such as topic modeling can offer valuable information on how and to whom internet-delivered cognitive behavioral therapies (iCBT) work. Although iCBT treatments provide convenient data for topic modeling, it has rarely been used in this context. Objective Our aims were to apply topic modeling to written assignment texts from iCBT for generalized anxiety disorder and explore the resulting topics’ associations with treatment response. As predetermining the number of topics presents a considerable challenge in topic modeling, we also aimed to explore a novel method for topic number selection. Methods We defined 2 latent Dirichlet allocation (LDA) topic models using a novel data-driven and a more commonly used interpretability-based topic number selection approaches. We used multilevel models to associate the topics with continuous-valued treatment response, defined as the rate of per-session change in GAD-7 sum scores throughout the treatment. Results Our analyses included 1686 patients. We observed 2 topics that were associated with better than average treatment response: “well-being of family, pets, and loved ones” from the data-driven LDA model (B=–0.10 SD/session/∆topic; 95% CI –016 to –0.03) and “children, family issues” from the interpretability-based model (B=–0.18 SD/session/∆topic; 95% CI –0.31 to –0.05). Two topics were associated with worse treatment response: “monitoring of thoughts and worries” from the data-driven model (B=0.06 SD/session/∆topic; 95% CI 0.01 to 0.11) and “internet therapy” from the interpretability-based model (B=0.27 SD/session/∆topic; 95% CI 0.07 to 0.46). Conclusions The 2 LDA models were different in terms of their interpretability and broadness of topics but both contained topics that were associated with treatment response in an interpretable manner. Our work demonstrates that topic modeling is well suited for iCBT research and has potential to expose clinically relevant information in vast text data.
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Shi, Lei, Junping Du, and Feifei Kou. "A Sparse Topic Model for Bursty Topic Discovery in Social Networks." International Arab Journal of Information Technology 17, no. 5 (September 1, 2020): 816–24. http://dx.doi.org/10.34028/iajit/17/5/15.
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Bursty topic discovery aims to automatically identify bursty events and continuously keep track of known events. The existing methods focus on the topic model. However, the sparsity of short text brings the challenge to the traditional topic models because the words are too few to learn from the original corpus. To tackle this problem, we propose a Sparse Topic Model (STM) for bursty topic discovery. First, we distinguish the modeling between the bursty topic and the common topic to detect the change of the words in time and discover the bursty words. Second, we introduce “Spike and Slab” prior to decouple the sparsity and smoothness of a distribution. The bursty words are leveraged to achieve automatic discovery of the bursty topics. Finally, to evaluate the effectiveness of our proposed algorithm, we collect Sina weibo dataset to conduct various experiments. Both qualitative and quantitative evaluations demonstrate that the proposed STM algorithm outperforms favorably against several state-of-the-art methods
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Meaney, Christopher, Michael Escobar, Therese A. Stukel, Peter C. Austin, and Liisa Jaakkimainen. "Comparison of Methods for Estimating Temporal Topic Models From Primary Care Clinical Text Data: Retrospective Closed Cohort Study." JMIR Medical Informatics 10, no. 12 (December 19, 2022): e40102. http://dx.doi.org/10.2196/40102.
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Background Health care organizations are collecting increasing volumes of clinical text data. Topic models are a class of unsupervised machine learning algorithms for discovering latent thematic patterns in these large unstructured document collections. Objective We aimed to comparatively evaluate several methods for estimating temporal topic models using clinical notes obtained from primary care electronic medical records from Ontario, Canada. Methods We used a retrospective closed cohort design. The study spanned from January 01, 2011, through December 31, 2015, discretized into 20 quarterly periods. Patients were included in the study if they generated at least 1 primary care clinical note in each of the 20 quarterly periods. These patients represented a unique cohort of individuals engaging in high-frequency use of the primary care system. The following temporal topic modeling algorithms were fitted to the clinical note corpus: nonnegative matrix factorization, latent Dirichlet allocation, the structural topic model, and the BERTopic model. Results Temporal topic models consistently identified latent topical patterns in the clinical note corpus. The learned topical bases identified meaningful activities conducted by the primary health care system. Latent topics displaying near-constant temporal dynamics were consistently estimated across models (eg, pain, hypertension, diabetes, sleep, mood, anxiety, and depression). Several topics displayed predictable seasonal patterns over the study period (eg, respiratory disease and influenza immunization programs). Conclusions Nonnegative matrix factorization, latent Dirichlet allocation, structural topic model, and BERTopic are based on different underlying statistical frameworks (eg, linear algebra and optimization, Bayesian graphical models, and neural embeddings), require tuning unique hyperparameters (optimizers, priors, etc), and have distinct computational requirements (data structures, computational hardware, etc). Despite the heterogeneity in statistical methodology, the learned latent topical summarizations and their temporal evolution over the study period were consistently estimated. Temporal topic models represent an interesting class of models for characterizing and monitoring the primary health care system.
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Hong-Hui, LI, Zhao Ai-Hua, and Zhang Jun-Wen. "Research of Software Reliability Test Based on Test Model." International Journal of Open Source Software and Processes 8, no. 3 (July 2017): 49–64. http://dx.doi.org/10.4018/ijossp.2017070103.
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This article describes how in recent years, models-based software reliability test methods have been a hot topic. In order to summarize the research results of the reliability test models created in recent years, and to find new research hot topics on this basis, two kinds of test models are in this article. These include the operational profile and the usage model which are introduced and compared. In addition, the methods of constructing a usage model are also discussed in detail. Finally, the topic of building a usage model is presented.
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Park, Sang-Min, Sung Joon Lee, and Byung-Won On. "Topic Word Embedding-Based Methods for Automatically Extracting Main Aspects from Product Reviews." Applied Sciences 10, no. 11 (May 31, 2020): 3831. http://dx.doi.org/10.3390/app10113831.
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Detecting the main aspects of a particular product from a collection of review documents is so challenging in real applications. To address this problem, we focus on utilizing existing topic models that can briefly summarize large text documents. Unlike existing approaches that are limited because of modifying any topic model or using seed opinion words as prior knowledge, we propose a novel approach of (1) identifying starting points for learning, (2) cleaning dirty topic results through word embedding and unsupervised clustering, and (3) automatically generating right aspects using topic and head word embedding. Experimental results show that the proposed methods create more clean topics, improving about 25% of Rouge–1, compared to the baseline method. In addition, through the proposed three methods, the main aspects suitable for given data are detected automatically.
Dissertations / Theses on the topic "Topic model methods"
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王詠媚 and Wing-mei Wong. "Some topics in model selection in financial time series analysis." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31225366.
