Shape Knowledge Research Articles

Product knowledge as a facilitator of organic purchase intention in emerging markets: Empirical evidence from India

Climate change due to anthropogenic activities is a major cause of concern. To support and implement the ongoing research and development in the field of sustainable production, it is equally imperative to examine the drivers of sustainable consumption. The present study addressed the gap in the literature by examining the individual effects of two types of product knowledge i.e., consumers’ subjective and objective knowledge in shaping their organic purchase intentions, in light of the theory of planned behavior. The data was obtained through self-administered questionnaires from 611 respondents from the emerging and fast-growing organic market of India. Structural equation modeling was employed to analyze the data. The model was found to be a good fit for the data and explained 69.9% variance in organic purchase intention. We found that organic purchase attitude and intention were majorly influenced by subjective norm and perceived price of organic products. Organic purchase attitude was found to have a partial mediating effect on the subjective norm to intention relationship, and a full mediating effect on the perceived price to intention relationship. Interestingly, subjective knowledge was found to have an insignificant impact on consumer attitude and intention. Objective knowledge, on the other hand, played a crucial role in strengthening the relationship between attitude and intention, and weakening the relationship between perceived price and attitude. Furthermore, we found a very weak correlation (r = 0.11) between subjective and objective knowledge, which points at the confusion and misinformation about organic products that might be prevailing among consumers. The findings of the study significantly contribute to the literature and may be utilized by the marketers and policymakers in formulating strategies and policies to foster organic consumption. This may be achieved by developing favourable attitude and intention among consumers through creating awareness regarding the benefits of organic consumption.

Making sense of sensory language: Acquisition of sensory knowledge by individuals with congenital sensory impairments

The present article provides a narrative review on how language communicates sensory information and how knowledge of sight and sound develops in individuals born deaf or blind. Studying knowledge of the perceptually inaccessible sensory domain for these populations offers a lens into how humans learn about that which they cannot perceive. We first review the linguistic strategies within language that communicate sensory information. Highlighting the power of language to shape knowledge, we next review the detailed knowledge of sensory information by individuals with congenital sensory impairments, limitations therein, and neural representations of imperceptible phenomena. We suggest that the acquisition of sensory knowledge is supported by language, experience with multiple perceptual domains, and cognitive and social abilities which mature over the first years of life, both in individuals with and without sensory impairment. We conclude by proposing a developmental trajectory for acquiring sensory knowledge in the absence of sensory perception.

Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing

Increasing digitalization enables the use of machine learning (ML) methods for analyzing and optimizing manufacturing processes. A main application of ML is the construction of quality prediction models, which can be used, among other things, for documentation purposes, as assistance systems for process operators, or for adaptive process control. The quality of such ML models typically strongly depends on the amount and the quality of data used for training. In manufacturing, the size of available datasets before start of production (SOP) is often limited. In contrast to data, expert knowledge commonly is available in manufacturing. Therefore, this study introduces a general methodology for building quality prediction models with ML methods on small datasets by integrating shape expert knowledge, that is, prior knowledge about the shape of the input–output relationship to be learned. The proposed methodology is applied to a brushing process with 125 data points for predicting the surface roughness as a function of five process variables. As opposed to conventional ML methods for small datasets, the proposed methodology produces prediction models that strictly comply with all the expert knowledge specified by the involved process specialists. In particular, the direct involvement of process experts in the training of the models leads to a very clear interpretation and, by extension, to a high acceptance of the models. While working out the shape knowledge requires some iterations in general, another clear merit of the proposed methodology is that, in contrast to most conventional ML, it involves no time-consuming and often heuristic hyperparameter tuning or model selection step.

Advances in the Cognitive Science and Their Implications for ABFM Knowledge Assessment

Since the founding of American Board of Family Practice (ABFP) in 1969, there have been major advances in the cognitive sciences of learning, memory retention, and problem solving. How should these insights shape knowledge assessment in ABFM's Board certification portfolio? To help answer this

MLFNet: Monocular lifting fusion network for 6DoF texture-less object pose estimation

