Construction Of Representations Research Articles

Economic analysis of hemodialysis and urgent-start peritoneal dialysis therapies.

Unplanned initiation of renal replacement therapy (RRT) in chronic kidney disease (CKD) patients is a common situation worldwide. In this scenario, peritoneal dialysis (PD) has emerged as a therapeutic option compared to hemodialysis (HD). In planned RRT, the costs of PD are lower than those of HD; however, the literature lacks such analyses when initiation is urgent. To clinically and economically evaluate, from the perspective of the Unified Health System (SUS, Sistema Único de Saúde), the strategy of initiating unplanned RRT using HD or PD in patients during their first year of therapy. Quasi-experimental study with cost-effectiveness analysis based on primary data from incident patients on RRT, over a twelve-month follow-up period, using the intention-to-treat approach. Data collection occurred prospectively, directly from medical records, computing data on the use of dialysis therapy, high-cost medications, procedures in dialysis accesses and recorded events. Costs were estimated using the amounts reimbursed by the SUS. In the economic analysis, the application of the bootstrap method and the construction of graphical representations were proposed. At the end of one year, there were no differences between costs and effectiveness when initiating unplanned RRT using either PD or HD. Starting RRT with PD is a similar option to starting with HD in patients requiring unplanned methods. The minimal initial investment required to establish PD slots makes it a strong option as a public health policy for expanding RRT in developing countries.

Using mass timber in multi-storey and non-residential construction projects: Motivations and barriers for professionals in Quebec

Despite their environmental benefits and technical viability, mass timber structures adoption remains limited. As an alternative to steel and concrete in non-residential and multi-storey construction, they represent only 10.2% of buildings four or fewer storeys high, 1% of those five or six storeys high and 4% of those seven to twelve storeys high in Quebec. Based on a purposive sample of 42 interviews with various construction industry professionals in Quebec (Canada), the representation of mass timber construction was highlighted. A thematic analysis approach enabled a study of the motivations and barriers to adopting mass timber and the specific reasons behind them, and to determine whether respondents’ perceptions differ significantly depending on their main professional activity. The results corroborate existing literature while offering deeper insights into motivations and barriers, revealing new viewpoints. Respondents cited construction costs, expertise, manufacturing capacity, regulatory limits, and material specifications as the most critical barriers, while environmental impact and aesthetics of wood as key motivators. The response profile analysis suggests that private developers and general contractors should be the primary targets of measures promoting mass timber adoption. This research will aid in refining policies and strategies to encourage the widespread adoption of mass timber in construction practices.

Low Versus High Levels of Social Cognition Impairment and Their Associations With Specific Schizophrenia Symptom Domains.

Social cognition is defined as the ability to construct mental representations about oneself, others, and one's relationships with others to guide social behaviors, including referring to mental states (cognitive factor) and understanding emotional states (affective factor). Difficulties in social cognition may be symptoms of schizophrenia. The authors examined associations between two factors of social cognition and specific schizophrenia symptoms, as well as a potential path from low-level affective perceptual social cognition to high-level social cognition, which may be associated with schizophrenia symptoms. The authors compared IQ, executive function, and social cognition scores of 41 patients with schizophrenia with those of a community-based group of 30 healthy individuals by using the Diagnostic Analysis of Nonverbal Accuracy 2-Taiwan version, the Movie for the Assessment of Social Cognition-Taiwan version, and the Chinese version of the theory of mind task. In analyses controlled for IQ and executive function scores, patients with schizophrenia were found to perform more poorly than individuals in the healthy comparison group on all social cognition tasks. Disorganized symptoms were associated with lower accuracy of recognizing happy and angry faces, a lower verbal theory of mind score, and altered low- and high-level social cognition scores. A potential causal link was identified between low-level affective perceptual social cognition and high-level social cognition, resulting in disorganized symptoms. These results indicate distinct roles of two factors of social cognition in schizophrenia symptomatology and provide a new direction for alleviating symptoms of this disorder by enhancing social cognition.

Advanced EPISM approach for holographic stereogram generation utilizing neural radiance fields.

