The approach of Norm Balance in predicting pharmacists' intention to collaborate with physicians to improve medication therapy.

Objective: Norm Balance is an approach under the Theory of Planned Behavior (TPB) where subjective norm is weighted by the relative importance of others and self-identity is weighted by the relative importance of self. The relative importance was measured previously by a trade-off measure. In this study, we developed separate measures for the relative importance. The study objectives were to: 1) assess the construct validity of the separate measures; 2) examine the approach of Norm Balance in predicting pharmacists' intention to collaborate with physicians to improve medication therapy; and 3) establish a modified TPB. Methods: We selected a random sample of 750 Iowa pharmacists and conducted two surveys. The first survey was to examine intention prediction, and the second survey was to examine behavior prediction by measuring behavior among respondents to the first survey. The relative importance was measured by both the trade-off measure and the separate measures. Exploratory factor analyses were performed for the relative importance of others (separate measures) and subjective norm, and for the relative importance of self (separate measures) and self-identity. Regressions for intention prediction were conducted for TPB with self-identity and the approach of Norm Balance. The same regressions were also conducted for three subgroups according to the median of the relative importance of others (trade-off measure). Moreover, another regression was conducted for behavior prediction. Results: 239 practicing pharmacists responded to the first survey, and 188 responded to the second survey. The separate measures had cross factor loadings, whereas the trade-off measure had low correlations with other constructs. Both regressions for intention prediction explained 75% of the variance, with self-efficacy and attitude being strong predictors. Self-identity was not a predictor in the TPB with self-identify, but self-identity weighted by the relative importance of self was a significant predictor in the approach of Norm Balance. Regression coefficients of subjective norm and self-identify varied across subgroups. The regression for behavior prediction explained 30% of the variance, with intention and self-efficacy being two predictors. Conclusion: The trade-off measure was better than separate measures. The approach of Norm Balance appears to be a better model than the TPB with self-identity to examine pharmacist-physician collaboration.

Children recognize and reject favoritism in norm enforcement.

The impartial enforcement of norms and laws is a hallmark of fair societies, yet partial, unequal norm enforcement is common, for example as a result of corruption. While children condemn norm violations and value impartiality in resource allocation contexts, children's understanding of unequal norm enforcement is currently underexplored. In three vignette studies, we investigated 4- to 8-year-old's (N = 192) developing recognition and condemnation of unequal norm enforcement, which presupposes a sensitivity to impartiality as a meta-norm. Children evaluated the actions of characters who enforced different norms equally or unequally. From age 5, children disapproved of unequal norm enforcement but approved of unequal treatment when justified (Study 1). Children of all ages accepted a lack of punishment when applied equally to all transgressors, suggesting that their negative evaluations of unequal norm enforcement were specifically guided by the element of partiality and not the desire to see transgressors sanctioned (Study 2). Further, children aged 6 years and older were sensitive to the reasons behind unequal punishment, condemning instances of favoritism while accepting selective leniency due to mitigating circumstances (Study 3). The findings show that, from around 5 to 6 years of age, children condemn unequal sanctions for equal transgressions, thereby demonstrating a deep appreciation of impartiality as a foundational principle of fair norm enforcement.

ISRSL0: compressed sensing MRI with image smoothness regularized-smoothed [Formula: see text] norm.

The reconstruction of MR images has always been a challenging inverse problem in medical imaging. Acceleration of MR scanning is of great importance for clinical research and cutting-edge applications. One of the primary efforts to achieve this is using compressed sensing (CS) theory. The CS aims to reconstruct MR images using a small number of sampled data in k-space. The CS-MRI techniques face challenges, including the potential loss of fine structure and increased computational complexity. We introduce a novel framework based on a regularized sparse recovery problem and a sharpening step to improve the CS-MRI approaches regarding fine structure loss under high acceleration factors. This problem is solved via the Half Quadratic Splitting (HQS) approach. The inverse problem for reconstructing MR images is converted into two distinct sub-problems, each of which can be solved separately. One key feature of the proposed approach is the replacement of one sub-problem with a denoiser. This regularization assists the optimization of the Smoothed [Formula: see text] (SL0) norm in escaping local minimums and enhances its precision. The proposed method consists of smoothing, feature modification, and Smoothed [Formula: see text] cost function optimization. The proposed approach improves the SL0 algorithm for MRI reconstruction without complicating it. The convergence of the proposed approach is illustrated analytically. The experimental results show an acceptable performance of the proposed method compared to the network-based approaches.

