Relative Importance Research Articles

Young Consumers’ Preferences for Natural Wine: An Italian Exploratory Study

Although much has been written about sustainable wine consumption, the topic of young consumers’ (age 18–39 years) preferences for natural wine in Italy has been poorly explored. Through an online survey of 640 Italian respondents, we investigate the characteristics that affect consumption of natural wine in Italy. First, a factor analysis was applied to reduce data to a smaller set of summary variables. A probit regression model was then used to explore the effects and evaluate the relative importance of identified factors on participants’ willingness to consume natural wine. The results reveal that being female, in the older age cohort (25–39 years), and being consumer of certified wine and/or biodynamic wine significantly increase the probability of drinking natural wine. The findings also showed that health benefits, natural product interest, previous experience with natural wine, and positive perception of natural wine also drive young consumers of natural wine.

A novel intelligent system based on machine learning for hydrochar multi-target prediction from the hydrothermal carbonization of biomass

Hydrothermal carbonization (HTC) is a thermochemical conversion technology to produce hydrochar from wet biomass without drying, but it is time-consuming and expensive to experimentally determine the optimal HTC operational conditions of specific biomass to produce desired hydrochar. Therefore, a machine learning (ML) approach was used to predict and optimize hydrochar properties. Specifically, biochemical components (proteins, lipids, and carbohydrates) of biomass were predicted and analyzed first via elementary composition. Then, accurate single-biomass (no mixture) based ML multi-target models (average R2 = 0.93 and RMSE = 2.36) were built to predict and optimize the hydrochar properties (yield, elemental composition, elemental atomic ratio, and higher heating value). Biomass composition (elemental and biochemical), proximate analyses, and HTC conditions were inputs herein. Interpretation of the model results showed that ash, temperature, and the N and C content of biomass were the most critical factors affecting the hydrochar properties, and that the relative importance of biochemical composition (25%) for the hydrochar was higher than that of operating conditions (19%). Finally, an intelligent system was constructed based on a multi-target model, verified by applying it to predict the atomic ratios (N/C, O/C, and H/C). It could also be extended to optimize hydrochar production from the HTC of single-biomass samples with experimental validation and to predict hydrochar from the co-HTC of mixed biomass samples reported in the literature. This study advances the field by integrating predictive modeling, intelligent systems, and mechanistic insights, offering a holistic approach to the precise control and optimization of hydrochar production through HTC.Graphical

Thermal Stability of F‐Rich Phlogopite and K‐Richterite During Partial Melting of Metasomatized Mantle Peridotite With Implications for Deep Earth Volatile Cycles

AbstractPhlogopite and K‐richterite constitute important carrier phases for H and F in Earth's lithosphere and mantle. The relative importance depends on their stabilities at high pressure and temperature, which in turn depends on bulk composition. Most previous experimental studies focused on the thermal stability of phlogopite and K‐richterite were conducted using simplified chemical compositions. Here, partial melting experiments on metasomatized and carbonated, OH ± F‐bearing near‐natural peridotite were performed at high pressures (2 and 5 GPa) and temperatures (1,100–1,350°C) to assess the thermal stability of F‐free versus F‐bearing phlogopite and K‐richterite. Experimental results demonstrate that the thermal stability of F‐bearing phlogopite is increased by >55°C/wt.% F, relative to F‐free phlogopite, whereas K‐richterite is absent in all experiments with significant degrees of melting (>2%). The thermal stability of phlogopite containing several wt.% F exceeds continental and oceanic geotherms within the upper 150 km. Fluorine‐rich phlogopite would therefore be stable in virtually all of the continental lithosphere, only to be decomposed during large, regional melting events such as continental break‐up, thereby acting as a major long‐term sink for F and/or H. This could even be the case for the oceanic asthenosphere, depending on the oceanic geotherm of the area of interest.

Disentangling the variability of symbiotic nitrogen fixation rate and the controlling factors.

