Implicit Variables Research Articles

Some or Other: Partitive Disanaphors Across Languages

Abstract We present a cross-linguistically robust pattern of polyfunctionality between indefinite, partitive, and disanaphoric (‘another’) use of a single lexical item. In examining the connection between these different uses, we argue that a uniform semantics underlies all of them. We employ the salience theory of definiteness, with the specific implementation in a dynamic semantics with choice functions, to model the full spectrum of the attested uses without invoking implicit variables. The core meaning is the partitive implication, whereas the indefinite and the discourse-familiar disanaphoric readings are underspecified in the semantics and are argued to be additional construals conditioned by discourse factors. The partitive meaning, however, is shown itself to be subject to variable pragmatic packaging across languages, from being a part of the truth conditions to a postsupposition.

Explainable and generalizable AI-driven multiscale informatics for dynamic system modelling

Ultra-precision machining requires system modelling that both satisfies explainability and conforms to data fidelity. Existing modelling approaches, whether based on data-driven methods in present artificial intelligence (AI) or on first-principle knowledge, fall short of these qualities in high-demanding industrial applications. Therefore, this paper develops an explainable and generalizable ‘grey-box’ AI informatics method for real-world dynamic system modelling. Such a grey-box model serves as a multiscale ‘world model’ by integrating the first principles of the system in a white-box architecture with data-fitting black boxes for varying hyperparameters of the white box. The physical principles serve as an explainable global meta-structure of the real-world system driven by physical knowledge, while the black boxes enhance local fitting accuracy driven by training data. The grey-box model thus encapsulates implicit variables and relationships that a standalone white-box model or black-box model fails to capture. Case study on an industrial cleanroom high-precision temperature regulation system verifies that the grey-box method outperforms existing modelling methods and is suitable for varying operating conditions.

Geotechnical analysis involving strain localization of overconsolidated soils based on unified hardening model with hardening variable updated by a composite scheme

AbstractStrain localization simulation of overconsolidated soils with high overconsolidation ratio (OCR) has been a long‐standing challenge. Some critical state soil models, including the modified Cam‐clay (MCC) model, have been widely applied, but they may not predict the shear dilatancy of overconsolidated soils well in some cases. Hence, the unified hardening (UH) model, which may be viewed as a generalized version of the MCC model, is implemented. It has been recognized, nonetheless, that without resorting to the regularization mechanism, the standard finite element method (FEM) or the second‐order cone programming optimized finite element method (FEM‐SOCP) often experiences instability or interruption of calculating the hardening‐softening responses of overconsolidated soils. To resolve the aforementioned difficulty, the UH model is developed and implemented in the framework of FEM‐SOCP based on the micropolar continuum (mpcFEM‐SOCP) to predict strain localizations of overconsolidated soils. Furthermore, to obviate non‐convexity of mpcFEM‐SOCP induced by material softening, an effective composite update scheme of hardening variable pc, which refers to the implicit variable (IV) scheme for the hardening stage and then refers to the explicit variable (EV) scheme for the softening stage, is proposed. Based on one biaxial compression problem and one rigid strip footing problem, numerical analyses disclose that by applying mpcFEM‐SOCP in conjunction with the composite update scheme of pc, the UH model of micropolar continuum can effectively predict the strain localization behavior of overconsolidated soil during its failure stage, and the stable hardening‐softening responses of overconsolidated soils can be readily attained.

Impact of travelers' consumer ethnocentrism on purchase and behavior intention: moderating effect of implicit prejudice

AbstractThis study investigated the effects of rigid thinking, ideology, and explicit and implicit prejudices on consumer ethnocentrism. Explicit and implicit prejudices significantly affected consumer ethnocentrism, but not travel behavioral intention. Among the three variances—Asian–European American, skin tone, and Arab‐Muslim Implicit Association Test (IAT)—used as implicit prejudice control variables, only implicit prejudice regarding Arab‐Muslim names significantly affected travel product purchase intention and travel behavior. The results revealed that consumers with strong consumer ethnocentrism are likely to avoid purchasing overseas travel products. These findings suggest the introduction of customized tourism marketing methods in countries with strong ethnocentrism.