Explanations of and interventions against affective polarization cannot afford to ignore the power of ingroup norm perception.

Affective polarization, or animosity toward opposing political groups, is a fundamentally intergroup phenomenon. Yet, prevailing explanations of it and interventions against it have overlooked the power of ingroup norm perception. To illustrate this power, we begin with evidence from 3 studies which reveal that partisans' perception of their ingroup's norm of negative attitudes toward the outgroup is exaggerated and uniquely predicts their own polarization-related attitudes. Specifically, our original data show that in predicting affective polarization (i.e. how one feels about one's partisan outgroup), the variance explained by ingroup norm perception is 8.4 times the variance explained by outgroup meta-perception. Our reanalysis of existing data shows that in predicting support for partisan violence (i.e. how strongly one endorses and is willing to engage in partisan violence), ingroup norm perception explains 52% of the variance, whereas outgroup meta-perception explains 0%. Our pilot experiment shows that correcting ingroup norm perception can reduce affective polarization. We elucidate the theoretical underpinnings of the unique psychological power of ingroup norm perception and related ingroup processes. Building on these empirical and theoretical analyses, we propose approaches to designing and evaluating interventions that leverage ingroup norm perception to curb affective polarization. We specify critical boundary conditions that deserve prioritized attention in future intervention research. In sum, scientists and practitioners cannot afford to ignore the power of ingroup norm perception in explaining and curbing affective polarization.

Sparse-Laplace hybrid graph manifold method for fluorescence molecular tomography.

Objective.Fluorescence molecular tomography (FMT) holds promise for early tumor detection by mapping fluorescent agents in three dimensions non-invasively with low cost. However, since ill-posedness and ill-condition due to strong scattering effects in biotissues and limited measurable data, current FMT reconstruction is still up against unsatisfactory accuracy, including location prediction and morphological preservation.Approach.To strike the above challenges, we propose a novel Sparse-Laplace hybrid graph manifold (SLHGM) model. This model integrates a hybrid Laplace norm-based graph manifold learning term, facilitating a trade-off between sparsity and preservation of morphological features. To address the non-convexity of the hybrid objective function, a fixed-point equation is designed, which employs two successive resolvent operators and a forward operator to find a converged solution.Main results.Through numerical simulations andin vivoexperiments, we demonstrate that the SLHGM model achieves an improved performance in providing accurate spatial localization while preserving morphological details.Significance.Our findings suggest that the SLHGM model has the potential to advance the application of FMT in biological research, not only in simulation but also inin vivostudies.

Research on ECG signal reconstruction based on improved weighted nuclear norm minimization and approximate message passing algorithm.

In order to improve the energy efficiency of wearable devices, it is necessary to compress and reconstruct the collected electrocardiogram data. The compressed data may be mixed with noise during the transmission process. The denoising-based approximate message passing (AMP) algorithm performs well in reconstructing noisy signals, so the denoising-based AMP algorithm is introduced into electrocardiogram signal reconstruction. The weighted nuclear norm minimization algorithm (WNNM) uses the low-rank characteristics of similar signal blocks for denoising, and averages the signal blocks after low-rank decomposition to obtain the final denoised signal. However, under the influence of noise, there may be errors in searching for similar blocks, resulting in dissimilar signal blocks being grouped together, affecting the denoising effect. Based on this, this paper improves the WNNM algorithm and proposes to use weighted averaging instead of direct averaging for the signal blocks after low-rank decomposition in the denoising process, and validating its effectiveness on electrocardiogram signals. Experimental results demonstrate that the IWNNM-AMP algorithm achieves the best reconstruction performance under different compression ratios and noise conditions, obtaining the lowest PRD and RMSE values. Compared with the WNNM-AMP algorithm, the PRD value is reduced by 0.17∼4.56, the P-SNR value is improved by 0.12∼2.70.

How big is too big? A qualitative study of discretionary food portion size norms among Australian consumers.