This paper presents a synthetic holographic stereogram printing approach that integrates neural radiance fields (NeRF) with the effective perspective images segmentation and mosaicking (EPISM) method. Sparse perspectives of a 3D scene are captured through random sampling and used to train a NeRF model with multi-resolution hash encoding, enabling rapid construction of an implicit scene representation. The EPISM method calculates the camera pose parameters needed for parallax images, which are rendered through the trained neural network. These rendered images are then encoded using the EPISM process to generate a sequence of synthetic effective perspective images for direct exposure printing. Experimental results demonstrate that the integration of EPISM and NeRF effectively addresses challenges such as inefficient light field acquisition, low computational efficiency, and the limitations of traditional techniques. This approach ensures rapid computation, precise scene reconstruction, and high-quality hologram printing, offering an end-to-end solution for acquiring and printing high-fidelity synthetic holographic stereograms of real-world scenes.

Memory Development, Configurations, Conjunctions, and the Hippocampal Index.

When I began my career, I had no idea that much of it would center around the hippocampus. Here I discuss some of the history of how this happened. I briefly mention my early undergraduate life and the problems it posed for getting into graduate school. I describe the unique circumstances that led me to Allan Wagner's laboratory and changed my career trajectory. My path to the hippocampus began with a decision to study memory development. This led to a collaboration with Rob Sutherland that produced the configural theory of the hippocampus. The idea was that the hippocampus facilitated the construction of representations of the co-occurring stimulus elements currently experienced by the organism. Thus, if two elements, A and B, occurred together, a representation, AB, could be constructed that could be discriminated from its elements, A and B. This idea was partially correct, but we missed an important property of the hippocampal system that was recognized by O'Keefe and Nadel, 1978 that is, that the hippocampus is an unmotivated, rapid learning system. Randy O'Reilly and I addressed this issue in what we called conjunctive representation theory and put forth a detailed cortical-hippocampus computational theory to explain how this could work I later realized that our ideas were remarkably like Tim Teyler's indexing theory of how the hippocampal system supports memory. At a Park City meeting, a chance encounter with Tim (whom I had never met) resulted in the opportunity to write a paper with Tim updating the indexing theory, It is my favorite theoretical paper.

The Representation of Orientation Semantics in Visual Sensory Memory.

Visual sensory memory constructs representations of the physical information of visual objects. However, few studies have investigated whether abstract information, such as semantic information, is also involved in these representations. This study utilized a masking technique combined with the partial report paradigm to examine whether visual sensory memory representation contains semantic information. Here, we regarded the concept of orientation carried by the visual stimulus as semantic information. In three experiments, participants were asked to remember the orientation of arrows. Visual stimuli with orientation information (triangles, rectangles, and Chinese characters) and without orientation information (circles, squares, and different Chinese characters) were used as masks. The results showed that memory performance was worse when masks contained orientation information compared to when they did not, as similar orientation semantic information between masks and targets created visual representation conflicts. These findings suggest that visual sensory memory representation includes the semantic information of orientation.

Integrating Metadiscourse Analysis with Transformer-Based Models for Enhancing Construct Representation and Discourse Competence Assessment in L2 Writing: A Systemic Multidisciplinary Approach

In recent years, large-scale language test providers have developed or adapted automated essay scoring systems (AESS) to score L2 writing essays. While the benefits of using AESS are clear, they are not without limitations, such as over-reliance on frequency counts of vocabulary and grammar variables. Discourse competence is one important aspect of L2 writing yet to be fully explored in AEE application. Evidence of discourse competence can be seen in the use of Metadiscourse Markers (MDM) to produce reader-friendly texts. The article presents a multidisciplinary study to explore the feasibility of expanding the construct representation of automated scoring models to assess discourse competence in L2 writing. Combining machine learning, automated textual analysis and corpus-linguistic methods to examine 2000 scripts across two tasks and five proficiency levels, the study investigates (1) in addition to frequency and range, whether accuracy of MDM is worth pursuing as a predictive feature in L2 writing, and (2) how identification and classification of MDM use might be fed into developing an automated scoring model using machine learning techniques. The contributions of this study are three-fold. Firstly, it offers valuable insights within the context of Explainable AI. By integrating MDM usage and accuracy into the scoring framework, this research moves beyond frequency-based evaluation. This study also makes significant contributions to the current understanding of L2 writing development that even lower-proficiency learners exhibit evidence of discourse competence through their accurate use of MDMs as well as their choice of MDMs in response to genre. From the perspective of expanding the construct representation in automated scoring systems, this study provides a critical examination of the limitations of many AEE models, which have heavily relied on vocabulary and grammar features. By exploring the feasibility of incorporating MDMs as predictive features, this research demonstrates the potential for construct expansion of L2 AEE. The results would support test providers in developing competence tests in various contexts and domains including manufacturing, medicine and so on.

