Relative Importance Research Articles

AdaVis: Adaptive and Explainable Visualization Recommendation for Tabular Data.

Automated visualization recommendation facilitates the rapid creation of effective visualizations, which is especially beneficial for users with limited time and limited knowledge of data visualization. There is an increasing trend in leveraging machine learning (ML) techniques to achieve an end-to-end visualization recommendation. However, existing ML-based approaches implicitly assume that there is only one appropriate visualization for a specific dataset, which is often not true for real applications. Also, they often work like a black box, and are difficult for users to understand the reasons for recommending specific visualizations. To fill the research gap, we propose AdaVis, an adaptive and explainable approach to recommend one or multiple appropriate visualizations for a tabular dataset. It leverages a box embedding-based knowledge graph to well model the possible one-to-many mapping relations among different entities (i.e., data features, dataset columns, datasets, and visualization choices). The embeddings of the entities and relations can be learned from dataset-visualization pairs. Also, AdaVis incorporates the attention mechanism into the inference framework. Attention can indicate the relative importance of data features for a dataset and provide fine-grained explainability. Our extensive evaluations through quantitative metric evaluations, case studies, and user interviews demonstrate the effectiveness of AdaVis.

Do successive performance matter for tone management? Evidence from China

ABSTRACT This paper investigates whether managers deliberately adjust the tone of their non-financial disclosure depending on their firm performance over multiple periods. Using 13,995 firm-year MD&As (Management Discussion and Analysis) in annual reports of companies listed on the Shanghai and Shenzhen Stock Exchanges from 2009 to 2019, we first observe a bias towards good news disclosure by managers in their MD&A section. More importantly, we find that this asymmetric tone management is more pronounced when a firm’s performance declines consecutively. In cross-sectional tests, we provide evidence that the relationship between asymmetric tone management and successive performance is weaker for firms with state ownership and those with a high marketability index. We suggest that in non-U.S. markets, such as China, asymmetric tone management is more likely to reflect the opportunistic behaviour of managers. Overall, our study sheds new light on tone management in emerging markets and highlights the importance of a multi-period relation in understanding managers’ tone management behaviour.

Neutrinos, light, matter, and the unification of gravitational and nuclear forces

Neutrinos, light, matter, and the unification of gravitational and nuclear forces The discovery of neutrinos and the measurement of their masses are significant events in the history of science. The Rotating Lepton Model provides a useful basis for understanding particles and nuclear reactions, highlighting the importance of Special Relativity, Gravity, and Quantum Mechanics in our universe. Professor Constantinos G. Vayenas explains. The discovery of neutrinos by Pauli some 90 years ago and the measurement of their masses by Kajita and McDonald (1) some 20 years ago constitute significant developments in the history of science. The recent (2023) (2) detection of neutrino production during the proton-proton collision experiments at CERN confirms the basic assumption of the Rotating Lepton Model (RLM) (2020), (3) i.e. that protons and neutrons comprise rotating neutrino triads, the former with a central positron. This implies that all matter in our Universe, including electromagnetic radiation, (4) comprises only five elementary particles: The three neutrinos (ν1, ν2, and ν3), the electron, and the positron. It also implies that two forces (gravity and electromagnetism) suffice for describing the interactions between these five particles and the concomitant production of all other composite particles, such as hadrons and bosons.

IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction.

Predicting the trajectory of pedestrians in crowd scenarios is indispensable in self-driving or autonomous mobile robot field because estimating the future locations of pedestrians around is beneficial for policy decision to avoid collision. It is a challenging issue because humans have different walking motions, and the interactions between humans and objects in the current environment, especially between humans themselves, are complex. Previous researchers focused on how to model human-human interactions but neglected the relative importance of interactions. To address this issue, a novel mechanism based on correntropy is introduced. The proposed mechanism not only can measure the relative importance of human-human interactions but also can build personal space for each pedestrian. An interaction module, including this data-driven mechanism, is further proposed. In the proposed module, the data-driven mechanism can effectively extract the feature representations of dynamic human-human interactions in the scene and calculate the corresponding weights to represent the importance of different interactions. To share such social messages among pedestrians, an interaction-aware architecture based on long short-term memory network for trajectory prediction is designed. Experiments are conducted on two public datasets. Experimental results demonstrate that our model can achieve better performance than several latest methods with good performance.