OBJECTIVE: The high availability of energy-dense nutrient-poor discretionary foods in large serving and package sizes may have shifted portion size norms (described as a typical perception of how much people choose to eat from a given food at a single eating occasion) towards larger sizes. Few public health recommendations exist around appropriate discretionary food portion sizes. This qualitative study aimed to explore the underlying rationale of portion size norms of discretionary foods among Australian adults 18-65 years. DESIGN: Four focus group sessions were conducted. Collected data were analysed using inductive thematic analysis. SETTING: Focus groups were held online via Zoom between September and October 2023. PARTICIPANTS: Thirty-four participants were recruited in the study (mean age 38 years, 19 females). RESULTS: The key themes raised from inductive analysis were personal factors, eating context factors, and food environment factors relevant to the portion size norms. A framework was established to illustrate the interaction across these themes during the conceptualisation of the norms. For serving size availability, consumers found that there were limited serving size choices when making portion size selections and lacked the knowledge and skills in portion control. CONCLUSIONS: These findings highlight the need to make positive changes to the current food environment and develop relevant public health guidelines around appropriate portion sizes to promote healthier portion size norms and enable better portion control.

PET image reconstruction using weighted nuclear norm maximization and deep learning prior.

The ill-posed Positron emission tomography (PET) reconstruction problem usually results in limited resolution and significant noise. Recently, deep neural networks have been incorporated into PET iterative reconstruction framework to improve the image quality. In this paper, we propose a new neural network-based iterative reconstruction method by using weighted nuclear norm (WNN) maximization, which aims to recover the image details in the reconstruction process. The novelty of our method is the application of WNN maximization rather than WNN minimization in PET image reconstruction. Meanwhile, a neural network is used to control the noise originated from WNN maximization. Our method is evaluated on simulated and clinical datasets. The simulation results show that the proposed approach outperforms state-of-the-art neural network-based iterative methods by achieving the best contrast/noise tradeoff with a remarkable contrast improvement on the lesion contrast recovery. The study on clinical datasets also demonstrates that our method can recover lesions of different sizes while suppressing noise in various low-dose PET image reconstruction tasks. Our code is available athttps://github.com/Kuangxd/PETReconstruction.

The impact of ethical commitments on the intention to adopt digital agricultural technologies.

Sustainable food security is a critical global concern and an urgent priority in developing countries such as Iran. Digital agricultural technologies (DAT) represent significant solutions in this regard, yet their adoptions and development in Iran face limitations. Theoretical studies have underscored the importance of ethical commitments in the adoption process. This study aims to investigate whether ethical commitments regarding food security can influence the intention to adopt digital technologies by farmers. The study employed the Norm Activation Model and integrated two additional components, namely perceived risk and social capital. We applied this framework to examine research works on farmers of Kermanshah Province in the west of Iran, using survey data (sample n = 384). Data analyses were done through structural equation modeling (SEM). Based on the results, the developed Norm Activation Model can be used to predict the adoption intention of DAT by farmers; with the model explaining 65% of total variance. Feeling guilt exhibited the highest direct effect, followed by feeling proud. Furthermore, ethical norms had a direct and indirect impact on intention through the mediating variables of feeling proud and Feeling guilt. The findings of this study contribute to facilitating innovation adoption strategies. It is recommended that, in order to facilitate and stabilize farmers' adoption of innovation, their sense of guilt should first be aroused. After stimulating the farmers' sense of pride toward the adoption; emphasis should be placed on ethical commitments. Ultimately, the introduction of technology and the facilitation of infrastructure should be pursued.

Bottom-up influences on social norms: How observers' responses to transgressions drive norm maintenance versus change.

Human behavior is heavily influenced by social norms. But when and how do norms persist or change? Complementing work on the role of top-down factors in the enforcement of normative behavior (e.g., sanctioning systems, organizational culture, formal leadership, corrective actions), I introduce a model of bottom-up influences on norm development. I argue that the trajectories of social norms are shaped by behavioral responses of observers to emergent norm violations. Research on such responses can be categorized in three broad clusters that have distinct implications for norm development. Oppositional responses to norm violations (punishment, confrontation, gossip, whistleblowing, derogation, social exclusion, emotional condemnation) discourage future transgressions, thereby contributing to norm maintenance. Acquiescent responses (avoidance, tolerance) leave room for future violations, thereby contributing to norm erosion. Supportive responses (emulation, endorsement) encourage future deviance and facilitate the spreading of counternormative behavior, thereby catalyzing norm change. By linking micro-level norm violations to macro-level normative systems, this approach illuminates how norms are dynamically negotiated through social interaction.

Norm Entrepreneurship in Digital Trade: The Singapore-led Wave of Digital Trade Agreements.

