Intrinsic Constraints Research Articles

Linking the intrinsic and environmental constraints with PwD tourists' behavioral intentions toward a travel destination: mediating role of learned helplessness

PurposeThe present study examines the antecedents of learned helplessness, i.e. intrinsic and environmental constraints and consequences, i.e. intention to travel and expectation in the context of people with disability (PwD) tourism context by applying the “Theory of Learned Helplessness”.Design/methodology/approachThe survey method was used to gather data from 209 physically disabled people who had visited/traveled to any tourist destination in the past twelve months. Structural equation modeling technique was used to analyze data.FindingsThe findings reveal that intrinsic and environmental constraints positively influence learned helplessness. Consequently, learned helplessness negatively effects intention to travel and positively affects expectation of PWD tourist' toward a travel destination. Furthermore, learned helplessness contributed as a mediator between intrinsic constraints and intention to travel toward a tourist destination.Originality/valueEven though the body of literature on associations studied pertaining the conceptual lens of learned helplessness is widely recognized, there is dearth of literature investigating the connections between travel constraints, learned helplessness, PwDs intention and their expectation in travel destination context.

Profiling the South African Recreational Domestic Tourist in the Era of COVID-19

The profiling crisis-induced domestic tourist behavior in contemporary tourism is a burgeoning discourse. This study addresses the urgent need to proactively predict and manage domestic tourist behavior. Therefore, the study aimed to profile the South African domestic tourist to predict better tourists' behavioral and demand responses considering the COVID-19 pandemic. Data were generated from a pre-recruited panel of South African consumers as potential domestic tourists via an online survey, resulting in a sample of n = 427. Exploratory Factor analysis and a two-step cluster analysis were used to identify the clusters based on the respondents' socio-demographic variables, intrinsic (push) and destination attribute-related pull motives, and constraints and willingness to pay. South African domestic recreational tourists are primarily motivated to travel by their relaxation and self-fulfillment needs and seek primarily nature and adventure activity, with palpable differences in their perceived intrinsic and extrinsic constraints and their willingness to pay for domestic tourism. The study is novel in profiling (the contemporary South African domestic tourist) and contextualizing (tourist behavior during a crisis) domestic tourists within an African tourism context. Thus, providing empirical evidence-based insights into the heterogeneity of domestic tourists based on innate psychographic characteristics and salient perception-oriented attributes as viable market segmentation bases.

Using decisive constraints to create design methods that guide ethical impact

Numerous methods have been designed to aid practitioners in identifying ethical concerns, imagining potential futures, defining values, and evaluating existing systems. However, there is little scholarship that addresses the design of these methods, including how ethical concerns are operationalized in these methods. In this paper, we report results of an interview study with twelve ethics-focused method designers, investigating their process of instigating, creating, and disseminating their method. We conducted a top-down thematic analysis using the Biskjaer and Halskov framework of decisive constraints, identifying intrinsic, extrinsic, and self-imposed constraints alongside iterative and evaluative resonance-seeking activities. This analysis provides a rich conceptual vocabulary to better describe the design of methods for ethical impact from the perspective of researchers and practitioners. • We use the language of decisive constraints to describe how methods are shaped. • We identify how design methods operationalize ethical perspectives through constraints. • We indicate opportunities for methods to support ethics-focused design processes.

A single level tunneling model for molecular junctions: evaluating the simulation methods.

A single level tunneling model has been the most popular model system in both experimental and theoretical studies of molecular junctions. We performed a detailed simulation study on the performance of the single level tunneling model for analyzing the charge transport in molecular junctions. Three different modeling methods, including the numerical integration of the Landauer formula and two approximated analytical formulas that are extensively used for extracting key transport parameters, i.e. the energy offset and the coupling strength between molecules and electrodes from current-voltage (I-V) characteristics were compared and evaluated for their applicability. The simulation of I-V plots shows that the applicability of the two approximated analytical models is dependent on the energy offset and coupling strength. Model analysis based on the three methods performed on experimental data obtained from representative literature papers revealed that the two approximated analytical methods are neither suitable for small coupling strength nor suitable for low energy offset, and they also deviated from the exact results at high bias. These results imply that the transport parameters by the model analysis can be wrong if the models were not correctly applied under their intrinsic constraints, therefore providing wrong physical information about the system. We finally provided an applicability map as a guide for different modeling methods for charge transport studies in molecular devices.