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Forster, Jeri E. "Varying-coefficient models for longitudinal data : piecewise-continuous, flexible, mixed-effects models and methods for analyzing data with nonignorable dropout /." Connect to full text via ProQuest. Limited to UCD Anschutz Medical Campus, 2006.
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Thesis (Ph.D. in Biostatistics) -- University of Colorado at Denver and Health Sciences Center, 2006.Typescript. Includes bibliographical references (leaves 72-75). Free to UCD Anschutz Medical Campus. Online version available via ProQuest Digital Dissertations;
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McCarthy, Shane A. "Topics in nonlinear self-dual supersymmetric theories." University of Western Australia. School of Physics, 2006. http://theses.library.uwa.edu.au/adt-WU2006.0045.
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[Truncated abstract. Formulae and special characters can only be approximated. See PDF version for accurate reproduction.] Theories of self-dual supersymmetric nonlinear electrodynamics are generalized to a curved superspace of 4D N = 1 supergravity, for both the old-minimal and the newminimal versions of N = 1 supergravity. We derive the self-duality equation, which has to be satisfied by the action functional of any U(1) duality invariant model of a massless vector multiplet, and show that such models are invariant under a superfield Legendre transformation. We construct a family of self-dual nonlinear models, which includes a minimal curved superspace extension of the N = 1 supersymmetric Born- Infeld action. The supercurrent and supertrace of such models are explicitly derived and proved to be duality invariant. The requirement of nonlinear self-duality turns out to yield nontrivial couplings of the vector multiplet to Kähler sigma models. We explicitly construct such couplings in the case when the matter chiral multiplets are inert under the duality rotations, and more specifically to the dilaton-axion chiral multiplet when the group of duality rotations is enhanced to SL(2,R). The component structure of the nonlinear dynamical systems introduced proves to be more complicated, especially in the presence of supergravity, as compared with well-studied effective supersymmetric theories containing at most two derivatives (including nonlinear Kähler sigma-models). As a result, when deriving their canonically normalized component actions, the traditional approach becomes impractical and cumbersome. We find it more efficient to follow the Kugo-Uehara scheme which consists of (i) extending the superfield theory to a super-Weyl invariant system; and then (ii) applying a plain component reduction along with imposing a suitable super-Weyl gauge condition. This scheme is implemented in order to derive the bosonic action of the SL(2,R) duality invariant coupling to the dilaton-axion chiral multiplet and a Kähler sigma-model.
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Syrén, Ludvig. "A method for introducing flexibility in rigid multibodies from reduced order elastic models." Thesis, Umeå universitet, Institutionen för fysik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160417.
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In multibody dynamics simulation of robots and vehicles it is common to model the systems as being composed of mainly rigid bodies with articulation joints. With the trend to more lightweight robots, however, the structural flexibility of the robots link’s needs to be considered for realistic dynamic simulations. The link’s geometries are complex and finite element models (FEM) are required to compute the deformations. However, FEM includes too many degrees of freedom for time-efficient dynamics simulation. A popular method is to generate reduced order models from the FE models, but with much fewer degrees of freedom, for fast and precise simulations. In this thesis a method for introducing reduced order models in rigid multibody systems was developed. The method is to divide a rigid body into two rigid bodies. Their relative movement is described by a six degree of freedom restoration force, determined with a reduced order model from Guyan reduction (static condensation). The method was validated for quasistatic deformation of a homogenous beam, a robot link arm with a more complex geometry and in multibody dynamics simulations. Finally the method was tested in simulation of a complete ABB robot with joint actuators, and any significant differences in the motion of the robot tool centre point due to replacing a rigid link arm by a flexible one was demonstrated.The method show good results for computing deformations of the homogenous beam, of the link arm and in the multibody simulation. The differences observed in simulation of a complete robot was expected and demonstrated the method to be applicable in robotic simulations.
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Wagner, Brandie D. "Permutation based microarray gene selection methods with covarience adjustment applicable to complex diseases /." Connect to full text via ProQuest. Limited to UCD Anschutz Medical Campus, 2007.
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Thesis (Ph.D. in Analytic Health Sciences) -- University of Colorado Denver, 2007.Typescript. Includes bibliographical references (leaves 57-60). Free to UCD affiliates. Online version available via ProQuest Digital Dissertations;
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Cohen, Margaret A. "Estimating the growth rate of harmful algal blooms using a model averaged method." View electronic thesis (PDF), 2009. http://dl.uncw.edu/etd/2009-1/rp/cohenm/margaretcohen.pdf.
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Kronberg, Simon. "Morphology Formation from Ternary Mixtures upon Evaporation : a Square Cell Model Approach." Thesis, Karlstads universitet, Institutionen för ingenjörsvetenskap och fysik (from 2013), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-72624.
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We present a novel modelling approach for the morphology formation from ternary mixtures upon evaporation, which takes into consideration two different length scales of the interaction, and further allows for these length scales to be altered. A quantitative study of the interfacial energy hints towards the existence of a mesoscopic regime - further research is needed to verify the validity of this claim however. We also demonstrate that the solvent initially follows a Fickian law of diffusion, then deviates from this behaviour, presumably due to the phase separated regions produced by the two remaining (active) components. We also attempt to bridge the gap between this work and a hypothetical three-dimensional model by considering a top-down view of the system. Here, we observe domain growth dominated by Ostwald ripening, with some coalescence. The domain growth was further characterised using Fourier image analysis.Vi presenterar en ny modellansats för morfologiformation från trekomponentsblandningar under avdunstning, som tar hänsyn till två olika längdskalor hos interaktionen, samt möjliggör förändring av dessa längdskalor. En kvantitativ studie av energin vid domängränserna tyder på att det finns en mesoskopisk regim - ytterligare forskning är dock nödvändig för att verifiera giltigheten av detta påstående. Vi visar också att lösningsmedlet ursprungligen följer en Fickiansk diffusionslag, för att senare avvika från detta beteende, förmodligen på grund av de tydliga domänerna som produceras av de två återstående (aktiva) komponenterna. Vi försöker också minska klyftan mellan det här arbetet och en hypotetisk tredimensionell modell genom att behandla systemet uppifrån. Här observerar vi domäntillväxt dominerad av 'Ostwald ripening', med viss koalescens. Domäntillväxten karakteriserades vidare med hjälp av Fourier-bildanalys.
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Podosinnikova, Anastasia. "Sur la méthode des moments pour l'estimation des modèles à variables latentes." Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLEE050/document.