This paper addresses the challenge of 6DoF texture-less object pose estimation from a single RGB image. Many recent works have shown that two-stage deep learning approaches based on the fusion of 2D geometric intermediate representations achieve remarkable results. These methods implicitly explore the mapping from the 2D appearance domain to the 3D structure domain. However, due to the lack of 3D geometric constraints from depth maps, it is difficult to extract enough clues based on appearance features to master the geometric relation of projection from 3D viewpoints to 2D planes, and this estimation process is extremely sensitive to occlusion. We propose a novel network called MLFNet that lifts the feature space from 2D to 3D based on hybrid 3D geometric intermediate representations. For the first time, we propose the surface normals in the object coordinate system as an intermediate representation of pose; its violent change provides strong clues for the keypoints usually located at the abrupt change of object surface. Dense 3D surfaces can enhance the geometric consistency of multi-representation constraints and retain more information in occluded scenes. With the proposed multi-modality dual attention mechanism and the embedding of standard 3D shape knowledge, the 2D geometric representation learning process explicitly depends on the fusion of 2D appearance features and 3D geometric features. This standardized information fusion pattern among 2D intermediate representations, 3D intermediate representations, and CAD models prior significantly reduces the network learning space. The proposed method achieves competitive performance on the Linemod dataset and outperforms the state-of-the-art methods on the Occlusion Linemod and T-Less datasets, which demonstrates the feasibility of the pose multi-representation fusion technique. The project site is at https://github.com/JJJano/MLFNet.

The Impact of Linguistic Complexity on Leadership in Online Q&A communities: Comparing Knowledge Shaping and Knowledge Adding

Understanding how to enhance online leadership in online Q&A communities is important because an online leader plays a role model or knowledge coordinator who can strengthen member commitment in the community. Considering the essential role of communication in establishing leadership, this study aims to understand how the linguistic complexity of two types of knowledge contribution, i.e., knowledge adding (KA) versus knowledge shaping (KS) that are targeted at two types of audience, may influence leadership in online Q&A communities. By analyzing the posting history of members from StackExchange, a massive network of online Q&A communities, our findings suggest that among the three linguistic complexity dimensions, readability and lexical diversity of KA have more positive impacts on online leadership than those of KS. However, the sentiment of KS has a more positive impact than the sentiment of KA. This study contributes to the online leadership research by highlighting the importance of adjusting linguistic styles based on types of communication behaviors (i.e., KA and KS) to earn leadership.

Hynoparenting untuk meningkatkan pengetahuan Ibu dalam membentuk Karakter anak

Phenomena that occur in society, most children's problems are caused by mistakes or ignorance of parents and the closest environment in how to communicate and convey good values ​​to children. shows deviations, does not follow parental advice or even fights, new parents panic and look for solutions. This should be avoided, children's character education should be carried out early before it is too late. The basic problem is that many parents do not understand when the time is right and how to educate children's character. One method that is expected to be a solution is the hypnoparenting technique.
 Hypnoparenting is a hypnotherapy technique that is specifically applied by parents in raising children. Broadly speaking, this technique is useful in improving the quality of communication and spiritual intelligence of parents and children. This method works directly on the child's subconscious, so that parents can apply their child's character education without coercion.
 This study aims to test the effectiveness of hypnoparenting counseling to increase parental knowledge in shaping children's character. This research was conducted in Al Wafa Kindergarten with the respondents being parents or guardians of Al Wafa Kindergarten students. This research is a pre-experimental study with a pretest-post test one group design. The interventions provided were in the form of counseling with interesting materials, video screenings, demonstrations, role plays, and sharing sessions.
 The results showed that there was a difference in knowledge before and after being given hypnoparenting counseling. The results of the paired T test showed a t-value of 7.023 (p-value 0.000 less than 0.05). Thus, it can be concluded that there is a significant difference between knowledge about mother's knowledge in shaping the child's character before and after being given counseling

Weakly supervised learning of multi-object 3D scene decompositions using deep shape priors

Representing scenes at the granularity of objects is a prerequisite for scene understanding and decision making. We propose PriSMONet, a novel approach based on Prior Shape knowledge for learning Multi-Object 3D scene decomposition and representations from single images. Our approach learns to decompose images of synthetic scenes with multiple objects on a planar surface into its constituent scene objects and to infer their 3D properties from a single view. A recurrent encoder regresses a latent representation of 3D shape, pose and texture of each object from an input RGB image. By differentiable rendering, we train our model to decompose scenes from RGB-D images in a self-supervised way. The 3D shapes are represented continuously in function-space as signed distance functions which we pre-train from example shapes in a supervised way. These shape priors provide weak supervision signals to better condition the challenging overall learning task. We evaluate the accuracy of our model in inferring 3D scene layout, demonstrate its generative capabilities, assess its generalization to real images, and point out benefits of the learned representation.

