Recursive Process Research Articles

A precise time-domain expanding boundary element method based on enthalpy formulation for non-isothermal phase change problems

A precise time-domain expanding boundary element method (PTEBEM) based on the enthalpy formulation is developed for solving non-isothermal phase change problems in this paper. Assume that temperature and enthalpy are independent field variables, and expand all variables over a time period. This allows the variation of variables to be described more accurately and the nonlinear governing equation to transform into a linear recursive form. Subsequently the boundary element method (BEM) is employed to solve recursive problems based on a fixed grid, and the radial integration method (RIM) is used to convert the resulting domain integrals into equivalent boundary integrals. In the recursive solution process, temperature and enthalpy are connected through the relationship between the two. Finally, a self-adaptive computation criterion is proposed to ensure sufficient computational accuracy. The phase change interfaces can be given by interpolation of the temperature field. No additional nonlinear iterations are required throughout the simulation. The calculation results are satisfactory and prove the accuracy and robustness of the proposed method.

Holistic Graph Neural Networks based on a global-based attention mechanism

Graph Neural Networks (GNNs) have become increasingly popular due to their impressive capacity to perform classification or regression on high-dimensional graph-structured data. However, standard message passing GNNs typically define nodes embeddings through a recursive neighborhood aggregation process which updates the representation vector of each node with reference to its neighborhood only. In this paper, we propose the Holistic Graph Neural Network (HGNN), a two-fold architecture which introduces a global-based attention mechanism for learning and generating nodes embeddings. The global features we inject, summarize the overall global behavior of the graph in addition to the local semantic and structural information. These global features will make each individual node aware of the global behavior of the graph outside the borders of the local neighborhood. We further propose a variant of the HGNN, we call HGNNα based on a more sophisticated hierarchical global-feature extraction mechanism. We explore diverse global pooling strategies to derive highly expressive global features. We also show that state-of-the-art GNNs can significantly benefit from the addition of the global-based attention introduced. Furthermore, we prove the efficiency of the HGNN model theoretically and adapt it to support graph data which carries edge attributes for example the Molecular datasets from the Open Graph Benchmark. Experiments on Bioinformatics datasets, Social Networks and Molecular datasets demonstrate that our proposed models achieve much better performance than state-of-the-art methods, for instance we achieved improvements of +11% on COLLAB and +13% on IMDB-BINARY datasets.

RECURSIVITY IN DIGITAL COLLABORATION PLATFORMS

This article investigates conditions of the reproduction of the techno-social component of recursivity with collective collaboration in the development and use of network information technologies. For this purpose, this article uses two cases that give a prototype of the institution of “governability without interference.” In the concept of the “recursive public” that underlies the first case, accessibility and changeability of the subject of its activity are important. Such conditions take place in the social environment of programmers who create and develop freely available software. In its scheme, passive and active recursive processes are distinguished. The first case is “consumption” of software by external stakeholders, while the second is about active participation of community members in its creation. Close to this concept are the ideas of “recursive organization,” “recursive citizenship,” and “recursive democracy” in a networked society. These are revealed in the second case using the example of the Decidim project. A distinctive feature of the digital political network that is being formed is the desire to meet basic requirements of radical democracy. It is shown that this orientation is largely associated with the ideas and experience of the social movement 15-M. Project participants, collaborating with each other, can consistently control the functioning of its digital platform and data, as well as modify other elements, including internal code and interface part. In the activities of the community that has developed in the project, the recursive process of creating organizational structures and their influence on the formation of the “action space” is highlighted. As in the first case, we can specify active and passive forms of recursion in the project. The understanding of technopolitical democratization, including the recursion mechanism, is disclosed. The process is presented in the form of an ontology schema. The results obtained concretize the understanding of recursivity and its impact on the development of digital collaboration platforms.

An LC-MS/MS assay for enzymatic characterization of methylthioalkylmalate synthase (MAMS) involved in glucosinolate biosynthesis.

