Issue Of Heterogeneity Research Articles

CEO overpower and corporate social responsibility of commercial banks: The moderating role of state ownership

This study examines the impacts of powerful CEOs and state ownership on commercial banks’ corporate social responsibility (CSR) in Vietnam, a transitional market in Asia. Given the differences between emerging and developed markets in terms of their institutions and governance, it is essential to explore the impact of CEO power on CSR disclosure in emerging markets. This study collects data from 37 Vietnamese commercial banks from 2010 to 2020. We employ a dynamic system Generalized Method of Moments to overcome endogeneity and heterogeneity issues. The findings show that powerful CEOs negatively reduce CSR programs. CEO power tends to focus less on CSR investments because CSR expenses reduce the operating free cash flow. Meanwhile, our findings indicate a positive relationship between state ownership and CSR developments. This study reports a moderating role of state ownership in empowering powerful CEOs to develop CSR programs in commercial banks. We also perform a robustness test to confirm the persistence of our main findings across subsamples by CEO ages. Our robustness test results indicate that CEOs have the lowest motivation to improve CSR when their ages are from 40 to 60. Our findings align with agency theory, stakeholder theory and prior literature. Finally, our study contributes practical implications for management and policymakers to develop CSR programs sustainably.

Undernutrition among the children below five years of age in Uganda: a spatial analysis approach

BackgroundUndernutrition is a health condition caused by a lack of enough food intake, not having enough of the right combination of food nutrients, or the body’s failure to utilize the food eaten resulting in either, stunting, being underweight, or wasting. Globally, undernutrition affects more than 149 million under-five children, while in Uganda about 3 in every 10 children suffer from undernutrition. Undernutrition and its risk factors among under-five children in Uganda were unevenly distributed across the country and a study that focused on spatial distribution was prudent to examine the nature of the problem and salient factors associated with it. The current study addressed the issues of spatial heterogeneity of undernutrition and its determinants with the goal to identify hot spots and advise policymakers on the best actions to be taken to address the problem.MethodsData were obtained from the 2016 Uganda Demographic and Health Survey. Prevalence rates and percentages of risk factors were combined with the Uganda district shape file to allow spatial analysis. Moran’s I, Getis-Ord (GI*), and Geographically Weighted Regressions were respectively used to establish the local, global, and geographically weighted regressions across the country. Stata 15 and ArcGIS 10. 7 soft wares were used.ResultsThe results indicate that undernutrition in Uganda shows varies spatially across regions. Evidence of hot spots exists in the Karamoja and Arua regions, cold spot areas exist around the central part of the country while the greatest part of Western Uganda, Northern, and Eastern were not significant.ConclusionThe study reveals that a variation in the distribution of undernutrition throughout the country. Significant spatial patterns associated with undernutrition as identified through the hotspot and cold spot analysis do exist in Uganda. Programs targeting to reduce the undernutrition of under-five children in Uganda should consider the spatial distribution of undernutrition and its determinants whereby priority should be given to hotspot areas. The spatial intensity of undernutrition and its determinants indicate that focus should be tailored to meet the local needs as opposed to a holistic national approach.

An omics-to-omics joint knowledge association subtensor model for radiogenomics cross-modal modules from genomics and ultrasonic images of breast cancers

The radiogenomics analysis can provide the connections between genomics and radiomics, which can infer the genomic features of tumors from their radiogenomic associations through the low-cost and non-invasiveness screening ultrasonic images. Although there are a number of pioneer approaches exploring the connections between genomic aberrations and ultrasonic features, these studies mainly focus on the relationship between ultrasonic features and only the most popular cancer genes, confronting two difficulties: missing many-to-many relationships as omics-to-omics view, and confounding group-specific associations with whole sample associations. To overcome the difficulty of omics-to-omics view and the issue of tumor heterogeneity, we propose an omics-to-omics joint knowledge association subtensor model. Specifically, the subtensor factorization framework can successfully discover the joint cross-modal module via an omics-to-omics view, while the sparse weight sample indication strategy can mine sample subgroups from the multi-omic data with tumor heterogeneity. The experimental evaluation result shows the jointness of the discovered modules across omics, their association with tumorigenesis contribution, and their relation for cancer related functions. In summary, our proposed omics-to-omics joint knowledge association subtensor model can serve as an efficient tool for radiogenomic knowledge associations, promoting the cross-modal knowledge graph construction of in explainable artificial intelligence cancer diagnosis.