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Les modèles linéaires latents sont des modèles statistique puissants pour extraire la structure latente utile à partir de données non structurées par ailleurs. Ces modèles sont utiles dans de nombreuses applications telles que le traitement automatique du langage naturel et la vision artificielle. Pourtant, l'estimation et l'inférence sont souvent impossibles en temps polynomial pour de nombreux modèles linéaires latents et on doit utiliser des méthodes approximatives pour lesquelles il est difficile de récupérer les paramètres. Plusieurs approches, introduites récemment, utilisent la méthode des moments. Elles permettent de retrouver les paramètres dans le cadre idéalisé d'un échantillon de données infini tiré selon certains modèles, mais ils viennent souvent avec des garanties théoriques dans les cas où ce n'est pas exactement satisfait. Dans cette thèse, nous nous concentrons sur les méthodes d'estimation fondées sur l'appariement de moment pour différents modèles linéaires latents. L'utilisation d'un lien étroit avec l'analyse en composantes indépendantes, qui est un outil bien étudié par la communauté du traitement du signal, nous présentons plusieurs modèles semiparamétriques pour la modélisation thématique et dans un contexte multi-vues. Nous présentons des méthodes à base de moment ainsi que des algorithmes pour l'estimation dans ces modèles, et nous prouvons pour ces méthodes des résultats de complexité améliorée par rapport aux méthodes existantes. Nous donnons également des garanties d'identifiabilité, contrairement à d'autres modèles actuels. C'est une propriété importante pour assurer leur interprétabilitéLatent linear models are powerful probabilistic tools for extracting useful latent structure from otherwise unstructured data and have proved useful in numerous applications such as natural language processing and computer vision. However, the estimation and inference are often intractable for many latent linear models and one has to make use of approximate methods often with no recovery guarantees. An alternative approach, which has been popular lately, are methods based on the method of moments. These methods often have guarantees of exact recovery in the idealized setting of an infinite data sample and well specified models, but they also often come with theoretical guarantees in cases where this is not exactly satisfied. In this thesis, we focus on moment matchingbased estimation methods for different latent linear models. Using a close connection with independent component analysis, which is a well studied tool from the signal processing literature, we introduce several semiparametric models in the topic modeling context and for multi-view models and develop moment matching-based methods for the estimation in these models. These methods come with improved sample complexity results compared to the previously proposed methods. The models are supplemented with the identifiability guarantees, which is a necessary property to ensure their interpretability. This is opposed to some other widely used models, which are unidentifiable
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Kim, Kwangmoo. "Topics in the theory of inhomogeneous media composite superconductors and dielectrics /." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1180537980.
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Das, Manirupa. "Neural Methods Towards Concept Discovery from Text via Knowledge Transfer." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1572387318988274.
Full textBooks on the topic "Topic model methods"
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Practical patient literacy: The medagogy model. New York: McGraw-Hill Medical, 2012.
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Antonella, Macagnano, ed. Advanced topics in cell model systems. Hauppauge, NY: Nova Science Publishers, 2009.
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Myopia: Animal models to clinical trials. New Jersey: World Scientific, 2010.
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Evaluation in nursing staff development: Methods and models. Rockville, Md: Aspen Systems Corp., 1985.
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Mark, Chang, ed. Adaptive design methods in clinical trials. 2nd ed. Boca Raton: Taylor & Francis, 2012.
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Maffett, Andrew Lewis. Topics for a statistical description of radar cross section. New York: Wiley, 1989.
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Palmer, Adele R. Existing chemicals regulation under the Toxic Substances Control Act: Models and methods for policy evaluation. Santa Monica, CA (P.O. Box 2138, Santa Monica 90406-2138): Rand, 1985.
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Statistical methods in drug combination studies. Boca Raton: CRC Press, Taylor & Francis Group, 2015.
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Julious, Steven A. An introduction to statistics in early phase trials. Chichester, West Sussex: John Wiley, 2010.
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Julious, Steven A. An introduction to statistics in early phase trials. Chichester, West Sussex: John Wiley, 2010.
Find full textBook chapters on the topic "Topic model methods"
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Isupova, Olga. "Proposed Learning Algorithms for Markov Clustering Topic Model." In Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video, 37–64. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75508-3_3.
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Vilalta, Ricardo, and Mikhail M. Meskhi. "Transfer of Knowledge Across Tasks." In Metalearning, 219–36. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-67024-5_12.
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AbstractThis area is often referred to as transfer of knowledge across tasks, or simply transfer learning; it aims at developing learning algorithms that leverage the results of previous learning tasks. This chapter discusses different approaches in transfer learning, such as representational transfer, where transfer takes place after one or more source models have been trained. There is an explicit form of knowledge transferred directly to the target model or to the meta-model. The chapter also discusses functional transfer, where two or more models are trained simultaneously. This situation is sometimes referred to as multi-task learning. In this approach, the models share their internal structure (or possibly some parts) during learning. Other topics include instance-, feature-, and parameter-based transfer learning, often used to initialize the search on the target domain. A distinct topic is transfer learning in neural networks, which includes, for instance, the transfer of a part of the network structure. The chapter also presents the double loop architecture, where the base-learner iterates over the training set in an inner loop, while the metalearner iterates over different tasks to learn metaparameters in an outer loop. Details are given on transfer learning within kernel methods and parametric Bayesian models.
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Koltai, Júlia, Zoltán Kmetty, and Károly Bozsonyi. "From Durkheim to Machine Learning: Finding the Relevant Sociological Content in Depression and Suicide-Related Social Media Discourses." In Pathways Between Social Science and Computational Social Science, 237–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-54936-7_11.
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AbstractThe phenomenon of suicide has been a focal point since Durkheim among social scientists. Internet and social media sites provide new ways for people to express their positive feelings, but they are also platforms to express suicide ideation or depressed thoughts. Most of these posts are not about real suicide, and some of them are a cry for help. Nevertheless, suicide- and depression-related content varies among platforms, and it is not evident how a researcher can find these materials in mass data of social media. Our paper uses the corpus of more than four million Instagram posts, related to mental health problems. After defining the initial corpus, we present two different strategies to find the relevant sociological content in the noisy environment of social media. The first approach starts with a topic modeling (Latent Dirichlet Allocation), the output of which serves as the basis of a supervised classification method based on advanced machine-learning techniques. The other strategy is built on an artificial neural network-based word embedding language model. Based on our results, the combination of topic modeling and neural network word embedding methods seems to be a promising way to find the research related content in a large digital corpus.Our research can provide added value in the detection of possible self-harm events. With the utilization of complex techniques (such as topic modeling and word embedding methods), it is possible to identify the most problematic posts and most vulnerable users.