Brassicaceae are blessed with specialized metabolites called glucosinolates (GSLs), which along with their degradation products, are beneficial in agriculture and human health. To date, more than 130 GSL structures have been identified, mostly derived from the amino acid methionine. The biosynthesis of methionine-derived aliphatic GSLs starts with a side-chain elongation step involving a recursive three-step cyclic process that incorporates a new methylene group into the 2-oxo acid to form a series of elongated 2-oxo acids. Methylthioalkylmalate synthase (MAMS) catalyzes the first committed step in the side-chain elongation of methionine-derived GSLs. The substrate specificity of MAMS with different 2-oxo acids determines whether reaction products of a given cycle enter for an additional round of chain elongation or enter into core GSLs structure formation. Multiple MAMS encoding genes are present in the Brassicaceae species and are known to play a central role in shaping the diverse profile of aliphatic GSLs. We recently established a highly sensitive LC-MS/MS-based methodology that quantifies the MAMS activity by estimating the amount of the next intermediate of the pathway, the 2-malate derivatives. Overall, this chapter describes the protocol for the expression, purification, and steady-state kinetic analysis of the recombinant MAMS protein.

RapidPlan hippocampal sparing whole brain model version 2-how far can we reduce the dose?

Whole-brain radiotherapy has been the standard palliative treatment for patients with brain metastases due to its effectiveness, availability, and ease of administration. Recent clinical trials have shown that limiting radiation dose to the hippocampus is associated with decreased cognitive toxicity. In this study, we updated an existing Knowledge Based Planning model to further reduce dose to the hippocampus and improve other dosimetric plan quality characteristics. Forty-two clinical cases were contoured according to guidelines. A new dosimetric scorecard was created as an objective measure for plan quality. The new Hippocampal Sparing Whole Brain Version 2 (HSWBv2) model adopted a complex recursive training process and was validated with five additional cases. HSWBv2 treatment plans were generated on the Varian HalcyonTM and TrueBeamTM systems and compared against plans generated from the existing (HSWBv1) model released in 2016. On the HalcyonTM platform, 42 cases were re-planned. Hippocampal D100% from HSWBv2 and HSWBv1 models had an average dose of 5.75 Gy and 6.46 Gy, respectively (p < 0.001). HSWBv2 model also achieved a hippocampal Dmean of 7.49 Gy, vs 8.10 Gy in HSWBv1 model (p < 0.001). Hippocampal D0.03CC from HSWBv2 model was 9.86 Gy, in contrast to 10.57 Gy in HSWBv1 (p < 0.001). For PTV_3000, D98% and D2% from HSWBv2 model were 28.27 Gy and 31.81 Gy, respectively, compared to 28.08 Gy (p = 0.020) and 32.66 Gy from HSWBv1 (p < 0.001). Among several other dosimetric quality improvements, there was a significant reduction in PTV_3000 V105% from 35.35% (HSWBv1) to 6.44% (HSWBv2) (p < 0.001). On 5 additional validation cases, dosimetric improvements were also observed on TrueBeamTM. In comparison to published data, the HSWBv2 model achieved higher quality hippocampal avoidance whole brain radiation therapy treatment plans through further reductions in hippocampal dose while improving target coverage and dose conformity/homogeneity. HSWBv2 model is shared publicly.

A Panoramic Synthetic Aperture Radar

This paper proposes a new synthetic aperture radar (SAR), named as panoramic SAR, based on a combination of linear and rotational SARs, by which a large 360-degree panoramic view of the observed scene can be reconstructed. Firstly, the system geometry and its imaging process based on the back-projection algorithm (BPA) are presented. The combined movement constitutes a two-dimensional synthetic aperture and thus higher imaging resolutions can be obtained. The corresponding resolution analysis and the sampling criteria are discussed accordingly. Then, a novel dynamic piecewise compensation (DPC) algorithm, a recursive imaging process, is proposed to reduce the processing complexity significantly. The imaging implementation and the complexity are also studied respectively. Finally, a prototype of panoramic SAR is built based on an frequency modulated continuous wave (FMCW) radar and a moving platform, and the simulation and experimental results are provided to validate the proposed panoramic SAR principle and the DPC algorithm.

Fabrication of Al-Zr -Mg-Ni matrix composite with TiC reinforcement by multi-pass recursive friction stir processing and its characterization

A titanium carbide (TiC)-reinforced Al-Zr-Mg-Ni matrix was utilized for conjunction with multi-pass friction stir processing to create the composite. Reverse engineering and geometric reconstruction techniques were employed to produce a spherical channel at a prearranged depth from the area using wire-EDM and filled with reinforcement elements to prevent the spread of reinforcement particles during fabrication. Multiple recursive passes during friction stir processing were studied to determine the effects on particle distribution and crystallographic texture, as well as surface roughness and mechanical properties. The results showed that increasing the number of passes significantly improved particle distribution and reduced defects such as particle agglomeration and voids. After six passes, the grain size was condensed to 2.40 nm while the dislocation density increased due to the uniform distribution of TiC particles. Grain refinement led to a significant increase in the texture index, which resulted in a surface roughness of 153.593 nm. After six passes, the composite's hardness and tensile strength had increased due to the even distribution of TiC particles and various strengthening mechanisms.

