Extant Models Research Articles

A consumer values-based approach to enhancing green consumption

Pro-environmental consumer behaviours are increasingly desirable. However, many studies report that gaps exist between consumers’ attitudes toward green products and their purchasing behaviours. Understanding these gaps is important to foster market demand for green products. Two sets of distinct consumer value systems offer insights into green consumer behaviour. Consumers’ personal values (PVs), i.e. their altruistic, biospheric and egoistic values, as well as consumers’ consumption values (CVs), e.g. the functional, psychological, economic or social perceived benefits consumers perceive in relation to a green offering. This paper examines current understanding of the individual and combined roles of consumers’ PVs and CVs in green consumption through an integrative literature review. Three knowledge gaps are identified, including that extant theories and models do not sufficiently consider associations between CVs and PVs in a green consumption context. To address the gaps, a conceptual model is proposed with eight testable propositions.

Modeling Gliding-based Target Selection for Blind Touchscreen Users.

Gliding a finger on touchscreen to reach a target, that is, touch exploration, is a common selection method of blind screen-reader users. This paper investigates their gliding behavior and presents a model for their motor performance. We discovered that the gliding trajectories of blind people are a mixture of two strategies: 1) ballistic movements with iterative corrections relying on non-visual feedback, and 2) multiple sub-movements separated by stops, and concatenated until the target is reached. Based on this finding, we propose the mixture pointing model, a model that relates movement time to distance and width of the target. The model outperforms extant models, improving R2 from 0.65 for Fitts' law to 0.76, and is superior in cross-validation and information criteria. The model advances understanding of gliding-based target selection and serves as a tool for designing interface layouts for screen-reader based touch exploration.

Family: Pacific perspectives

AbstractModels that conceptualize family are employed by psychologists, governments, legislators, and policymakers to inform professional practice and formulate public policy. However, extant models are notably Western‐centric in both origin and context. Given the cultural diversity of today's globalized world, it is timely to question whether such models can adequately represent populations within their ambit. This paper takes Bronfenbrenner's ecological systems model as one representative way of thinking about family and employs it as the point of contrast with Pacific family arrangements. Particular consideration is given to Indigenous Fijian (iTaukei), Tongan, and Māori perspectives. The model was found to be inadequate for representing Pacific family arrangements. This finding has important implications as it creates a space for new insights and possibilities. To this end, key family‐related metaphors common among Pacific peoples have been incorporated to conceptualize one potential alternative.

Low-carbon tourism strategy evaluation and selection using interval-valued intuitionistic fuzzy additive ratio assessment approach based on similarity measures.

Recently, the assessment and selection of most suitable low-carbon tourism strategy has gained an extensive consideration from sustainable perspectives. Owing to participation of multiple qualitative and quantitative attributes, the low-carbon tourism strategy (LCTS) selection process can be considered as multi-criteria decision-making (MCDM) problem. As uncertainty is usually occurred in LCTSs evaluation, the theory of interval-valued intuitionistic fuzzy sets (IVIFSs) has been established as more flexible and efficient tool to model the uncertain decision-making problems. The idea of the present study is to develop an extended method using additive ratio assessment (ARAS) framework and similarity measures in a way to find an effective solution to the decision-making problems using IVIFSs. The bases of the proposed method are the IVIFSs operators, some modifications in the traditional ARAS framework and a calculation procedure of the weights of the criteria. To calculate criterion weight, new similarity measures for IVIFSs are developed aiming at the achievement of more realistic weights. Also, a comparison is demonstrated to the currently used similarity measures in order to show the efficiency of the developed approach. To confirm that the developed IVIF-ARAS approach can be successfully employed to practical decision-making problems, a case study of LCTS selection problem is considered. The final results from the developed approach and the extant models are compared for the validation of the proposed approach in this study.