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 prevalence of placentally derived composite of adverse pregnancy outcomes (CAPO) and composite of severe adverse pregnancy outcomes (CAPO-S). This was a single-center retrospective cohort study of data obtained in a tertiary maternity unit. Data regarding ethnicity and socioeconomic deprivation (as measured with indices of multiple deprivation) were collected for 13 165 singleton pregnancies screened routinely in the first trimester for pre-eclampsia using the Fetal Medicine Foundation combined risk-assessment algorithm. CAPO was defined as the presence of one or more interrelated outcomes associated with placental dysfunction, namely, hypertensive disorders of pregnancy, preterm birth, birth weight ≤ 10th centile and stillbirth. CAPO-S was defined as the presence of one or more of the following: hypertensive disorders of pregnancy at < 37 + 0 weeks, preterm birth at < 34 + 0 weeks, birth weight ≤ 5th centile and stillbirth at < 37 + 0 weeks. The prevalence of CAPO was 16.3% in white women, 29.3% in black women and 29.3% in South Asian women. However, half (51.7%) of all CAPO cases occurred in white women. There was a strong interaction between ethnicity and socioeconomic deprivation, with a correlation coefficient of -0.223. Both ethnicity and socioeconomic deprivation influenced the prevalence of CAPO and CAPO-S, with the contribution of ethnicity being the strongest. Black and Asian ethnicity, as well as socioeconomic deprivation, influence the prevalence of placenta-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 placenta-mediated CAPO. © 2024 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Social protection strategies in relations between Ukraine and the European Union

In today’s world, social protection is becoming an increasingly important topic that combines economic, political, and social aspects. Regardless of the level of development of a country, people are constantly looking for ways to ensure their well-being and protect themselves from negative influences. Social protection strategies are of particular importance in relations between Ukraine and the European Union. Ukraine, as a sovereign state, monitors the needs of its population and tries to adapt its social system to European standards. Therefore, the purpose of the study is to analyze the current state and prospects for the development of social protection strategies in Ukraine in the context of cooperation with the European Union to identify possible areas for improving the social sphere and ensuring a high level of social protection for the Ukrainian population. The study uses a variety of methods, including analysis, synthesis, induction, deduction, comparative legal, dialectical, and others. Considering cooperation with the European Union helps to solve socioeconomic problems and ensure a high level of social protection.

The relation of clinical governance climate on quality of care mediated by patient safety culture

The hospital organization determines quality of care (QOC) from health services; however, there are many challenges due to the different backgrounds of healthcare workers. Essentially, governance is needed to guarantee a process oriented toward clinical outcomes. Unfortunately, research on clinical management with dimensional measurements conducted in private hospitals is still scanty. This study aimed to investigate the relationship between clinical governance climate (CGC) on QOC mediated by a patient safety culture (PSC) and the control variable adaptation to workload in relation to QOC. Participants were healthcare workers in two private hospitals that have been fully accredited; 416 participants met the requirements. Data were collected by distributing questionnaires in March 2023. Data were analyzed through partial least square – structural equation model (PLS-SEM). The study result indicated a significant positive relationship between CGC and PSC (β=0.851, p-value&amp;lt;0.05). Further, a positive relationship between PSC to QOC (β=0.654, p-value&amp;lt;0.05) was established. However, insufficient evidence indicates a direct relation between CGC on QOC (p-value&amp;gt;0.05). The role of PSC as a full mediating was confirmed (β=0.557, p-value&amp;lt;0.05, CI 95% 0.441-0.677). The finding of this study is the importance of CGC relation to QOC mediated by PSC in private hospitals.

