Recursive Process Research Articles

The Language Conceptual Formation to Inspire Intelligent Systems

The semantic web invests in systems that work collaboratively. In this article we show that the collaborative way is not enough, because the system must ‘understand’ the data resources that are provided to it, to organize them in the direction indicated by the system’s core, the algorithm. In order for intelligent systems to imitate human cognition, in addition to technical skills to model algorithms, we show that the specialist needs a good knowledge of the principles that explain how human language constructs concepts. The content of this article focuses on the principles of the conceptual formation of language, pointing to aspects related to the environment, to logical reasoning and to the recursive process. We used the strategy of superimposing the dynamics of human cognition and intelligent systems to open new frontiers regarding the formation of concepts by human cognition. The dynamic aspect of the recursion of the human linguistic process integrates visual, auditory, tactile input stimuli, among others, to the central nervous system, where meaning is constructed. We conclude that the human linguistic process involves axiomatic (contextual/biological) and logical principles, and that the dynamics of the relationship between them takes place through recursive structures, which guarantee the construction of meanings through long-range correlation under scale invariance. Recursion and cognition are, therefore, interdependent elements of the linguistic process, making it a set of sui generis structures that evidence that the essence of language, whether natural or artificial, is a form and not a substance.

Effects of Online Collaborative Writing via Google Docs on Learners’ Writing Performance and Interaction: A Case Study of Asian EFL Learners

This study investigated the effects of online collaborative writing and writing contributions in Google Docs of 35 Asian EFL university learners in a composition course. Data were collected from students’ pre- and post-test writing, two extended online collaborative tasks: writing descriptive and argumentative essays, and student reflections. Students’ writing performances were assessed using Jacobs et al.’s (1981) composition analytic scoring rubric. Paired sample t-test analysis showed that academic writing performance significantly increased after participating in two extended collaborative writing tasks. Learners developed more extended written texts and texts were more accurate in the post-test writing. Furthermore, Pearson correlation coefficient (Pearson’s r) analysis showed that learners’ text contributions, their use of writing change functions and language functions during the collaborative process was positively correlated with their post-writing performance. This study supports the notion of writing as an exploratory and recursive process, but not linear.

Process Metaphysics and Mutative Life: Sketches of Lived Time

Process Metaphysics and Mutative Life: Sketches of Lived Time

Highly accurate oil production forecasting under adjustable policy by a physical approximation network

Petroleum is widely used as one of the important energy sources. Engineers need to continuously adjust strategies to maximize oil recovery during development. Then strategy adjustment requires a lot of cost, so it is necessary to select the optimal strategy through production forecasting. In this paper, a self-defined Double-channel Heterogeneous Dynamical Graph network (DHDG) is proposed deal with this problem. It can receive different development strategies predict future production indices base on that. Particularly, the concept of graph network is used for the first time to simulate the well pattern and predict production of all wells in the block at same time. At the same time, in order to fully fit the seepage process between wells, we complicate the edge features of the graph, so that it has enough capacity to express the state information of the formation between wells. Finally, we amplify the features of network output through engineering calculations, which further ensured the integrity of the information transmitted in the recursive process. The example results in two benchmark reservoirs show that it is accurate enough to predict oil production rate in future 600 days and more accurate than LSTM networks.

High-reflective surfaces shape measurement technology based on adaptive fringe projection

Fringe projection profilometry (FPP) is limited by the pixel depth of the camera and cannot correctly reconstruct complex objects with highly reflective surfaces. In this paper, we proposed an improved adaptive fringe technique to avoid image saturation. This method only needs to project a uniform gray level image and a set of horizontal and vertical phase-shifting fringe patterns to generate adaptive fringes. The gray level of saturated regions can be fitted by the uniform image and the coordinates of the areas are obtained by calculating the horizontal and vertical fringes. To avoid the process of recursion, we introduce the concept of the grayscale correction function. Furthermore, a modified mapping formula is adopted and refined, which enhances the mapping effect and reconstruction accuracy. Compared with the original adaptive fringe projection (AFP), the modified method further decreases the number of projected fringe patterns. We analyzed the error between the fitted value and the standard value to evaluate the performance of the method. Experimental results demonstrated that the proposed method maintains the accuracy of reconstruction while improving time efficiency compared to other high dynamic range (HDR) measurement methods. • The contribution of different fringe projection technology with highly reflective objects is investigated. • A novel adaptive fringe is proposed to measure the depth information of HDR objects. • An idea is proposed for the improvement of adaptive fringe projection.