Symbiotic nitrogen (N) fixation (SNF), replenishing bioavailable N for terrestrial ecosystems, exerts decisive roles in N cycling and gross primary production. Nevertheless, it remains unclear what determines the variability of SNF rate, which retards the accurate prediction for global N fixation in earth system models. This study synthesized 1230 isotopic observations to elucidate the governing factors underlying the variability of SNF rate. The SNF rates varied significantly from 3.69 to 12.54 g N m-2 year-1 across host plant taxa. The traits of host plant (e.g. biomass characteristics and taxa) far outweighed soil properties and climatic factors in explaining the variations of SNF rate, accounting for 79.0% of total relative importance. Furthermore, annual SNF yield contributed to more than half of N uptake for host plants, which was consistent across different ecosystem types. This study highlights that the biotic factors, especially host plant traits (e.g. biomass characteristics and taxa), play overriding roles in determining SNF rate compared with soil properties. The suite of parameters for SNF lends support to improve N fixation module in earth system models that can provide more confidence in predicting bioavailable N changes in terrestrial ecosystems.

Determinants of Managerial Pay: The Relative Contribution of Compensation Predictors

Firm characteristics that determine CEO pays are closely interrelated with one another and make the partitioning of variances among correlated multiple predictors difficult. We decompose the interrelated predictors by orthogonalizing each predictor based on Tonidandel, LeBreton’s (2015) relative weight analysis on both normal and crisis period. In the process, we can rank the relative importance of each predictor and investigate its evolution over the economic crisis period. Firm size is the most dominant determinant, occupying over 60% relative weight. Wage discrimination against small company is obvious. ROA contributes 8.7% for the normal period and 10.8% for the crisis, which implies that CEOs’ ability to generate profits in crisis is particularly valued high and companies reward managers accordingly. The prolonged good performance is especially valued higher (13.9%) than the short-term performance. Risk and cash flow volatility occupy 3.6% and 1.8%, respectively, and the use of funds, such as capital expenditure and interest payment triggered by leverage occupy only marginal portions. This suggests that firms may lower CEO pays to reserve cash when they face risks or new investment opportunities, but the amount of extraction may not be high. In crisis, credit information can potentially outweigh the importance of many other typical predictors.

Weak evidence of density dependent population regulation when using the ability of two simple density dependent models to predict population size

The relative importance of density dependence regulation in natural population fluctuations has long been debated. The concept of density dependence implies that current abundance is determined by historical abundance. We have developed four models—two density dependent and two density independent—to predict population size one year beyond the training set and used predictive performance on more than 16,000 populations from 14 datasets to compare the understanding captured by those models. For 4 of 14 datasets the density dependent models make better predictions (i.e., density dependent regulated) than either of the density independent models. However, neither of the density dependent models is statistically significantly superior to density independent models for any of the 14 datasets. We conclude that the evidence for widespread density dependent population regulation in the forms represented by these two simple density-dependent models is weak. However, the density dependent models used here—the Logistic and Gompertz models—are simple representations of how population density might regulate natural populations and only examine density-dependent effects on population size. A comprehensive assessment of the relative importance of density-dependent population regulation will require testing the predictive ability of a wider range of density-dependent models including models examining effects on population characteristics other than population size.

Historical change and variability of spectral wind wave climate in the New York Bight

AbstractThe sea surface is a complex superposition of various wave systems, thus, analysing their characteristics provides more meaningful and accurate information compared to conventional integrated wave parameter approaches. The concept of wave family—a population of wave systems within a specific region in spectral space—provides the foundation for investigating the long‐term climatology of wave systems. This study adopts a wave family approach to investigate the climatology of wave systems and their long‐term climate controllers at the location of a coastal buoy in the western North Atlantic Ocean. The frequency‐direction wave spectrum is constructed for a historical 20‐year period by applying the Maximum Entropy Method on directional buoy measurements acquired from the National Data Buoy Center station 44025, located in the New York Bight. The two‐dimensional wave spectrum is partitioned, using the watershed partitioning algorithm, and the occurrence distribution of spectral partitions is calculated to identify predominant wave families. Each of the four identified wave families carries unique characteristics based on spatial formation and duration of propagation prior to arrival at the study site. The study quantifies long‐term trends and variability in wave height of each wave family on a monthly, seasonal and annual level, and their relative importance to the bulk sea‐state climatology. The nexus between the bulk or wave family seasonal wave heights and the climate indices is quantified to identify the wave climate controllers in the study area.