A novel EKC perspective: do agricultural production, energy transition, and urban agglomeration achieve ecological sustainability?

We aim to analyze the existence of a novel agricultural production-augmented environmental Kuznets curve (EKC-AP) theory for energy transition, urban agglomeration, and financial depth. To conduct this research, we use a global panel of selected 54 countries belonging to low, medium, and high levels of economic development from 1971 to 2017. Since our data are distributed into three development levels, it is highly likely to face cross-sectional dependence across the countries within and between the development levels, which may provide biased empirical results. To outpace such possibility, we employ advanced econometric strategies, which are robust to cross-sectional dependence and slope heterogeneity issues. We found that agricultural production, energy transition, urban agglomeration, financial depth, and ecological degradation experience a long-term cointegrating equilibrium association. We also confirm that exists an EKC-AP linkage of agricultural production with ecological degradation in highly developed economies, while those at the low and medium levels of development reveal a positive exponential and monotonic impact, respectively, of agricultural production on ecological degradation. The energy transition is found to promote ecological sustainability in developed countries through technique impact, while it deteriorates the ecological quality in countries at the medium development level. Moreover, urban agglomeration adversely impacts the ecological quality in the economies at low and medium development levels, whereas it improves the ecological sustainability in developed economies. Besides, financial depth proves harmful to ecological sustainability, with less subtle effects in developed economies. Based on our findings, for countries at low and medium levels of development, we propose advancing farming techniques using ecologically friendly technologies, promoting green energy transition, expanding the share of the services sector, and providing green financial systems to support green investment projects to achieve the Sustainable Development agenda of the United Nations.

How renewable energy investment, environmental regulations, and financial development derive renewable energy transition: Evidence from G7 countries

Renewable energy transition is an imperative agenda of the Conference of the Parties. It has a tremendous role in limiting carbon emissions, and environmentalists have recently highlighted the critical need to identify the factors influencing the energy transition. By evaluating the evidence of the G7 from 2000 to 2020, this study broadens the scope of this argument by providing potential determinants of the energy transition. This study explores the dynamic influence of renewable energy investment, financial structure, and environmental regulation on renewable energy transition. The study applied the cross-section autoregressive distributed lag (CS-ARDL) model to resolve the issues of slope heterogeneity and cross-section dependency in panel data analysis. The results exhibit that green energy investment, financial development, and environmental policy stringency stimulate sustainable energy transition in the long run. Manifestly, the interaction term of financial development and ecological regulations offer comparatively stronger influence than their individual effects, implying that effective environmental regulations direct the movement of financial resources toward the renewable energy transition. Similar results are also affirmed through the augmented mean group estimator. It suggests that the policymakers would formulate integrated policies to strengthen the ecological regulations and financial sector development, reducing financial barriers and introducing new green financial products.

Integrating multi-type aberrations from DNA and RNA through dynamic mapping gene space for subtype-specific breast cancer driver discovery.

Driver event discovery is a crucial demand for breast cancer diagnosis and therapy. In particular, discovering subtype-specificity of drivers can prompt the personalized biomarker discovery and precision treatment of cancer patients. Still, most of the existing computational driver discovery studies mainly exploit the information from DNA aberrations and gene interactions. Notably, cancer driver events would occur due to not only DNA aberrations but also RNA alternations, but integrating multi-type aberrations from both DNA and RNA is still a challenging task for breast cancer drivers. On the one hand, the data formats of different aberration types also differ from each other, known as data format incompatibility. On the other hand, different types of aberrations demonstrate distinct patterns across samples, known as aberration type heterogeneity. To promote the integrated analysis of subtype-specific breast cancer drivers, we design a "splicing-and-fusing" framework to address the issues of data format incompatibility and aberration type heterogeneity simultaneously. To overcome the data format incompatibility, the "splicing-step" employs a knowledge graph structure to connect multi-type aberrations from the DNA and RNA data into a unified formation. To tackle the aberration type heterogeneity, the "fusing-step" adopts a dynamic mapping gene space integration approach to represent the multi-type information by vectorized profiles. The experiments also demonstrate the advantages of our approach in both the integration of multi-type aberrations from DNA and RNA and the discovery of subtype-specific breast cancer drivers. In summary, our "splicing-and-fusing" framework with knowledge graph connection and dynamic mapping gene space fusion of multi-type aberrations data from DNA and RNA can successfully discover potential breast cancer drivers with subtype-specificity indication.

