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Journal articles on the topic 'Framework RWA'

Author: Grafiati

Published: 11 March 2023

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1

Hadarics, Márton, and Anna Kende. "A closer look at intergroup threat within the dual process model framework: The mediating role of moral foundations." Psychological Thought 10, no. 1 (April 28, 2017): 167–77. http://dx.doi.org/10.5964/psyct.v10i1.210.

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In our study we investigated how right-wing authoritarianism (RWA) and social dominance orientation (SDO) are related to perceived intergroup threat, and also tested the potential mediating role of individualizing and binding moral foundations within this relationship pattern. According to our results, both RWA and SDO enhanced the perceived threat related to immigration. Furthermore, the effect of SDO was partly mediated by individualizing moral foundations, while the effect of RWA was partly mediated by both kinds of moral foundations. It seems that perceived intergroup threat, at least to some extent, is influenced by personal moral preferences that can be derived from individual dispositions and motivations.

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Mohamad, Siti Nor Amira, Mohamad Yazis Ali Basah, and Muhammad Ridhwan Ab Aziz. "Examining Risk-Weighted Assets (RWA) Performance after Recent Financial Crisis in Malaysian Banking System." International Journal of Economics and Finance 10, no. 5 (April 7, 2018): 129. http://dx.doi.org/10.5539/ijef.v10n5p129.

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Until recently, there has been only muted debate on the stability of RWA and after the recent financial crisis, the new regulatory framework was introduced that will enrich the quality and level of capital ratios for the banking system. However these capital ratios required to be based on specific risk measurement that permits for appropriate comparison as these gives new prominence to the stability of the underlying RWA. The aim of this paper is to examine the RWA performance after recent global financial crisis in Malaysian banking system. The study uses quantitative approach to examine in detail the RWA performance from year 2012 to 2016 using secondary analysis of bank’s annual report.

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Raju, Chitra, Sudarmani Rajagopal, Kanagaraj Venusamy, Kannadhasan Suriyan, and Manjunathan Alagarsamy. "SDSFLF: fault localization framework for optical communication using software digital switching network." International Journal of Reconfigurable and Embedded Systems (IJRES) 12, no. 1 (March 1, 2023): 113. http://dx.doi.org/10.11591/ijres.v12.i1.pp113-124.

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Optical network is an emerging technology for data communication inworldwide. The information is transmitted from the source to destination through the fiber optics. All optical network (AON) provides good transmission transparency, good expandability, large bandwidth, lower bit error rate (BER), and high processing speed. Link failure and node failure haveconsistently occurred in the traditional methods. In order to overcome the above mentioned issues, this paper proposes a robust software defined switching enabled fault localization framework (SDSFLF) to monitor the node and link failure in an AON. In this work, a novel faulty node localization (FNL) algorithm is exploited to locate the faulty node. Then, the software defined faulty link detection (SDFLD) algorithm that addresses the problem of link failure. The failures are localized in multi traffic stream (MTS) and multi agent system (MAS). Thus, the throughput is improved in SDSFLF compared than other existing methods like traditional routing and wavelength assignment (RWA), simulated annealing (SA) algorithm, attackaware RWA (A-RWA) convex, longest path first (LPF) ordering, and biggest source-destination node degree (BND) ordering. The performance of the proposed algorithm is evaluated in terms of network load, wavelength utilization, packet loss rate, and burst loss rate. Hence, proposed SDSFLF assures that high performance is achieved than other traditional techniques.

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Basdogan, Ipek, Laila Mireille Elias, Frank Dekens, and Lisa Sievers. "Predicting the Optical Performance of the Space Interferometry Mission Using a Modeling, Testing, and Validation Methodology." Journal of Vibration and Acoustics 129, no. 2 (March 17, 2006): 148–57. http://dx.doi.org/10.1115/1.2202152.

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This paper presents the modeling, testing, and validation methodologies developed to predict the optical performance of the Space Interferometry Mission (SIM) at the Jet Propulsion Laboratory (JPL). The modeling methodology combines structural, optical, and control system design within a common state space framework and incorporates reaction wheel assembly (RWA) disturbances to evaluate the end-to-end performance of the system requirements. The validation methodology uses the Micro-Precision Interferometer (MPI) testbed, which is a ground-based, representative hardware model of SIM. In this study, the integrated model of the MPI testbed was used to calculate the transfer functions from RWA input to optical performance output. The model-predicted transfer functions were compared with the MPI testbed measurements, and the accuracy of the integrated model was quantified using a metric that was based on output power of the transfer functions. The RWA disturbances were then propagated through the modeled and measured transfer functions to predict the optical performance of the MPI testbed. This method is called the “decoupled disturbance analysis” and relies on the “blocked” RWA disturbances, measured with the RWA hardmounted to a rigid surface. These predictions were compared with the actual (measured) optical performance of MPI, measured with the RWA mounted to MPI, to evaluate the accuracy of the decoupled disturbance analysis method. The results show that this method is not an accurate representation of the coupled boundary conditions that occurs when the RWA is mounted to the flexible MPI structure. In order to correct for the blocked RWA disturbance boundary conditions, the “coupled disturbance analysis” method was developed. This method uses “force filters” that depend on estimates of the interface accelerances of the RWA and the MPI structure to effectively transform the blocked RWA disturbance measurements into their corresponding “coupled” disturbances (the disturbances that would occur at the coupled RWA-MPI interface). Compared to the decoupled method, the coupled method more accurately predicts the system’s performance. Additionally, the RWA cross-spectral density terms were found to be influential in matching the performance predictions to the measured optical performance of MPI.