Rulemaking in digital trade is proceeding apace. Many preferential trade agreements contain dedicated e-commerce or digital trade chapters and some states have entered into stand-alone digital economy agreements. This article seeks to establish whether, and to what extent, normative change is occurring in digital trade agreements, the nature of any changes, and identify which states are acting as norm entrepreneurs. We employ a new method of legal coding, systematically comparing the nature and prescriptiveness of digital provisions in 12 trade agreements concluded between 2019 and 2023. We find evidence of substantial policy innovation, and identify Singapore as the key norm entrepreneur. A new wave of 'Singapore-led' agreements substantially expands the scope of digital trade, to cover areas such as digital identities, e-invoicing and e-payments, the governance of AI, and regulation of new digital technologies. Commitments are typically couched as soft rather than hard law, reflecting the nascent stages of rulemaking. Norm entrepreneurship on the part of Singapore and its allies reflects a desire to position themselves as 'digital hubs' in the global economy, spur rulemaking in areas where innovation is ahead of regulation, and promote digital interconnectivity at time of regulatory divergence and geopolitical rivalry.

MCGDM approach based on (p, q, r)-spherical fuzzy Frank aggregation operators: applications in the categorization of renewable energy sources.

The growing demand for energy, driven by population growth and technological advancements, has made ensuring a sufficient and sustainable energy supply a critical challenge for humanity. Renewable energy sources, such as biomass, solar, wind, and hydro, are inexhaustible and environmentally friendly, offering a viable solution to both the energy crisis and the fight against global warming. However, selecting the optimal renewable energy source remains a complex decision-making problem due to the varying characteristics and impacts of these sources. Motivated by the need for more accurate and nuanced decision-making tools in this domain, this paper introduces a novel multicriteria group decision-making (MCGDM) approach based on [Formula: see text]spherical fuzzy Frank aggregation operators. By integrating Frank t-norm with [Formula: see text]spherical fuzzy sets, we develop aggregation operators (AOs) that effectively manage membership, neutral, and non-membership degrees through parameters [Formula: see text], [Formula: see text], and [Formula: see text]. These AOs provide a more refined framework for decision-making, leading to improved outcomes. We apply this approach to evaluate and identify the superior and optimal renewable energy source using artificial data, demonstrating the advantages of the proposed operators compared to existing methods. This work contributes to the field by offering a robust tool for addressing the energy crisis and advancing sustainable energy solutions.

Coevolution of norm psychology and cooperation through exapted conformity.

People willingly follow norms and values, often incurring material costs. This behaviour supposedly stems from evolved norm psychology, contributing to large-scale cooperation among humans. It has been argued that cooperation is influenced by two types of norms: injunctive and descriptive. This study theoretically explores the socialisation of humans under these norms. Our agent-based model simulates scenarios where diverse agents with heterogeneous norm psychologies engage in collective action to maximise their utility functions that capture three motives: gaining material payoff, following injunctive and descriptive norms. Multilevel selective pressure drives the evolution of norm psychology that affects the utility function. Further, we develop a model with exapted conformity, assuming selective advantage for descriptive norm psychology. We show that norm psychology can evolve via cultural group selection. We then identify two normative conditions that favour the evolution of norm psychology, and therefore cooperation: injunctive norms promoting punitive behaviour and descriptive norms. Furthermore, we delineate different characteristics of cooperative societies under these two conditions and explore the potential for a macro transition between them. Together, our results validate the emergence of large-scale cooperative societies through social norms and suggest complementary roles that conformity and punishment play in human prosociality.

Genetic determinism of sensitivity to environmental challenges using daily feed intake records in three lines of pigs.