Contestant Heterogeneity and Hack-a-Shaq Strategy in Team Efforts in Professional Basketball Games

Abstract: This article addresses the issue of contestant heterogeneity (CH) in team efforts in National Basketball Association games for the three seasons from 2013/14 to 2015/16. The results show that two teams’ total efforts regarding rebounds and fouls increase as CH becomes smaller. The evidence thus indicates that the two teams play harder the smaller that CH is. In the analysis of the effects of heterogeneity on the favorite’s and the underdog’s efforts, the results show that each tries harder when CH is reduced and makes less effort as it becomes larger. These results support the contamination hypothesis. The checks for robustness in the panel regressions and results of Hack-a-Shaq strategy reinforce these conclusions. The evidence also shows that the appearance of the previous year’s champion in a game decreases the two teams’ total number of rebounds and fouls. Overwhelming CH makes both favorites and underdogs exert less effort.

Personalized Edge Intelligence via Federated Self-Knowledge Distillation

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Federated Learning</i> (FL) is an emerging approach in edge computing for collaboratively training machine learning models among multiple devices, which aims to address limited bandwidth, system heterogeneity, and privacy issues in traditional centralized training. However, the existing federated learning methods focus on learning a shared global model for all devices, which may not always be ideal for different devices. Such situations become even worse when each edge device has its own data distribution or task. In this paper, we study personalized federated learning in which our goal is to train models to perform well for individual clients. We observe that the initialization in each communication round causes the forgetting of historical personalized knowledge. Based on this observation, we propose a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Personalized Federated Learning</i> (PFL) framework via self-knowledge distillation, named pFedSD. By allowing clients to distill the knowledge of previous personalized models to current local models, pFedSD accelerates the process of recalling the personalized knowledge for the latest initialized clients. Moreover, self-knowledge distillation provides different views of data in feature space to realize an implicit ensemble of local models. Extensive experiments on various datasets and settings demonstrate the effectiveness and robustness of pFedSD.

Accelerating Federated Edge Learning via Topology Optimization

Federated edge learning (FEEL) is envisioned as a promising paradigm to achieve privacy-preserving distributed learning. However, it consumes excessive learning time due to the existence of straggler devices. In this article, a novel topology-optimized FEEL (TOFEL) scheme is proposed to tackle the heterogeneity issue in federated learning and to improve the communication-and-computation efficiency. Specifically, a problem of jointly optimizing the aggregation topology and computing speed is formulated to minimize the weighted summation of energy consumption and latency. To solve the mixed-integer nonlinear problem, we propose a novel solution method of penalty-based successive convex approximation (SCA), which converges to a stationary point of the primal problem under mild conditions. To facilitate real-time decision making, an imitation-learning-based method is developed, where deep neural networks (DNNs) are trained offline to mimic the penalty-based method, and the trained imitation DNNs are deployed at the edge devices for online inference. Thereby, an efficient imitation-learning-based approach is seamlessly integrated into the TOFEL framework. Simulation results demonstrate that the proposed TOFEL scheme accelerates the federated learning process and achieves a higher energy efficiency. Moreover, we apply the scheme to 3-D object detection with multivehicle point cloud data sets in the CARLA simulator. The results confirm the superior learning performance of the TOFEL scheme over conventional designs with the same resource and deadline constraints.

The effect of military spending on economic growth in MENA: evidence from method of moments quantile regression

This study adopted a novel quantile regression via moments to explore the effects of military spending on the distribution of economic growth of 14 MENA countries over the period from 1981 to 2019. The method, apart from enabling us to investigate the effects of military spending on the distribution of economic growth at different quantiles, also helps to address issues of heterogeneity and endogeneity characterising the panel studies. Our results showed that irrespective of measures of military spending and economic growth, an upsurge in military spending leads to a positive effect on economic growth at different quantiles, suggesting that military spending is productive and growth-enhancing in the MENA countries.