Ultrafast imaging of terahertz electric waveforms using quantum dots

Microscopic electric fields govern the majority of elementary excitations in condensed matter and drive electronics at frequencies approaching the Terahertz (THz) regime. However, only few imaging schemes are able to resolve sub-wavelength fields in the THz range, such as scanning-probe techniques, electro-optic sampling, and ultrafast electron microscopy. Still, intrinsic constraints on sample geometry, acquisition speed and field strength limit their applicability. Here, we harness the quantum-confined Stark-effect to encode ultrafast electric near-fields into colloidal quantum dot luminescence. Our approach, termed Quantum-probe Field Microscopy (QFIM), combines far-field imaging of visible photons with phase-resolved sampling of electric waveforms. By capturing ultrafast movies, we spatio-temporally resolve a Terahertz resonance inside a bowtie antenna and unveil the propagation of a Terahertz waveguide excitation deeply in the sub-wavelength regime. The demonstrated QFIM approach is compatible with strong-field excitation and sub-micrometer resolution—introducing a direct route towards ultrafast field imaging of complex nanodevices in-operando.

Robust Graph Regularized Nonnegative Matrix Factorization

Nonnegative Matrix Factorization (NMF) has become a popular technique for dimensionality reduction, and been widely used in machine learning, computer vision, and data mining. Existing unsupervised NMF methods impose the intrinsic geometric constraint on the encoding matrix, which only indirectly affects the base matrix. Moreover, they ignore the global structure of the data space. To address these issues, in this paper we propose a novel unsupervised NMF learning framework, called Robust Graph regularized Nonnegative Matrix Factorization (RGNMF). RGNMF constructs a sparse graph imposed on the basis matrix to catch the global structure and preserve the discriminative information. And it models the local structure by building a k-NN graph constrained on the encoding matrix, which gains the compact representation. Consequently, RGNMF not only respects the global structure, but also depicts the local structure. In addition, it employs such a L_2,1-norm cost function to decompose the basis matrix and encoding matrix that its robustness can be improved. Further, it imposes the L_2,1-norm constraint on the basis matrix to choose the discriminative feature. Hence, RGNMF can gain the robust discriminative representation by combining structure learning and L_2,1-norm constraints imposed on the basis matrix and encoding matrix. Extensive experiments on real-world problems demonstrate that RGNMF achieves better clustering results than the state-of-the-art approaches.

A Total Variation Regularized Bipartite Network for Unsupervised Change Detection

Detecting changes in complicated remote sensing images have been gaining much attention. One of the main challenges lies in how to detect intrinsic changes robustly while avoiding the false alarms caused by various challenging factors, such as spatial illumination variations, small viewpoint differences, noises and outliers between multi-temporal remote sensing images. To reduce the influence of these factors, in this paper, we propose an unsupervised joint learning model based on a total variation regularization and bipartite deep convolutional neural network, called total variation regularized bipartite network (TVRBN). In this model, parametric feature differences are initialized by a bipartite autoencoder. An objective function is defined as a parametric feature difference term integrated with a change pre-detection constraint term and a total variation regularization to the change probability map with intrinsic changes. Then the unified objective function is optimized jointly to learn the bipartite network parameters and detect an intrinsic change map. Due to the intrinsic difference constraints in feature space and total variation regularization, the proposed TVRBN method can compute higher-quality and smoother change maps, suppress non-intrinsic changes, and overcome small viewpoint differences in comparable images. Extensive experiments on both homogeneous and heterogeneous images demonstrate the robustness of the proposed method by comparing it with state-of-the-art methods.