In pig breeding, environmental challenges can affect the welfare and productivity of animals. Resilient animals have the capacity to be minimally affected by these environmental challenges. Understanding the genetic basis of sensitivity to these environmental challenges is crucial for selecting more resilient animals, thereby enhancing welfare and productivity. The aims of this study were to: (1) estimate the probability of the occurrence of an unrecorded environmental challenge at a given day using daily feed intake (DFI) data, and (2) evaluate the genetic determinism of environmental sensitivity in three pig lines bred in real selection conditions. Data comprised of 100,799, 186,247, and 304,826 DFI records from 1,618, 2,517, and 3,788 Landrace (LA), Large White (LW) and Piétrain (PI) male pigs, respectively. The pedigree included 3,730, 5,649, and 9,293 animals for LA, LW, and PI, respectively. The probabilities of the occurrence of an unrecorded environmental challenge at a given day were estimated via a mixture model. The probabilities (p) of being "high CV days" were then taken as reference and used in genetic analysis as an environmental descriptor to describe the environment. DFI records were analysed using two linear models: a linear reaction norm animal model (RNAM) and the animal model. (Co)variance components were estimated using average-information restricted maximum likelihood (AI-REML). The means of the probabilities of the occurrence of an environmental challenge for LA, LW, and PI were 0.24, 0.10, and 0.22, respectively, indicating that the probability of an environmental challenge was low for most of the days. The genetic correlations between the intercept and the slope obtained from the RNAM for LA, LW, PI were -0.52, 0.06, and -0.36, respectively. These findings suggest that selecting hypothetically for decreased DFI in non-stressful conditions would result in pigs with increased DFI in stressful conditions in the LA and PI lines, whereas it would have a minor impact on the environmental sensitivity of LW. The proportion of resilient animals for LA, LW, and PI was 75.0, 74.2, and 72.2%, respectively, implying that most of the animals were resilient. The study demonstrated that the slope of DFI is heritable and can effectively be used as an indicator of sensitivity to environmental challenges. These results are valuable in improving the resilience of livestock species to environmental challenges through genetic selection.

2-Rotund norms for unconditional and symmetric sequence spaces.

A reflexive Banach space with an unconditional basis admits an equivalent 1-unconditional 2R norm and embeds into a reflexive space with a 1-symmetric 2R norm. Partial results on 1-symmetric 2R renormings of spaces with a symmetric basis are obtained.

Efficacy of Social Norm Interventions on Addictive Behaviours-A Systematic Review and Meta-Analysis of Randomised Controlled Trials.

Mental health-related behaviours including addictive behaviours contribute significantly to the global burden of disease. Social norm interventions appear to be a cost-effective means of reducing addictive behaviour. We conducted a systematic review and meta-analysis of the efficacy of social norm interventions for addictive behaviours. We searched the databases Medline and PsycInfo from inception to April 2024 as well as reference lists of eligible studies and related systematic reviews for randomised controlled trials (RCTs) comparing the efficacy of social norm interventions for addictive behaviours to control conditions. Out of the 11,515 potentially eligible RCTs, 52 trials with a total of 31,764 adult participants met inclusion criteria, with 45 trials targeting alcohol consumption, three trials targeting Marijuana use, two trials targeting other substance abuse and two trials targeting gambling. Overall, 37 trials were included in the random-effects meta-analysis. The comparison of social norm interventions to control conditions at posttreatment showed a small but statistically significant effect (g = -0.12; 95% CI = -0.22 to -0.02; p < 0.01). Risk of bias was rated low in 37 RCTs, 14 RCTs were rated as having some risk of bias concerns and one RCT was rated as having high risk of bias. Social norm interventions can be an effective intervention method for reducing substance abuse and gambling. Yet, data is largely derived from studies targeting alcohol consumption and current trials suffer from methodological and practical limitations. The small effect sizes need to be appraised in the context of cost-effectiveness of these interventions.

Busy Ethic: A Validation of a Popular Concept.

BACKGROUND AND OBJECTIVES: The busy ethic for retirement, as proposed by Ekerdt (1986), is a prescriptive norm that esteems an occupied, active lifestyle. This research is a first attempt to measure the busy ethic in a standardized way and apply it to a population-based sample. Objectives are: to examine whether a busy ethic is affirmed by retirees; to test busy ethic endorsement by different segments of the retired population; and to examine whether endorsement is associated with selected activities. RESEARCH DESIGN AND METHODS: We developed a scale measuring the busy ethic for a survey among 1,143 Dutch retirees. We tested two sets of hypotheses about social factors that might explain subscription to a busy norm: a hypothesis about modernization (i.e., individual autonomy from social control) that would reduce busy ethic endorsement and a hypothesis about differential exposure to expectations. RESULTS: Greater consent to the busy ethic was associated with circumstances that enable active lifestyles (perceived income adequacy, self-reported health), that raise one's social value (education), and that entail more social connectedness (religious service attendance). Busy ethic agreement was positively associated with engagement in paid work, productive social activities, and group recreation. DISCUSSION AND IMPLICATIONS: We found substantial endorsement of the importance of activity for oneself and others. The idealization of a busy retirement as a good retirement may be a seeming way for retirees to defer old age. At the same time, a prescriptive norm of activation may put strain on older adults who are less capable of conforming.

