Multidimensional Index Research Articles

Impact of age and sex on the relationship between carotid intima-media thickness and frailty level in the Canadian Longitudinal Study of Aging

BackgroundCarotid intima-media thickness (cIMT) provides an index of arterial injury. Frailty is an indicator of vulnerability to adverse health outcomes. It is unclear whether cIMT is associated with the multi-dimensional frailty index and/or if this relationship is age- or sex-specific. The aim was to determine the impact of age and sex on the relationship between cIMT and frailty level in middle-aged and older adults. MethodsFrailty and cIMT data were extracted from the Canadian Longitudinal Study of Aging baseline comprehensive cohort of middle-aged (45–64 years) and older adults (>65 years) (n = 10,209). cIMT was assessed via high-resolution ultrasound. Frailty was determined using a 52-item index. Covariate-adjusted ordinary least squares regressions were conducted separately for middle-aged males (n = 3178), middle-aged females (n = 3125), older males (n = 2031), and older females (n = 1875). ResultsAverage cIMTs were larger in older versus middle-aged adults and in males versus females (all, p < 0.001). Average cIMT was positively associated with frailty level in adjusted linear regression models in middle-aged males [adj. R2 = 0.09; β = 0.015 (95 % CI: 0.005–0.026), p = 0.004], middle-aged females [adj. R2 = 0.11; β = 0.040 (95 % CI: 0.025–0.054), p < 0.001], older males [adj. R2 = 0.12; β = 0.019 (95 % CI: 0.004–0.034), p = 0.01], and older females [adj. R2 = 0.11; β = 0.020 (95 % CI: 0.002–0.039), p = 0.03]. ConclusioncIMT was an independent contributor to frailty level regardless of age group (middle-aged/older adults) or sex, with the strongest effect observed in middle-aged females. Our cross-sectional study documents the independent relationship between a marker of cardiovascular function and an increased vulnerability to adverse health outcomes in middle-aged and older males and females.

Digital financial capability and household consumption: evidence from China

ABSTRACT Utilizing the 2017 China Household Finance Survey (CHFS) data, we design a novel multidimensional index of consumers’ digital financial capability (DFC) and find that consumers’ DFC significantly and positively influences their total household consumption and the proportion of online consumption. Our analysis reveals consumers’ DFC impacts household consumption through mediating channels, i.e. easing credit constraints and promoting online shopping, information search, and social interaction. Overall, it is beneficial for consumers to enhance their DFC, not only their financial literacy, to better use digital financial services to improve their financial well-being in the digital era.

Impact of climate-smart agriculture adoption on food security and multidimensional poverty of rural farm households in the Central Rift Valley of Ethiopia

BackgroundClimate change has perverse effects on the natural resource base and agricultural productivity, negatively affecting the well-being of households and communities. There are various attempts by the government and NGOs to promote climate-smart agricultural (CSA) practices to help farmers adapt to and mitigate these negative impacts. This study aimed to identify CSA practices widely adopted in the study area and examined their impacts on rural farm households’ food security and multidimensional poverty. A three-stage proportional to size sampling procedure was followed to select four districts out of nine districts, and 278 households were randomly selected from two kebeles from each district. A cross-sectional data of the 2020–2021 cropping season were collected using a structured and pretested survey questionnaire. The food consumption score, dietary diversity score, food insecurity experience scale, and multidimensional poverty index, constructed out of 9 indicators, were used to assess households’ food security and poverty status, respectively. A multinomial endogenous switching regression model was used to assess average treatment effects on these outcome indicators.ResultsWidely adopted CSA practices are conservation agriculture, soil fertility management, crop diversification, and small-scale irrigation. The results illustrated that adopter households on average showed more food consumption score, dietary diversity score, and less food insecurity experience scale than non-adopters. The results also showed that CSA adopter households, on average, have a low deprivation score in multidimensional poverty than non-adopter households. Accelerating wider adoption of CSA through up-scaling incentives is quite important.ConclusionThis study showed that CSA adoption improves households’ food security and reduces multidimensional poverty. We conclude that up-scaling of CSA practices is important for contributing to the achievement of SDG1, SDG2 and SDG13 targets.