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Ricciardi, Sergio, Francesco Palmieri, Ugo Fiore, Davide Careglio, Germán Santos-Boada, and Josep Solé-Pareta. "An energy-aware dynamic RWA framework for next-generation wavelength-routed networks." Computer Networks 56, no. 10 (July 2012): 2420–42. http://dx.doi.org/10.1016/j.comnet.2012.03.016.

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Jijun Zhao, Wei Li, Xin Liu, Wenyu Zhao, and M. Maier. "Physical Layer Impairment (PLI)-Aware RWA Algorithm Based on a Bidimensional QoS Framework." IEEE Communications Letters 17, no. 6 (June 2013): 1280–83. http://dx.doi.org/10.1109/lcomm.2013.043013.130331.

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7

Lamprou, Ioannis, Russell Martin, and Paul Spirakis. "Cover Time in Edge-Uniform Stochastically-Evolving Graphs." Algorithms 11, no. 10 (October 2, 2018): 149. http://dx.doi.org/10.3390/a11100149.

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We define a general model of stochastically-evolving graphs, namely the edge-uniform stochastically-evolving graphs. In this model, each possible edge of an underlying general static graph evolves independently being either alive or dead at each discrete time step of evolution following a (Markovian) stochastic rule. The stochastic rule is identical for each possible edge and may depend on the past k ≥ 0 observations of the edge’s state. We examine two kinds of random walks for a single agent taking place in such a dynamic graph: (i) The Random Walk with a Delay (RWD), where at each step, the agent chooses (uniformly at random) an incident possible edge, i.e., an incident edge in the underlying static graph, and then, it waits till the edge becomes alive to traverse it. (ii) The more natural Random Walk on what is Available (RWA), where the agent only looks at alive incident edges at each time step and traverses one of them uniformly at random. Our study is on bounding the cover time, i.e., the expected time until each node is visited at least once by the agent. For RWD, we provide a first upper bound for the cases k = 0 , 1 by correlating RWD with a simple random walk on a static graph. Moreover, we present a modified electrical network theory capturing the k = 0 case. For RWA, we derive some first bounds for the case k = 0 , by reducing RWA to an RWD-equivalent walk with a modified delay. Further, we also provide a framework that is shown to compute the exact value of the cover time for a general family of stochastically-evolving graphs in exponential time. Finally, we conduct experiments on the cover time of RWA in edge-uniform graphs and compare the experimental findings with our theoretical bounds.

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Sudama, I. Nyoman. "Conflict within tri hita karana’s fields: A conceptual review." International journal of linguistics, literature and culture 6, no. 6 (October 4, 2020): 8–23. http://dx.doi.org/10.21744/ijllc.v6n6.992.

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The study on Tri Hita Karana mostly focuses on the application of the concept in particular area. Its lack of conjectural discussion opens opportunities for further research. Specifically, conflict is infrequently embraced, despite the fact that dispute is inevitably part of another Balinese balance concept, namely, rwa bhineda. This paper aims to draft a discourse of THK within the standpoint of cultural sociology by tailoring several concepts to identify the raise and possibility of conflict in Balinese social systems. Thence, the paper first reviews THK literatures to examine research conceptions on THK within Balinese customs. Second, the paper proposes extended THK framework to illustrate how conflict can emerge in Balinese practices and explain further understanding of interrelation of THK’s worlds. This paper highlights how the THK can be seen from cultural sociology perspectives to frame the dynamic of Balinese traditions within the contestation of spiritual, nature, and human fields. This study shows that capital play important roles on the Balinese life balance. Capital determines the positions, statuses, competitions, and hence symbolic values of agents in the fields. The paper advocates future theory developments to construct on different framework of THK to better describe the Balinese transitions toward modern cultures.

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Vozzella, Pietro, and Giampaolo Gabbi. "What is good and bad with the regulation supporting the SME’s credit access." Journal of Financial Regulation and Compliance 28, no. 4 (May 1, 2020): 569–86. http://dx.doi.org/10.1108/jfrc-10-2019-0132.

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Purpose This analysis asks whether regulatory capital requirements capture differences in systematic risk for large firms and micro-, small- and medium-sized enterprises (MSMEs). The authors explore whether bank capital regulations intended to support SMEs’ access to borrowing are effective. The purpose of this paper is to find out whether the regulatory design (particularly the estimate of asset correlations) positively affects the lending process to small and medium enterprises, compared to large corporates. Design/methodology/approach The authors investigate the appropriateness of bank capital requirements considering default risk of loans to MSMEs and distortions in capital charges between MSMEs and large firms under the Basel III framework. The authors compiled firm-level data to capture the proportions of MSMEs and large firms in Italy during 2000–2014. The data set is drawn from financial reports of 708,041 firms over 15 years. Unlike most empirical studies that correlate assets and defaults, this study assesses a firm’s creditworthiness not by agency ratings or by sampling banks but by a specific model to estimate one-year probabilities of default. Findings The authors found that asset correlations increase with firms’ size and that large firms face considerably greater systematic risk than MSMEs. However, the empirical values are much lower than regulatory values. Moreover, when the authors focused on the MSME segment, systematic risk is rather stable and varies significantly with turnover. This analysis showed that the regulatory supporting factor represents a valuable attempt to treat MSME loans more fairly with respect to banks’ capital requirements. Basel III-internal ratings-based approach results show that when the supporting factor is applied, the Risk-Weighted-Assets (RWA) differences between MSMEs and large firms increase. Research limitations/implications The implications of this research is that banking regulators to make MSMEs support more effective should review asset correlation estimation criteria, refining the fitting with empirical evidence. Practical implications The asset correlation parameter stipulated by the Basel framework is invariant with economic cycles, decreases with borrowers’ probability of default and increases with borrowers’ assets. The authors found that those relations do not hold. This way, asset correlations fall below parameters defined by regulatory formula, and SMEs’ credit risk could be overstated, resulting in a capital crunch. Originality/value The original contribution of this paper is to demonstrate that the gap between empirical and regulatory capital charge remains high. When the authors examined the Basel III-IRBA, results showed that when the supporting factor is applied, the RWA differences between MSMEs and large firms increase. This is particularly strong for loans to small- and medium-sized companies. Correctly calibrating asset correlations associated with the supporting factor eliminates regulatory distortions, reducing the gap in capital charges between loans to large corporate and MSMEs.