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Felli, Paolo, Marco Montali, and Sarah Winkler. "CTL$$^*$$ Model Checking for Data-Aware Dynamic Systems with Arithmetic." In Automated Reasoning, 36–56. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10769-6_4.
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AbstractThe analysis of complex dynamic systems is a core research topic in formal methods and AI, and combined modelling of systems with data has gained increasing importance in applications such as business process management. In addition, process mining techniques are nowadays used to automatically mine process models from event data, often without correctness guarantees. Thus verification techniques for linear and branching time properties are needed to ensure desired behavior.Here we consider data-aware dynamic systems with arithmetic (DDSAs), which constitute a concise but expressive formalism of transition systems with linear arithmetic guards. We present a CTL$$^*$$ ∗ model checking procedure for DDSAs that addresses a generalization of the classical verification problem, namely to compute conditions on the initial state, called witness maps, under which the desired property holds. Linear-time verification was shown to be decidable for specific classes of DDSAs where the constraint language or the control flow are suitably confined. We investigate several of these restrictions for the case of CTL$$^*$$ ∗ , with both positive and negative results: witness maps can always be found for monotonicity and integer periodicity constraint systems, but verification of bounded lookback systems is undecidable. To demonstrate the feasibility of our approach, we implemented it in an SMT-based prototype, showing that many practical business process models can be effectively analyzed.
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Meadows, Edward S., and James B. Rawlings. "Topics in Model Predictive Control." In Methods of Model Based Process Control, 331–47. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-0135-6_13.
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Pelz, Peter F., Robert Feldmann, Christopher M. Gehb, Peter Groche, Florian Hoppe, Maximilian Knoll, Jonathan Lenz, et al. "Our Specific Approach on Mastering Uncertainty." In Springer Tracts in Mechanical Engineering, 43–111. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78354-9_3.
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AbstractThis chapter serves as an introduction to the main topic of this book, namely to master uncertainty in technical systems. First, the difference of our approach to previous ones is highlighted. We then discuss process chains as an important type of technical systems, in which uncertainty propagates along the chain. Five different approaches to master uncertainty in process chains are presented: uncertainty identification, uncertainty propagation, robust optimisation, sensitivity analysis and model adaption. The influence of the process on uncertainty and methods depends on whether it is dynamic/time-varying and/or active. This brings us to the main strategies for mastering uncertainty: robustness, flexibility and resilience. Finally, three different concrete technical systems that are used to demonstrate our methods are presented.
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Danelutto, Marco, Denis Caromel, Duane Szafron, and Fernando Silva. "Topic 9 Parallel Programming: Models, Methods and Languages." In Euro-Par 2005 Parallel Processing, 685. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11549468_75.
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Cunha, José C., Sergei Gorlatch, Daniel Quinlan, and Peter H. Welch. "Topic 9: Parallel Programming: Models, Methods and Languages." In Euro-Par 2006 Parallel Processing, 603. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11823285_62.
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Indukaev, Andrey. "Studying Ideational Change in Russian Politics with Topic Models and Word Embeddings." In The Palgrave Handbook of Digital Russia Studies, 443–64. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42855-6_25.
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AbstractThis chapter applies computational methods of textual analysis to a large corpus of media texts to study ideational change. The empirical focus of the chapter is on the ideas of the political role of innovation, technology, and economic development that were introduced into Russian politics during Medvedev’s presidency. The chapter uses topic modeling, shows the limitations of the method, and provides a more nuanced analysis with the help of word embeddings. The latter method is used to analyze semantic change and to capture complex semantic relationships between the studied concepts.
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Dral, Pavlo O., Fuchun Ge, Bao Xin Xue, Yi-Fan Hou, Max Pinheiro, Jianxing Huang, and Mario Barbatti. "MLatom 2: An Integrative Platform for Atomistic Machine Learning." In Topics in Current Chemistry Collections, 13–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07658-9_2.
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AbstractAtomistic machine learning (AML) simulations are used in chemistry at an everincreasing pace. A large number of AML models has been developed, but their implementations are scattered among different packages, each with its own conventions for input and output. Thus, here we give an overview of our MLatom 2 software package, which provides an integrative platform for a wide variety of AML simulations by implementing from scratch and interfacing existing software for a range of state-of-the-art models. These include kernel method-based model types such as KREG (native implementation), sGDML, and GAP-SOAP as well as neuralnetwork- based model types such as ANI, DeepPot-SE, and PhysNet. The theoretical foundations behind these methods are overviewed too. The modular structure of MLatom allows for easy extension to more AML model types. MLatom 2 also has many other capabilities useful for AML simulations, such as the support of custom descriptors, farthest-point and structure-based sampling, hyperparameter optimization, model evaluation, and automatic learning curve generation. It can also be used for such multi-step tasks as Δ-learning, self-correction approaches, and absorption spectrum simulation within the machine-learning nuclear-ensemble approach. Several of these MLatom 2 capabilities are showcased in application examples.
Conference papers on the topic "Topic model methods"
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Bhatia, Shraey, Jey Han Lau, and Timothy Baldwin. "Topic Intrusion for Automatic Topic Model Evaluation." In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/d18-1098.
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Jin, Yuan, He Zhao, Ming Liu, Lan Du, and Wray Buntine. "Neural Attention-Aware Hierarchical Topic Model." In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.emnlp-main.80.
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Sasaki, Kentaro, Tomohiro Yoshikawa, and Takeshi Furuhashi. "Online topic model for Twitter considering dynamics of user interests and topic trends." In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2014. http://dx.doi.org/10.3115/v1/d14-1212.
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Bianchi, Annamaria, Camilla Salvatore, and Silvia Biffignandi. "Communicating Corporate Social Responsibility through Twitter: a topic model analysis on selected companies." In CARMA 2020 - 3rd International Conference on Advanced Research Methods and Analytics. Valencia: Universitat Politècnica de València, 2020. http://dx.doi.org/10.4995/carma2020.2020.11646.
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Social media are fundamental in creating new opportunities for firms and they represent a relevant tool for the communication and the engagement with customers. The purpose of this paper is to analyse the communication of Corporate Social Responsibility (CSR) activities on Twitter. We consider the listed companies included in the Dow Jones Industrial Average Index and we implement a topic model analysis on their timelines. In order to identify the topic discussed, their correlation, and their evolution over time and sectors,we apply the Structural Topic Model algorithm, which allows estimating the model including document-level metadata. This model proves to be a powerful tool for topic detection and for estimating the effects of document-level metadata. Indeed, we find that the topics are overall well identified, and the model allows catching signals from the data. Finally, we discuss issues related to the validity of the analysis, including data quality problems.