Urban Resilience and Climate Change: Developing a Multidimensional Index to Adapt against Climate Change in the Iranian Capital City of Tehran

Urban resilience studies have increased during recent years due to the significance of climate change as an alarming issue in centralized and highly populated cities where urban functionalities are disintegrated. Towards this aim, an Exploratory Factor Analysis (EFA) was enrolled to streamline the urban resilience to climate change over the 22 districts in Tehran after assessing the resilience objectivity. Based on the results, the city coverage was classified into best (41%), moderate (15%), low (14%), and least resilient (30%). In addition, the urban municipal districts were classified into five functional zones including Wellbeing-wealth (WWZ), Ecological Conservation (ECZ), Core (CZ), Downtown (DZ), and Neutral Zone (NZ) after evaluating the concept of urban functionality in the resilience framework. The results indicated that the socio-cultural component is considered as the fundamental necessity, along with eco-environmental and economic components in capacity building to urban climate resilience. In fact, more than half of the Tehran coverage is regarded as resilient. Thus, the rest should be prioritized, despite the need to inspire from top-ranked districts, especially D4. However, downtown and neutral zones, especially D9 and D21, which account for up to 12% of the least resilient areas, should be evaluated seriously. Finally, the robustness of the proposed methodology was compared to the studies addressing the same concept, and we offer some preparatory and adaptive measures for urban planners and policymakers.

Older People Hospitalized for COVID-19: Prognostic Role of Multidimensional Prognostic Index and Other Prognostic Scores.

During the SARS-CoV-2 pandemic, frailty and patients’ poor outcomes seem to be closely related. However, there is no clear indication of the significance of this connection and the most adequate risk index in clinical practice. In this study, we compared a short version of MPI (multidimensional prognostic index) and other two prognostic scores for COVID-19 as potential predictors of poor patient outcomes. The patients were consecutively enrolled in the hospital of Palermo for COVID-19. The accuracy of Brief-MPI, 4C score and COVID-GRAM score in points was evaluated using the area under the curve (AUC) with 95% CI, taking mortality or sub-ICU admission as outcome. The study included 112 participants (mean age 77.6, 55.4% males). During a mean of 16 days of hospitalization, Brief-MPI significantly increased by 0.03 ± 0.14 (p = 0.04), whilst COVID-GRAM did not. Brief-MPI, 4C score and COVID-GRAM scores had good accuracy in predicting negative outcomes (AUC > 0.70 for all three scores). Brief-MPI was significantly associated with an increased mortality/ICU admission risk, indicating the importance of multidimensional impairment in clinical decision-making with an accuracy similar to other prognostic scores commonly used in COVID-19 study, providing information regarding domains for which interventions can be proposed.

The index of clothing for assessing tourism climate comfort: Development and application

Climate comfort is a significant factor in analyzing the effects of climate change on tourism, and considerable research has used multidimensional climate indices to evaluate climate comfort. In particular, the index of clothing (ICL) is recognized as one of the most popular climate indices and has been widely applied in many studies. While few studies focused on the calculation method of the index of clothing model’s surface solar radiation (Ract), the computed value was greater than that observed at ground stations. Thus, this study tried to improve solar radiation energy calculation on the Earth’s surface in the index of clothing model with the method recommended by the International Food and Agriculture Organization (FAO), and then validated the new model based on the meteorological data of 31 provincial capitals in mainland China during 1980–2019. Results showed that: 1) The value of Ract calculated by the International Food and Agriculture Organization (FAO) method was close to the site observations (Pbais &amp;lt; 15%), and was suggested to be used in enhancing the estimate approach for Ract in the index of clothing; 2) Different from the original index of clothing, ICL-new is significantly more effective in evaluating climate comfort in middle and low latitude regions; 3) Climate change had a considerable influence on the climate comfort of cities in mainland China. Since 1980, the climate comfort of cities in eastern China had increased in spring, while that of cities in western China had declined, and most cities had a decreasing trend in summer. Finally, our findings revealed that ICL-new can realistically and precisely depicts the actual scenario than the original index of clothing, and it is more suitable to provide scientific impact assessment and tourism management for government agencies and destination management.