Creativity in problem solving: integrating two different views of insight

Even after many decades of productive research, problem solving instruction is still considered ineffective. In this study we address some limitations of extant problem solving models related to the phenomenon of insight during problem solving. Currently, there are two main views on the source of insight during problem solving. Proponents of the first view argue that insight is the consequence of analytic thinking and a sequence of conscious and stepwise steps. The second view suggests that insight is the result of unconscious processes that come about only after an impasse has occurred. Extant models of problem solving within mathematics education tend to highlight the first view of insight, while Gestalt inspired creativity research tends to emphasize the second view of insight. In this study, we explore how the two views of insight—and the corresponding set of models—can describe and explain different aspects of the problem solving process. Our aim is to integrate the two different views on insight, and demonstrate how they complement each other, each highlighting different, but important, aspects of the problem solving process. We pursue this aim by studying how expert and novice mathematics students worked on two ill-defined mathematical problems. We apply both a problem solving model and a creativity model in analyzing students’ work on the two problems, in order to compare and contrast aspects of insight during the students’ work. The results of this study indicate that sudden and unconscious insight seems to be crucial to the problem solving process, and the occurrence of such insight cannot be fully explained by problem solving models and analytic views of insight. We therefore propose that extant problem solving models should adopt aspects of the Gestalt inspired views of insight.

A Modified UK Real Value/Short-Cut DCF Model for the Valuation of a Nigerian Sub-Leased Commercial Property

A UK real value/short-cut DCF model that addresses the valuation of geared profit rents generated by sub-let commercial investment properties especially in climes with nationalized land policy has been elusive. This study examines the modification of the extant UK real value/short-cut DCF model for the valuation of head-leased occupancy rights with fixed and non-revisable ground rents pursuant to the provisions of the Nigeria Land Use Act. The model is developed by blending the inputs of the modified rational model with that of the Crosby's real value/short-cut DCF model leading to a valuation model that appears simplified compared to the existing UK contemporary value models. The gearing effect for the hypothetical case of sub-let head-lease occupancy right in Nigeria is manifested as having a diminishing impact of fixed ground rent on profit rent calculation. For the valuation case study involving this hypothetical head-leased occupancy right in Nigeria, it was found that the modified UK real value/short-cut DCF model produced valuation that is identical to those churned out from the full DCF, modified rational, and Crosby's real value/short-cut DCF models. For the same hypothetical case study, less than 1% difference is observed between valuations from the all risks real yield (ARRY) model, which evolved in New Zealand and the valuation produced from the modified UK real value/short-cut DCF model, thereby validating all methods against each other. In consonance with the International Valuation Standards pertaining to income valuation approach, a sustained pedagogy of emerging real value models alongside the extant nominal valuation models is suggested as a way forward. Nevertheless, the modified UK real value/short-cut DCF model is recommended as an alternative to the extant variants of real value and explicit DCF techniques for the valuation of sub-let head-lease occupancy rights with fixed/non-revisable ground rents payable especially in climes with nationalized land policy.

A novel method for online real-time forecasting of crude oil price

Improving the accuracy of crude oil price forecasting is helpful for stabilizing financial markets and oil import and export trade. However, the extant models rarely focus on the online forecasting and uncertainty of crude oil price. Motivated by these, a new forecasting method is proposed. Firstly, the price sequence is decomposed to get the regular sub-sequences and noise sequence by the improved variational mode decomposition, whose parameters are optimized by particle swarm optimization based on average maximum envelope entropy. Secondly, the proposed hybrid model of point prediction is established by the characteristics of sub-sequences, and the interval prediction model is constructed by combining the point prediction model and Bootstrap sampling. Finally, the proposed point prediction model and interval prediction model are formed into a predictor to realize online real-time prediction. The empirical results show that, compared with other competing models, the proposed model for point prediction improves basically by over 10% on loss functions in the different frequency data, which verifies that it is superior to other models in accuracy and robustness. The coverage rate and fluctuation consistency are almost all more than 70% in the interval prediction results. In a word, the proposed method has high flexibility and accuracy, which can provide referring information for practitioners working with oil.