Nothing as Expected: The Year that Was…

Nothing as Expected: The Year that Was…

Feed for Thought: Factors Predicting Public Support for Funding and Labeling Preferences of Alternative Aquafeed

ABSTRACT Alternative aquafeed is currently being researched as a sustainable and environmentally friendly way to bolster aquaculture production, which plays an integral role in supplying global seafood demand. Guided by the cognitive miser model, this study aims to examine factors predicting public support for funding the development of alternative aquafeed and public preference for labeling fish fed with alternative aquafeed. This study found that heuristics were more influential than knowledge in shaping the dependent variables. This study also investigated how heuristics serve as perceptual filters, interacting with knowledge when influencing public support for funding and preference for labeling. This indicates that dependent on their predispositions and available heuristic cues, individuals might interpret the same information differently. Most importantly, the findings suggest that labels may serve varying roles and can be an effective way for actors in the food industry to communicate to the public.

Deal Me in: Playing Cards in the Home to Learn Math

Recent meta-analyses have demonstrated a significant association between children’s early math achievement and their experiences with math at home, including their caregivers’ talk about math. However, few studies have investigated the relations between caregiver math talk and children’s learning with experimental designs. Eighty-six children (M = 5.0 years) and their caregivers were randomly assigned to play either a numeracy or a shape card game at home for six weeks. Data were collected on children’s number and shape knowledge and families’ math talk during gameplay. There was substantial participant attrition (42% did not return completed materials), however, both an intent-to-treat analysis of the sample that received study materials and a subgroup analysis of study completers showed that children who played the shape game significantly improved their shape naming and matching skills relative to children who played the number game. Children who played the number game did not significantly improve their numerical skills relative to children who played the shape game. Mathematical talk during gameplay varied between families but was correlated over time within families. Caregivers’ and children’s talk about matching cards by shape or color predicted children’s learning from the shape game. The results suggest that despite receiving uniform instructions and materials, there was significant variability in children’s home math experiences that predicted their learning from the card game.

A Novel Computer-Aided Diagnostic System for Early Detection of Diabetic Retinopathy Using 3D-OCT Higher-Order Spatial Appearance Model.

Early diagnosis of diabetic retinopathy (DR) is of critical importance to suppress severe damage to the retina and/or vision loss. In this study, an optical coherence tomography (OCT)-based computer-aided diagnosis (CAD) method is proposed to detect DR early using structural 3D retinal scans. This system uses prior shape knowledge to automatically segment all retinal layers of the 3D-OCT scans using an adaptive, appearance-based method. After the segmentation step, novel texture features are extracted from the segmented layers of the OCT B-scans volume for DR diagnosis. For every layer, Markov–Gibbs random field (MGRF) model is used to extract the 2nd-order reflectivity. In order to represent the extracted image-derived features, we employ cumulative distribution function (CDF) descriptors. For layer-wise classification in 3D volume, using the extracted Gibbs energy feature, an artificial neural network (ANN) is fed the extracted feature for every layer. Finally, the classification outputs for all twelve layers are fused using a majority voting schema for global subject diagnosis. A cohort of 188 3D-OCT subjects are used for system evaluation using different k-fold validation techniques and different validation metrics. Accuracy of , , and are achieved using 4-, 5-, and 10-fold cross-validation, respectively. Additional comparison with deep learning networks, which represent the state-of-the-art, documented the promise of our system’s ability to diagnose the DR early.

Misinformedness about the European Union and the Preference to Vote to Leave or Remain.

European politicians have become increasingly concerned about the possible distorting effects of citizens not only being uninformed, but systematically misinformed about the European Union (EU). Against this background, this study assesses the role of EU knowledge in shaping the preference to vote to leave or remain in a (hypothetical) referendum on EU membership using cross‐national survey data that were collected simultaneously in eight EU countries during the run‐up to the 2019 EP elections. The surveys included a newly designed item battery of EU knowledge capturing both the accuracy as well as confidence in knowledge of the respondents. The results show that misinformedness is associated with a preference to leave the EU, the uninformed citizens tend to be undecided or not intending to vote, while the well‐informed prefer to remain. Overall, our findings contribute to the ongoing debates about the role of misinformation in politics.