Recursive Gaussian Process over graphs for Integrating Multi-timescale Measurements in Low-Observable Distribution Systems

The transition to a smarter grid is empowered by enhanced sensor deployments and smart metering infrastructure in the distribution system. Measurements from these sensors and meters can be used for many applications, including distribution system state estimation (DSSE). However, these measurements are typically sampled at different rates and could be intermittent due to losses during the aggregation process. These multi time-scale measurements should be reconciled in real-time to perform accurate grid monitoring. This paper tackles this problem by formulating a recursive multi-task Gaussian process (RGP-G) approach that sequentially aggregates sensor measurements. Specifically, we formulate a recursive multi-task GP with and without network connectivity information to reconcile the multi time-scale measurements in distribution systems. The proposed framework is capable of aggregating the multi-time scale measurements batch-wise or in real-time. Following the aggregation of the multi time-scale measurements, the spatial states of the consistent time-series are estimated using matrix completion based DSSE approach. Simulation results on IEEE 37 and IEEE 123 bus test systems illustrate the efficiency of the proposed methods from the standpoint of both multi time-scale data aggregation and DSSE.

A Novel CFFBP Algorithm With Noninterpolation Image Merging for Bistatic Forward-Looking SAR Focusing

Fast factorized back-projection (FFBP) has significant advantages for bistatic forward-looking synthetic aperture radar (BFSAR) imaging with arbitrary geometry and complex configuration. Conventional FFBP is generally based on the polar coordinate system (PCS) for recursive processing; however, it involves huge interpolations and causes computational inefficiency. In this article, a novel FFBP is developed for BFSAR focusing based on the Cartesian coordinate system (CCS), which is referred to as Cartesian fast factorized back-projection (CFFBP). In the new algorithm, a two-step spectrum correction is designed to avoid spectrum aliasing, and the Nyquist sampling requirement (NSR) for the BFSAR image spectrum can be decreased significantly. With low NSR in CCS, subimage merging can be implemented with noninterpolation processing, so that the proposed algorithm can achieve high performance in both accuracy and efficiency. Moreover, the practical problem of motion error is particularly considered in algorithm development, and well-adapted data-driven motion compensation (DDMC) is integrated with CFFBP based on which a new Cartesian fast time-domain (CFTD) processing framework is developed for BFSAR application. Promising results from both simulation and raw data experiments are provided and analyzed to validate the high performance of the proposed algorithm.

Reconsideration of the recursive processing model in the problem formulation and solving processes

In this study, we identified human cognitive processing in the problem formulation and solving process. In previous studies, this was a unidirectional process. Herein, we propose a model to realistically explain the recursion in the process of problem formulation and solving. Applying this model to real cases is a topic for future research.

Youth media repertoires: Age ranges, context factors and privacy management

The overall aim of the study is to trace the interaction between the composition of the media repertoire and the everyday world of adolescents, also looking at privacy management in the course of acquiring digital communication media as part of the media repertoire. In order to do justice to this complexity, young people were not considered as a uniform demographic group, but were divided into three stages. Through this differentiation, a recursive process is to be worked out that makes it possible to also include contextual influencing factors such as peer group, family environment etc. and to expand previous findings on the media repertoire of young people. As a result of this approach, a multi-stage development process was elaborated as well as the privacy management of digital communication media of young people.