Learning the lie of the land: How people construct mental representations of distributions.

An unexamined assumption in many studies of learning and decision making is that people learn underlying probability distributions. However, the acquisition of distributional knowledge is rarely the focus of investigations. We report five experiments (N = 580 adults) that provide this focus and highlight the factors that impact people's ability to accurately learn and reproduce underlying distributions. We find that people accurately reproduced the distribution only when either the environmental signal is strong (e.g., discrete bimodal distributions) or sufficient cues are provided to aid construction of mental representations (e.g., items from the modes in a noisy bimodal distribution are presented in different colors). We interpret these results in terms of participants testing and learning discrete rules corresponding to salient features of the environment rather than spontaneously representing entire distributions. As such, the findings challenge strong assumptions about the role of probability distribution knowledge in explanations of learning and decision making. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Exploring Peace Journalism Practices in Reporting on the Israel-Palestine Conflict

This research examines the application of peace journalism principles in the coverage of the Israel-Palestine conflict during the period of 2023–2024, a time marked by renewed tensions and global attention. Drawing on Johan Galtung’s framework, the study evaluates how major international news outlets, including The New York Times, The Guardian, Al Jazeera, BBC News, and Haaretz, report on the conflict, focusing on criteria such as the emphasis on peace initiatives, avoidance of demonization, use of multi-perspectivity, avoidance of victimization language, and use of de-escalation language. Through content analysis of 200 articles, the study reveals significant variations in adherence to peace journalism principles across different news outlets and temporal phases of the conflict. While BBC News and The Guardian demonstrated strong adherence to peace journalism principles, Al Jazeera exhibited lower adherence in certain areas. The findings underscore the challenges and potential of implementing peace journalism in conflict reporting and highlight the role of educational initiatives in promoting balanced and constructive media representation. Overall, the study contributes to the discourse on media ethics and the role of journalism in conflict zones, providing insights to inform future reporting practices and promote understanding and reconciliation.

From primordial inscription to word representation.

This work seeks to examine two aspects of the passage from the somato-sensory function to the psyche, in an attempt to explore in greater detail the construction of mental representations. The first aspect is the primordial inscription, which ends in the formation of a pictogram. This is an attempt to represent the "thing". The relevance of this work stems from the fact that the number of patients with difficulties in representation is increasing in clinical work. The second aspect addressed is word representation, which is necessary for the expression of meanings and for the development of a signifying chain, an essential element in the establishment of mental representations. Two questions arose during the writing of this work. The first: What enables a word to be incorporated in the psychic apparatus, with the functions of denoting, expressing and performing, thus achieving its relational and representational potential? The second and more important question: What enables words to come together and form the most varied and complex thoughts?

Catching the flowing consumers: media representation effects of tourism livestreaming

ABSTRACT Algorithm-based social platforms like TikTok have changed livestreaming tourism. Many countries and regions promote tourism via livestreaming. Tourism livestreaming often has low viewer numbers, unstable viewer numbers, and unsatisfactory effects like a lack of travel intention or consumption. Based on the characteristics of space of flows, this paper analyses how tourism livestreaming on social media platforms can improve communication through media reproduction and increase travel intension. Study 1 involved in-depth interviews with 40 social media users who watched tourism livestreaming for over two years. Functional application and reproduction effects embedded in the space of flows, as well as the ambience and anchor capabilities built into the livestreaming, are reasons for audiences to stay. Study 2 uses a questionnaire to examine media representation and travel intention. Results show that tourism livestreaming's objective presentation and representational construction increase travel intention. The mediating effect of sense of place is also revealed.

The representation of social concept in Coca-Cola advertisement

The aim of this research is to understand how Coca-Cola advertisements construct representations of social concepts in Coca-Cola advertisements. This research utilized qualitative methods to describe data analysis. The results of the first research showed that the Coca-Cola advertising sign contains a social concept in every advertisement displayed in advertisements on YouTube. Coca-Cola advertisements often show many people involved in them, such as interactions, relationships and dynamics between individuals or groups in society which have positive values, so that social value concepts appear in them, namely social communication, togetherness, tolerance and also concern for the environment. In the first study, researchers used the semiotic theory triangle based on Charles Sanders Peirce to describe social concepts in Coca-Cola YouTube advertisements. The author uses semiotic theory based on Charles Sanders Peirce. This theory aims to determine the relationship between representation, objects and interpretants in Coca-Cola. Furthermore, the results of the research by the two researchers found the value of the social concept in Coca-Cola's YouTube advertisement, which shows the value of social communication, togetherness, tolerance, and also concern for the environment. In addition, the researcher explains the values found, so this research is: The social signs of the concepts displayed in Coca-Cola advertisements certainly have social values that we can encounter in everyday life.