Direct Conversion of Methane to C2Hydrocarbons in Solid-State Membrane Reactors at High Temperatures

Direct conversion of methane to C2 compounds by oxidative and nonoxidative coupling reactions has been intensively studied in the past four decades; however, because these reactions have intrinsic severe thermodynamic constraints, they have not become viable industrially. Recently, with the increasing availability of inexpensive "green electrons" coming from renewable sources, electrochemical technologies are gaining momentum for reactions that have been challenging for more conventional catalysis. Using solid-state membranes to control the reacting species and separate products in a single step is a crucial advantage. Devices using ionic or mixed ionic-electronic conductors can be explored for methane coupling reactions with great potential to increase selectivity. Although these technologies are still in the early scaling stages, they offer a sustainable path for the utilization of methane and benefit from the advances in both solid oxide fuel cells and electrolyzers. This review identifies promising developments for solid-state methane conversion reactors by assessing multifunctional layers with microstructural control; combining solid electrolytes (proton and oxygen ion conductors) with active and selective electrodes/catalysts; applying more efficient reactor designs; understanding the reaction/degradation mechanisms; defining standards for performance evaluation; and carrying techno-economic analysis.

Gramian-constrained optimization process for the stiffness model identification of industrial manipulators

AbstractThis work deals with the elastostatic identification of industrial manipulators. By reviewing the basics of the physical elastic properties of both links and joints in the framework of the lumped stiffness modeling techniques, the Gramian nature of the stiffness matrices has been found out adequate to do so. Then, a novel optimization method has been developed, which incorporates the Gramian matrix formulation along a non-linear optimization process, acting as an intrinsic constraint for the conservativeness of the elastostatic modeling. Numerical and experimental analyses evince the effectiveness of the proposed method, as the elastostatic models obtained by means of the proposed technique predict more than 93.7% of the compliance deviations of a real industrial robot. The proposed method is simple enough to be jointly applicable to the most recent elastostatic model reduction techniques.

The Effect of Context and Individual Differences in Human-Generated Randomness.

Many psychological studies have shown that human‐generated sequences are hardly ever random in the strict mathematical sense. However, what remains an open question is the degree to which this (in)ability varies between people and is affected by contextual factors. Herein, we investigated this problem. In two studies, we used a modern, robust measure of randomness based on algorithmic information theory to assess human‐generated series. In Study 1 (N=183), in a factorial design with task description as a between‐subjects variable, we tested the effects of context and mental fatigue on human‐generated randomness. In Study 2 (N=266), in online research, in experimental design, we further investigated the effect of mental fatigue on the randomness of human‐generated series and the relationship between the need for cognition (NFC) and the ability to produce random‐like series. Results of Study 1 show that the activation of the ability to produce random‐like series depends on the relevance of the contextual cues (χ2(2)=7.9828,p=.0192), whether they activate known representations of a random series generator and consequently help to avoid the production of trivial sequences. Our findings from both studies on the effect of mental fatigue (Study 1 – t(47,529.5568)=−18.62,p<.001; Study 2 – F(edf=3.587,Ref.df=3.587)=11.863,p<.0001) and cognitive motivation (t(180)=2.66,p=.009) demonstrate that regardless of the context or task's novelty people quickly lose interest in the random series generation. Therefore, their performance decreases over time. However, people high in the NFC can maintain the cognitive motivation for a longer period and consequently on average generate more random series. In general, our results suggest that when contextual cues and intrinsic constraints are in optimal interaction people can temporarily escape the structured and trivial patterns and produce more random‐like sequences.