Becoming first-generation

PurposeThe purpose of this paper is to understand how I – and many other students – became first-generation college students (FGCSs) by exploring the rise and retraction of TRIO. Originally, TRIO was a set of three college access and retention programs created in the 1960s to address the needs of a population designated as academically and economically disadvantaged. The author uses the term “becoming” as a process of how a person is constructed socially, in this case, as a FGCS and faculty member. The author uses. social construction of targeted populations theory to explore my testimonio as a beneficiary of opportunity programs.Design/methodology/approachTo explore how FGCS experience policy changes affecting TRIO, I use my testimonio to build on the historical literature that covers 1968–2001. I begin my testimonio by first reviewing my background as a second generation Ecuadorian American. I then review how I encountered opportunity programs and why I continued to be a participant in these programs, specifically, TRIO. I end my testimonio by countering criticism of TRIO programs using research on and lived experiences with TRIO.FindingsDuring an era when TRIO budgets remain under constant attack, testimonios shed light on participants’ experiences with policy changes. Specifically, I reveal how economic budget cuts create policy changes to terms such as “academically and economically disadvantaged” and shapes perceptions and knowledge about who are FGCS.Research limitations/implicationsResearch limitations include application of social construction of targeted group theory to one testimonio. Implications include more studies on how policy changes, specifically budget allocations could affect characterizations and images of targeted populations such as FGCS.Practical implicationsUnderstanding the role of policy changes to TRIO program funding benefits students from misperceptions by educational administrators and other stakeholders.Originality/valueMy testimonio also builds on work regarding the impacts of CARPS on FGCS like me. Most importantly, my testimonio contributes to the efforts necessary to ensure these programs continue to exist and succeed. I use my testimonio side by side with research literature on TRIO to become both the subject of inquiry and the inquirer, simultaneously a beneficiary and an informant of how these programs have influenced my persistence through the postsecondary education system and shaped my understanding of FGCS.

A fibre-based 2D-slit homogenizer concept for high-precision space-based spectrometer missions

The measurement accuracy of recent and future space-based imaging spectrometers with a high spectral and spatial resolution suffer from the inhomogeneity of the radiances of the observed Earth scene. The Instrument Spectral Response Function (ISRF) is distorted due to the inhomogeneous illumination from scene heterogeneity. This gives rise to a pseudo-random error on the measured spectra. In order to assess the spectral stability of the spectrograph, stringent requirements are typically defined on the ISRF such as shape knowledge and the stability of the centroid position of the spectral sample. The high level of spectral accuracy is particularly crucial for missions quantifying small variations in the total column of well-mixed trace gases like hbox {CO}_{2}. In the framework of the hbox {CO}_{2} Monitoring Mission (CO2M) industrial feasibility study (Phase A/B1 study), we investigated a new slit design called 2D-Slit Homogenizer (2DSH). This new concept aims to reduce the Earth scene contrast entering the instrument. The 2DSH is based on optical fibre waveguides assembled in a bundle, which scramble the light in across-track (ACT) and along-track (ALT) direction. A single fibre core dimension in ALT defines the spectral extent of the slit and the dimension in ACT represents the spatial sample of the instrument. The full swath is given by the total size of the adjoined fibres in ACT direction. In this work, we provide experimental measurement data on the stability of representative rectangular core shaped fibre as well as a preliminary pre-development of a 2DSH fibre bundle. In our study, the slit concept has demonstrated significant performance gains in the stability of the ISRF for several extreme high-contrast Earth scenes, achieving a shape stability of <0.5{%} and a centroid stability of <0.25 text {pm} (NIR). Given this unprecedented ISRF stabilization, we conclude that the 2DSH concept efficiently desensitizes the instrument for radiometric and spectral errors with respect to the heterogeneity of the Earth scene radiance.

Can guidance during play enhance children's learning and development in educational contexts? A systematic review and meta-analysis.

This systematic review and meta‐analysis considered evidence of guided play compared to direct instruction or free play to support children's learning and development. Interventions from 39 studies were reviewed (published 1977–2020); 17 were included in meta‐analysis (N total = 3893; M childage = 1–8 years; M girls 49.8%; M ethnicity White 41%, African American/Black 28%, Hispanic 19%). Guided play had a greater positive effect than direct instruction on early maths skills (g = 0.24), shape knowledge (g = 0.63), and task switching (g = 0.40); and than free play on spatial vocabulary (g = 0.93). Differences were not identified for other key outcomes. Narrative synthesis highlighted heterogeneity in the conceptualization and implementation of guided play across studies.