On J-folded Alcove Paths and Combinatorial Representations of Affine Hecke Algebras

We introduce the combinatorial model of J-folded alcove paths in an affine Weyl group and construct representations of affine Hecke algebras using this model. We study boundedness of these representations, and we state conjectures linking our combinatorial formulae to Kazhdan-Lusztig theory and Opdam’s Plancherel Theorem.

Analisis Motivasi Kerja, Lingkungan Kerja, dan Produktivitas Kerja Pegawai Pada Pusat Pengelolaan Pendapatan Daerah Wilayah Karawang

This research aims to find out and understand the results of the analysis of work motivation, work environment and employee work productivity at the Regional Revenue Management Center for the Karawang Region. The location of this research is the Regional Revenue Management Center for the Karawang Region. This research method uses a qualitative descriptive approach, expressed through word selection, sentence construction, detailed narrative and visual representation. Data collection techniques using interviews, observation and documentation. The results of research on work motivation provided at the Regional Revenue Management Center for the Karawang Region are quite good. The working environment at the Regional Revenue Management Center in the Karawang Region is still less comfortable for working employees, because several facilities were found to be not very functional. The work productivity of employees at the Regional Revenue Management Center for the Karawang Region can be seen from work efficiency, always trying to provide fast and precise services to the community, but increasing work productivity has not been achieved or is not yet optimal. In this way, of course, to increase employee work productivity at the Karawang Region Regional Revenue Management Center, it is necessary to pay attention to work motivation, especially the work environment so that it can leave a deep impression on employees, which will produce better results for employees.

Revictimization of women victims of intimate partner violence in the French press: A social representations approach.

This study aims to analyze how the press portrays cases of revictimization experienced by women in France, through the lens of social representation theory. An exploratory, descriptive study was conducted using a corpus of 157 online press articles. A total of 187,773 words and 5,240 segments were analyzed using Iramuteq version 7, employing top-down hierarchical classification and lexical similarity analysis. The analysis identified four classes: 1. Violent incidents (25.5%); 2. Political actions against violence (26%); 3. Institutional revictimization (26.6%) and 4. Violence and breaking the silence (21.9%). The findings illustrate how media portrayals contribute to the construction of social representations surrounding revictimization. Class 1 reveals a focus on extreme cases of violence, such as femicides, and highlights a potential gender bias in media reporting through the omission of terms like 'femicide'. Class 2 demonstrates the influence of ideological perspectives on the portrayal of political measures against violence, with conservative outlets framing such actions within traditional values, while progressive newspapers advocate for systemic reform. Class 3 shows a gap in the conceptualization of institutional revictimization in the French press compared to other countries, revealing how conservative portrayals can minimize systemic issues and perpetuate harmful stereotypes. Class 4 shows the role of female journalists in addressing various forms of violence, despite the limited impact of the #MeToo movement in recent coverage. Collectively, these findings elucidate how media representations shape public attitudes and institutional responses to gender-based violence through the lens of social representations.

Travel influencers and their social identity: a phenomenological approach

ABSTRACT The role of travel influencers has become increasingly popular, having a significant impact on tourists and tourism businesses. Influencers negotiate and navigate challenges such as content quality and authenticity while fostering a sense of community and influencing industry standards. Little is known, however, about how travel influencers construct their social identities. Based on social identity theory, and by interviewing 19 Chinese travel influencers, this study developed the following three-stage representation of travel influencer social identity construction: ‘transformation’, ‘maintenance’, and ‘identity value presentation’. It makes significant contributions to extant tourism literature by providing unique insights into the construction process associated with the identities of travel influencers.