Transtibial Anterior Cruciate Ligament Reconstruction: Tips for a Successful Anatomic Reconstruction

Anterior cruciate ligament (ACL) reconstruction is one of the most commonly performed orthopaedic sports procedures. Two main techniques are used for accomplishing an ACL reconstruction: transtibial and anteromedial portal techniques. The transtibial technique has been criticized for its inability to create an anatomic femoral tunnel given the intrinsic constraint of the tibial tunnel during drilling. However, technical modifications of the transtibial technique can result in anatomic tunnel entrance positioning and a properly oriented graft. This Technical Note presents our technique for anatomic transtibial ACL reconstruction.

대학생의 여가동기별 군집에 따른 여가제약이 회복탄력성에 미치는 영향

초록·키워드 목차 오류제보하기 This study aimed to investigate the effect of leisure constraints on university students" resilience according to the cluster by their leisure motives. To achieve this objective, data were collected using a questionnaire consisting of leisure motives, leisure constraints, and resilience from 215 students enrolled at universities in Gyeonggi-do who were collected using the cluster sampling method, and the analysis results are as follows. First, the number of clusters according to the leisure motives of university students was divided into two. Cluster 1 is a group with relatively high physical, cultural, and exploratory motives, and Cluster 2 is a group with a relatively high motive for interpersonal relationships. Second, among the leisure constraints of university students, intrinsic constraints have a statistically significant negative effect on the self-optimism and grateful attitudes about resilience. In other words, in order to increase the self-optimism and grateful attitudes about resilience, the intrinsic constraints of university students should be reduced. Third, the effect of leisure constraints on the resilience of university students according to the cluster by their leisure motive was analyzed. As a result, in the effect of intrinsic constraints on grateful attitudes, there was a moderating effect according to the cluster by leisure motive. In other words, since the intrinsic constraints are relatively related to the grateful attitude about resilience among university students with cluster 1 motivational orientation, a reduction of the intrinsic constraints can have an positive effect on the resilience of university students with cluster 1 motivational orientation. #leisure constraints #Resilience #cluster analysis #leisure motive AbstractⅠ. 서론Ⅱ. 연구방법Ⅲ. 결과Ⅳ. 논의Ⅴ. 결론 및 제언참고문헌

여성 에어로빅 참가자의 여가제약이 여가제약협상, 여가활동 전문화와 여가만족에 미치는 영향분석

The purpose of this study is to analyze the effects of leisure constraints on leisure constraints negotiation, recreation specialization and leisure satisfaction of female aerobics participants. Therefore, a total of 249 female participants who participated in aerobics as a leisure activity in the H city of Gyeongsangbuk-do were used for actual analysis. For data processing, frequency analysis, exploratory factor analysis, reliability analysis, correlation analysis, and multiple regression analysis were performed using SPSS23.0. Through this, the following results were derived. First, structural constraints, which are sub-factors of leisure constraints, were found to have a positive (+) significant effect on the time and cost management effort of negotiating leisure constraints. Second, intrinsic constraints, a sub-factor of leisure constraints, were found to have a negative (-) significant effect on recreational specialization. Third, intrinsic and structural constraints, which are sub-factors of leisure constraints, were found to have a negative (-) significant effect on leisure satisfaction. Fourth, it was found that the effort to manage time and cost and the effort to change aspirations, which are sub-factors of negotiation of leisure constraints, had a positive (+) significant effect on the recreational specialization. Fifth, it was found that the effort to change aspirations, which is a sub-factor of negotiation of leisure constraints, had a positive (+) significant effect on leisure satisfaction. Sixth, it was found that recreational specialization had a positive (+) significant effect on leisure satisfaction.