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Romanov, Dmitry B., Kira G. Serdakova, Olga V. Smirnikova, Ilya I. Khersonsky, and Svetlana E. Kovaleva. "Formation of authoritarian personality within the framework of educational models involving soviet and post-soviet philosophical and ideological specifics." Perspectives of Science and Education 50, no. 2 (May 1, 2021): 40–55. http://dx.doi.org/10.32744/pse.2021.2.3.

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Introduction. The study of authoritarian personality formation factors is becoming increasingly important in connection with the need to assess the efficacy of democratisation and humanisation of the socio-cultural environment. Despite the fact that the dominant trend in modern education is the striving to maximise the resource domain for due realisation of the individual, the authoritarian type of management is quite frequent, continuing to develop in many spheres of social relations. Materials and methods. The authors, in the course of the empirical research, explore the authoritarian personality potential in respondents representing different learning models, by measuring pronouncement of traits characteristic of the authoritarian syndrome, assessing the disposition towards choosing the authoritarian regime as a way to get protected against danger, along with the levels of manifestation of three types of authoritarianism: interpersonal, organisational and socio-political. The designated parametric characteristics are surveyed in respondents with the use of a set of standardised valid diagnostic methods: T. Adorno’s questionnaire “The Authoritarian Personality (F-scale)” (adapted by Denisova); Altemeyer’s questionnaire “Right-Wing Authoritarianism” (RWA) (adapted by Dyakonova); Dakkit’s questionnaire “Faith in a Dangerous World” (adapted by Gulevich, Anikeenok, Bezmenova); Bayazitov’s and Alishev’s questionnaire “The Authoritarian Stereotype”. Results. Following the comparative analysis of the empirical data obtained in the course of the study, a connection was established between the extent of individual’s authoritativeness and the specificity of learning models. It was found out during the analysis of the obtained data, that the level of right-wing authoritarianism measured according to Altemeyer’s method was higher in the elder learners group than in the younger group (the younger group – 15.24 points, the older group – 19.12 points). The level of authoritarianism in the group of older respondents turned out to be statistically much higher according to the consolidated figures under the “Authoritarian stereotype” methodology by Bayazitov and Alishev (the younger group – 4.10 points, the elder group – 4.55 points). Conclusions. The philosophical and ideological platform of educational models influences the process of formation of the authoritarian personality. The influence of the philosophical and ideological platform on the personality may be corrected only through extension of individual resources for comprehending the global sociocultural situation and one’s place in it. The progress in this domain may be facilitated by increasing the credibility of fundamental disciplines, philosophy in the first place, and by the introduction of psychological competence courses in schools for schoolchildren, their parents and teaching staff.

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Anisimov, Roman Sergeevich, and Vyacheslav Yur'evich Kombarov. "Right-wing authoritarianism in Modern Russia in the conditions of the “state of emergency”." Политика и Общество, no. 4 (April 2021): 17–37. http://dx.doi.org/10.7256/2454-0684.2021.4.33876.

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This article is dedicated to examination of the phenomenon of right-authoritarian personality (RWA), or more specifically, the theoretical, methodological and applied use of this concept within the framework of Giorgio Agamben’s concept of the "state of emergency". This study reveals the intolerance and lack of flexibility of an increasing number of people in modern Russia as deliberately cultivated by the state apparatus for the more effective ideological and hybrid military confrontation with many countries of the world. This provision is interpreted as the "state of emergency", in which according to the theory of Giorgio Agamben, punitive measures can be applied against the citizens bypass the law. Personality is viewed through the prism of actualization by the situation of the "state of emergency" of the authoritarian right-wing traits within its structure that contribute to the maintenance of totalitarian tendencies in the society. The scientific novelty consists in examination of the role and nature of the impact of social transformations defined as emergency upon actualization of the right-authoritarian traits in the structure of personality. Verification of the hypotheses is carried out in the course of empirical research with the use of various questionnaires for determining certain preferences in the cultural and social spheres of life, as well as what can be perceived as the social threat by right-wing authoritarians in the conditions of the “state of emergency”. Particular attention is given to the mechanism of actualization of right authoritarian tendencies in a person, as well as to the analysis of Giorgio Agamben’s concept of the “state of emergency”. Examination of this concept from sociological perspective is a long-run objective. The are no similar studies in the Russia science; thus, it is of particular interest to consider the results of analysis based on certain peculiarities in Russia.

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Quadri, Rana, and Anupama Deshpande. "Deep Learning-Based Segmentation and Classification of COVID-19 Infection Severity Levels from CT Scans." Revue d'Intelligence Artificielle 36, no. 1 (February 28, 2022): 41–48. http://dx.doi.org/10.18280/ria.360105.