Households’ Agribusiness Status and Food Security in Burkina Faso

Food insecurity remains a major challenge worldwide. The political discourse calls for increasing agricultural productivity through agribusiness as a key driver of food and nutrition security. This is because the dominant agricultural system, that is, small farms, appears inefficient. However, the policy definition of agribusiness may exclude actors who fit into rural households in low-income countries. This study contributes to the literature by comparing the relationship between agribusiness and non-agribusiness households and by focusing on how this affects their respective food security. It develops an analytical framework that helps decision makers redirect agribusiness policies by clearly considering rural households as potential agribusinesses. Using a dataset covering the entire rural area of Burkina Faso, this study adopts an endogenous sample selection method to define the agribusiness status of these rural agricultural households. An instrumental variable (IV) probit regression was used to analyse the determining factors of agribusiness status, as it addresses the problem of selection bias or endogeneity. A simple linear regression was then used through which a multidimensional food security index was regressed over agribusiness and non-agribusiness household characteristics. The study finds that in rural areas, the level of agricultural technologies adopted, the input cost, and the quantity of family labour used in farming are among the determining factors of a household’s agribusiness. Land size and the quantity of sales within a household are key determinants of the level of food security. As the level of food security in agribusiness households is (two times) high as in non-agribusiness households, this study recognises the relevance of targeting agribusiness as an enabler for achieving food security. This paper proposes that decision-makers extend their definition of agribusiness by including rural non-legally recognised agribusiness actors to increase fiscal income. This study is the first to address agribusiness and food security issues in Burkina Faso.

Financial inclusion and energy poverty nexus in the era of globalization: Role of composite risk index and energy investment in emerging economies

Even in the modern era with increasing globalization and technological innovation, the issue of energy poverty is still debatable in many parts of the world. Unlike developed countries, developing countries struggle to satisfy the growing energy demand. To evaluate the role of financial inclusion and its impact on energy poverty in six representative emerging economies including Brazil, China, India, Indonesia, Mexico, and Russia from 2004 to 2019, this paper examines the role of the composite risk index (a combination of political, economic, and financial risk), investment in energy with public-private participation, gross domestic product, human capital, globalization, and renewable energy. This study first constructed a multidimensional index to measure the weighted average energy poverty and composite energy poverty index along with a novel index for financial inclusion. Moreover, this study uses novel panel data approaches such as Westerlund's cointegration and cross-sectionally augmented autoregressive distributed lags model (CS-ARDL). The study established long-run cointegrating relationship between energy poverty and its detereminants, i.e., financial inclusion, globalization, investment in energy, human capital index, income and composite risk index. The empirical results found that financial inclusion, renewable energy electricity, globalisation, income, human capital index and energy investment are the key determinants of energy poverty in emerging economies. Furthermore, financial inclusion is found to curb energy poverty. Human capital index and globalization also help to reduce energy poverty. Given the empirical results of the nexus and impacts of financial inclusion on energy poverty using the multidimensional proxies, the study provides critical implications to researchers and policymakers in mitigating the consequences of energy poverty and the development of financial systems and their co-movement with energy poverty-related issues.

Intraurban Geographic and Socioeconomic Inequalities of Mortality in Four Cities in Colombia.