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Salvatore, Camilla, Alice Madonna, Annamaria Bianchi, Albachiara Boffelli, and Matteo Giacomo Maria Kalchschmidt. "Collaborate for what: a structural topic model analysis on CDP data." In CARMA 2022 - 4th International Conference on Advanced Research Methods and Analytics. valencia: Universitat Politècnica de València, 2022. http://dx.doi.org/10.4995/carma2022.2022.15074.
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The aim of this paper is to understand why firms engage with their suppliers to collaborate for sustainability. To this purpose, we use the Carbon Disclosure Project (CDP) Supply Chain dataset and apply the Structural Topic Model to 1) identify the topics discussed in an open-ended question related to climate-related supplier engagement and 2) estimate the differences in the discussion of such topics between CDP members and non-members, respectively focal firms and first-tier suppliers. The analysis highlights that the two most prevalent reasons firms engage with their suppliers relate to several aspects of the management of the supply chain, and the services and goods mobility efficiency. It is further noted how first-tier suppliers do not dispose of established capabilities and, therefore, are still in the course of improving their processes. On the contrary, focal firms have more structured capabilities so to manage supplier engagement for information collection. This study demonstrates how big data and machine learning methods can be applied to analyse unstructured textual data from traditional surveys.
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Gui, Lin, Jia Leng, Gabriele Pergola, Yu Zhou, Ruifeng Xu, and Yulan He. "Neural Topic Model with Reinforcement Learning." In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-1350.
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Niu, Yue, and Hongjie Zhang. "A Self-Aggregated Hierarchical Topic Model for Short Texts." In 2nd International Conference on Machine Learning, IOT and Blockchain (MLIOB 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111212.
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With the growth of the internet, short texts such as tweets from Twitter, news titles from the RSS, or comments from Amazon have become very prevalent. Many tasks need to retrieve information hidden from the content of short texts. So ontology learning methods are proposed for retrieving structured information. Topic hierarchy is a typical ontology that consists of concepts and taxonomy relations between concepts. Current hierarchical topic models are not specially designed for short texts. These methods use word co-occurrence to construct concepts and general-special word relations to construct taxonomy topics. But in short texts, word cooccurrence is sparse and lacking general-special word relations. To overcome this two problems and provide an interpretable result, we designed a hierarchical topic model which aggregates short texts into long documents and constructing topics and relations. Because long documents add additional semantic information, our model can avoid the sparsity of word cooccurrence. In experiments, we measured the quality of concepts by topic coherence metric on four real-world short texts corpus. The result showed that our topic hierarchy is more interpretable than other methods.
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Zou, Bowei, Guodong Zhou, and Qiaoming Zhu. "Unsupervised Negation Focus Identification with Word-Topic Graph Model." In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2015. http://dx.doi.org/10.18653/v1/d15-1187.
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Zhao, Yilang. "Exploring Redditors’ Topics with Natural Language Processing." In CARMA 2022 - 4th International Conference on Advanced Research Methods and Analytics. valencia: Universitat Politècnica de València, 2022. http://dx.doi.org/10.4995/carma2022.2022.15022.
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This paper examines how people in Reddit develop topics across threads in a given subreddit and how discussions concentrate on the topic in given threads with natural language processing (NLP) methods. By implementing an LDA topic model and TF-IDF models, this paper discovers people’s aggregated concerns are related to real-world issues and their discussions are concentrative considering the topics they discuss.
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Butt, Saad Masood, Azura Onn, Moaz Masood Butt, Nadra Tabassam Inam, and Shahid Masood Butt. "Incorporation of usability evaluation methods in agile software model." In 2014 IEEE 17th International Multi-Topic Conference (INMIC). IEEE, 2014. http://dx.doi.org/10.1109/inmic.2014.7097336.
Full textReports on the topic "Topic model methods"
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Tanny, Josef, Gabriel Katul, Shabtai Cohen, and Meir Teitel. Micrometeorological methods for inferring whole canopy evapotranspiration in large agricultural structures: measurements and modeling. United States Department of Agriculture, October 2015. http://dx.doi.org/10.32747/2015.7594402.bard.
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Original objectives and revisions The original objectives as stated in the approved proposal were: (1) To establish guidelines for the use of micrometeorological techniques as accurate, reliable and low-cost tools for continuous monitoring of whole canopy ET of common crops grown in large agricultural structures. (2) To adapt existing methods for protected cultivation environments. (3) To combine previously derived theoretical models of air flow and scalar fluxes in large agricultural structures (an outcome of our previous BARD project) with ET data derived from application of turbulent transport techniques for different crops and structure types. All the objectives have been successfully addressed. The study was focused on both screenhouses and naturally ventilated greenhouses, and all proposed methods were examined. Background to the topic Our previous BARD project established that the eddy covariance (EC) technique is suitable for whole canopy evapotranspiration measurements in large agricultural screenhouses. Nevertheless, the eddy covariance technique remains difficult to apply in the farm due to costs, operational complexity, and post-processing of data – thereby inviting alternative techniques to be developed. The subject of this project was: 1) the evaluation of four turbulent transport (TT) techniques, namely, Surface Renewal (SR), Flux-Variance (FV), Half-order Time Derivative (HTD) and Bowen Ratio (BR), whose instrumentation needs and operational demands are not as elaborate as the EC, to estimate evapotranspiration within large agricultural structures; and 2) the development of mathematical models able to predict water savings and account for the external environmental conditions, physiological properties of the plant, and structure properties as well as to evaluate the necessary micrometeorological conditions for utilizing the above turbulent transfer methods in such protected environments. Major conclusions and achievements The major conclusions are: (i) the SR and FV techniques were suitable for reliable estimates of ET in shading and insect-proof screenhouses; (ii) The BR technique was reliable in shading screenhouses; (iii) HTD provided reasonable results in the shading and insect proof screenhouses; (iv) Quality control analysis of the EC method showed that conditions in the shading and insect proof screenhouses were reasonable for flux measurements. However, in the plastic covered greenhouse energy balance closure was poor. Therefore, the alternative methods could not be analyzed in the greenhouse; (v) A multi-layered flux footprint model was developed for a ‘generic’ crop canopy situated within a protected environment such as a large screenhouse. The new model accounts for the vertically distributed sources and sinks within the canopy volume as well as for modifications introduced by the screen on the flow field and microenvironment. The effect of the screen on fetch as a function of its relative height above the canopy is then studied for the first time and compared to the case where the screen is absent. The model calculations agreed with field experiments based on EC measurements from two screenhouse experiments. Implications, both scientific and agricultural The study established for the first time, both experimentally and theoretically, the use of four simple TT techniques for ET estimates within large agricultural screenhouses. Such measurements, along with reliable theoretical models, will enable the future development of lowcost ET monitoring system which will be attainable for day-to-day use by growers in improving irrigation management.