Neural Causal Information Extractor for Unobserved Causes.

Causal inference aims to faithfully depict the causal relationships between given variables. However, in many practical systems, variables are often partially observed, and some unobserved variables could carry significant information and induce causal effects on a target. Identifying these unobserved causes remains a challenge, and existing works have not considered extracting the unobserved causes while retaining the causes that have already been observed and included. In this work, we aim to construct the implicit variables with a generator-discriminator framework named the Neural Causal Information Extractor (NCIE), which can complement the information of unobserved causes and thus provide a complete set of causes with both observed causes and the representations of unobserved causes. By maximizing the mutual information between the targets and the union of observed causes and implicit variables, the implicit variables we generate could complement the information that the unobserved causes should have provided. The synthetic experiments show that the implicit variables preserve the information and dynamics of the unobserved causes. In addition, extensive real-world time series prediction tasks show improved precision after introducing implicit variables, thus indicating their causality to the targets.

Analysis of factors affecting the transformation of scientific and technological achievements in universities based on structural equation modeling

Abstract Based on structural equation modeling, this paper measures and evaluates the transformation of scientific and technological achievements of colleges and universities in terms of implicit and explicit variables. The PLS algorithm is used to measure the intrinsic factor, which is designed to follow the Gaussian distribution and reach the expected value through iterative loops. The indicators and parameters of the explicit variables are evaluated by combining the principal component analysis and the great likelihood method. According to the results, the factors that are influenced by the policy environment account for 16.5%, while the problem of capital investment accounts for 36.3%. The number of invention patents increased by 8.93% after measuring and assessing them using the method presented in this paper. It demonstrates that universities can explore their technological achievements in depth and enhance their scientific research level.

The role of time in healthy living environment networks: current research and future directions

Abstract Background Societal challenges in the context of healthy living environments are complex, requiring networked collaboration from heterogenous actors. As it takes time before environmental investments accrue health effects, long-term networks emerge with dynamics that are unpredictable. Recent literature gives reason to believe that time plays an important role in managing networks, and can potentially help understand the dynamics in healthy living environment contexts. This study aims to identify gaps in how the literature reports on the role of time in healthy living environment networks. We identify avenues for future research. Methods A scoping review of the literature yielded 27 papers published between 2012 and 2022 about collaborative practices for a healthy living environment. We collected data on general characteristics, and references to time in relation to the context, collaborative process, and outcomes of networks described in each paper. Results The majority of the literature does not report on temporal dimensions in networks for a healthy living environment. In the literature that does include references to time, temporal dimensions remain an implicit and passive variable. For example, studies describe the evolution of environmental or health problems and their time horizon. If temporal dimensions are manifest in descriptions of the collaboration itself it mostly concerns the evolution of processes, their time horizon, and references to tempo. Strategic use of timing and adaption, as well as realizing long-term impact are the most important dimensions in terms of network actions. Conclusions No studies systematically analyze the role of time. Future research should fill this gap to enrich our understanding of how, when, and why time shapes networked governance and outcomes for a healthy living environment. Applying these concepts further can help to develop new steering tools for practitioners involved in collaboration. Key messages • The role of time in governance of healthy living environment networks remains limitedly studied. • Future research should study the (strategic) role of temporal dimensions in healthy living environment governance.