Stress and Adjustment during the COVID-19 Pandemic: A Qualitative Study on the Lived Experience of Canadian Older Adults

In response to the COVID-19 pandemic, social distancing measures were put into place to flatten the pandemic curve. It was projected that older adults were at increased risk for poor psychological and health outcomes resulting from increased social isolation and loneliness. However, little research has supported this projection among community-dwelling older adults. While a growing body of research has examined the impact of the COVID-19 pandemic on older adults, there is a paucity of qualitative research that captures the lived experience of community-dwelling older adults in Canada. The current study aimed to better understand the lived experience of community-dwelling older adults during the first six months of the pandemic in Ontario, Canada. Semi-structured one-on-one interviews were conducted with independent-living older adults aged 65 years and older. A total of 22 interviews were analyzed using inductive thematic analysis. Following a recursive process, two overarching themes were identified: perceived threat and challenges of the pandemic, and coping with the pandemic. Specifically, participants reflected on the threat of contracting the virus and challenges associated with living arrangements, social isolation, and financial insecurity. Participants shared their coping strategies to maintain health and wellbeing, including behavioral strategies, emotion-focused strategies, and social support. Overall, this research highlights resilience among older adults during the first six months of the pandemic.

Reliable Template Matching for Image Detection in Vision Sensor Systems.

Template matching is a simple image detection algorithm that can easily detect different types of objects just by changing the template without tedious training procedures. Despite these advantages, template matching is not currently widely used. This is because traditional template matching is not very reliable for images that differ from the template. The reliability of template matching can be improved by using additional information (depths for the template) available from the vision sensor system. Methods of obtaining the depth of a template using stereo vision or a few (two or more) template images or a short template video via mono vision are well known in the vision literature and have been commercialized. In this strategy, this paper proposes a template matching vision sensor system that can easily detect various types of objects without prior training. To this end, by using the additional information provided by the vision sensor system, we study a method to increase the reliability of template matching, even when there is a difference in the 3D direction and size between the template and the image. Template images obtained through the vision sensor provide a depth template. Using this depth template, it is possible to predict the change of the image according to the difference in the 3D direction and the size of the object. Using the predicted changes in these images, the template is calibrated close to the given image, and then template matching is performed. For ease of use, the algorithm is proposed as a closed form solution that avoids tedious recursion or training processes. For wider application and more accurate results, the proposed method considers the 3D direction and size difference in the perspective projection model and the general 3D rotation model.

A Gaussian Process Iterative Learning Control for Aircraft Trajectory Tracking

This article proposes a recursive Gaussian process regression with a joint optimization-based iterative learning control algorithm to estimate and predict disturbances and model uncertainties affecting a flight. The algorithm proactively compensates for the predicted disturbances, improving precision in aircraft trajectory tracking. Higher precision in trajectory tracking implies an improvement of the aircraft trajectory predictability and, therefore, of the air traffic management system efficiency. Airlines can also benefit from this higher predictability by reducing the number of alterations when following their designed trajectories, which entails a reduction of costs and emissions. The iterative learning control algorithm is divided into two steps: first, a recursive Gaussian process regression estimates and predicts perturbations and model errors with no need for prior knowledge about their dynamics and with low computational cost, and second, this information is used to update the control inputs so that the subsequent aircraft intending to fly the same planned trajectory will follow it with greater precision than the previous ones. This method is tested on a simulated commercial aircraft performing a continuous climb operation and compared to an iterative learning algorithm using a Kalman filter estimator in a similar scenario. The results show that the proposed approach provides 62 and 42% precision improvement in tracking the desired trajectory, as compared to the Kalman filter approach, in two experiments, where no prior knowledge of the unmodeled dynamics was available, also achieving it in less iterations.

Towards Assessment as Learning: Findings from online courses for secondary school teachers

Today's school has to deal with several critical issues related to assessment, such as the excessive focus on grades and the lack of a shared understanding of assessment methods and aims. Assessment becomes formative when the evidence gathered is used to adapt teaching practice to pupils' learning needs. Teachers should move towards Assessment as Learning, characterised by appropriate tasks, development of evaluative competence, and involvement of students in feedback processes. This contribution presents two online research-training courses on assessment in secondary schools. They involved 240 secondary school teachers and consisted of a training and a workshop part. Courses were mainly based on teacher involvement, allowing for recursive feedback processes. We analysed data from an entry questionnaire, a One Minute Paper submitted during the course, and the workshop. The data analysis highlights the main difficulties related to assessment, suggestions, and reflections of the participants. We noticed the importance of workshop and group work to reflect on the transformation of practices. Finally, the interaction with the teachers was indeed valuable in understanding the sustainability of the proposal and refining it.