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McKay, S., Nate Richards, and Todd Swannack. Ecological model development : evaluation of system quality. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45380.
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Ecological models are used throughout the US Army Corps of Engineers (USACE) to inform decisions related to ecosystem restoration, water operations, environmental impact assessment, environmental mitigation, and other topics. Ecological models are typically developed in phases of conceptualization, quantification, evaluation, application, and communication. Evaluation is a process for assessing the technical quality, reliability, and ecological basis of a model and includes techniques such as calibration, verification, validation, and review. In this technical note (TN), we describe an approach for evaluating system quality, which generally includes the computational integrity, numerical accuracy, and programming of a model or modeling system. Methods are presented for avoiding computational errors during development, detecting errors through model testing, and updating models based on review and use. A formal structure is proposed for model test plans and subsequently demonstrated for a hypothetical habitat suitability model. Overall, this TN provides ecological modeling practitioners with a rapid guide for evaluating system quality.
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Chornodon, Myroslava. FEAUTURES OF GENDER IN MODERN MASS MEDIA. Ivan Franko National University of Lviv, February 2021. http://dx.doi.org/10.30970/vjo.2021.49.11064.
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The article clarifies of gender identity stereotypes in modern media. The main gender stereotypes covered in modern mass media are analyzed and refuted. The model of gender relations in the media is reflected mainly in the stereotypical images of men and woman. The features of the use of gender concepts in modern periodicals for women and men were determined. The most frequently used derivatives of these macroconcepts were identified and analyzed in detail. It has been found that publications for women and men are full of various gender concepts that are used in different contexts. Ingeneral, theanalysisofthe concept-maximums and concept-minimum gender and their characteristics is carried out in the context of gender stereotypes that have been forme dand function in the society, system atizing the a ctual presentations. The study of the gender concept is relevant because it reveals new trends and features of modern gender images. Taking into account the special features of gender-labeled periodicals in general and the practical absence of comprehensive scientific studies of the gender concept in particular, there is a need to supplement Ukrainian science with this topic. Gender psychology, which is served by methods of various sciences, primarily sociological, pedagogical, linguistic, psychological, socio-psychological. Let us pay attention to linguistic and psycholinguistic methods in gender studies. Linguistic methods complement intelligence research tasks, associated with speech, word and text. Psycholinguistic methods used in gender psychology (semantic differential, semantic integral, semantic analysis of words and texts), aimed at studying speech messages, specific mechanisms of origin and perception, functions of speech activity in society, studying the relationship between speech messages and gender properties participants in the communication, to analyze the linguistic development in connection with the general development of the individual. Nowhere in gender practice there is the whole arsenal of psychological methods that allow you to explore psychological peculiarities of a person like observation, experiments, questionnaires, interviews, testing, modeling, etc. The methods of psychological self-diagnostics include: the gender aspect of the own socio-psychological portrait, a gender biography as a variant of the biographical method, aimed at the reconstruction of individual social experience. In the process of writing a gender autobiography, a person can understand the characteristics of his gender identity, as well as ways and means of their formation. Socio-psychological methods of studying gender include the study of socially constructed women’s and men’s roles, relationships and identities, sexual characteristics, psychological characteristics, etc. The use of gender indicators and gender approaches as a means of socio-psychological and sociological analysis broadens the subject boundaries of these disciplines and makes them the subject of study within these disciplines. And also, in the article a combination of concrete-historical, structural-typological, system-functional methods is implemented. Descriptive and comparative methods, method of typology, modeling are used. Also used is a method of content analysis for the study of gender content of modern gender-stamped journals. It was he who allowed quantitatively to identify and explore the features of the gender concept in the pages of periodicals for women and men. A combination of historical, structural-typological, system-functional methods is also implemented in the article. Descriptive and comparative methods, method of typology, modeling are used. A method of content analysis for the study of gender content of modern gender-labeled journals is also used. It allowed to identify and explore the features of the gender concept quantitatively in the periodicals for women and men. The conceptual perception and interpretation of the gender concept «woman», which is highlighted in the modern gender-labeled press in Ukraine, requires the elaboration of the polyfunctionality of gender interpretations, the comprehension of the metaphorical perception of this image and its role and purpose in society. A gendered approach to researching the gender content of contemporary periodicals for women and men. Conceptual analysis of contemporary gender-stamped publications within the gender conceptual sphere allows to identify and correlate the meta-gender and gender concepts that appear in society.
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McKinnon, Mark, Daniel Madryzkowksi, and Craig Weinschenk. Development of a Database of Contemporary Material Properties for Fire Investigation Analysis - Materials and Methods. UL Firefighter Safety Research Institute, July 2020. http://dx.doi.org/10.54206/102376/zmpa6638.
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Meetings with the majority of the Technical Panel for the Development of an Interactive Database of Contemporary Material Properties for Fire Modeling project were held on June 29 and June 30, 2020. The major subjects of discussion included the list of proposed materials to be tested and characterized, the properties for the database, and the experimental and analytical methods to determine the properties for the database. A list of 101 materials divided into 11 categories were identified for inclusion in the database. The topics of variability in materials and aging of products and furniture items was discussed and it was concluded that investigating these variations is outside the scope of the project in this phase. The list of properties to be stored in the database for each material as well as proposed experimental methods to determine each property were discussed in the Technical Panel meetings. The discussion emphasized that the priorities for the properties represented in the database are dependent on the expected users for the database. Three potential user groups and the sets of properties that each group would likely require were identified. To ensure that the data contained in the database is useful for modeling, it was determined that prioritization would be given to complete sets of properties to be measured and stored in the database. Over the course of the two meetings, several tools were proposed to make the database easier for model practitioners to use. Once such tool included functionality to output lines of code for the models or entire model input files to simplify the process of inserting the properties into computa- tional fire models. Another tool that was discussed would involve automatically extracting derived properties from data sets or translating between complex and simple representations of burning. The next phase of the project includes conducting research to finalize the structure of the database and finalizing experimental procedures and protocols to populate the database.