An exact penalty function optimization method and its application in stress constrained topology optimization and scenario based reliability design problems

A smooth penalty function method which does not involve dual and slack implicit variables to formulate constrained optimization conditions is proposed. New active and loss functions are devised to independently and adaptively handle the violation of each constraint function. A single unconstrained minimization of the proposed penalty function produces a solution to the original optimization problem. The constrained optimization conditions depending only on the primal variable are derived and reduce to the canonical Karush-Kuhn-Tucker(KKT) conditions in a much more general framework. The derivative of the proposed loss functions with respect to constraint function can be interpreted as the Lagrange multiplier of the traditional Lagrangian function. The appearance of the first-order Hessian information is unveiled. This is in contrast to the existing works where the classical Lagrange Hessian contains only second order derivatives. Finally, some numerical examples including medium-scale stress constrained topology optimization and scenario based reliability design problems are presented to demonstrate the effectiveness of the proposed methodology.

Approach to Quality of Work-Life and Affective Commitment in Sustainable HRM Practices for the improvement of Organizational Citizenship Behavior

This study aims to to measure and explain the practice of implementing Sustainable HRM to improve Organizational Citizenship Behavior by using the variables of Quality of Work-Life (QWL) and Affective Commitment, especially in the automotive retail industry. Data analysis uses a path to show a specific relationship between exogenous variables, which are variables (X), endogenous variables which are effect variables (Y), and implicit variables (ϵ). This data was collected through an array of questions to be answered by replies from as many as 59 motorcycle automotive retail company employees. The QWL and affective commitment have a favourable and considerable impact on OCB in employees. External influences still need to be followed up by further research, which can complement the determination of OCB in addition to QWL and Affective Commitment. Subsequent research focuses on environmental performance management, the design of training and development, and environmental organizational culture.

Bioenergy Model of Plant Raw Materials and Assessment of Feed Production Technologies

It is noted that the effectiveness of the technological processes in agriculture is usually evaluated according to economic, operational and relative criteria. Such an estimate often proves to be inaccurate for solving applied problems. The functioning of the biotechnical system "operator – feed – machine – animal" reveals this discrepancy to the highest extent. The paper confirms the necessity and relevance of a fact-based analysis of energy flows in the biotechnical system, as well as the energy flows interaction with each other and with the external environment. (Research purpose) To substantiate a methodological approach to assessing the fodder production efficiency based on a bioenergetic analysis of fodder production technologies. (Materials and methods) The research involves the comparative analysis of exchange energy accumulation by plants before harvesting and the parameters of its reduction in the plant raw materials, taking into account technological impacts. (Results and discussion) It is obtained that a one-megajoule input of mechanical energy in the cultivation of cereal-grass forage mixtures provides 121 megajoules of exchange energy. The exchange energy content in the cereal-grass forage mixture is calculated to be 44,850 megajoules per hectare (1950 megajoules per ton) while the specific mechanical energy consumption amounts to 370.6 megajoules per hectare (16.1 megajoules per ton) for the feed mixture cultivation. (Conclusions) It is established that the dynamics of energy accumulation during the vegetable raw material cultivation and the character of a decrease in its energy content during fodder preparation and storage have a sustainable common trend. The growth trend of energy accumulation is proved to have a linear character for both various types of fodder crops and the decrease in the feed energy content. Efficiency assessment criteria are proposed for the mechanical energy consumption during fodder crop cultivation, plant material processing and feed storage. A mathematical model is developed making it possible to study implicit variables in a complex system of accumulation and entropy of feed exchange energy, take optimal engineering decisions to justify and improve the technologies for feed cultivation, harvesting and preparation, as well as its effective use.

Mind the Large Gap: Novel Algorithm Using Seasonal Decomposition and Elastic Net Regression to Impute Large Intervals of Missing Data in Air Quality Data

Air quality data sets are widely used in numerous analyses. Missing values are ubiquitous in air quality data sets as the data are collected through sensors. Recovery of missing data is a challenging task in the data preprocessing stage. This task becomes more challenging in time series data as time is an implicit variable that cannot be ignored. Even though existing methods to deal with missing data in time series perform well in situations where the percentage of missing values is relatively low and the gap size is small, their performances are reasonably lower when it comes to large gaps. This paper presents a novel algorithm based on seasonal decomposition and elastic net regression to impute large gaps of time series data when there exist correlated variables. This method outperforms several other existing univariate approaches namely Kalman smoothing on ARIMA models, Kalman smoothing on structural time series models, linear interpolation, and mean imputation in imputing large gaps. However, this is applicable only when there exists one or more correlated variables with the time series with large gaps.