A Novel Signal Processing Method for Friction and Sliding Wear

Abstract This current study proposed a new computationally efficient and comparatively accurate algorithm for calculating both static and dynamic coefficients of friction from high-frequency data. Its scope embraced an application in a real-time friction-based system, such as active braking safety systems in automobile industries. The signal sources were from a heavy-duty reciprocating dry sliding wear test platform, focused on experimental data related to friction induced by stick-slip phenomena. The test specimen was a polytetrafluoroethylene (PTFE)-coated basalt/vinyl ester composite material, tested at a large scale. The algorithm was primarily aimed to provide scalability for processing significantly large tribological data in a real-time. Besides a computational efficiency, the proposed method adopted to evaluate both static and dynamic coefficients of friction using the statistical approach exhibited a greater accuracy and reliability when compared with the extant models. The result showed that the proposed method reduced the computation time of processing and reduced the variation of the absolute values of both static and dynamic frictions. However, the variation of dynamic friction was later increased at a particular threshold, based on the test duration.

What Drives Consumers’ Decisions to Use Intelligent Agent Technologies? A Systematic Review

As artificial intelligence continues to advance, it will increasingly empower the successful use of intelligent agent (IA) technologies in marketing practices. The purpose of this paper is to summarize the state-of-the-art literature and present a holistic view of different types of antecedents of IA technology use in marketing from the consumer’s perspective. This paper uses the systematic literature review method and covers 107 articles published in scientific journals between 2000 and 2020. The identified antecedents are categorized into IA characteristics, consumer perceptions, external conditions, as well as individual characteristics and analyzed at the individual level of use. Future research should focus on investigating the relative importance of the effects of IA characteristics, consumer perceptions, external, and individual factors on consumers’ intentions to use IAs. This paper argues that while extant technology acceptance models contribute to the understanding of IA use, IAs, due to their unique characteristics (e.g., anthropomorphism) and dimensions (e.g., IA as an interface, as a proxy for the system and an autonomous aggregator and agent), require a new lens to explain the drivers of IA use in data-rich and process-rich environments. The traditional technology acceptance theories provide a valuable, yet incomplete understanding of how consumers use IAs. Drawing from representation theory, this paper proposes a theoretical framework of IA use and argues that IAs act as representations to facilitate the primary goal.

An image-computable model of how endogenous and exogenous attention differentially alter visual perception

Attention alters perception across the visual field. Typically, endogenous (voluntary) and exogenous (involuntary) attention similarly improve performance in many visual tasks, but they have differential effects in some tasks. Extant models of visual attention assume that the effects of these two types of attention are identical and consequently do not explain differences between them. Here, we develop a model of spatial resolution and attention that distinguishes between endogenous and exogenous attention. We focus on texture-based segmentation as a model system because it has revealed a clear dissociation between both attention types. For a texture for which performance peaks at parafoveal locations, endogenous attention improves performance across eccentricity, whereas exogenous attention improves performance where the resolution is low (peripheral locations) but impairs it where the resolution is high (foveal locations) for the scale of the texture. Our model emulates sensory encoding to segment figures from their background and predict behavioral performance. To explain attentional effects, endogenous and exogenous attention require separate operating regimes across visual detail (spatial frequency). Our model reproduces behavioral performance across several experiments and simultaneously resolves three unexplained phenomena: 1) the parafoveal advantage in segmentation, 2) the uniform improvements across eccentricity by endogenous attention, and 3) the peripheral improvements and foveal impairments by exogenous attention. Overall, we unveil a computational dissociation between each attention type and provide a generalizable framework for predicting their effects on perception across the visual field.

Decision-making model of machine tool remanufacturing alternatives based on dual interval rough number clouds

Remanufacturing of machine tools has become one of the crucial topical subjects as it can effectively reduce the manufacturing cost compared with traditional new product development. The assessment and selection of different remanufacturing alternatives are vital for successfully implementing the remanufacturing of machine tools. Although numerous fuzzy theory-based methods have been developed to evaluate and select the reasonable remanufacturing alternatives in the machine tool remanufacturing process, they still have some drawbacks, such as requiring extra assumptions, ignoring the impact of internal relationships among distinct criteria, and lacking the mechanism of manipulating diverse uncertain information. To cover such limitations, this study develops an applicable decision-making method to assist managers in extracting the essential remanufacturing alternatives for product improvement. First, a new concept named dual interval rough number clouds (DIRNCs) is developed by combining the merit of interval rough numbers (IRNs) and interval cloud model in handling uncertain information. Then, an applicable decision-making support model of remanufacturing alternatives is constructed based on DIRNCs, two non-linear weighting methods, and a technique for order performance by similarity to ideal solution (TOPSIS). Finally, an assessment of remanufacturing alternatives of machine tools is presented to illustrate the model, whose effectivity is demonstrated by comparing extant models. Results display that the developed DIRNC-based model is more advantageous than triangular fuzzy-based, cloud model-based, fuzzy rough-based, and IRN-based methods.