ОБ ИТОГАХ ТРЕТЬЕЙ ВСЕРОССИЙСКОЙ КОНФЕРЕНЦИИ «УСТОЙЧИВОЕ РАЗВИТИЕ РОССИИ», ПРОШЕДШЕЙ В РОССИЙСКОМ ГОСУДАРСТВЕННОМ ГУМАНИТАРНОМ УНИВЕРСИТЕТЕ

New, previously unfamiliar to mankind global challenges and systemic crises (technogenic, biogenic, sociocultural, environmental), cyber- terrorism and ideological extremism, deep contradictions, growing instability and uncertainty that the world faced at the turn of the 20th – 21st centuries, along with emerging the new geological Anthropocene era, led to the urgent need to realize the importance of sustainable development and rethink the role of education (including the humanities). UN Secretary-General António Guterres recently spoke of a growing catastrophe in the field of education. The independent authoritative International Commission of UNESCO stated that certain approaches to education have largely exhausted themselves, and in April 2021 formulated its updated goal – to show that in times of deep crises in health, social disasters, governance, economics and the environment – educa- tion should become regenerative: aimed at updating existing and restoring lost or damaged values and principles of education. The UNESCO global initiative in the context of rethinking the role of knowledge and education in shaping the future of mankind requires the creation in Russia of new scientific and research centers for sustainable development, which will form new ideas in the field of education and tools for their implementation

Dual Convolutional Neural Networks for Breast Mass Segmentation and Diagnosis in Mammography

Deep convolutional neural networks (CNNs) have emerged as a new paradigm for Mammogram diagnosis. Contemporary CNN-based computer-aided-diagnosis systems (CADs) for breast cancer directly extract latent features from input mammogram image and ignore the importance of morphological features. In this paper, we introduce a novel end-to-end deep learning framework for mammogram image processing, which computes mass segmentation and simultaneously predicts diagnosis results. Specifically, our method is constructed in a dual-path architecture that solves the mapping in a dual-problem manner, with an additional consideration of important shape and boundary knowledge. One path, called the Locality Preserving Learner (LPL), is devoted to hierarchically extracting and exploiting intrinsic features of the input. Whereas the other path, called the Conditional Graph Learner (CGL), focuses on generating geometrical features via modeling pixel-wise image to mask correlations. By integrating the two learners, both the cancer semantics and cancer representations are well learned, and the component learning paths in return complement each other, contributing an improvement to the mass segmentation and cancer classification problem at the same time. In addition, by integrating an automatic detection set-up, the DualCoreNet achieves fully automatic breast cancer diagnosis practically. Experimental results show that in benchmark DDSM dataset, DualCoreNet has outperformed other related works in both segmentation and classification tasks, achieving 92.27% DI coefficient and 0.85 AUC score. In another benchmark INbreast dataset, DualCoreNet achieves the best mammography segmentation (93.69% DI coefficient) and competitive classification performance (0.93 AUC score).

Fincher-Kiefer, R. (2021). How the body shapes knowledge: Empirical support for embodied cognition (M. Mochizuki, R. Iseki, &amp; E. Kawasaki Trans.). Shinyosha.

Fincher-Kiefer, R. (2021). How the body shapes knowledge: Empirical support for embodied cognition (M. Mochizuki, R. Iseki, &amp; E. Kawasaki Trans.). Shinyosha.

Generalized Convexity Properties and Shape-Based Approximation in Networks Reliability

Some properties of generalized convexity for sets and functions are identified in case of the reliability polynomials of two dual minimal networks. A method of approximating the reliability polynomials of two dual minimal network is developed based on their mutual complementarity properties. The approximating objects are from the class of quadratic spline functions, constructed based on both interpolation conditions and shape knowledge. It is proved that the approximant objects preserve both the high-order convexity and some extremum properties of the exact reliability polynomials. It leads to pointing out the area of the network where the maximum number of paths is achieved. Numerical examples and simulations show the performance of the algorithm, both in terms of low complexity, small error and shape preserving. Possibilities of increasing the accuracy of approximation are discussed.