Machine learning approaches to enhance diagnosis and staging of patients with MASLD using routinely available clinical information.

Metabolic dysfunction Associated Steatotic Liver Disease (MASLD) outcomes such as MASH (metabolic dysfunction associated steatohepatitis), fibrosis and cirrhosis are ordinarily determined by resource-intensive and invasive biopsies. We aim to show that routine clinical tests offer sufficient information to predict these endpoints. Using the LITMUS Metacohort derived from the European NAFLD Registry, the largest MASLD dataset in Europe, we create three combinations of features which vary in degree of procurement including a 19-variable feature set that are attained through a routine clinical appointment or blood test. This data was used to train predictive models using supervised machine learning (ML) algorithm XGBoost, alongside missing imputation technique MICE and class balancing algorithm SMOTE. Shapley Additive exPlanations (SHAP) were added to determine relative importance for each clinical variable. Analysing nine biopsy-derived MASLD outcomes of cohort size ranging between 5385 and 6673 subjects, we were able to predict individuals at training set AUCs ranging from 0.719-0.994, including classifying individuals who are At-Risk MASH at an AUC = 0.899. Using two further feature combinations of 26-variables and 35-variables, which included composite scores known to be good indicators for MASLD endpoints and advanced specialist tests, we found predictive performance did not sufficiently improve. We are also able to present local and global explanations for each ML model, offering clinicians interpretability without the expense of worsening predictive performance. This study developed a series of ML models of accuracy ranging from 71.9-99.4% using only easily extractable and readily available information in predicting MASLD outcomes which are usually determined through highly invasive means.

A perceptual approach to address complex water management issues in lowland permeable catchments

Water quality management is a pressing global concern, and an increasingly complex issue due to climate and land-use change, legacy pollution, and the persistent release of well-known and emerging contaminants from diffuse and point sources. The increasing availability of high-frequency monitoring data is leading existing, often heuristic approaches, to be judged inadequate. Water managers frequently rely on simple qualitative and/or quantitative approaches for decision-making, but a lack of tangible improvement in freshwater quality outcomes is demanding new and innovative approaches that rely more on physical process understanding, rather than precedent. In this study, we drew upon local geological, hydrogeological, and hydrological data to infer a high-level perceptual model of surface/groundwater interactions in a chalk stream in Dorset, UK. We used the perceptual model to interrogate spatial and temporal trends in historical water quality data and to construct reach-scale nutrient mass balances. Through novel representation with Sankey diagrams, the perceptual model highlighted the relative importance of different hydrological features. Surface/groundwater interactions were found to occur predominantly by spring flow. We demonstrate that river flow accretion was dominated by the Chalk aquifer despite only occupying ca. 15 % of the surface bedrock area, and that spring sources, whilst vital to dilute treated sewage inputs in baseflow conditions, were also major sources of legacy nitrate. Nutrient mass balances showed that sewage treatment works contributed ca. 13 % to soluble reactive phosphorus load, with groundwater accounting for ca. 48 %. Thus, a determinand often associated with point-source pollution was shown to be diffuse dominated in this river. The study demonstrated how a multi-disciplinary approach to water management, based on a comprehensive perceptual modelling approach, could identify hitherto unknown sources and relative contributors to freshwater pollution and allow flow and load apportionment studies to provide useful decision-support to manage nutrient pollution. The novel application of perceptual modelling tools, such as the Sankey diagram, allows different source attributions to be presented in an accessible manner, and can be readily transferred to other study areas.