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Due to the emergence of coronavirus disease 2019 (COVID-19) around the globe, the detection and treatment of COVID-19 patients is an extremely essential process in healthcare systems. Among many imaging technologies, Computed Tomography (CT) analyses with deep learning frameworks offer better efficiency than the other imaging modalities. To segment the infected COVID-19 Region-Of-Interests (ROIs), an Enriched 2.5D U-Net (E2.5D U-Net)-based deep learner has been used. In contrast, it requires categorization of infection severity to identify the patients in the prior stage. Hence, in this article, feature extraction with a classification framework is proposed to learn deep features related to the infection disease severity. In this framework, each segment from the E2.5D U-Net is fed to a deep learner such as DenseNet201 to extract the deep features. These features are learned independently by different machine learning classifiers to categorize the infection severity levels. It aids physicians in diagnosing COVID-19 patients in advance. But it needs other infection-related features to enhance the efficiency. Therefore, a multi-modeling classification framework is proposed. This framework extracts the handcrafted features from CT scans and concatenates them with the deep features to get the unified feature vector. Moreover, these feature vectors are trained by using the multi-modeling classifier for predicting the infection severity levels with higher accuracy. At last, the testing outcomes exhibit that the multi-modal classifier establishes a higher efficiency than the standard classifier frameworks.

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Aditama, Redi, Zulfikar Achmad Tanjung, Widyartini Made Sudania, and Toni Liwang. "SMART-RDA: A Galaxy Workflow for RNA-Seq Data Analysis." KnE Life Sciences 3, no. 4 (March 27, 2017): 186. http://dx.doi.org/10.18502/kls.v3i4.703.

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<p class="Els-Abstract-text">RNA-seq using the Next Generation Sequencing (NGS) approach is a common technology to analyze large-scale RNA transcript data for gene expression studies. However, an appropriate bioinformatics tool is needed to analyze a large amount of transcriptomes data from RNA-seq experiment. The aim of this study was to construct a system that can be easily applied to analyze RNA-seq data. RNA-seq analysis tool as SMART-RDA was constructed in this study. It is a computational workflow based on Galaxy framework to be used for analyzing RNA-seq raw data into gene expression information. This workflow was adapted from a well-known Tuxedo Protocol for RNA-seq analysis with some modifications. Expression value from each transcriptome was quantitatively stated as Fragments Per Kilobase of exon per Million fragments (FPKM). RNA-seq data of sterile and fertile oil palm (Pisifera) pollens derived from Sequence Read Archive (SRA) NCBI were used to test this workflow in local facility Galaxy server. The results showed that differentially gene expression in pollens might be responsible for sterile and fertile characteristics in palm oil Pisifera.</p><p><strong>Keywords:</strong> FPKM; Galaxy workflow; Gene expression; RNA sequencing.</p>

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McManamay, R. A., D. J. Orth, C. A. Dolloff, and E. A. Frimpong. "REGIONAL FRAMEWORKS APPLIED TO HYDROLOGY: CAN LANDSCAPE-BASED FRAMEWORKS CAPTURE THE HYDROLOGIC VARIABILITY?" River Research and Applications 28, no. 9 (June 9, 2011): 1325–39. http://dx.doi.org/10.1002/rra.1535.

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Wang, Yisong, He Jia, Xin Fang, Ziyang Qiu, and Tao Du. "CO2 and water vapor adsorption properties of framework hybrid W-ZSM-5/silicalite-1 prepared from RHA." RSC Advances 10, no. 41 (2020): 24642–52. http://dx.doi.org/10.1039/d0ra03736b.

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Bhattacharyya, Arnab, Elena Grigorescu, and Asaf Shapira. "A unified framework for testing linear-invariant properties." Random Structures & Algorithms 46, no. 2 (June 27, 2013): 232–60. http://dx.doi.org/10.1002/rsa.20507.

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Janardhanan, Jitha, and Umamaheswari Subbian. "Recognizing Multiple Human Activities Using Deep Learning Framework." Revue d'Intelligence Artificielle 36, no. 05 (December 23, 2022): 791–99. http://dx.doi.org/10.18280/ria.360518.

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Rochmania, Nur, Indri Rozas, and Ilham Ilham. "Tren Penggunaan Framework COBIT, ITIL, dan ISO 27001 Pada Rentang Tahun 2014-2018 di Indonesia." Edumatic: Jurnal Pendidikan Informatika 4, no. 2 (December 20, 2020): 10–19. http://dx.doi.org/10.29408/edumatic.v4i2.2249.

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The use of Information Technology (IT) frameworks from year to year in Indonesia has shown an increase. However, there is not much research on the use of IT frameworks. The research on IT frameworks can be used by a company to analyze trends in the use of frameworks in Indonesia. That way, it can help a company consider choosing a framework to use. The IT frameworks discussed in this study are COBIT, ITIL, and ISO 27001. Data related to research IT frameworks taken in the 2014-2018 period. Data collection through the Google Scholar web search engine using the Publish or Perish software. The data obtained were analyzed using descriptive quantitative methods. The results of this study can use to determine the use of the IT framework in Indonesia. The amount of raw data obtained from the data collection process was 5,708 data with Authors, Title, Years, and Publisher variables. Research data on titles and keywords obtained in the Indonesian domain amounted to 754 and overseas amounted to 194. From the research results, it can conclude that the most widely used framework is the COBIT framework, with a percentage of 69.3% in the Indonesian and 58.7% in the foreign domain.

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Zhang, Zehua, and Ziheng Huang. "Design and Application of Logistics Robot based on RPA." Frontiers in Computing and Intelligent Systems 1, no. 1 (September 1, 2022): 66–75. http://dx.doi.org/10.54097/fcis.v1i1.1464.