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Kozlovsky, Evgen O., and Hennadiy M. Kravtsov. Мультимедийная виртуальная лаборатория по физике в системе дистанционного обучения. [б. в.], August 2018. http://dx.doi.org/10.31812/0564/2455.
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Research goals: the description of technology of software development in Physics Virtual Laboratory for Distance Learning System. Research objectives: the architecture of client and server parts of the lab, the functionality of the system modules, user roles, as well as the principles of virtual laboratory use on a personal computer. Object of research: the distance learning system “Kherson Virtual University”. Subject of research: virtual laboratory for physics in the distance learning. Research methods used: analysis of statistics and publications. Results of the research. The development of the software module “Virtual Lab” in distance learning system “Kherson Virtual University” (DLS KVU) applied to the problems of physics on topics kinematics and dynamics. The information technology design and development, the structure of the virtual laboratory, and its place in the DLS KVU are described. The principal modes of the program module operation in the system and methods for its use in the educational process are described. The main conclusions and recommendations. The use of this software interface allows teachers to create labs and use them in their distance courses. Students, in turn, will be able to conduct research, carrying out virtual laboratory work.
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Stavland, Arne, Siv Marie Åsen, Arild Lohne, Olav Aursjø, and Aksel Hiorth. Recommended polymer workflow: Lab (cm and m scale). University of Stavanger, November 2021. http://dx.doi.org/10.31265/usps.201.
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Polymer flooding is one of the most promising EOR methods (Smalley et al. 2018). It is well known and has been used successfully (Pye 1964; Standnes & Skjevrak 2014; Sheng et al. 2015). From a technical perspective we recommend that polymer flooding should be considered as a viable EOR method on the Norwegian Continental Shelf for the following reasons: 1. More oil can be produced with less water injected; this is particularly important for the NCS which are currently producing more water than oil 2. Polymers will increase the aerial sweep and improve the ultimate recovery, provided a proper injection strategy 3. Many polymer systems are available, and it should be possible to tailor their chemical composition to a wide range of reservoir conditions (temperature and salinity) 4. Polymer systems can be used to block water from short circuiting injection production wells 5. Polymer combined with low salinity injection water has many benefits: a lower polymer concentration can be used to reach target viscosity, less mechanical degradation, less adsorption, and a potential reduction in Sor due to a low salinity wettability effect. There are some hurdles when considering polymer flooding that needs to be considered: 1. Many polymer systems are not at the present considered as green chemicals; thus, reinjection of produced water is needed. However, results from polymer degradation studies in the IORCentre indicates that a. High molecular weight polymers are quickly degraded to low molecular weight. In case of accidental release to the ocean low molecular weight polymers are diluted and the lifetime of the spill might be quite short. According to Caulfield et al. (2002) HPAM is not toxic, and will not degrade to the more environmentally problematic acrylamide. b. In the DF report for environmental impact there are case studies using the DREAM model to predict the transport of chemical spills. This model is coupled with polymer (sun exposure) degradation data from the IORCentre to quantify the lifetime of polymer spills. This approach should be used for specific field cases to quantify the environmental risk factor. 2. Care must be taken to prepare the polymer solution offshore. Chokes and vales might be a challenge but can be mitigating according to the results from the large-scale testing done in the IORCentre (Stavland et al. 2021). None of the above-mentioned challenges are server enough to not consider polymer flooding. HPAM is neither toxic, nor bio-accumulable, or bio-persistent and the CO2 footprint from a polymer flood may be significantly less than a water flood (Dupuis et al. 2021). There are at least two contributing factors to this statement, which we will return in detail to in the next section i) during linear displacement polymer injection will produce more oil for the same amount of water injected, hence the lifetime of the field can be shortened ii) polymers increase the arial sweep reducing the need for wells.
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Choudhary, Ruplal, Victor Rodov, Punit Kohli, Elena Poverenov, John Haddock, and Moshe Shemesh. Antimicrobial functionalized nanoparticles for enhancing food safety and quality. United States Department of Agriculture, January 2013. http://dx.doi.org/10.32747/2013.7598156.bard.
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Original objectives The general goal of the project was to utilize the bactericidal potential of curcumin- functionalizednanostructures (CFN) for reinforcement of food safety by developing active antimicrobial food-contact surfaces. In order to reach the goal, the following secondary tasks were pursued: (a) further enhancement of the CFN activity based on understanding their mode of action; (b) preparing efficient antimicrobial surfaces, investigating and optimizing their performance; (c) testing the efficacy of the antimicrobial surfaces in real food trials. Background to the topic The project dealt with reducing microbial food spoilage and safety hazards. Cross-contamination through food-contact surfaces is one of the major safety concerns, aggravated by bacterial biofilm formation. The project implemented nanotech methods to develop novel antimicrobial food-contact materials based on natural compounds. Food-grade phenylpropanoidcurcumin was chosen as the most promising active principle for this research. Major conclusions, solutions, achievements In agreement with the original plan, the following research tasks were performed. Optimization of particles structure and composition. Three types of curcumin-functionalizednanostructures were developed and tested: liposome-type polydiacetylenenanovesicles, surface- stabilized nanoparticles and methyl-β-cyclodextrin inclusion complexes (MBCD). The three types had similar minimal inhibitory concentration but different mode of action. Nanovesicles and inclusion complexes were bactericidal while the nanoparticlesbacteriostatic. The difference might be due to different paths of curcumin penetration into bacterial cell. Enhancing the antimicrobial efficacy of CFN by photosensitization. Light exposure strengthened the bactericidal efficacy of curcumin-MBCD inclusion complexes approximately three-fold and enhanced the bacterial death on curcumin-coated plastic surfaces. Investigating the mode of action of CFN. Toxicoproteomic study revealed oxidative stress in curcumin-treated cells of E. coli. In the dark, this effect was alleviated by cellular adaptive responses. Under light, the enhanced ROS burst overrode the cellular adaptive mechanisms, disrupted the iron metabolism and synthesis of Fe-S clusters, eventually leading to cell death. Developing industrially-feasible methods of binding CFN to food-contact surfaces. CFN binding methods were developed for various substrates: covalent binding (binding nanovesicles to glass, plastic and metal), sonochemical impregnation (binding nanoparticles to plastics) and electrostatic layer-by-layer coating (binding inclusion complexes to glass and plastics). Investigating the performance of CFN-coated surfaces. Flexible and rigid plastic materials and glass coated with CFN demonstrated bactericidal activity towards Gram-negative (E. coli) and Gram-positive (Bac. cereus) bacteria. In addition, CFN-impregnated plastic material inhibited bacterial attachment and biofilm development. Testing the efficacy of CFN in food preservation trials. Efficient cold pasteurization of tender coconut water inoculated with E. coli and Listeriamonocytogeneswas performed by circulation through a column filled with CFN-coated glass beads. Combination of curcumin coating with blue light prevented bacterial cross contamination of fresh-cut melons through plastic surfaces contaminated with E. coli or Bac. licheniformis. Furthermore, coating of strawberries with CFN reduced fruit spoilage during simulated transportation extending the shelf life by 2-3 days. Implications, both scientific and agricultural BARD Report - Project4680 Page 2 of 17 Antimicrobial food-contact nanomaterials based on natural active principles will preserve food quality and ensure safety. Understanding mode of antimicrobial action of curcumin will allow enhancing its dark efficacy, e.g. by targeting the microbial cellular adaptation mechanisms.