Guiding Students towards an Understanding of Climate Change through a Teaching–Learning Sequence

In this paper, we put forward a proposal for the design and the evaluation of teaching–learning sequences (TLSs) on the greenhouse effect (GHE), relying on the educational reconstruction model (MER). The first design, which starts from a critical analysis of textbook treatments of the GHE, is followed by a cyclic, recursive process, which consists of theoretical reflection, conceptual analysis, design, and test of a sequence. At each iteration, the analysis of the students’ learning progression provided relevant information for addressing the persistent hurdles and misunderstandings that affect it. Our findings show how design choices can support the learning of the GHE, leading to the formulation of design principles that help foster understanding. The iterative approach strongly improved the design and evaluation and allowed for a significant refinement of the TLSs. The implementation and evaluation process, which went on from 2017 to 2021, involved undergraduate students attending a course on “experimental physics laboratory” at the University of Trento in those years. The results indicate that, in the end, students can reach an effective understanding of the physical grounds of the GHE.

Minimal-Approximation-Based Adaptive Event-Triggered Control of Switched Nonlinear Systems with Unknown Control Direction

In this paper, the adaptive neural network event-triggered tracking problem is investigated for a class of uncertain switched nonlinear systems with unknown control direction and average dwell time switching. To reduce the communication network traffic, an event-triggering mechanism based on the tracking error is explored in the controller-to-actuator channel. Additionally, the minimal approximation technology, which designs virtual control laws as the unavailable intermediate signals, is introduced to reduce the difficulty of the controller design process. Compared with the existing adaptive backstepping designs using the filtering technology, the virtual controllers are recursed into a lumped nonlinear function to settle the explosion of complexity, and one neural network is employed in the recursive process. Meanwhile, a boundedness lemma on Nussbaum function is given to address the unknown control direction under the minimal approximation design framework. The stability of the overall closed-loop system is rigorously proved by the Lyapunov stability theory, and the rationality of the proposed strategy is verified by a simulation example. According to the proposed event-triggered mechanism, 81.25% of the communication resources are saved in the simulation example.

The status of internalized prejudice in leader self-development

PurposeLittle research directly examines leader self-development among marginalized workers. The authors offer a framework to explain the role of internalized prejudice in limiting leader self-development, and the authors use that framework to suggest organizational interventions aimed at enhancing leader self-development among marginalized workers.Design/methodology/approachThe framework is grounded in the incompleteness thesis with its attention to the mutual shaping of culture and mind, and the interventions are drawn from principles of social movements with their focus on changing culture and the minds of individuals. The framework and interventions address the role of status in internalized prejudice.FindingsStatus-related experiences in culture shape status-related thoughts in the mind, resulting in internalized prejudice. Internalized prejudice reduces the status-related behavior of leader self-development, which serves to shape status in the culture. This repeats in an ongoing, recursive process that can be disrupted through organizational interventions. The social movement principles of common purpose and networking can provide new status-related experiences to reduce internalized prejudice, and habit-breaking can stop automatic self-limiting behaviors that can arise from internalized prejudice.Originality/valueBy focusing on status, the authors provide a framework that allows integration of literature across marginalized groups, providing a guide for understanding both commonality and uniqueness of experience. The authors bring principles of social movement to the discussion of leader self-development among marginalized workers as a guide for developing organizational interventions.

Optimized thermal management of a battery energy-storage system (BESS) inspired by air-cooling inefficiency factor of data centers

Inspired by the ventilation system of data centers, we demonstrated a solution to improve the airflow distribution of a battery energy-storage system (BESS) that can significantly expedite the design and optimization iteration compared to the existing process. A defective cooling system of a BESS decreases the overall operational efficiency and increases the risk of thermal runaway, but current design optimizations rely on a case-by-case approach. The solutions of this fashion are both time-consuming and costly because of the laborious recursive process. The desire to eliminate the time for development motivates us to pursue a more general path for design optimization. In this work, we identified the similarity of geometry between the data center and the BESS, as well as the factors that induced the unbalanced airflow distribution. Inspired by the data-center thermal management, we propose a generalized solution of layout arrangement that we applied to the BESS design. We performed a quantitative analysis of cooling performance and investigated the flow patterns under given operating configurations. After modification, the maximum temperature difference of the battery cells drops from 31.2°C to 3.5°C, the average temperature decreases from 30.5°C to 24.7°C, and the coefficient of performance (COP) increases four-fold. The modification shows an improvement in temperature uniformity, overall temperature and COP. The cooling solution applicable to the general container BESS design demonstrates the enormous potential for an effective and rapid design optimization.