Perceived Health Outcomes of Recreation Scale: Measurement Invariance over Gender.

Research handling structural differences among groups presume that the measurement tool works similarly among the groups and the results of measurements provide similar psychometric properties. Therefore, the aim of the study is to provide evidence for measurement invariance of the construct validity Perceived Health Outcomes of Recreation Scale (PHORS). The research sample consisted of a total of 1984 adults who exercise, including 864 women and 1120 men during 2021-2022 in Antalya City, Turkey. The MI of the PHORS was tested by multigroup confirmatory factor analyses, which test the invariance of the covariance structures within the scope of structural equation modelling. Invariance tests were gradually conducted for the implicit variables in the model, CFI (comparative fit index criteria) and AIC (Akaike information criterion) were inquired between structural invariance, where no restriction was applied on the analyses and the other invariance tests (metric invariance, scalar invariance and string invariance respectively) where more restraints are applied. The study yielded evidence showing that the measurement model defined for the factor structure of the scale provided measurement invariance by gender. ΔCFI values were ≤0.010 in all subscales for metric and scalar invariance. The items of PHORS represented the same psychological structure, different groups responded to the items in the same way, the constant values in regression equations generated for the items in regression equations were equal/invariable between the groups.

An efficient optimal energy flow model for integrated energy systems based on energy circuit modeling in the frequency domain

With more energy networks being interconnected to form integrated energy systems (IESs), the optimal energy flow (OEF) problem has drawn increasing attention. Extant studies on OEF models mostly utilize the finite difference method (FDM) to address partial-differential-equation (PDE) constraints related to the dynamics in natural gas networks (NGNs) and district heating networks (DHNs). However, this time-domain approach suffers from a heavy computational burden with regard to achieving high finite-difference accuracy. In this paper, a novel OEF model that formulates NGN and DHN constraints in the frequency domain and corresponding model compaction techniques for efficient solving are contributed. First, an energy circuit method (ECM) that algebraizes the PDEs of NGNs and DHNs in the frequency domain is introduced. Then, an ECM-based OEF model is formulated, which contains fewer variables and constraints than an FDM-based OEF model and thereby yields better solving efficiency. Finally, variable space projection is employed to remove implicit variables, by which another constraint generation algorithm is enabled to remove redundant constraints. These two techniques further compact the OEF model and bring about a second improvement in solving efficiency. Numerical tests on actual systems indicate the final OEF model reduces variables and constraints by more than 95% and improves the solving efficiency by more than 10 times. In conclusion, the proposed OEF model and solving techniques well meet the optimization needs of large-scale IESs.

Pay online or pay on delivery? An ERP study of how payment methods affect online purchase decisions for search vs. experience products

The payment method is a key factor influencing online consumers’ purchase decisions. However, little is known about its underlying neural basis, which could help to reveal the mechanism by which payment methods affect online purchase decisions. Combined with the event-related potentials (ERPs), a neuroscience technique with the advantage of measuring implicit psychological variables to reveal the mechanism behind behaviors, this study uncovers consumers’ discrepant perception between pay-online and pay-on-delivery in different purchase contexts through an online purchase task. Behavioral results showed that purchase intention is higher for pay-online than pay-on-delivery, regardless of product type. At the brain level, we found consumers induce higher perceived risk (indicated in larger N2 amplitudes) and smaller negative emotion (mirrored by larger P3 amplitudes) for pay-online than pay-on-delivery, especially when shopping for search products. However, this effect disappeared when purchasing experience products. Moreover, the larger perceived risk for experience products than search products may lead consumers to ignore the difference between the two payment methods. This study helps online sellers optimize payment services for specific products.