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In the logistics management process, tedious and repetitive tasks are very common, which is inseparable from a large number of manual data entry and processing of a large number of orders to complete the daily logistics workflow. The work of entering data and processing orders takes a lot of time and high repetition. The application of RPA robot (Robotic Process Automation) is particularly important, which can reduce a lot of labor cost and time cost, improve accuracy, and is of great significance for logistics management. This paper designs a RPA robot framework for logistics management, which is directly used in the development tool Uibot Creator. Developers carry out the second development on the basis of this framework, which can simplify the development process, improve the efficiency of development, save project preparation time, and improve the efficiency of logistics operation and management. The core functions of the framework include logistics web portal framework, logistics information capture, input, logistics documents, mail automation and other frameworks. Finally, the designed Robotic Process Automation framework is applied to the logistics management process to verify the effect of the optimization.

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Ponnusamy, Vijayakumar, Sowmya Natarajan, Nandakumar Ramasamy, Christopher Clement, Prithiviraj Rajalingam, and Makino Mitsunori. "An IoT- Enabled Augmented Reality Framework for Plant Disease Detection." Revue d'Intelligence Artificielle 35, no. 3 (June 30, 2021): 185–92. http://dx.doi.org/10.18280/ria.350301.

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Jayaswal, Ruchi, and Manish Dixit. "A Framework for Anomaly Classification Using Deep Transfer Learning Approach." Revue d'Intelligence Artificielle 35, no. 3 (June 30, 2021): 255–63. http://dx.doi.org/10.18280/ria.350309.

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Over the last few years, surveillance CCTV cameras have rapidly grown to monitor human activities. Suspicious activities like assault, gun violence, kidnapping need to be observed in public places like malls, public roads, colleges, etc. There is a need for such a surveillance system that automatically recognizes human behavior, such as violent and non-violent actions. Action recognition has become an active research topic for researchers within the computer vision field. However, the human behavior recognition community has mainly focused only on regular actions like walking, running, jogging, etc. Though, detecting behavior in anomaly subjects like assault violence, gun violence, or general aggressive behavior has been comparatively less research in these specific events due to a lack of datasets and algorithms. Thus, there is an increasing demand for datasets to develop abnormal behavior algorithms that can classify anomaly actions. In this paper, the novel dataset is proposed named Human Behavior Dataset 2021 (HBD21). There are four categories of videos available in this dataset: Assault violence, Gun violence, Sabotage violence, and Normal events. This proposed dataset contains a total of 456 videos. Each video has the same length of each category. This paper aims to make a robust surveillance system framework with the help of a deep transfer learning approach and proposed a novel hybrid model. In this view, the current research work is categorized into three phases. Firstly, the preprocessing technique is applied to enhance the brightness of videos, and for resizing then, frames are extracted from each video. Secondly, the transfer learning-based Xception model is used to extract relevant features from frames. The third phase is a classification of behaviors in which a modified LSTM technique is applied. The model is trained using LSTM on the HBD21 dataset. Moreover, using proposed methods on the HBD21 dataset, the accuracy is obtained 97.25% overall.

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Gleitsman, Kristin R., and Daniel H. Herschlag. "A kinetic and thermodynamic framework for theAzoarcusgroup I ribozyme reaction." RNA 20, no. 11 (September 22, 2014): 1732–46. http://dx.doi.org/10.1261/rna.044362.114.

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Horne, Avril, Michael Stewardson, John Freebairn, and Thomas A. McMahon. "Using an economic framework to inform management of environmental entitlements." River Research and Applications 26, no. 6 (June 21, 2009): 779–95. http://dx.doi.org/10.1002/rra.1275.

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Brummer, T. J., A. E. Byrom, J. J. Sullivan, and P. E. Hulme. "A Quantitative Framework to Derive Robust Characterization of Hydrological Gradients." River Research and Applications 32, no. 7 (February 5, 2016): 1517–29. http://dx.doi.org/10.1002/rra.3001.

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Call, E. M., and M. L. Hunter. "Evaluating the Framework of a New River Bird Survey Method." River Research and Applications 33, no. 4 (January 9, 2017): 495–504. http://dx.doi.org/10.1002/rra.3119.

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Steinbaugh, Michael J., Lorena Pantano, Rory D. Kirchner, Victor Barrera, Brad A. Chapman, Mary E. Piper, Meeta Mistry, et al. "bcbioRNASeq: R package for bcbio RNA-seq analysis." F1000Research 6 (June 20, 2018): 1976. http://dx.doi.org/10.12688/f1000research.12093.2.

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RNA-seq analysis involves multiple steps, from processing raw sequencing data to identifying, organizing, annotating, and reporting differentially expressed genes. bcbio is an open source, community-maintained framework providing automated and scalable RNA-seq methods for identifying gene abundance counts. We have developed bcbioRNASeq, a Bioconductor package that provides ready-to-render templates, objects and wrapper functions to post-process bcbio RNA sequencing output data. bcbioRNASeq helps automate the generation of high-level RNA-seq reports, facilitating the quality control analyses, identification of differentially expressed genes and functional enrichment analyses.

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Hunka, Frantisek. "Managing Processes in the REA Framework." Journal of Emerging Technologies in Accounting 11, no. 1 (December 1, 2014): 27–39. http://dx.doi.org/10.2308/jeta-51108.