Mortality inequalities have been described across Latin American countries, but less is known about inequalities within cities, where most populations live. We aimed to identify geographic and socioeconomic inequalities in mortality within the urban areas of four main cities in Colombia. We analyzed mortality due to non-violent causes of diseases in adults between 2015 and 2019 using census sectors as unit of analysis in Barranquilla, Bogotá, Cali, and Medellín. We calculated smoothed Bayesian mortality rates as main health outcomes and used concentration indexes (CInd) for assessing inequalities using the multidimensional poverty index (MPI) as the socioeconomic measure. Moran eigenvector spatial filters were calculated to capture the spatial patterns of mortality and then used in multivariable models of the association between mortality rates and quintiles of MPI. Social inequalities were evident but not consistent across cities. The most disadvantaged groups showed the highest mortality rates in Cali. Geographic inequalities in mortality rates, regardless of the adults and poverty distribution, were identified in each city, suggesting that other social, environmental, or individual conditions are impacting the spatial distribution of mortality rates within the four cities.

Measuring happiness for social policy evaluation: a multidimensional index of happiness

There are multiple indices based on positive psychology and the economics of happiness for measuring, evaluating and developing social policies. Based on univariable scales, they reflect a more hedonic view rather than a complex perspective on social reality and human behavior, which renders them unfit for purpose in the field of sociology. This paper proposes a multidimensional model that includes social relationships and human happiness, focusing more on eudaimonic than hedonic happiness. It contains an affective dimension, an ethical dimension, and a welfare system’s evaluation dimension. Based on the sociology of emotions, it includes elements from Shalom Schwartz’s theory of values and Jacques Thomassen’s study of societies with consolidated political and welfare systems. The model was validated using an Exploratory Factor Analysis with 28 variables from the European Social Survey (ESS). A multidimensional index is proposed, composed of five factors: the welfare system, positive and negative emotional states, altruism, and egocentrism.

Assessment of the urban waterlogging resilience and identification of its driving factors: A case study of Wuhan City, China

With rapid urbanization and extreme rainstorm events associated with climate change, urban waterlogging has become one of the most frequent and severe disasters globally. In this study, a multi-dimensional and multi-process index system based on the Pressure-State-Response (PSR) framework was developed to measure the level of urban waterlogging resilience (UWR). The spatial distribution of UWR on a block scale was explored based on the entropy weight method with the natural breakpoint method (EWM-NBM) in the central district of Wuhan City, China. In addition, the effects of the runoff control facilities and early warning measures on UWR were also quantified. Further, the Geodetector was used to investigate the main driving factors of UWR and their interactions. Results showed that the constructed index system for UWR based on the PSR framework performed reasonably, and the EWM-NBM was validated to be effective in the integrated assessment. In terms of the validation results, 82.72 % of the recorded waterlogging points belonged to high and very-high risk levels. The spatial heterogeneity of UWR was significant in the study area where the higher-level UWR mainly appears in the areas near the undeveloped suburban and water bodies (lakes and rivers), and the lower-level UWR was concentrated in central urban areas with more impervious surfaces. There was a clear increasing trend in UWR after the implementation of runoff control facilities and early warning measures, but its spatial distribution remained almost invariant. Among all the indexes, the impervious surface percentage had the strongest (69.58 %) explanatory ability for the UWR, and mean annual precipitation (15.51 %), GDP (14.03 %), and population density (11.98 %) also demanded attention. Most driving factors of UWR showed nonlinear interactions. This research could provide a benchmark for urban planning to enhance UWR to mitigate the waterlogging within the main urban area.

Poles Apart? Alternative Welfare Trajectories under Finance-Dominated Capitalism

By connecting the post-Keynesian view of financialization with Comparative Political Economy, we suggest some elements for an updated interpretation of the existing models of welfare. As a main element of novelty, we consider how high-income countries are reacting to the pressures of finance-dominated capitalism, namely by extending, keeping, or retrenching their welfare systems. The contribution of the article is threefold. First, we elaborate on the nexus between welfare and financialization. Second, we introduce a multidimensional index aimed at assessing the degree of welfare of 32 OECD countries for the period 1990–2015. Third, we compare our index with the socio-economic models identified in the literature and with the patterns of financialization. Two trajectories emerge from our exploration: on the one side, Scandinavian and Continental European countries moved toward relatively higher levels of welfare; on the other side, lower levels of welfare feature Anglo-Saxon, Asian, Mediterranean, and Central and Eastern European countries. Besides such divergence, we find that the process of financialization was more intense in countries with stronger welfare, which likely opted for a compensation strategy. Our work highlights the relevance of policies and institutions in shaping different welfare systems and coping with the challenges of the current phase of capitalism.