Understanding the heating characteristics in microwave heating moving and deforming foods by a hybrid double-layer ALE/implicit algorithm

Multiphysics modeling is an essential tool to understand the heating characteristics of microwave processing food products. However, when the heated sample is moving and deforming during the heating process (e.g. the long-term microwave drying process), the huge mesh distortions caused by the sample's motion and deformation will make the simulation impossible. To solve this problem, a hybrid double-layer Arbitrary-Lagrange-Eulerian (ALE)/implicit algorithm is proposed in this paper, where the sample's motion and deformation is tracked by a double-layer ALE framework and convert to time-varying implicit variables. By doing so, the influence of the sample motion and deformation on the electromagnetic field distribution will be characterized by the evolution of time-varying implicit variables, rather than by explicit modeling, which reduces the mesh distortion and makes the calculation possible. A two-dimensional and a three-dimensional model of microwave cavities containing a rotating and deforming food are given to describe the algorithm in detail. Experiments for the three-dimensional model are also conducted to validate the proposed method. Results show that the shrinkage of the food may have a negative impact on the heating performance (especially energy efficiency) and corresponding remedies need to be performed for long-term microwave heating processes.

Participant or spectator? Comprehending the sport sponsorship process from different perspectives

PurposeDespite the fact this is already known over how sports spectators interpret sponsorship content, less is known about participants’ sentiments toward sponsors, which are sometimes crucial to the survival of participation sporting events. Various researchers have examined at how spectators recognize or recall sponsors, but very few have explored it from the viewpoint of sport participants. This research portrays a shift in focus from spectator-based to participant-based marketing by studying the differences over how participants and spectators perceive sport sponsorship, recognizing the current knowledge gap regarding the process of sponsorship in participant-based sports. Sponsorship previous research has focused on the effectiveness of sport sponsorships utilizing variables such sponsor recognition, desire to sponsors’ products and anticipated sponsorship advantages (Koronios and Dimitropoulos, 2020). The examination of sport sponsorship from the standpoint of the sport spectator is something that many of the past studies have in common. The application of past research analysing sport sponsorship from a distinct perspective: that of the sport participant, distinguishes the present study. In addition, the present study explores the distinctions in sponsorship effectiveness between spectators and participants, a topic which hasn't been addressed at before in sponsorship literature.Design/methodology/approachThis study depicts a move away from spectator-based to participant-based marketing, utilizing contemporary implicit physiological variables to measure participants’ and spectators’ awareness of and attitude toward sponsors, and it proposes a model of their purchase intentions toward real sponsors of a martial event. A total of 1,332 questionnaires were collected and analysed.FindingsFactors such as sport involvement, social media use, sincerity and beliefs of sponsorship were found to have a significant effect on purchase intention of spectators and participants. And crucial differences in the significance of various antecedent factors between spectators and participants were detected in the formation of positive purchase and word-of-mouth intentions.Originality/valueThis paper gives prominence to contemporary methodological advances in the field of implicit social cognition and their relevance to sponsorship. This is the first sponsorship research which examines the effectiveness of sponsorship variables under the implicit–explicit variables dichotomy.