Lethal coalitionary attacks of chimpanzees (Pan troglodytes troglodytes) on gorillas (Gorilla gorilla gorilla) in the wild

Intraspecies violence, including lethal interactions, is a relatively common phenomenon in mammals. Contrarily, interspecies violence has mainly been investigated in the context of predation and received most research attention in carnivores. Here, we provide the first information of two lethal coalitionary attacks of chimpanzees (Pan troglodytes troglodytes) on another hominid species, western lowland gorillas (Gorilla gorilla gorilla), that occur sympatrically in the Loango National Park in Gabon. In both events, the chimpanzees significantly outnumbered the gorillas and victims were infant gorillas. We discuss these observations in light of the two most widely accepted theoretical explanations for interspecific lethal violence, predation and competition, and combinations of the two-intraguild predation and interspecific killing. Given these events meet conditions proposed to trigger coalitional killing of neighbours in chimpanzees, we also discuss them in light of chimpanzees’ intraspecific interactions and territorial nature. Our findings may spur further research into the complexity of interspecies interactions. In addition, they may aid in combining field data from extant models with the Pliocene hominid fossil record to better understand behavioural adaptations and interspecific killing in the hominin lineage.

A Review of Biomarkers for Ischemic Stroke Evaluation in Patients With Non-valvular Atrial Fibrillation

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide and results in a significantly increased ischemic stroke (IS) risk. IS risk stratification tools are widely being applied to guide anticoagulation treatment decisions and duration in patients with non-valvular AF (NVAF). The CHA2DS2-VASc score is largely validated and currently recommended by renowned guidelines. However, this score is heavily dependent on age, sex, and comorbidities, and exhibits only moderate predictive power. Finding effective and validated clinical biomarkers to assist in personalized IS risk evaluation has become one of the promising directions in the prevention and treatment of NVAF. A number of studies in recent years have explored differentially expressed biomarkers in NVAF patients with and without IS, and the potential role of various biomarkers for prediction or early diagnosis of IS in patients with NVAF. In this review, we describe the clinical application and utility of AF characteristics, cardiac imaging and electrocardiogram markers, arterial stiffness and atherosclerosis-related markers, circulating biomarkers, and novel genetic markers in IS diagnosis and management of patients with NVAF. We conclude that at present, there is no consensus understanding of a desirable biomarker for IS risk stratification in NVAF, and enrolling these biomarkers into extant models also remains challenging. Further prospective cohorts and trials are needed to integrate various clinical risk factors and biomarkers to optimize IS prediction in patients with NVAF. However, we believe that the growing insight into molecular mechanisms and in-depth understanding of existing and emerging biomarkers may further improve the IS risk identification and guide anticoagulation therapy in patients with NVAF.

Multi-criteria food waste treatment method selection using single-valued neutrosophic-CRITIC-MULTIMOORA framework

Proper management and treatment of food waste have become a key concern due to its significant environmental, social, and economic ramifications. The selection of the most appropriate food waste treatment method among a set of alternative methods can be regarded as a multi-criteria decision-making problem because of the association of numerous qualitative and quantitative attributes. In this paper, we offer a novel integrated framework by combining criteria interaction through inter-criteria correlation (CRITIC) and multi-objective optimization based on ratio analysis with the full multiplicative form (MULTIMOORA) methods with single-valued neutrosophic sets (SVNSs) for assessing the multi-criteria food waste treatment methods selection. In this methodology, the CRITIC technique is applied for computing the attribute weights, and the MULTIMOORA model is employed for estimating the ranking of the options within SVNSs context. To examine the introduced methodology’s efficiency and achievability, a case study of food waste treatment method (FWTM) assessment is discussed in the SVNSs setting. Further, comparative study and sensitivity investigation are offered to certify the presented framework for prioritizing FWTMs. The final results indicate that the proposed approach achieves better solutions than the extant models.