Computational procedures for quality assessment of latent concepts

The estimation of a latent construct is a crucial issue when a phenomenon is a consequence of the joint occurrence of multiple features. Several techniques for estimating latent phenomena are proposed in literature, but since the variable under study is unobservable, and the law by which the features combine together is unknown, comparison of results obtained with different methods is achieved through simulation‐based analyses. The latent construct is measured by a multidimensional index (MI), which determines a ranking among the observations. It is therefore relevant to assess the reliability of the methods by which such rankings are produced. In this paper, we propose two procedures evaluating the assessment of latent constructs and the corresponding rankings. The first one is a simulation‐based mean square error, which evaluates the MI accuracy, while the second one uses a simulation average split ranking index, which assesses its reliability. Both methodologies are illustrated with an application to the assessment of the urban sustainability, and three methodologies of building MIs are compared: the first one based on structural equation models; the second one based on principal component analysis; and the third one that uses an equal weight method.

Predicting ecosystem productivity based on plant community traits.

With the rapid accumulation of plant trait data, major opportunities have arisen for the integration of these data into predicting ecosystem primary productivity across a range of spatial extents. Traditionally, traits have been used to explain physiological productivity at cell, organ, or plant scales, but scaling up to the ecosystem scale has remained challenging. Here, we show the need to combine measures of community-level traits and environmental factors to predict ecosystem productivity at landscape or biogeographic scales. We show how theory can extend the production ecology equation to enormous potential for integrating traits into ecological models that estimate productivity-related ecosystem functions across ecological scales and to anticipate the response of terrestrial ecosystems to global change.

Préparation estimée et vulnérabilité des pays face à la pandémie de COVID-19: Elaboration et approbation de l'Index global

La pandémie de COVID-19 et ses conséquences socio-économiques ont provoqué une augmentation notable du rôle de l'estimation de la préparation et la vulnérabilité des pays. Cela s'ajoute aux investigations existantes dans le cadre des ont internationales et des classements multidimensionnels, et présente des résultats d'évaluation complexe au niveau national de la préparation et la vulnérabilité face à la pandémie, à partir des index multidimensionnels proposés par les auteurs. L'objectif de cette recherche était d'estimer le niveau de préparation national et la vulnérabilité d'un point de vue complexe. Un index multidimensionnel global a été calculé et utilisé pour donner un classement des pays à partir de cette méthodologie. L'index a permis aux auteurs de conclure qu'il y a une différence importante dans le niveau de préparation face à une pandémie et une disproportion entre certains éléments du système pour chaque pays. L'analyse des données, les sources et les rapports ont souligné le besoin d'étendre la liste des indicateurs pour évaluer le niveau de vulnérabilité. L'opportunité d'utiliser cet index est prouvée par l'analyse corrélative statistique entre les sous-index et le niveau de morbidité et mortalité due au COVID-19. L'index est utilisé pour former des groupes multidimensionnels et le partitionnement de données permet d'estimer les conséquences économiques de la pandémie. Les résultats du partitionnement de données ont permis de former quatre groupes de pays : (1) très vulnérables avec faible niveau de préparation ; (2) vulnérables avec faible niveau de préparation ; (3) vulnérables avec haut niveau de préparation ; (4) peu vulnérables avec haut niveau de préparation. L'analyse comparative des conséquences économiques (changement annuel PIB en 2020-2019 par rapport à PIB en 2019-2017), taux de chômage et changement de l'inflation ont montré des disproportions de l'influence de la pandémie par groupes de pays.