People’s preferred balance between politicians, citizens, and experts in policy-making decisions

ABSTRACT Most prior studies examining citizens’ preferences for “who should govern” assume that people prefer either politicians, citizens, or experts to exclusively influence policy decisions. Our approach posits that individuals may actually prefer a mix of these actors. Across two studies, we discovered that people indeed favour the involvement of all three actors in policy decisions, but with specific relative importance assigned to each of them. Notably, our second study clarified that which actor should have the largest say depends on the specific issue at hand, with citizens outweighing experts and politicians for ideological issues and experts outweighing citizens and politicians for technical issues. These findings are particularly relevant, given that these two actors were found to outperform each other in a different legitimacy dimension. That is, citizens’ contribution is seen most in terms of input legitimacy, whereas experts are perceived as contributing most to output legitimacy, particularly for technical issues. In contrast, politicians are considered to contribute the least to all three legitimacy dimensions. To enhance the perceived legitimacy of policy decisions among the citizenry, it becomes crucial for policymakers to embrace a more “hybrid” perspective, acknowledging the value of a more diverse collaboration between politicians, citizens, and experts.

Diatom metacommunity structuring in a large lake: geomorphic, water chemistry and dispersal effects on diatom guilds in Lake Ladoga (north-western Russia)

The relative importance of environmental filtering and dispersal in structuring metacommunities of littoral periphytic diatoms was assessed within a large lake (Lake Ladoga, north-western Russia). We hypothesized that different diatom functional guilds (high-profile, low-profile, motile and planktic) would respond differently to environmental and spatial variables, depending on their mode of attachment and dispersal ability. Guilds showed distinct distributional patterns along a gradient of wave-induced disturbance: low-profile diatoms dominated in exposed shorelines (high disturbance), whereas high-profile and motile diatoms were more abundant in coastal wetlands (low disturbance). Redundancy analysis with variation partitioning of the entire diatom community revealed that environmental variables (littoral geomorphology and water chemistry) outperformed spatial variables, indicating a predominant role of species sorting. When the diatom community was deconstructed into functional groups, benthic guilds (i.e., low-profile, high-profile and motile) showed higher dependence on environmental factors, whereas the planktic guild was primarily predicted by spatial factors. Thus, benthic taxa with lower dispersal abilities were less impacted by mass effects than planktic taxa. Among benthic guilds, motile diatoms were less sensitive to water chemistry compared to low-profile and high-profile diatoms, suggesting that a group-specific habitat preference may interfere with diatom responses to water quality changes. Hence, the response of diatom guilds to water quality parameters was related to their resistance to wave action. Overall, we concluded that the higher importance of environmental filtering than dispersal-related processes in shaping the diatom metacommunity resulted from a combination of a strong environmental gradient in geomorphology/water chemistry and the intermediate spatial scale of our study.

Maternal ethnicity and socioeconomic deprivation: influence on adverse pregnancy outcomes.

To evaluate the relative importance of ethnicity and socioeconomic deprivation in determining the likelihood and the percentage of composite adverse pregnancy outcomes (CAPO) and composite of severe adverse pregnancy outcomes (CAPO-S) METHODS: This is a single centre retrospective cohort study conducted in a tertiary maternity unit. Data regarding the ethnicity and socioeconomic deprivation were collected for 13,165 singleton pregnant women routinely screened in the first trimester for preeclampsia using the Fetal Medicine Foundation combined algorithm. The prevalence or risk of CAPO was 16.3% for White women, 29.3% for Black women and 29.3% for South Asian women. However, half of all CAPO cases (51.7%) occurred in White women. There is a strong interaction between ethnicity and socioeconomic deprivation (as measured with indices of multiple deprivation). Both influence the prevalence of CAPO and CAPO-S, with the contribution of ethnicity being strongest. Black and Asian ethnicity as well as socioeconomic deprivation influence the prevalence of placentally-mediated adverse pregnancy outcomes. Despite this, most adverse pregnancy outcomes occur in White women, who represent the majority of the population and are also affected by socioeconomic deprivation. For these reasons, inclusion of socioeconomic deprivation should be considered in early pregnancy risk assessment for placentally-mediated CAPO. This article is protected by copyright. All rights reserved.

컨조인트 분석을 활용한 홍삼커피 선택속성의 상대적 중요도 분석

컨조인트 분석을 활용한 홍삼커피 선택속성의 상대적 중요도 분석

Use of machine learning to identify characteristics associated with severe hypoglycemia in older adults with type 1 diabetes: a post-hoc analysis of a case–control study

IntroductionSevere hypoglycemia (SH) in older adults (OAs) with type 1 diabetes is associated with profound morbidity and mortality, yet its etiology can be complex and multifactorial. Enhanced tools to identify...