SVQ-MAE: an efficient speech pre-training framework with constrained computational resources

Self-supervised learning for speech pre-training models has achieved remarkable success in acquiring superior speech contextual representations by learning from unlabeled audio, excelling in numerous downstream speech tasks. However, the pre-training of these models necessitates significant computational resources and training duration, presenting a high barrier to entry into the realm of pre-training learning. In our efforts, by amalgamating the resource-efficient benefits of the generative learning model, Masked Auto Encoder, with the efficacy of the vector quantization method in discriminative learning, we introduce a novel pre-training framework: Speech Vector Quantization Masked Auto Encoder (SVQ-MAE). Distinct from the majority of SSL frameworks, which require simultaneous construction of speech contextual representations and mask reconstruction within an encoder-only module, we have exclusively designed a decoupled decoder for pre-training SVQ-MAE. This allows the additional decoupled decoder to undertake the mask reconstruction task solely, reducing the learning complexity of pretext tasks and enhancing the encoder’s efficiency in extracting speech contextual representations. Owing to this innovation, by using only 4 GPUs, SVQ-NAE can achieve high performance comparable to wav2vec 2.0, which requires 64 GPUs for training. In the Speech Processing Universal Performance Benchmark, SVQ-MAE surpasses wav2vec 2.0 in both keyword spotting and emotion recognition tasks. Furthermore, in cross-lingual ASR for Mandarin, upon fine-tuning on AISHELL-1, SVQ-MAE achieves a Character Error Rate of 4.09%, outperforming all supervised ASR models.

Multi-particle quantum walks on 3D integrated photonic chip

Quantum walks provide a speed-up in computational power for various quantum algorithms and serve as inspiration for the construction of complex graph representations. Many pioneering works have been dedicated to expanding the experimental state space and the complexity of graphs. However, these experiments are mostly limited to small experimental scale, which do not reach a many-body level and fail to reflect the multi-particle quantum interference effects among non-adjacent modes. Here, we present a quantum walk with three photons on a two-dimensional triangular lattice, which is mapped to a 19 × 19 × 19 high-dimensional state space and constructs a complex graph with 6859 nodes and 45,486 edges. By utilizing the statistical signatures of the output combinations and incorporating machine learning techniques, we successfully validate the nonclassical properties of the experiment. Our implementation provides a paradigm for exponentially expanding the state space and graph complexity of quantum walks, paving the way for surmounting the classical regime in large-scale quantum simulations.

Maximizing wind farm production through pitch control using graph neural networks and hybrid learning methods

AbstractThis article presents a novel methodology to maximize wind farm power generation by integrating graph neural networks (GNN), supervised learning, and reinforcement learning techniques. First, the article introduces a graph‐based representation of the wind farm, capturing wind turbines as vertices and the inter‐turbine wake interactions as edges. The construction of this graph representation integrates the Jensen wake model, which includes insights derived from prior knowledge of wind farm aerodynamics. Subsequently, a detailed description of the GNN model's architecture, incorporating a message passing mechanism, is outlined. This GNN model is trained initially with supervised learning using a dataset of optimal pitch angles generated from the analytical results derived from Jensen wake model. Moreover, to improve the GNN model's accuracy and adaptability, reinforcement learning techniques are employed. The GNN model interacts with a high‐fidelity wind farm simulation environment, receiving feedback in the form of rewards derived from the wind farm's actual power output. Through a policy gradient approach, the GNN parameters undergo iterative updates, enabling the model to learn and adapt to dynamic wind conditions and intricate turbine interactions. The effectiveness and advantages of the proposed methodology are demonstrated through comprehensive case studies across various wind farm layouts.

Psychometric Evaluation of Cognitive and Positive Valence Tasks Chosen for the NIMH Research Domain Criteria Project.

The NIMH Research Domain Criteria (RDoC) initiative seeks to utilize multidimensional patterns of socio-cognitive behavior to improve understanding of mental illness. We aimed to evaluate the psychometric properties of a subset of RDoC tasks. Specifically, we investigated two positive valence tasks and five cognitive tasks. Participants (N = 320) were recruited through an online research platform. We used generalizability theory to estimate reliability, and factor analysis to examine factor structure. Reliability was average to excellent with some notable exceptions. Factor analysis results raised concerns about whether the factor structure of task scores aligns with the proposed RDoC model. Effects of cognitive manipulations generally supported the construct representation of tasks. Results indicate that the majority of RDoC task scores examined have acceptable reliability or can be made reliable through modest increases in task length. Future research in diverse populations is needed to better understand the factor structure of RDoC cognitive and positive valence measures.