Energy-Saving Bias in Green Technology Innovation A Spatial and Temporal Analysis of manufacturing in China

This study presents an in-depth analysis of the energy-saving bias in green technology innovation across 30 provinces in manufacturing from 2011 to 2021, utilizing a novel Malmquist-Luenberger multidimensional decomposition index based on the directional distance function. The research reveals that green innovation, characterized predominantly by energy conservation, plays a pivotal role in driving China's green total factor productivity. The impetus for innovation in energy saving is found to surpass that of emission reduction in manufacturing enterprises. Energy-saving biased green technology innovation, originating in economically advanced provinces, has gradually expanded to the northern region, and it encompassed the majority of provinces in China. This type of innovation serves as the primary driver of regional green innovation. The study also identifies a conspicuous spatial aggregation effect of energy-saving biased green technology innovation, linked intrinsically to the degree of industrial aggregation and the spatial correlation effect of innovation.

Vibration Signal Augmentation Method for Fault Diagnosis of Low-Voltage Circuit Breaker Based on W-CGAN

Low-voltage circuit breaker (LVCB) fault diagnosis based on artificial intelligence (AI) algorithm has always been a research hotspot and got some recent advances. However, AI algorithms usually require sufficient data to train the model, so intelligent fault diagnosis is a challenging task when lack of fault signals. To solve this problem, a fault vibration signal augmentation method based on Wasserstein distance (WD) and conditional generative adversarial networks (CGANs) is proposed in this article. The proposed method uses WD to optimize the adversarial training of generator and discriminator, and thus, the generator can generate vibration signals under different fault conditions, which can be used to extend the training dataset. In order to verify the improvement effect of this method on the accuracy of LVCB fault diagnosis, multiple fault classifiers are trained using generated and real fault signals, and a multidimensional evaluation index system is built to evaluate the classification effect. Experimental results reveal that the method can generate fault signals with high similarity and improve the accuracy of fault diagnosis.

The Poverty Reduction Effect of People's Livelihood Fiscal Expenditure—A Study Based on Multi-dimensional Poverty Index

The Poverty Reduction Effect of People's Livelihood Fiscal Expenditure—A Study Based on Multi-dimensional Poverty Index

A New Power System Source-End Low Carbonization Evaluation System Considering Carbon Control Model

Under the global goal of “carbon peaking and carbon neutrality”, new power system is facing a new energy development trend of increasing the capacity of new energy and reducing the capacity and quantity of coal power. Furthermore, new power systems are also moving towards new models such as the Energy Internet and energy storage diversification. This paper combines the needs of new power systems’ “source-load interaction” and a differentiated analysis of the specific carbon emission characteristics of each subject of the Energy Internet “source-network-load-storage”. This paper analyzes the energy efficiency coefficient and carbon emission factors of each link of “source-grid-load-storage”. Therefore, the paper proposes a carbon control model for the Energy Internet. This paper focuses on the characteristics of coal power with the largest carbon reduction. Therefore, this article constructs a multi-dimensional low-carbonization evaluation index system for coal-fired power plants. In addition, this article designs a fusion weight calculation method for subjective and objective indicators based on CRITIC-G1. Additionally, this paper uses the ideal solution–gray relational method to evaluate the specified samples. Finally, this article verifies the effectiveness and feasibility of the implementation of the low-carbonization evaluation index system for coal-fired power plants.

Empatia afetiva e cognitiva entre estudantes universitários da saúde, exatas e humanas: um estudo transversal

Abstract Objective The aim of the present study was to compare levels of affective and cognitive empathy among university students of the health field, exact sciences, and humanities. Method A cross-sectional study was conducted in an online format with the participation of 519 students of the health field, exact sciences, and humanities. Data collection involved the use of a sociodemographic questionnaire and the Multidimensional Interpersonal Reactivity Index. Results The female sex predominated the overall sample (74.4%). Statistically significant differences were found regarding total empathy as well as the affective and cognitive domains. Students of in the health field had the highest levels of total and affective empathy. Students of the exact sciences had higher levels of cognitive empathy compared to those in the health sciences and humanities. Conclusion In conclusion, students of the health sciences have higher levels of empathy and the affective domain, whereas those of the exact sciences have higher levels of cognitive empathy.