Genomic prediction and validation strategies for reproductive traits in Holstein cattle across different Chinese regions and climatic conditions.

Accurate genomic predictions of breeding values for traits included in the selection indexes are paramount for optimizing genetic progress in populations under selection. The size of the reference populations is a major factor influencing the accuracy of genomic predictions, which is even more important for lowly-heritable traits such as fertility and reproduction indicators. Combining data from different geographical regions or countries can be beneficial for genomic prediction of these lowly heritable traits. Therefore, the objectives of this study were to: 1) evaluate the benefits of performing across-regional genomic evaluations for reproduction traits in Chinese Holstein cattle; and, 2) assess the feasibility of validating genomic predictions across environments based on reaction norm models (RNM) and the Linear Regression (LR) method after accounting for genotype-by-environment interactions. Phenotypic records from 194,574 cows collected across 47 farms located in 2 regions of China were used for this study. The reference and validation populations were defined based on birth year for applying the LR validation method. The traits evaluated included: interval from first to last insemination (IFL), conception rate at the first insemination (CR_f), and number of inseminations (NS) recorded in heifers and first-parity cows. The results indicated that combining data from different regions resulted in greater genomic prediction accuracies compared with using data from single regions, with increases ranging from 2.74% to 93.81%. This improvement was particularly notable for the region with the least amount of available data, where the increases ranged from 26.49% to 93.81%. Furthermore, the predictive abilities could be validated for all studied traits based on the LR method across different environments when fitting RNM. The prediction accuracies and bias of genomic breeding values based on RNM were better than regular single-trait animal models in extreme climatic conditions for IFL and NS, whereas limited increases in predictive abilities were observed for CR_f. Across-regional genomic prediction by RNM can account for genotype-by-environment interactions, potentially increase the accuracy of genomic prediction, and predict the performances of individuals in the environments with limited phenotypic data available.

Clarifying social norms which have robustness against reputation costs and defector invasion in indirect reciprocity.

The evolution of cooperation through indirect reciprocity is a pivotal mechanism for sustaining large-scale societies. Because third parties return cooperative behaviour in indirect reciprocity, reputations that assess and share these third parties' behaviour play an essential role. Studies on indirect reciprocity have predominantly focused on the costs associated with cooperative behaviour, overlooking the costs tied to the mechanisms underpinning reputation sharing. Here, we explore the robustness of social norms necessary to secure the stability of indirect reciprocity, considering both the costs of reputation and the resilience against perfect defectors. Firstly, our results replicate that only eight social norms, known as the 'leading eight,' can establish a cooperative regime. Secondly, we reveal the robustness of these norms against reputation costs and perfect defectors. Our analysis identifies four norms that exhibit resilience in the presence of defectors due to their neutral stance on justified defection and another four that demonstrate robustness against reputation costs through their negative evaluation of unjustified cooperation. The study underscores the need to further research how reputational information is shared within societies to promote cooperation in diverse and complex environments.

Genetic and Environmental Patterns Underlying Phenotypic Plasticity in Flowering Time and Plant Height in Sorghum.

Phenotypic plasticity is the property of a genotype to produce different phenotypes under different environmental conditions. Understanding genetic and environmental factors behind phenotypic plasticity helps answer some longstanding biology questions and improve phenotype prediction. In this study, we investigated the phenotypic plasticity of flowering time and plant height with a set of diverse sorghum lines evaluated across 14 natural field environments. An environmental index was identified to quantitatively connect the environments. Reaction norms were then obtained with the identified indices for genetic dissection of phenotypic plasticity and performance prediction. Genome-wide association studies (GWAS) detected different sets of loci for reaction-norm parameters (intercept and slope), including 10 new genomic regions in addition to known maturity (Ma1) and dwarfing genes (Dw1, Dw2, Dw3, Dw4 and qHT7.1). Cross-validations under multiple scenarios showed promising results in predicting diverse germplasm in dynamic environments. Additional experiments conducted at four new environments, including one from a site outside of the geographical region of the initial environments, further validated the predictions. Our findings indicate that identifying the environmental index enriches our understanding of gene-environmental interplay underlying phenotypic plasticity, and that genomic prediction with the environmental dimension facilitates prediction-guided breeding for future environments.