Social Interpretable Tree for Pedestrian Trajectory Prediction

Understanding the multiple socially-acceptable future behaviors is an essential task for many vision applications. In this paper, we propose a tree-based method, termed as Social Interpretable Tree (SIT), to address this multi-modal prediction task, where a hand-crafted tree is built depending on the prior information of observed trajectory to model multiple future trajectories. Specifically, a path in the tree from the root to leaf represents an individual possible future trajectory. SIT employs a coarse-to-fine optimization strategy, in which the tree is first built by high-order velocity to balance the complexity and coverage of the tree and then optimized greedily to encourage multimodality. Finally, a teacher-forcing refining operation is used to predict the final fine trajectory. Compared with prior methods which leverage implicit latent variables to represent possible future trajectories, the path in the tree can explicitly explain the rough moving behaviors (e.g., go straight and then turn right), and thus provides better interpretability. Despite the hand-crafted tree, the experimental results on ETH-UCY and Stanford Drone datasets demonstrate that our method is capable of matching or exceeding the performance of state-of-the-art methods. Interestingly, the experiments show that the raw built tree without training outperforms many prior deep neural network based approaches. Meanwhile, our method presents sufficient flexibility in long-term prediction and different best-of-K predictions.

Variables in early algebra: exploring didactic potentials in programming activities

In this paper we consider implications of the current world-wide inclusion of computational thinking in relation to children’s development of algebraic thinking. Little is known about how newly developed visual programming environments such as Scratch could enhance early algebra learning. The study is based on examples of programming activities used by mathematics teachers in Sweden, teaching students aged 10–12 years during the first two years of implementing programming in the mathematics curriculum. Informed by Chevallard’s praxeology in terms of praxis and logos, we describe, unpack, discuss and expand these activities. Core issues related to algebra found in the three activities are as follows: making implicit variables explicit; using a counter variable; and identifying parameters as a specific type of variable. Our findings show that, in addition to already identified uses of variables in early algebra, programming activities in the early years bring in new aspects and new ways of treating variables that could, potentially, enhance students’ understanding of variables and generalization, provided that programming praxis is embedded in an appropriate algebra logos.

Chaotically Enhanced Meta-Heuristic Algorithms for Optimal Design of Truss Structures with Frequency Constraints

The natural frequencies of any structure contain useful information about the dynamic behavior of that structure, and by controlling these frequencies, the destructive effects of dynamic loads, including the resonance phenomenon, can be minimized. Truss optimization by applying dynamic constraints has been widely welcomed by researchers in recent decades and has been presented as a challenging topic. The main reason for this choice is quick access to dynamic information by examining natural frequencies. Also, frequency constraint relations are highly nonlinear and non-convex and have implicit variables, so using mathematical and derivative methods will be very difficult and time consuming. In this regard, the use of meta-heuristic algorithms in truss weight optimization with frequency constraints has good results, but with the introduction of form variables, these algorithms trap at local optima. In this research, by applying chaos map in meta-heuristic algorithms, suitable conditions have been provided to escape from local optima and access to global optimums. These algorithms include Chaotic Cyclical Parthenogenesis Algorithms (CCPA), Chaotic Biogeography-Based Optimization (CBBO), Chaotic Teaching-Learning-Based Optimization (CTLBO) and Chaotic Particle Swarm Optimization (CPSO), respectively. Also, by using different scenarios, a good balance has been achieved between the exploration and exploitation of the algorithms.

Development of Academic Writing Block Scale (AWBS): A Validity and Reliability Study

Academic writing block is a psychology-based writing problem that has a productivity-lowering effect on the publishing process. This study aimed to develop a valid and reliable assessment tool to measure academic writing block. To this end, during the pre-trial and trial applications, academicians were interviewed face to face. The main application was realized through the online form. A pre-reliability test, exploratory factor analysis (EFA) and total item correlation were conducted in the trial application. In the main application, reliability test, confirmatory factor analysis (CFA) and total item correlation were performed for construct validity. After the trial application, it was determined that the scale has a four-factor structure consisting of 23 items. Cronbach Alpha values were calculated to determine the preliminary reliability of the scale. CFA was conducted to test the construct validity of the Academic Writing Block Scale (AWBS). It was observed that the structure with four implicit variables determined in CFA was confirmed by the remaining 18 items in the scale. Besides, the total item correlation was used to test construct validity. Cronbach Alpha values were calculated to determine the reliability of AWBS. According to these values, it can be said that AWBS is a valid and reliable assessment instrument that can be used to measure academic writing block.