Hybrid sentiment analysis with textual and interactive information

In the past decade, various methods have been proposed to address document-level sentiment classification. However, the exploration of user–product interactions has not been sufficiently studied in the literature. In this work, we aim to investigate the effectiveness of exploiting user–product relations, and propose a hybrid semantic and interactive model for the classification task. The novelty of the proposed method is a ranking graph module and a latent matching module, where the former is capable of capturing high-order connectivity among the nodes, while the later is able to preserve semantics of local connectivity during the recursive graph learning processing. These two modules are seamlessly incorporated, enabling the proposed model to learn comprehensive and discriminative representations for the specific classification task. We conduct extensive experiments on three public datasets, and demonstrate the advantage of the proposed model over the state-of-the-art baselines.

Recursive Convex Model for Optimal Power Flow Solution in Monopolar DC Networks

This paper presents a new optimal power flow (OPF) formulation for monopolar DC networks using a recursive convex representation. The hyperbolic relation between the voltages and power at each constant power terminal (generator or demand) is represented as a linear constraint for the demand nodes and generators. To reach the solution for the OPF problem a recursive evaluation of the model that determines the voltage variables at the iteration t+1 (vt+1) by using the information of the voltages at the iteration t (vt) is proposed. To finish the recursive solution process of the OPF problem via the convex relaxation, the difference between the voltage magnitudes in two consecutive iterations less than the predefined tolerance is considered as a stopping criterion. The numerical results in the 85-bus grid demonstrate that the proposed recursive convex model can solve the classical power flow problem in monopolar DC networks, and it also solves the OPF problem efficiently with a reduced convergence error when compared with semidefinite programming and combinatorial optimization methods. In addition, the proposed approach can deal with radial and meshed monopolar DC networks without modifications in its formulation. All the numerical implementations were in the MATLAB programming environment and the convex models were solved with the CVX and the Gurobi solver.

More Gamer, Less Girl: Gendered Boundaries, Tokenism, and the Cultural Persistence of Masculine Dominance

Abstract How do exclusionary boundaries persist in consumption subcultures amid increased progress, representation, and inclusion? In video gaming, women have come to represent nearly half of the market; yet, this is a limited indicator of gender-based progress. A culture of masculine dominance persists. Extending previous research on boundary work, the authors employ a cultural perspective of tokenism to examine how gendered boundaries in consumption subcultures persist despite efforts to transform or even eradicate them. This qualitative study draws on interviews with 23 gamers who identify as women (ages 19–29 years), coupled with data from social media platforms, news media, and industry reports. Empirical findings capture the recursive process of maladaptive boundary crossing: how women’s efforts to subvert gendered boundaries at the micro level (e.g., through response enactments) get churned through the structuring tokenistic mechanics of boundary work at the meso level and result in the inadvertent cultural persistence of masculine dominance. The analysis offers a conceptual framework that explains how micro–meso level dynamics perpetuate and conceal inequity in consumption subcultures. Implications address the precarious promise of progress and the cultural legacy of tokenism in the marketplace with particular relevance to broader systems of domination.

The Feedback Loop of Flow: Controlled Experiment Shows Task-relevant Feedback Increases Flow

Flow theory suggests three flow conditions lead to flow: optimal challenges, clear goals, and immediate feedback. Previous research has only confirmed the link between optimal challenges and flow with controlled experiments. Inspired by Miller’s test-operate-test-exit (TOTE) units and Csikszentmihalyi’s flow theory, we introduce a feedback loop of flow model of the cognitive processes that lead to flow in computer-based tasks. This model illustrates how the flow conditions come together to create the flow experience. In particular, the feedback loop of flow model we present suggests the feedback must be task-relevant to generate a flow state, which means there must be feedback that pertains to the goals of the task. We conducted a controlled experiment to test the causal relationship between task-relevant feedback and flow. Participants who experienced designs with task-relevant feedback rated their experience higher on a measure of flow than those in either the no feedback or randomized feedback control groups. Our findings provide evidence that feedback increases flow and specifically that designs with task-relevant feedback increase flow. We present a design for flow model and discuss design guidelines for how to create interactive systems that will facilitate users getting into a flow state. Task-relevant feedback communicates how well users are performing actions that make progress towards the goal of the optimally challenging task that is getting them into flow and how they can get better at that task. As with TOTE units, the feedback loop of flow is a recursive process, which suggests task-relevant feedback must be presented continuously and for each subtask of the overall task.