Roles of Thermal Forced and Eddy‐Driven Effects in the Northward Shifting of the Subtropical Westerly Jet Under Recent Climate Change

AbstractThe zonal‐mean subtropical westerly jet (SWJ) in boreal winter shows a significant northward shift trend under recent climate change. Previous studies proposed thermal forcing—represented by the thermal wind associated with the temperature gradient—and the driving effect of the eddy momentum flux (EMF) convergence that leads to the eddy‐driven jet as explanations for this process; however, their relative importance in influencing the SWJ shift requires further investigation. In this study, we examined the roles of thermal forced and eddy‐driven jet components in the zonal‐mean northward SWJ shift. We also investigated the role of Hadley circulation because its poleward boundary is related to the zonal‐mean SWJ. Results suggest that thermal forced component plays a major role, while EMF‐driven component plays a secondary role. Specifically, the subtropical warming, which is primarily influenced by enhanced adiabatic downward motion of the Hadley circulation, increases the meridional temperature gradient and the associated thermal wind poleward of the SWJ. It also reduces atmospheric static stability aloft and converges the EMF on the poleward side of the climatological SWJ. Enhanced meridional temperature gradient and EMF convergence on the poleward side push the SWJ northward. Results from further mathematical analysis indicate that thermal forced and eddy‐driven zonal wind components account for 72% and 28% of the shift distance, respectively. In addition to elucidating the relative importance of thermal and EMF forcings, this study emphasizes the critical role of the subtropical warming driven by the intensified local descending branch of the Hadley circulation in shifting the zonal‐mean SWJ.

Assessing the long-term priorities of pancreaticoduodenectomy survivors

BackgroundThis study assessed the long-term quality of life (QOL) and priorities of pancreaticoduodenectomy (PD) survivors. MethodsSurvivors were surveyed via internet-based support groups. The relative importance of longevity, experience, costs, and QOL were assessed. ResultsThe PD cohort (n = 247, 35%) was 60 ± 12 years, 71% female, and 93% white. With moderate agreement, patients ranked survival most important, followed by functional and emotional well-being; costs and experience were least important (W = 35.7%, p < 0.001). Well-being improved throughout survivorship (P-QOL: 39 ± 12 at ≤3 mo vs 43 ± 12 at >10 y, p = 0.170; M-QOL: 38 ± 13 at ≤3 mo vs 44 ± 16 at >10 y; p = 0.015) but remained below the general population (p < 0.001). PD patients with benign diagnoses ranked functional independence as most important (2.00 ± 1.13 vs 2.63 ± 1.19, p < 0.001, W = 41.1%); PD patients with malignant diagnoses regarded overall survival most important (2.10 ± 1.20 vs 1.82 ± 1.22, p < 0.16, W = 35.1%). The mean rank order of priorities remained concordant between short-term (<1 year) and long-term (>5 years) survivors. ConclusionPD survivors experience long-term mental and physical health impairments, underscoring the importance of functional and emotional support. Survivors place paramount importance on overall survival, functional independence, and emotional well-being. Cancer survivors prioritize longevity, while survivors of chronic benign conditions prioritize functional independence.