A Basic Model to Predict Enteric Methane Emission from Dairy Cows and Its Application to Update Operational Models for the National Inventory in Norway.

Simple SummaryMany techniques exist to quantify enteric methane (CH4) emissions from dairy cows. Since measurement on the entire national cow populations is not possible, it is necessary to use estimates for national inventory reporting. This study aimed to develop (1) a basic equation of enteric CH4 emissions from individual animals based on feed intake and nutrient contents of the diet, and (2) to update the operational way of calculation used in the Norwegian National Inventory Report based on milk yield and concentrate share of the diet. An international database containing recently published data was used for this updating process. By this the accuracy of the CH4 production estimates included in the national inventory was improved.The aim of this study was to develop a basic model to predict enteric methane emission from dairy cows and to update operational calculations for the national inventory in Norway. Development of basic models utilized information that is available only from feeding experiments. Basic models were developed using a database with 63 treatment means from 19 studies and were evaluated against an external database (n = 36, from 10 studies) along with other extant models. In total, the basic model database included 99 treatment means from 29 studies with records for enteric CH4 production (MJ/day), dry matter intake (DMI) and dietary nutrient composition. When evaluated by low root mean square prediction errors and high concordance correlation coefficients, the developed basic models that included DMI, dietary concentrations of fatty acids and neutral detergent fiber performed slightly better in predicting CH4 emissions than extant models. In order to propose country-specific values for the CH4 conversion factor Ym (% of gross energy intake partitioned into CH4) and thus to be able to carry out the national inventory for Norway, the existing operational model was updated for the prediction of Ym over a wide range of feeding situations. A simulated operational database containing CH4 production (predicted by the basic model), feed intake and composition, Ym and gross energy intake (GEI), in addition to the predictor variables energy corrected milk yield and dietary concentrate share were used to develop an operational model. Input values of Ym were updated based on the results from the basic models. The predicted Ym ranged from 6.22 to 6.72%. In conclusion, the prediction accuracy of CH4 production from dairy cows was improved with the help of newly published data, which enabled an update of the operational model for calculating the national inventory of CH4 in Norway.

A new transit assignment model based on line and node strategies

Passengers traveling on transit networks involve two kinds of decision-making strategies: deciding which lines are attractive at an origin or transfer node (denoted line strategy, LS), or deciding which node to transfer at when riding a line (denoted node strategy, NS). Combining these two strategies, this paper proposes a novel variational inequality formulation for the user equilibrium passenger assignment problem. The inclusion of the NS eliminates the need for passenger assignment on a large augmented graph, reducing the modeling complexity and making it easier to track all passengers’ travel routes. Moreover, constraints on the maximal number of transfers—which are crucial in practical decision-making on transit networks—are explicitly included, further drastically reducing the set of passengers’ feasible strategies. Furthermore, some extant strategy-based transit assignment models are shown to be a special case of the proposed model when the transfer constraint is removed. Finally, the properties of the proposed model are illustrated on a small network, and the model and algorithm exhibit huge advantages on the chosen transit subnetwork of Beijing.

Novel Beings and Assisted Nonexistence.

This article engages with the legal regulation of end-of-existence decisionmaking for novel beings, specifically assisted nonexistence for such entities. The author explains the concept of a legal model for assisted death by reference to the substantive features of legal regimes in three jurisdictions in which assisted suicide or euthanasia is lawful. He considers how these models might fit novel beings who may require or prefer assistance to end their own existence by reference to the constituent features-abstract legal ingredients-that models for assisted death share. The author argues that extant models may block some novel beings' access to end-of-existence assistance or fail to track what matters to them. He then examines the merits of adopting a universal model for assisted nonexistence, that is, a legal framework whose substantive features capture the end-of-existence concerns of both human and novel beings. Consideration of a unified legal framework may illuminate the discussion of assisted nonexistence for humans and novel beings. However, the paper proposes that whereas novel beings may have similar interests to humans, they may be relevantly different also. The prima facie case for adopting a one regime to rule us all approach to assisted nonexistence may be defeated by reasons for divergent regulation.