Everybody Does It: The Pragmatics and Perceptions of International Chinese Graduate Students and their American Peers Regarding Gossip

Integrating natural observation, interviews, and quantitative analysis, we used a mixed design to compare the socio-linguistic judgments of international Chinese students at a private University on the East Coast of the United States (US) with those of their native English-speaking peers regarding a critical incident involving gossip. Ninety-two participants evaluated alternative sociolinguistic strategies offered in addressing the incident on semantic differential scales. Judgments by each group regarding four alternative responses were surveyed and compared. Twenty participants, ten from each group, participated in semi-structured interviews. Themes were developed through a recursive process: interpretations were validated by a bilingual bicultural expert. Several distinctions in judgments emerged. The most preferred alternative to dealing with a group gossiping about a friend for Americans was to say honestly that it made them uncomfortable while Chinese participants preferred requesting a change in topic. Such contrasts were found to be representative of underlying sociocultural values for each group. Intercultural pragmatic distinctions such as these could lead to pragmatic failure and have the potential to interfere with the development of intercultural friendship among the members of the two groups. Implications for pedagogy and developing cross-cultural insight are offered.

Abolish! Against the Use of Risk Assessment Algorithms at Sentencing in the US Criminal Justice System

In this article, I show why it is necessary to abolish the use of predictive algorithms in the US criminal justice system at sentencing. After presenting the functioning of these algorithms in their context of emergence, I offer three arguments to demonstrate why their abolition is imperative. First, I show that sentencing based on predictive algorithms induces a process of rewriting the temporality of the judged individual, flattening their life into a present inescapably doomed by its past. Second, I demonstrate that recursive processes, comprising predictive algorithms and the decisions based on their predictions, systematically suppress outliers and progressively transform reality to match predictions. In my third and final argument, I show that decisions made on the basis of predictive algorithms actively perform a biopolitical understanding of justice as management and modulation of risks. In such a framework, justice becomes a means to maintain a perverse social homeostasis that systematically exposes disenfranchised Black and Brown populations to risk.

Children’s Views on Children’s Rights: A Systematic Literature Review

Abstract Children’s rights are set out in the United Nations Convention on the Rights of the Child. This systematic literature review aimed to investigate children’s views of children’s rights, at a broad level. Nine papers were included, from a range of countries and contexts. They all accessed the views of children and young people (aged up to 18 years). A content analysis was carried out using a recursive process of hybrid aggregative-configurative synthesis, and themes within children’s views and factors that may affect these were identified. These were ‘awareness of rights’, ‘value placed on (importance of) rights’, ‘impact of having/not having rights fulfilled’, ‘realisation and respect of rights’, ‘equality of rights’, ‘identifying and categorising of rights’, and ‘factors that may affect children’s views’. These were developed into a progression of rights realisation and implications for practice and further research were considered.

The Patch: An Artful Syn(aes)thetic Mapping of Linguistic Data through Collaborative Digital / Analogue Literacy Processes

The Patch workshop explores creative / critical analyses that can map the collectively relevant topoi of semiosis in linguistic texts according to the three ecologies as articulated by Félix Guattari. As creative pedagogues both in service and critical of creative economics, we valourize a generative practice, one that results in successive creative readings, writings, visualizations, sonifications and audiovisual artifacts. The Patch is a human-computer procedural algorithm, engaging a series of recursive and recombinant processes that utilize several software programs, collaborative writing and performance practices to bridge analogue and digital literacies. A total of 80 teacher education students, graduate students and faculty, working with a single input text, provided the data reported in this paper.

Legitimacy Processes and Trajectories of Co-Prosumption Services: Insights from Coworking Spaces

Our study applies legitimacy theorizing to service research, zooming in on co-prosumption service business models, which reside on significant direct contacts among provider-actors and customers as well as fellow customers in the service space. Our findings are based on a longitudinal flexible pattern matching method on 17 coworking spaces. The service cocreation nuances the double role of customers as evaluators and cocreators of legitimacy. This is because customers can have immediate perceptions of the actions and values of the services in their legitimacy evaluation while cocreating the service. Legitimacy shaped via social and recursive processes occurs in three stages: provisional, calibrated, and affirmed legitimacy. Findings inform four trajectory mechanisms of value-in-use pattern provenance, emergent Business Model development adaptive to the spatial context and loyal customers, visible trances as well as inside-out and outside-in identification processes. Further, the processes in the micro-ecosystem of an interstitial service space can develop a superordinate logic which overlays the potentially present coopetive and heterogenous institutional logics and interests of service customers.