Teaming up to traverse loneliness: a co-creative journey toward a home care work model for supporting social participation among older adults

BackgroundParticipatory research is particularly suitable in adressing know-do gaps in health systems. There is a disparity between what is known about the benefits of social participation and home care’s responsibility to provide conditions amenable to older adults’ social participation, and what is accomplished in home care practice. Home care workers are a large, low-power group, whose competences should be better harnessed. We carried out a participatory action research (PAR) project with the goal of generating an improved structure for identifying and alleviating loneliness. This article aims to explore the co-creative process of designing a work model that guides home care workers in supporting social participation among older care recipients.MethodsMultimodal data from 16 PAR workshops with 14 home care workers were described and explored through the ‘recursive PAR process’ and the ‘framework for occupational enablement for change in community practice”.ResultsThe PAR process is outlined through the objectives, activities, and work model, as well as enablement strategies employed throughout the PAR process; as are its opportunities, challenges and implications. The work model describes how care workers can act as discoverers of care recipients’ unmet social needs, employ intentional communication, and link to relevant professions or community services to alleviate loneliness among older home care recipients.ConclusionsThis research process included opportunities of collaborating with enthusiastic and competent home care workers, but also challenges of moving between theory and practice and maintaining active participation between workshops. The resulting work model is in step with the requirements of elderly care, is unique in its field and could comprise a first step toward a more systematic approach of assessing and addressing loneliness. The vivid delineation of the PAR process provided in this paper can aid other researchers in navigating participatory research in home care contexts.

Introducing the Collective Action Recursive Empowerment (CARE) Model: How Small‐Scale Protests Led to Large‐Scale Collective Action in Armenia's Velvet Revolution

The current study examined the motivations and outcomes of the Armenian Velvet Revolution through the voice of 18 protesters with qualitative interviews, exploring their motivations for joining the collective action, their perceptions of its implementation, and resultant psychological changes. Participants distinguished between individual‐ and group‐level processes and bypassed social divisions through a shared belief in the value of “Rejecting Serzh” (i.e., opposing then President Serzh Sargsyan). Results also illustrated a recursive process of collective action supporting the development of a new theoretical CARE (collective action recursive empowerment) model. The model understands successful social change as a function of both individual and group identity informing collective‐action processes: Smaller acts of protest increase individual feelings of shared group identity and empowerment/efficacy beliefs, thus strengthening motivations for continued protest and making large‐scale collective action achievable. By linking together multiple time points of a collective‐action process in one recursive model, the current work maps out one specific case of successful collective action in recent history, which serves as a basis for broader theoretical generalization and future empirical research.

Contending with the unforeseen “messiness” of the qualitative data analysis process

Qualitative data analysis (QDA) is often depicted as a linear process that employs an organised structure to derive themes inductively or deductively. However, I realised the “messiness” of the process during my doctoral studies on primary teachers’ use of digital technologies in ESL classrooms in Maldivian schools. My own experience revealed that QDA is a nonlinear, iterative and recursive process. Adopting a phenomenological research design to capture my nine participants’ “lived” experiences, I gathered data from various sources over eight months. Shared through this paper is a selection of my experiences that exemplify the nonlinearity of QDA, including the emergent need to develop an NVivo-enhanced Spiral-QDA process to accommodate my unforeseen data analysis experience. Therefore, reflections from my doctoral research experiences may be edifying and helpful for postgraduate students and supervisors in their own QDA process.