Explicit and implicit depth-cue integration: Evidence of systematic biases with real objects

In previous studies using VR, we found evidence that 3D shape estimation agrees to a superadditivity rule of depth-cue combination, by which adding depth cues leads to greater perceived depth and, in principle, to depth overestimation. Superadditivity can be quantitatively accounted for by a normative theory of cue integration, via adapting a model termed Intrinsic Constraint (IC). As for its qualitative nature, it remains unclear whether superadditivity represents the genuine readout of depth-cue integration, as predicted by IC, or alternatively a byproduct of artificial virtual displays, because they carry flatness cues that can bias depth estimates in a Bayesian fashion, or even just a way for observers to express that a scene “looks deeper” with more depth cues by explicitly inflating their depth judgments. In the present study, we addressed this question by testing whether the IC model’s prediction of superadditivity generalizes to real world settings. We asked participants to judge the perceived 3D shape of cardboard prisms through a matching task. To control for the potential interference of explicit reasoning, we also asked participants to reach-to-grasp the same objects and we analyzed the in-flight grip size throughout the reaching. We designed a novel technique to carefully control binocular and monocular 3D cues independently, allowing to add or remove depth information seamlessly. Even with real objects, participants exhibited a clear superadditivity effect in both tasks. Furthermore, the magnitude of this effect was accurately predicted by the IC model. These results confirm that superadditivity is an inherent feature of depth estimation.

Complex relationships between beta diversity and dispersal in meta‐community models

Dispersal is one of the most important drivers of community assembly. Understanding how dispersal impacts spatial variations in community composition (beta diversity) is crucial for predicting biodiversity change during the Anthropocene. Classic theories and a large amount of empirical evidence have led to a common belief that increasing dispersal rate should lead to regional community homogenization (lower beta diversity). However, recent empirical studies show that increasing dispersal can also lead to regional community divergence when interacting with other processes such as disturbance and priority effect. The underappreciation of the complex relationship between beta diversity and dispersal might impede our ability to make accurate ecological forecasts. To address this concern, I examine the beta diversity–dispersal relationship through extinction and colonization dynamics in meta‐community models. I find that a wide range of beta diversity–dispersal relationships (e.g. negative, positive, hump‐shaped) can be generated by changes in species‐level occurrence probabilities when meta‐population dispersal (rather than mainland–island dispersal), disturbance and strong biotic interactions are at play. Specifically, reducing the variance of species‐level occurrence probabilities leads to community divergence and increasing the mean occurrence probability leads to community homogenization; a positive or hump‐shaped beta diversity–dispersal relationship emerges when the effect of the decreasing variance is stronger than the effect of increasing mean. These findings highlight the intrinsic constraint of occupancy stochasticity on beta diversity changes and echo the call for more realistic manipulations of stochastic dispersal and a detailed understanding of the interaction networks to test various beta diversity–dispersal relationships in future empirical studies.

The Intrinsic Shapes of Low Surface Brightness Galaxies (LSBGs): A Discriminant of LSBG Galaxy Formation Mechanisms

Abstract We use the low surface brightness galaxy (LSBG) samples created from the Hyper Suprime-Cam Subaru Strategic Program (781 galaxies), the Dark Energy Survey (20977 galaxies), and the Legacy Survey (selected via H i detection in the Arecibo Legacy Fast ALFA Survey, 188 galaxies) to infer the intrinsic shape distribution of the LSBG population. To take into account the effect of the surface brightness cuts employed when constructing LSBG samples, we simultaneously model both the projected ellipticity and the apparent surface brightness in our shape inference. We find that the LSBG samples are well characterized by oblate spheroids, with no significant difference between red and blue LSBGs. This inferred shape distribution is in good agreement with similar inferences made for ultra-diffuse cluster galaxy samples, indicating that environment does not play a key role in determining the intrinsic shape of LSBGs. We also find some evidence that LSBGs are more thickened than similarly massive high surface brightness dwarfs. We compare our results to intrinsic shape measures from contemporary cosmological simulations, and find that the observed LSBG intrinsic shapes place considerable constraints on the formation path of such galaxies. In particular, LSBG production via the migration of star formation to large radii produces intrinsic shapes in good agreement with our observational findings.

Just Noticeable Differences in 3D Shape Perception: A Measure of Estimation Noise or Cue Gain?