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ABSTRACT The resources, events, agents (REA) ontology provides rules and guidelines for business process modeling utilizing the value-modeling approach. The core element of this approach is an REA model at the business process level. The REA model is usually governed by an information entity, such as a contract or a schedule. Although the REA model provides all necessary entities and relationships for value modeling, its support for information entity value creation, and its flow to the related process that it will govern, deserves deeper elaboration. The aim of this paper is to examine and propose a solution to information entity value creation within the REA model and its transfer to a related business process in compliance with the REA value chain concept. In order to distinguish standard REA business processes from those in which the information entities are created, these processes are called managing processes.

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Tommandru, Suresh, and Domnic Sandanam. "An Automated Framework for Patient Identification and Verification Using Deep Learning." Revue d'Intelligence Artificielle 34, no. 6 (December 31, 2020): 709–19. http://dx.doi.org/10.18280/ria.340605.

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Automated patient identification and verification are very important at a medical emergency and when patients are not carrying his/her identity. It is a risk factor that identifying the correct patient identity for doctors to provide medical treatment. The majority of the identification or verification is being done by wristbands, RFID tags, fingerprint, face detection by using handcraft feature-based face recognition systems. A new framework based on robust deep learning model and contrast enhancement is proposed in this paper. In the proposed work, the light illumination problem has been addressed by the contrast enhancement technique for deep learning models to recognize the face. It is proved that the inclusion of contrast enhancement is improving patient identification and verification. To evaluate the deep learning framework, the proposed deep learning models have been trained on our own dataset and have been tested with a real-time medical providing agency. The experimental results show that the proposed framework exhibits more robust test results with accuracy than existing hand-crafted techniques under the live webcam video capture for the real-time patient detection system.

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Banday, Imran Rashid, Majid Zaman, Syed Mohammad Khurshid Quadri, Sheikh Amir Fayaz, and Muheet Ahmed Butt. "Big Data in Academia: A Proposed Framework for Improving Students Performance." Revue d'Intelligence Artificielle 36, no. 4 (August 31, 2022): 589–95. http://dx.doi.org/10.18280/ria.360411.

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The way people learn has radically changed as a result of information technology. As an informal method of learning, fragmented learning has become a popular way to learn new technology and expertise. Academic organizations generate a large amount of heterogeneous data, and academic leaders want to make the most of it by analyzing the large amount of data in order to make better decisions. The volume isn't the only issue; the organization's data structure (structured, semi structured, and unstructured) adds to the complexity of academic work and decision-making on a daily basis. As big data has become more prevalent in educational settings, new data-driven techniques to enhance informed decision-making and efforts to improve educational efficacy have emerged. Traditional data sources and approaches were previously too expensive to obtain with digital traces of student behaviour, which offer more scalable and finer-grained comprehension and support of learning processes. This study provides a fragmented learning solution for students in a data environment that can suggest subjects to them based on their geographical location, gender, and district of residence, among other factors. This suggested framework is expected to play a key role in directing the development of a society that values lifelong learning.

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Sharma, Rohit, and Nishtha Hooda. "Optimized Ensemble Machine Learning Framework for High Dimensional Imbalanced Bio Assays." Revue d'Intelligence Artificielle 33, no. 5 (November 30, 2019): 387–92. http://dx.doi.org/10.18280/ria.330509.

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White, W. R., and T. L. Crisman. "Headwater Streams of Florida: Types, Distribution and a Framework for Conservation." River Research and Applications 32, no. 3 (October 8, 2014): 452–61. http://dx.doi.org/10.1002/rra.2845.

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Vanwe, Mr Vivek Ajinath. "Robotic Process Automation using Virtual Environment." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 4814–18. http://dx.doi.org/10.22214/ijraset.2022.45056.

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Abstract: Ventures are starting to see a computerized labor force as a feature of their computerized change technique by joining components of RPA, man-made reasoning (AI), and examination to mechanize business processes. While carrying out associations receive the above rewards of effective RPA execution, the RPA item sellers and specialist co-ops likewise go through a steady development cycle to handle specialized hindrances that taking on associations face in mechanizing processes and RPA apparatus mix/communication with all applications/frameworks inside their current circumstance. The RPA market is growing quickly, yet to be maintained, RPA should offer more extensive help for application types, and for both virtual and cloud-based frameworks. As RPA keeps on advancing both in capacity and extension, business use cases are growing past centralized servers, heritage, work areas, and web applications into the domain of remote and virtualized conditions. We are now seeing a solid blend of robotization conditions like actual machines, Windows servers, terminal servers, virtual machines (VMs), virtual work area framework (VDI), and distant work area convention (RDP).

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Steinbaugh, Michael J., Lorena Pantano, Rory D. Kirchner, Victor Barrera, Brad A. Chapman, Mary E. Piper, Meeta Mistry, et al. "bcbioRNASeq: R package for bcbio RNA-seq analysis." F1000Research 6 (November 8, 2017): 1976. http://dx.doi.org/10.12688/f1000research.12093.1.

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RNA-seq analysis involves multiple steps from processing raw sequencing data to identifying, organizing, annotating, and reporting differentially expressed genes. bcbio is an open source, community-maintained framework providing automated and scalable RNA-seq methods for identifying gene abundance counts. We have developed bcbioRNASeq, a Bioconductor package that provides ready-to-render templates and wrapper functions to post-process bcbio output data. bcbioRNASeq automates the generation of high-level RNA-seq reports, including identification of differentially expressed genes, functional enrichment analysis and quality control analysis.

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Satish Babu, J., and G. Krishna Mohan. "Design of an Intelligent Hierarchical Level Structural Framework for Cyber-Physical Systems." Revue d'Intelligence Artificielle 36, no. 1 (February 28, 2022): 93–103. http://dx.doi.org/10.18280/ria.360111.