Point matching based on affine invariant centroid trees

Object detection can be formulated as a point matching problem when objects are modeled by point sets. Moments, which have been widely used for point matching, are limited to affine transformations as their support point sets cannot keep invariant. To address this problem, we developed an affine invariant centroid tree (AICT) to obtain a rigorous affine invariant support point set (SPS). The algorithm is constructed by a recursive process: the point set is first divided by the vector from the certain point to the centroid of the point set, and the centroids of subsets are used to generate vectors for renewed partitions. In addition, the centroids of the subsets are stored to form an AICT. The AICT represents the inherent structure of the point set. It is highly tolerant to noise and outliers due to the partitions on the whole point set. More importantly, it is affine invariant owing to the affine invariance of partition. Therefore, we can get rigorous affine invariant descriptors while moments are combined with AICT. The experimental results on synthesized and real data verify that our proposed algorithm outperforms the state-of-the-art point matching methods including shape context, iterative closet point, and the method adopting thin plate spline for rigid robust point matching (TPS-RPM).

“Self-Cultivation as the Root of All”—The Individual in the Recursive Process of “Ensuring Peace for All Under Heaven, Good State Governance and Regulation of Family Affairs, and Self-Cultivation”

Abstract Today, “regulation of family affairs, good state governance and ensuring peace to all under heaven” has become a fixed-format narrative through which China remains attached to its traditions. However, most such expressions digress from the original context of The Great Learning and so have become a way of conveying modern and international ideas instead. “Regulation of family affairs, good state governance and ensuring peace to all under heaven” in the context of The Great Learning are the last three of the “eight essential principles (studying things, acquiring knowledge, being sincere in thought, rectifying one’s heart/mind, cultivating oneself, regulating one’s family affairs, governing the state well, and ensuring peace to all under heaven),” and seemingly separate from the other five. However, only by connecting the eight principles together, both progressively and recursively, can one accurately and fully understand the meaning of the last three (“regulation of family affairs, good state governance and ensuring peace to all under heaven”). They are listed in a progressive order but only by going backward to “studying things” can we truly understand the profound meaning of the “complete eight.” In these two-way orders, “self-cultivation” by the individual is the key link. Such an understanding helps to focus on the basic collectivist ideas of Confucian thought, and to highlight its modern significance.

EAGCN: An Efficient Adaptive Graph Convolutional Network for Item Recommendation in Social Internet of Things

In the era of Internet of Things (IoT), intelligent recommendation is playing an important role in our daily life. How to provide personalized information to users is the core concern of Internet content service providers. To improve the recommendation quality, it is a hot topic to go beyond merely user–item interaction records and take social relations into account in IoT. Recently, emerged graph neural networks (GNNs) shine a light on simulating the recursive social diffusion process, to refine user embedding learning. Nevertheless, two key issues have not been well studied in previous studies: 1) they usually model user preference and social influence within a same semantic space and fail to simultaneously inject high-order connectivity information reflected in both user–item interaction graph and user–user social graph and 2) they typically rely on negative sampling to optimize a recommendation model, which makes them highly sensitive to the design of the sampler and hardly makes full use of GPU’s computing ability. In light of this, we propose a novel framework for item recommendation, namely, an efficient adaptive graph convolutional network (EAGCN). Specifically, we introduce a space-adaptive graph convolutional module, which could jointly explore the propagation process of user interest and social influence. Furthermore, a user-specific gating mechanism is designed to aggregate user representations from both spaces. To make EAGCN practical in social IoT, we devise a fast nonsampling leaner to optimize EAGCN’s parameters with better leveraging matrix computing of GPU. Extensive experiments under four scenarios show that our solution consistently and significantly outperforms strong baseline methods in both model effectiveness and training efficiency.

Identification Approach for Nonlinear MIMO Dynamics of Closed-Loop Active Magnetic Bearing System

A systematic identification approach for the rotor/radial active magnetic bearing (rotor/RAMB) system is presented in this study. First, the system identification of the controller of commercial TMP is undertaken, and the corresponding linear dynamic models are constructed. To perfectly excite the nonlinearities of the rotor/RAMB system, a parallel amplitude-modulated pseudo-random binary sequence (PAPRBS) generator, which possesses the merits of no correlation among the perturbation signals, is employed. The dynamics of the rotor/RAMB system is identified with a Hammerstein–Wiener model. To reduce the difficulty of the identified two nonlinear blocks, the output nonlinear characteristics are estimated prior to the recursive process. Two conventional nonlinear model structures, i.e., NARX and NARMAX, are employed for comparison to verify the effectiveness of the identified Hammerstein–Wiener model. The averaged fit values of the Hammerstein–Wiener model, NARX model, and NARMAX model are 93.25%, 88.36%, and 76.91%, respectively.