Determinants of livestock depredation risk by Persian leopards in southern Iran

Human-wildlife conflict is a major conservation challenge worldwide, with negative consequences for people and wildlife alike. Large carnivores, despite their key role in ecosystem functioning, are persecuted throughout their ranges as a response to perceived or actual risks to livestock and humans. To promote human-carnivore coexistence, a better understanding of the determinants of conflict is necessary. A range of factors have been hypothesized to influence livestock depredation risk, including carnivore habitat suitability and connectivity, prey availability, and anthropogenic factors. Yet, the relative importance of these factors is often unclear. We analyzed and mapped livestock depredation risk for the Persian leopard (Panthera pardus tulliana) in southern Iran using a species distribution modeling framework. We used livestock depredation reports and interviews (n = 199) from 25 randomly selected villages together with predictors capturing leopard habitat suitability, movement corridors, wild and domestic prey, and anthropogenic pressure. Our leopard habitat suitability and connectivity analyses predicted 28 core habitat patches (>250km2) associated with mountainous and conserved areas, with partly severed corridors between them. Our risk mapping indicated that the chance of livestock depredation increased in areas with higher leopard habitat suitability. Conversely, depredation risk was lower in areas with more wild prey. We predicted the highest depredation risk in about 12 % of the landscape where urgent conflict mitigation measures would be most beneficial. More generally, our study shows that while conservation areas provide refuge for carnivores, these areas can also lead to higher levels of conflict, hence mitigation measures should be prioritized in their surroundings.

Intrinsic Lipid Curvature and Bilayer Elasticity as Regulators of Channel Function: A Comparative Single-Molecule Study.

Perturbations in bilayer material properties (thickness, lipid intrinsic curvature and elastic moduli) modulate the free energy difference between different membrane protein conformations, thereby leading to changes in the conformational preferences of bilayer-spanning proteins. To further explore the relative importance of curvature and elasticity in determining the changes in bilayer properties that underlie the modulation of channel function, we investigated how the micelle-forming amphiphiles Triton X-100, reduced Triton X-100 and the HII lipid phase promoter capsaicin modulate the function of alamethicin and gramicidin channels. Whether the amphiphile-induced changes in intrinsic curvature were negative or positive, amphiphile addition increased gramicidin channel appearance rates and lifetimes and stabilized the higher conductance states in alamethicin channels. When the intrinsic curvature was modulated by altering phospholipid head group interactions, however, maneuvers that promote a negative-going curvature stabilized the higher conductance states in alamethicin channels but destabilized gramicidin channels. Using gramicidin channels of different lengths to probe for changes in bilayer elasticity, we found that amphiphile adsorption increases bilayer elasticity, whereas altering head group interactions does not. We draw the following conclusions: first, confirming previous studies, both alamethicin and gramicidin channels are modulated by changes in lipid bilayer material properties, the changes occurring in parallel yet differing dependent on the property that is being changed; second, isolated, negative-going changes in curvature stabilize the higher current levels in alamethicin channels and destabilize gramicidin channels; third, increases in bilayer elasticity stabilize the higher current levels in alamethicin channels and stabilize gramicidin channels; and fourth, the energetic consequences of changes in elasticity tend to dominate over changes in curvature.

Organization of production and income inequality

AbstractThis paper contributes to the literature that postulates a relationship between income inequality and the relative importance of “market” and “organization” in the direction of resources. The paper emphasizes that both are endogenous; therefore, the empirical associations observed in the empirical data between inequality measures and production organization variables cannot be interpreted as indicative of causal relationships. The paper solves for the composition and size of occupational groups, the distribution of firm size, and the distribution of income as market equilibrium outcomes of an occupational choice economy, and performs a comparative static analysis. We find that the interaction between cross‐economy differences in the distribution of general skills in the labor force and the loss of control in the supervision of workers by managers can explain the empirical regularities observed in the relationship between the organization of production (distribution of firm size) and income inequality (distribution of labor income). This explanation of the empirical regularities differs from others proposed in the literature, such as those based on institutional constraints or the market power of firms.

Training self-learning circuits for power-efficient solutions

As the size and ubiquity of artificial intelligence and computational machine learning models grow, the energy required to train and use them is rapidly becoming economically and environmentally unsustainable. Recent laboratory prototypes of self-learning electronic circuits, such as “physical learning machines,” open the door to analog hardware that directly employs physics to learn desired functions from examples at a low energy cost. In this work, we show that this hardware platform allows for an even further reduction in energy consumption by using good initial conditions and a new learning algorithm. Using analytical calculations, simulations, and experiments, we show that a trade-off emerges when learning dynamics attempt to minimize both the error and the power consumption of the solution—greater power reductions can be achieved at the cost of decreasing solution accuracy. Finally, we demonstrate a practical procedure to weigh the relative importance of error and power minimization, improving the power efficiency given a specific tolerance to error.