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Fahlgren, N., C. M. Sullivan, K. D. Kasschau, E. J. Chapman, J. S. Cumbie, T. A. Montgomery, S. D. Gilbert, et al. "Computational and analytical framework for small RNA profiling by high-throughput sequencing." RNA 15, no. 5 (March 24, 2009): 992–1002. http://dx.doi.org/10.1261/rna.1473809.

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Zalewski, Pawel, Letizia Marchegiani, Atis Elsts, Robert Piechocki, Ian Craddock, and Xenofon Fafoutis. "From Bits of Data to Bits of Knowledge—An On-Board Classification Framework for Wearable Sensing Systems." Sensors 20, no. 6 (March 16, 2020): 1655. http://dx.doi.org/10.3390/s20061655.

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Wearable systems constitute a promising solution to the emerging challenges of healthcare provision, feeding machine learning frameworks with necessary data. In practice, however, raw data collection is expensive in terms of energy, and therefore imposes a significant maintenance burden to the user, which in turn results in poor user experience, as well as significant data loss due to improper battery maintenance. In this paper, we propose a framework for on-board activity classification targeting severely energy-constrained wearable systems. The proposed framework leverages embedded classifiers to activate power-hungry sensing elements only when they are useful, and to distil the raw data into knowledge that is eventually transmitted over the air. We implement the proposed framework on a prototype wearable system and demonstrate that it can decrease the energy requirements by one order of magnitude, yielding high classification accuracy that is reduced by approximately 5%, as compared to a cloud-based reference system.

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Barrangou, Rodolphe. "Finding SECURE Ground: USDA Edits the Biotechnology Regulatory Framework." CRISPR Journal 3, no. 3 (June 1, 2020): 136–37. http://dx.doi.org/10.1089/crispr.2020.29096.rba.

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Battula, Bhanu Prakash, and Duraisamy Balaganesh. "Medical Image Data Classification Using Deep Learning Based Hybrid Model with CNN and Encoder." Revue d'Intelligence Artificielle 34, no. 5 (November 20, 2020): 645–52. http://dx.doi.org/10.18280/ria.340516.

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Healthcare sector is one of the prime and different from other trade. Society expects high priority and highest level of services and care irrespective of money. Presently medical field suffers from accurate diagnosis of diseases and it create huge loss to society. The prime factor for this is due to the nature of medical data, it is a combination of all varieties of data. Medical image analysis is a key method of Computer-Aided Diagnosis (CAD) frameworks. Customary strategies depend predominantly on the shape, shading, and additionally surface highlights just as their mixes, a large portion of which are issue explicit and have demonstrated to be integral in medical images, which prompts a framework that does not have the capacity to make portrayals of significant level issue area ideas and that has poor model speculation capacity. In this paper we are attempting a medical image data classification technique using hybrid deep learning technique based on Convolutional Neural Network (CNN) and encodes. What's more, we assess the proposed approach on two benchmark clinical picture datasets: HIS2828 and ISIC2017. The proposed algorithm is applied on the considered 2 datasets for performing data classification using deep learning based CNN and encoders. The proposed model is compared with the traditional methods and the results show that proposed model classification accuracy is better than the existing models.

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Hu, Chi, Li-li Xu, Jing Jia, Kan Li, Hui Xu, and Bin Di. "Pharmaceutical cocrystals prepared with a cucurbit[8]uril framework for intestine-targeted drug delivery." SDRP Journal of Food Science & Technology 4, no. 3 (2019): 696–706. http://dx.doi.org/10.25177/jfst.4.3.ra.509.

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Tuan Anh, Le, and Dao Thi Thanh Binh. "Portfolio optimization under mean-CVaR simulation with copulas on the Vietnamese stock exchange." Investment Management and Financial Innovations 18, no. 2 (June 11, 2021): 273–86. http://dx.doi.org/10.21511/imfi.18(2).2021.22.

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This paper studies how to construct and compare various optimal portfolio frameworks for investors in the context of the Vietnamese stock market. The aim of the study is to help investors to find solutions for constructing an optimal portfolio strategy using modern investment frameworks in the Vietnamese stock market. The study contains a census of the top 43 companies listed on the Ho Chi Minh stock exchange (HOSE) over the ten-year period from July 2010 to January 2021. Optimal portfolios are constructed using Mean-Variance Framework, Mean-CVaR Framework under different copula simulations. Two-thirds of the data from 26/03/2014 to 27/1/2021 consists of the data of Vietnamese stocks during the COVID-19 recession, which caused depression globally; however, the results obtained during this period still provide a consistent outcome with the results for other periods. Furthermore, by randomly attempting different stocks in the research sample, the results also perform the same outcome as previous analyses. At about the same CvaR level of about 2.1%, for example, the Gaussian copula portfolio has daily Mean Return of 0.121%, the t copula portfolio has 0.12% Mean Return, while Mean-CvaR with the Raw Return portfolio has a lower Return at 0.103%, and the last portfolio of Mean-Variance with Raw Return has 0.102% Mean Return. Empirical results for all 10 portfolio levels showed that CVaR copula simulations significantly outperform the historical Mean-CVaR framework and Mean-Variance framework in the context of the Vietnamese stock exchange.

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Sawhney, Rahul, Shilpi Sharma, Purvit Vashishtha, Arun Kothari, Vibhu Gupta, and Tanupriya Choudhury. "An Efficient Deep Neural Framework for Nucleus Semantic Segmentation with Enhanced U-Net." Revue d'Intelligence Artificielle 36, no. 3 (June 30, 2022): 475–81. http://dx.doi.org/10.18280/ria.360316.