Ethnopharmacological Survey of Medicinal Plants Used by Local Herbalists and Traditional Healers for the Treatment of Cancer in Jordan

Background: As many Jordanians include herbs in their treatment, an ethnopharmacological survey was conducted to identify the most prescribed plants for the treatment of cancer by local herbalists and traditional healers in Jordan, aiming to encourage the prescription that is based on scientific evidence, to tackle some important practices in traditional healing, and to provide candidates for the isolation of new anticancer agents. Methods: Thirty-two Jordanian herbalists and traditional healers were interviewed. Both the use value and frequency of citation were implemented as quantitative methods to demonstrate the relative importance of a chosen species for the treatment of cancer. Furthermore, literature was screened for evidences that supported or opposed the use of the plants of interest Results: It was revealed that 31 plants are currently used in Jordan for the treatment of cancer, with Curcuma longa and Ephedra foeminea being the most used ones. Conclusions: It was noticed that the use of many prescribed plants could be rationalised by in vitro and/or in vivo studies. However, more attention should be paid by practitioners to some important aspects, such as the method of use and preparation of plants, their interaction with chemotherapeutic agents and the differences in their efficacy against different cancer types.

HelloTalk Application as an Alternative for Building Students’ Reading Ability on Narative Texts

English was regarded as a foreign language in Indonesia and holds significant importance in various domains, including education, economy, international relations, and technology. Consequently, the Indonesian government has designated English as the primary foreign language, mandatory for learning in both schools and universities. Thus, English has incorporated into the curriculum from elementary level up to university. This research aimed to determine if there is a significant difference in the reading abilities of students taught using the Hellotalk application compared to those taught through conventional methods.in this school most of students still low to read the English text so the Reseacher interesting to give new method for them. The method used Quantitative research, with an experimental method and quasi-experimental design. The target population consisted of seventh-grade students. Purposive sampling was utilized to select a sample of 25 students for the experimental group and 25 students for the control group. Data collection involved the use of a multiple-choice test, which was subsequently analyzed using the independent t-test in SPSS 25. The research findings revealed a significant distinction between the experimental group and the control group. This meant that Hellotalk Application was suitable to use in teaching reading on narrative texts at the seventh grade.

MHGCN+: Multiplex Heterogeneous Graph Convolutional Network

Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous graph data, ranging from link prediction to node classification. However, most existing works ignore the relation heterogeneity with multiplex networks between multi-typed nodes and the different importance of relations in meta-paths for node embedding, which can hardly capture the heterogeneous structure signals across different relations. To tackle this challenge, this work proposes a M ultiplex H eterogeneous G raph C onvolutional N etwork (MHGCN+) for multiplex heterogeneous network embedding. Our MHGCN+ can automatically learn the useful heterogeneous meta-path interactions of different lengths with different importance in multiplex heterogeneous networks through multi-layer convolution aggregation. Additionally, we effectively integrate both multi-relation structural signals and attribute semantics into the learned node embeddings with both unsupervised and semi-supervised learning paradigms. Extensive experiments on seven real-world datasets with various network analytical tasks demonstrate the significant superiority of MHGCN+ against state-of-the-art embedding baselines in terms of all evaluation metrics. The source code of our method is available at: https://github.com/FuChF/MHGCN-plus .

An improved habitat suitability index for the great crested newt Triturus cristatus

An improved habitat suitability index for the great crested newt Triturus cristatus

Analysis of Factors Affecting the Implementation of Sustainable Building Projects

Abstract: Sustainability concept can be defined as “meeting the needs of the present without compromising the needs of the future” [7]. Conventional buildings approximately consumes more than 30% of energy while utilising around 40% of resources while also simultaneously generating 40% of wastes and 35% of harmful green-house gases [8]. The sustainable building construction approach has the potential to make a significant contribution to sustainable development, especially considering the building industry’s considerable influence. Based on our literature assessment, we discovered that multiple research reveal a general lack of consistency across outcomes in different geographical locations regarding factors impacting the implementation of sustainable building projects. Therefore this study is based on the analysis of factors that impact the implementation of sustainable building projects and the effects of sustainable building projects on various critical factors. For the result of the research analysis, the Reliability analysis which was conducted based on Cronbach's Alpha Coefficient for the factors affecting the implementation of sustainable building projects were found to be 0.76658442 which can conclude that the reliability level of the questionnaires responses was good while based on Relative Importance Index (RII) analysis factors affecting the implementation of sustainable building projects were ranked, with material related factors being the most significant (RII 0.705) among other factors. Additionally, the impact of implementing sustainable building projects on different critical factors was determined, with RII analysis indicating that the most significant impact will occur in terms of economic benefits (RII 0.767). Overall, the study tried fulfilling all the objectives which were stated.