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Thompson, Joseph. How WASH Programming has Adapted to the COVID-19 Pandemic. Institute of Development Studies (IDS), December 2020. http://dx.doi.org/10.19088/slh.2021.001.
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Since first appearing at the end of 2019, the novel coronavirus disease (COVID-19) has spread at a pace and scale not seen before. On 11 March 2020, the World Health Organization (WHO) declared COVID-19 a pandemic. A rapid response was called for, and actors across the globe worked quickly to develop sets of preventative measures to contain the disease. One mode of transmission identified early on in the crisis was via surfaces and objects (fomites) (Howard et al. 2020). To combat this, hand hygiene was put forward as a key preventative measure and heralded as ‘the first line of defence against the disease’ (World Bank 2020). What followed was an unprecedented global focus on handwashing with soap. Health messages on how germs spread, the critical times at which hands should be washed, and methods for correct handwashing were shared (Centers for Disease Control and Prevention 2020). Political leaders around the world promoted handwashing and urged people to adopt the practice to protect against the coronavirus. The primary and secondary impacts of COVID-19 have affected people and industries in a variety of different ways. For the WASH sector, the centring of handwashing in the pandemic response has led to a sudden spike in hygiene activity. This SLH Rapid Topic Review takes stock of some of the cross-cutting challenges the sector has been facing during this period and explores the adaptations that have been made in response. It then looks forwards, thinking through what lies ahead for the sector, and considers the learning priorities for the next steps.
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Thompson, Joseph. How WASH Programming has Adapted to the COVID-19 Pandemic. The Sanitation Learning Hub, Institute of Development Studies, December 2020. http://dx.doi.org/10.19088/slh.2021.0015.
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Since first appearing at the end of 2019, the novel coronavirus disease (COVID-19) has spread at a pace and scale not seen before. On 11 March 2020, the World Health Organization (WHO) declared COVID-19 a pandemic. A rapid response was called for, and actors across the globe worked quickly to develop sets of preventative measures to contain the disease. One mode of transmission identified early on in the crisis was via surfaces and objects (fomites) (Howard et al. 2020). To combat this, hand hygiene was put forward as a key preventative measure and heralded as ‘the first line of defence against the disease’ (World Bank 2020). What followed was an unprecedented global focus on handwashing with soap. Health messages on how germs spread, the critical times at which hands should be washed, and methods for correct handwashing were shared (Centers for Disease Control and Prevention 2020). Political leaders around the world promoted handwashing and urged people to adopt the practice to protect against the coronavirus. The primary and secondary impacts of COVID-19 have affected people and industries in a variety of different ways. For the WASH sector, the centring of handwashing in the pandemic response has led to a sudden spike in hygiene activity. This SLH Rapid Topic Review takes stock of some of the cross-cutting challenges the sector has been facing during this period and explores the adaptations that have been made in response. It then looks forwards, thinking through what lies ahead for the sector, and considers the learning priorities for the next steps.
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Hulata, Gideon, Thomas D. Kocher, and Micha Ron. Elucidating the molecular pathway of sex determination in cultured Tilapias and use of genetic markers for creating monosex populations. United States Department of Agriculture, January 2007. http://dx.doi.org/10.32747/2007.7695855.bard.
Full textAbstract:
The objectives of this project were to: 1) Identify genetic markers linked to sex-determining genes in various experimental and commercial stocks of O. niloticusand O. aureus, as well as red tilapias; 2) Develop additional markers tightly linked to these sex determiners, and develop practical, non-destructive genetic tests for identifying genotypic sex in young tilapia; A third aim, to map sex modifier loci, was removed during budget negotiations at the start of the project. Background to the topic. A major obstacle to profitable farming of tilapia is the tendency of females to reproduce at a small size during the production cycle, diverting feed and other resources to a large population of small, unmarketable fish. Several approaches for producing all-male fingerlings have been tried, including interspecific hybridization, hormonal masculinization, and the use of YY-supermale broodstock. Each method has disadvantages that could be overcome with a better understanding of the genetic basis of sex determination in tilapia. The lack of sex-linked markers has been a major impediment in research and development of efficient monosex populations for tilapia culture. Major conclusions, solutions, achievements. We identified DNA markers linked to sex determining genes in six closely related species of tilapiine fishes. The mode of sex determination differed among species. In Oreochromis karongaeand Tilapia mariaethe sex-determining locus is on linkage group (LG) 3 and the female is heterogametic (WZ-ZZ system). In O. niloticusand T. zilliithe sex-determining locus is on LG1 and the male is heterogametic (XX-XY system). We have nearly identified the series of BAC clones that completely span the region. A more complex pattern was observed in O. aureus and O. mossambicus, in which markers on both LG1 and LG3 were associated with sex. We found evidence for sex-linked lethal effects on LG1, as well as interactions between loci in the two linkage groups. Comparison of genetic and physical maps demonstrated a broad region of recombination suppression harboring the sex-determining locus on LG3. We also mapped 29 genes that are considered putative regulators of sex determination. Amhand Dmrta2 mapped to separate QTL for sex determination on LG23. The other 27 genes mapped to various linkage groups, but none of them mapped to QTL for sex determination, so they were excluded as candidates for sex determination in these tilapia species. Implications, both scientific and agricultural. Phylogenetic analysis suggests that at least two transitions in the mode of sex determination have occurred in the evolution of tilapia species. This variation makes tilapias an excellent model system for studying the evolution of sex chromosomes in vertebrates. The genetic markers we have identified on LG1 in O. niloticusaccurately diagnose the phenotypic sex and are being used to develop monosex populations of tilapia, and eliminate the tedious steps of progeny testing to verify the genetic sex of broodstock animals.