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With the advancement of medicine, there is a terrific requirement to process an increasing number of medical imaging where image segmentation comes into play. Single cell division is generally among the first as well as most essential tasks in image-based cell analysis. The identification of the nuclei allows pathologists to determine each cell in the sample, and by measuring how cells respond to various treatments, they can comprehend the fundamental biological processes in work. In this paper we gathered a deep learning network which detects and parts the cell microscopy image. Highly advanced performance is achieved in image segmentation tasks through deep learning-based techniques. These procedures are complex and need the support of compelling computational resources. This paper emphasizes the basic principles of the methods used to segment an image. We have implemented U-Net for the semantic segmentation of nucleus. A successful implementation will aid researchers immensely in their fight to find pharmaceutical solutions to medical crises while saving both valuable research time and funding.

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Halperin, Eran, and Uri Zwick. "A unified framework for obtaining improved approximation algorithms for maximum graph bisection problems." Random Structures and Algorithms 20, no. 3 (April 4, 2002): 382–402. http://dx.doi.org/10.1002/rsa.10035.

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43

Wallis, C., I. Maddock, F. Visser, and M. Acreman. "A framework for evaluating the spatial configuration and temporal dynamics of hydraulic patches." River Research and Applications 28, no. 5 (October 29, 2010): 585–93. http://dx.doi.org/10.1002/rra.1468.

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44

Podolak, C. J. P. "A VISUAL FRAMEWORK FOR DISPLAYING, COMMUNICATING AND COORDINATING A RIVER RESTORATION MONITORING PROJECT." River Research and Applications 30, no. 4 (March 27, 2013): 527–35. http://dx.doi.org/10.1002/rra.2651.

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Wright, K. A., D. H. Goodman, N. A. Som, J. Alvarez, A. Martin, and T. B. Hardy. "Improving Hydrodynamic Modelling: an Analytical Framework for Assessment of Two-Dimensional Hydrodynamic Models." River Research and Applications 33, no. 1 (August 2, 2016): 170–81. http://dx.doi.org/10.1002/rra.3067.

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Siljee, Johanneke, Ivor Bosloper, and Jos Nijhuis. "A Quality Framework for the Storage and Retrieval of Context." Revue d'intelligence artificielle 19, no. 3 (June 1, 2005): 499–517. http://dx.doi.org/10.3166/ria.19.499-517.

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Venkatraman, Deepa, and Radha Narayanan. "Integrated Framework for Intrusion Detection Through Adversarial Sampling and Enhanced Deep Correlated Hierarchical Network." Revue d'Intelligence Artificielle 36, no. 4 (August 31, 2022): 597–605. http://dx.doi.org/10.18280/ria.360412.

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Intrusion Detection Systems (IDSs) play a critical role in detecting malicious assaults and threats in the network system. This research work proposed a network intrusion detection technique, which combines an Adversarial Sampling and Enhanced Deep Correlated Hierarchical Network for IDS. Initially, the proposed Enhanced Generative Adversarial Networks (EGAN) method is used to raise the minority sample. A balanced dataset can be created in this way, allowing the model to completely learn the properties of minority samples while also drastically minimizing the model training time. Then, create an Enhanced Deep Correlated Hierarchical Network model by using a Bi-Directional Long Short-Term Memory (BiLSTM) to collect temporal characteristics and Cross-correlated Convolution Neural Network (CCNN) to retrieve spatial characteristics. The softmax classifier at the end of BiLSTM is used to classify intrusion data. The traditional NSL-KDD dataset is utilized for the experimentation of the proposed model.

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Fleit, Gábor, Sándor Baranya, Tamás Krámer, Hans Bihs, and János Józsa. "A practical framework to assess the hydrodynamic impact of ship waves on river banks." River Research and Applications 35, no. 9 (August 16, 2019): 1428–42. http://dx.doi.org/10.1002/rra.3522.

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Makris, Evangelos, Angelos Kolaitis, Christos Andrikos, Vrettos Moulos, Panayiotis Tsanakas, and Christos Pavlatos. "Knotify+: Toward the Prediction of RNA H-Type Pseudoknots, Including Bulges and Internal Loops." Biomolecules 13, no. 2 (February 6, 2023): 308. http://dx.doi.org/10.3390/biom13020308.

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The accurate “base pairing” in RNA molecules, which leads to the prediction of RNA secondary structures, is crucial in order to explain unknown biological operations. Recently, COVID-19, a widespread disease, has caused many deaths, affecting humanity in an unprecedented way. SARS-CoV-2, a single-stranded RNA virus, has shown the significance of analyzing these molecules and their structures. This paper aims to create a pioneering framework in the direction of predicting specific RNA structures, leveraging syntactic pattern recognition. The proposed framework, Knotify+, addresses the problem of predicting H-type pseudoknots, including bulges and internal loops, by featuring the power of context-free grammar (CFG). We combine the grammar’s advantages with maximum base pairing and minimum free energy to tackle this ambiguous task in a performant way. Specifically, our proposed methodology, Knotify+, outperforms state-of-the-art frameworks with regards to its accuracy in core stems prediction. Additionally, it performs more accurately in small sequences and presents a comparable accuracy rate in larger ones, while it requires a smaller execution time compared to well-known platforms. The Knotify+ source code and implementation details are available as a public repository on GitHub.

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Hawkins, Les, Hien Nguyen, and Linda Smith Griffin. "RDA and Serials: Transitioning to RDA Within a MARC 21 Framework." Serials Librarian 66, no. 1-4 (May 2014): 7–19. http://dx.doi.org/10.1080/0361526x.2014.879015.

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