Information and analytical support for economic evaluation and digital indication of innovative development of a developer enterprise in construction

In the conditions of functioning of construction enterprises, the importance of relations between the development of a construction and investment project and its stakeholders (stakeholders), which influence various aspects of project activities in order to make a profit, is growing. Reducing the effectiveness of interaction between construction developers and their partners, such as customers, contractors, design organizations, competitors and public organizations, requires the development of theoretical and methodological foundations for the implementation of a stakeholder-oriented strategy for managing construction enterprises. This strategy includes the organization of financing and implementation of a real estate development project, ta king into account the constraints and with the aim of obtaining commercial benefits. In today's world, construction development is a n area that is constantly being influenced by innovative technologies and strategies. The economic development of a country is determined by the efficiency of construction enterprises, which are an important indicator of the status and trends of the internal and external economic environment. The article explores innovative directions aimed at updating business processes and organizational structures of enterprises in the field of construction development. By analyzing and systematizing modern approaches to management and the use of advanced technologies, the article considers such innovative areas as the introduction of the Building Information Modeling (BIM) system, the use of intelligent management systems, the development of digital platforms, and others. Coverage of these areas will allow to provide an economic assessment and determine the criteria for qualimetric and factor analysis of indicators of innovative development of enterprises in the construction industry and their impact on the efficiency of business processes and competitiveness of enterprises. The economic assessment of innovative development in the development system is an important tool for enterprises seeking to achieve sustainable growth and success. Innovations have become a key success factor in the modern economy, especially in the field of construction and real estate development. Ukraine, as a country with active construction activity, has a great potential for innovative changes in this sector. However, for the effective implementation of innovations in the activities of construction enterprises, a deep economic assessment and economic and managerial analysis are required. This article serves as a source for studying and understanding innovative trends in construction development and considers the possibilities of their implementation to improve the efficiency of management and economic diagnostics and digital administration of directive indicators for assessing the innovative development of a construction developer enterprise.

Sustainable Consumption Behaviour and Common Goods: Education, Culture, Rule of Law, Work, and Solidarity

It is increasingly imperative to foster consumption behaviours that are more environmentally responsible. In order to design impact and sustainable actions that promote a transition to a more sustainable society, it is crucial to understand the underlying motivators that drive this change. Within this framework, this study sought to demonstrate how the comprehensive experience of common goods (solidarity, work, rule of law, culture, and education) is linked to such consumption behaviours. In order to test this hypothesis, a hypothetical-deductive quantitative approach was utilised, employing the second-generation statistical technique of partial least squares structural equation modeling (PLS-SEM) for data analysis. The results revealed that only the common good of Solidarity exhibits a significant relationship with Sustainable Consumption Behaviour. Additionally, Work and Education as common goods demonstrate a relationship of relative importance with this behaviour. Additionally, relationships were found between the common good, showcasing the interconnectedness rooted in cultural norms that strengthen individuals' experiences and, thus, cultivate a sense of community - an essential component for promoting consciousness and modifying behaviours. The novelty of this work is that it was not only focused on governing the commons, but also on how they are distributed and maintained for the long term. The theoretical basis was that natural resources are finite goods to which everyone should have access; however, not everyone enjoys them. That is why a limitation in agency impedes the experience of common goods and, therefore, in sustainable consumption. This study demonstrated that prioritising the welfare of others in addition to oneself, known as altruism, enhances the satisfaction derived from selfless consumption. The implications of this research include the development of instructional interventions incorporating motivational aspects to encourage sustainable consumption practices, ultimately supporting the shift necessary for the well-being of future generations.