The present study sought to test two fundamentally distinct interpretations of the Just Noticeable Difference (JND) for depth discrimination. According to the widely accepted Bayesian Maximum Likelihood Estimation (MLE) model of 3D processing, the JND measures the standard deviation of the noise corrupting a depth estimate. The Intrinsic Constraint (IC) model of cue integration alternatively suggests that the JND is the result of task related decision noise rather than noise directly corrupting the depth estimate. According to this account, the JND is inversely proportional to the slope, or gain, of the perceptual function relating physical depth to perceived depth. To test this novel interpretation, we tested depth discrimination with a classic 2-Interval Forced Choice task in which the gains of a fixed standard stimulus and varying comparison stimulus could either have a High or Low value, or, according to Bayesian models, High or Low reliabilities. Bayesian models predict that the JND should depend on both the reliability of the standard and comparison stimuli. However, according to the IC model, the JND only depends on the gain of the comparison stimulus, and therefore there should be no difference between conditions where the comparison has the same gain. Empirical results with texture stimuli closely align with the IC predictions. In conclusion, since JNDs do not measure the magnitude of the noise of 3D estimates as assumed by the Bayesian approach, these findings lead to a radically different interpretation of previous depth discrimination data.

Experimental implementation of precisely tailored light-matter interaction via inverse engineering

Accurate and efficient quantum control in the presence of constraints and decoherence is a requirement and a challenge in quantum information processing. Shortcuts to adiabaticity, originally proposed to speed up the slow adiabatic process, have nowadays become versatile toolboxes for preparing states or controlling the quantum dynamics. Unique shortcut designs are required for each quantum system with intrinsic physical constraints, imperfections, and noise. Here, we implement fast and robust control for the state preparation and state engineering in a rare-earth ions system. Specifically, the interacting pulses are inversely engineered and further optimized with respect to inhomogeneities of the ensemble and the unwanted interaction with other qubits. We demonstrate that our protocols surpass the conventional adiabatic schemes, by reducing the decoherence from the excited-state decay and inhomogeneous broadening. The results presented here are applicable to other noisy intermediate-scale quantum systems.

목표지향적 행동모형과 여가제약-협상 모형을 활용한 프로배구 관람객의 의사결정과정

The purpose of this study is to verify the decision-making process of professional volleyball spectators using a goal-oriented behavior model and a leisure constraint-negotiation model, and to provide empirical basic data for efficient strategy establishment. First, it was found that attitude, positive anticipation, negative anticipation, and perceived behavioral control had a positive effect on aspiration, and subjective norms did not. Second, aspiration positively influenced behavioral intention. Third, perceived behavior controllability had no influence on behavioral intention. Fourth, intrinsic constraints, a sub-factor of leisure constraints, were found to have an effect on negotiation, and interpersonal constraints and structural constraints did not.Fifth, it was found that the aspiration of the goal-oriented behavior model had a significant effect on the negotiation. Sixth, it was found that negotiation did not affect the intention of action. Therefore, it is expected that the results of this study will be the basic data for the decision-making of professional volleyball visitors.

Sterically Encumbered Coordination Sites. Iron(II) Complexes of Jäger‐type ligands with a Terphenyl Backbone

AbstractA Schiff base‐like ligand H2LTerPh featuring a para‐terphenyl backbone was synthesized and converted in two steps to an octahedral iron(II) coordination polymer with 1,2‐bis‐(4‐pyridyl)‐ethylene as bridging ligand. Single crystal X‐ray structure analysis was possible for the free ligand and the two octahedral iron(II) complexes. The Schiff base‐like ligand features a [N2O2]2− coordination sphere for the metal center and the para‐terphenyl backbone introduces intrinsic steric constraint. Characterization of the coordination polymer with an N4O2 coordination sphere around the iron center via magnetic measurements (SQUID) and room temperature Mössbauer spectroscopy revealed HS character in the entire temperature range investigated. Structure modelling with DFT calculations supports the findings, but support also the possibility of spin crossover in solution.