Advanced boiling heat transfer model for a horizontal tube with numerical analysis of bubble behaviours

The boiling heat transfer model for a horizontal tube is developed herein, with numerical investigations to realistically simulate bubble nucleation site distributions and bubble behaviours. Accordingly, a new heat partitioning model was proposed to reflect the bubble sliding phenomena more realistically than the extant heat partitioning models for horizontal tubes. The bubble trajectories, merging, lift-off, and distribution of nucleation sites were numerically analysed based on the force balance model, bubble tracking method, and Latin-hypercube sampling. The proposed approach can overcome the limitations of the arithmetic heat partitioning model, which does not reflect bubble mergers and the random nature of the nucleation site distribution. Distinctive bubble behaviours on a horizontal tube can be reproduced by the present model, including bubble sliding, departure, lift-off, and merger. The numerical model presented here considers bubble sliding based on a mechanistic force balance model and bubble merger based on the bubble tracking method. The Monte Carlo method was applied to determine the spatial distribution of the nucleation sites, and statistical information on the wall heat transfer and contribution of each heat transfer mechanism were obtained via extensive calculations. The method developed herein was applied to evaluate the wall heat transfer in horizontal heaters. Further, the proposed numerical boiling heat transfer model was validated against an extant experimental database of horizontal heat exchanger tubes, and the model was observed to predict the wall heat flux with satisfactory accuracy.

Response shift in patient-reported outcomes: definition, theory, and a revised model

PurposeThe extant response shift definitions and theoretical response shift models, while helpful, also introduce predicaments and theoretical debates continue. To address these predicaments and stimulate empirical research, we propose a more specific formal definition of response shift and a revised theoretical model.MethodsThis work is an international collaborative effort and involved a critical assessment of the literature.ResultsThree main predicaments were identified. First, the formal definitions of response shift need further specification and clarification. Second, previous models were focused on explaining change in the construct intended to be measured rather than explaining the construct at multiple time points and neglected the importance of using at least two time points to investigate response shift. Third, extant models do not explicitly distinguish the measure from the construct. Here we define response shift as an effect occurring whenever observed change (e.g., change in patient-reported outcome measures (PROM) scores) is not fully explained by target change (i.e., change in the construct intended to be measured). The revised model distinguishes the measure (e.g., PROM) from the underlying target construct (e.g., quality of life) at two time points. The major plausible paths are delineated, and the underlying assumptions of this model are explicated.ConclusionIt is our hope that this refined definition and model are useful in the further development of response shift theory. The model with its explicit list of assumptions and hypothesized relationships lends itself for critical, empirical examination. Future studies are needed to empirically test the assumptions and hypothesized relationships.

Exploring the Adventist Health-lifestyle: A Social Marketing Perspective

Some studies have linked the Seventh-day Adventist (SDA) church health-lifestyle with better wellbeing and longevity. However, there are suggestions that current initiatives by the SDA-church to prompt health behavior modifications have been inadequate. Current attempts seem to be largely downstream approaches without clear marketing guidelines. While social marketing has been noted to be a key success factor in behavior modification initiatives, it does not seem to have been used in promoting SDA health-lifestyle initiatives. Hence the study sought to bring in a social marketing perspective to the current initiatives being done to make the SDA health-lifestyle modifications attainable for more people. A multi-method design combining grounded theory and appreciative inquiry was used. Data was collected from SDA-public health academics and practitioners, health-lifestyle program participants as well as social marketing extant studies and models. The sources of data were 30 interviews, 20 extant studies on social marketing, 5 social marketing models and 5 forum presentations. The major finding of the study is a social marketing theory for SDA health-lifestyle modification suggesting that health-lifestyle behavior modification is driven by centers of influence that run benchmarked programs shaped by distinctively Adventist worldview, and guided via marketing design criteria. Social marketing is suggested as the missing link that makes it easier to implement health-lifestyle behavior adoption efforts.