Explainable machine learning for predicting stomatal conductance across multiple plant functional types

Stomatal conductance (gs) is a key leaf-level function controlling water, carbon, and energy exchange between vegetation and the surrounding environment. Conventionally, semi-empirical models have been used to model gs, but these models require re-parameterization as ecosystems undergo phenological changes over the growing season. In contrast, machine learning (ML) models offer a potential path to overcome this problem but are less interpretable than process-based models. This study explores ML as an approach to develop flexible and robust models of gs for a range of plant functional types (PFTs), including C3 crops, C3 grasses, shrubs, and tree species across different continents. An explainable machine-learning approach (eXML) was used here to provide novel interpretations and insights into the ML model formulations and relative predictor importance. We contrast the performance of three ML architectures: extreme gradient boosting, random forests, and neural networks. Models were developed and examined using many combinations of environmental and physiological predictors. The results demonstrated that ML models significantly outperform conventional semi-empirical models in predicting gs responses to the environment, while not requiring re-parameterization as is required in the semi-empirical paradigm. Particular focus is placed on models formulated around predictor sets that are: (a) relevant to gs estimation in modern terrestrial biophysical simulation models, and (b) composed of variables describing environmental and physiological drivers that can be remotely sensed non-invasively. “Generalized” models developed using data from all four PFTs demonstrated strong predictive performance using only three predictor variables, capturing 63–80 % of the variability in stomatal conductance across all ML architectures. Four predictor variables resulted in models capturing 79–83 % of gs variability, and models developed using all five predictor variables examined here were able to capture as much as 87 % of gs variability across all PFTs. Uncertainty in gs predictions was quantified using quantile regression. Shapley additive explanations was applied to unravel instance-based positive and negative contributions of environmental and physiological predictors to gs modeling, while illustrating that the models are consistent with the underlying ecophysiology. This work demonstrates the power of ML to introduce a new paradigm in the simulation of highly dynamic ecophysiological processes critical to environmental prediction.

Community Preferences for the Care of Older People at the End of Life: How Important is the Disease Context?

Population preferences for care at the end of life can inform palliative care policy and direction. Research investigating preferences for care at the end of life has focused predominantly on the context of advanced cancer, with relatively little attention to other life-limiting illnesses that are common causes of death. We aimed to investigate preferences for the care of older people at the end of life in three different disease contexts. The purpose was to understand if population preferences for care in the last 3 weeks of life would differ for patients dying from cancer, heart failure or dementia. Three discrete choice experiments were conducted in Australia with a general population sample using similar methods but different end-of-life disease contexts. Some attributes were common across the three experiments and others differed to accommodate the specific disease context. Each survey was completed by a different panel sample aged ≥45years (cancer, n=1548; dementia, n=1549; heart failure, n=1003). Analysis was by separate mixed logit models. The most important attributes across all three surveys were costs to the patient and family, patient symptoms and informal carer stress. The probability of choosing an alternative was lowest (0.18-0.29) when any one of these attributes was at the least favourable level, holding other attributes constant across alternatives. The cancer survey explored symptoms more specifically and found patient anxiety with a higher relative importance score than the symptom attribute of pain. Dementia was the only context where most respondents preferred to not have a medical intervention to prolong life; the probability of choosing an alternative with a feeding tube was 0.40 (95% confidence interval 0.36-0.43). This study suggests a need for affordable services that focus on improving patient and carer well-being irrespective of the location of care, and this message is consistent across different disease contexts, including cancer, heart failure and dementia. It also suggests some different considerations in the context of people dying from dementia where medical intervention to prolong life was less desirable.

An integrated conceptual model for enhancing refugee education

AbstractThis paper introduces a unified conceptual framework for understanding refugee identity construction within educational contexts, drawing on theories of social justice (Fraser, 2008), affective relations (Lynch, 2012), the resumption of ordinary life (Kohli, 2011) and ecological systems (Bronfenbrenner, 1977). Addressing a gap in the existing scholarship, the model emphasises the importance of affective relations in shaping refugee identities in education alongside other factors. The study examines the macro constructs of redistribution, recognition, representation and relational affect, highlighting their impact on refugee children's educational experiences. Further, it considers the micro constructs of safety, sense of belonging and success in integration and attainment in educational environments. The constructs are examined across various levels of Bronfenbrenner's ecological system, demonstrating the complexity of refugees’ needs. This model aims to facilitate a holistic, justice‐oriented approach to refugee education, which is crucial given the rise in global displacement. The study has significant implications for refugee education research and policymaking, potentially informing targeted interventions and programmes that foster academic success, overall well‐being, and practice in refugee education.

How Segregation Ruins Inference: A Sociological Simulation of the Inequality Equilibrium

Abstract Why do many people underestimate economic and racial inequality and maintain that theirs is a meritocratic society? Existing work suggests that people are rationalizing, misinformed, or misled. This article proposes an additional explanation: Inequality itself makes economic and racial disparities difficult to understand. In unequal societies, individuals establish their networks at formative institutions patterned by class and race. As a result, they unwittingly condition on key causal pathways when making descriptive and causal inferences about inequality. We use a simple agent-based model to show that, under circumstances typical to highly stratified societies, individuals will underestimate the extent of economic and racial inequality, downplay the importance of inherited advantages, and overestimate the relative importance of individual ability. Moreover, we show that they will both underestimate the extent of racial discrimination and overestimate its relative importance. Because segregated social worlds bias inference in these ways, all individuals (rich and poor) have principled reasons to favor less redistribution than they would if their social worlds were more integrated.

Interpreting biogeochemical processes through the relationship between total alkalinity and dissolved inorganic carbon: Theoretical basis and limitations

AbstractThe marine carbonate system is influenced by anthropogenic CO2 uptake, biogeochemical processes, and physical changes that involve freshwater input and removal. Two frequently used parameters to quantify seawater carbonate system are total alkalinity (TA) and total dissolved inorganic carbon (DIC). To account for the physical changes, both TA and DIC are usually normalized to a reference salinity (i.e., nTA and nDIC), and then the relationship between nTA and nDIC is used to identify major biogeochemical processes that regulate the carbonate system, based on process‐specific reaction stoichiometry. However, the theoretical basis of this interpretation has not been holistically examined. In this study, we validated this method under idealized conditions and discussed the associated assumptions and limitations. Furthermore, we applied this method to interpret field TA and DIC data from a lagoonal estuary in the northwestern Gulf of Mexico. Our results demonstrated that evaluating field data that encompass multiple stations and time periods could be problematic. In addition, various combinations of biogeochemical processes can lead to the same nTA–nDIC relationship, even though the relative importance of each individual process may vary significantly. Therefore, the stoichiometric relationship relying solely on TA and DIC data is not a definitive approach for uncovering dominant biogeochemical processes. Instead, measurements of process‐specific parameters are necessary.

Body condition and diet of the dyeing poison frog Dendrobates tinctorius (Cuvier 1797) (Amphibia: Dendrobatidae) from the eastern Brazilian Amazonia

The present study adds new data on the natural history of Dendrobatestinctorius. We conducted our fieldwork during the rainy season of the years2018 and 2019 in two protected areas (Parque Natural Municipal do Cancão–PNMC and Reserva Extrativista Beija-Flor Brilho de Fogo–REBFBF) in the state of Amapá, Brazil. Through active visual searches we sampled 22 individuals of D. tinctorius, of which 10 were countedfor the population of PNMC and 12 for the population of REBFBF. We calculatedthe Body Condition Index (BCI) for all individuals and, for dietary data, each frog’s stomach was flushed. Females from the REBFBF were larger than males whencompared to the PNMC population. BCI of females from the REBFBF showed higher values than males. Gravid females of both populations showed higher BCI. Retrievedstomachs revealed a total of 290 prey items divided into four main taxonomiccategories of Arthropods. The Index of Relative Importance revealed that antsof the genus Solenopsis were the majority of the diet, and mites represented only 4.86%. The niche breadth for both populations was narrow, indicating a specialist diet, as expected for the species. Our study evidences the importance of integration between BCI and dietary aspects.