Influence of renewable energy infrastructure, Chinese outward FDI, and technical efficiency on ecological sustainability in belt and road node economies

Renewable energy infrastructure is highly interrelated to environmental sustainability and the accomplishment of sustainable development goals. A significant portion of China's outward foreign direct investment (FDI) has been allocated to energy production and transmission across the Belt and Road Initiative (BRI) region. Therefore, we examine the influence of renewable energy infrastructure, China's outward FDI, and the host country's technical efficiency on ecological sustainability. This research uses the cross-sectional autoregressive distributive lag (CS-ARDL) model to address the issues of parameter and cross-sectional heterogeneity. The findings show that renewable energy infrastructure and technical efficiency significantly reduce carbon emissions in the long run. Likewise, FDI inflows and economic growth increase emissions in the BRI region due to higher energy consumption. Moreover, the direct effect of FDI on environmental quality is negative, while the indirect impact through moderation of FDI and renewable energy infrastructure leads to lower emissions. These results implied that the reallocation of inward FDI in energy infrastructure neutralizes the negative consequences, improves energy efficiency, and resultantly lower emissions. Therefore, it opens new policy implications that we focus more on investing FDI in the energy infrastructure sector.

Normativity and Variation in the Address Terms System Practiced among the Jordanian Youth Community

This study investigates the key forms of address used amongst Jordanian university students, the impact of gender on using these forms and what accounts for the variation in their address system. By addressing the issue of normativity and heterogeneity in the use of address terms, in different social settings, the study enriches the understanding of the internal variation of the address term system. Data collected through questionnaires and semi-structured interviews were analysed, based on Watts’ discursive approach to politeness and Agha’s approach of indexicality. The results revealed that the identified normative patterns represent Jordanian university politic behaviours, which index different social meanings and relations among the youth community, in relation to specific social contexts. The most frequent strategies university students use for addressing others are personal names, innovative terms, descriptive phrases, pronouns, titles, teknonyms, and religious, military, attention attractors, as well as a combination of these terms. It also seems that there are no absolute stable patterns of address term usage among the youth community, speaking Jordanian Arabic. Rather, there is an infinite society-internal heterogeneity in the address terms usage. The results also revealed that an intra-group variation signifies social struggles over the norms of address term usage and potentially normative incertitude.

Similarity Feature Construction for Matching Ontologies through Adaptively Aggregating Artificial Neural Networks

Ontology is the kernel technique of Semantic Web (SW), which enables the interaction and cooperation among different intelligent applications. However, with the rapid development of ontologies, their heterogeneity issue becomes more and more serious, which hampers communications among those intelligent systems built upon them. Finding the heterogeneous entities between two ontologies, i.e., ontology matching, is an effective method of solving ontology heterogeneity problems. When matching two ontologies, it is critical to construct the entity pair’s similarity feature by comprehensively taking into consideration various similarity features, so that the identical entities can be distinguished. Due to the ability of learning complex calculating model, recently, Artificial Neural Network (ANN) is a popular method of constructing similarity features for matching ontologies. The existing ANNs construct the similarity feature in a single perspective, which could not ensure its effectiveness under diverse heterogeneous contexts. To construct an accurate similarity feature for each entity pair, in this work, we propose an adaptive aggregating method of combining different ANNs. In particular, we first propose a context-based ANN and syntax-based ANN to respectively construct two similarity feature matrices, which are then adaptively integrated to obtain a final similarity feature matrix through the Ordered Weighted Averaging (OWA) and Analytic hierarchy process (AHP). Ontology Alignment Evaluation Initiative (OAEI)’s benchmark and anatomy track are used to verify the effectiveness of our method. The experimental results show that our approach’s results are better than single ANN-based ontology matching techniques and state-of-the-art ontology matching techniques.

Single cell RNA sequencing research in maternal fetal interface.

The maternal-fetal interface is an essential environment for embryonic growth and development, and a successful pregnancy depends on the dynamic balance of the microenvironment at the maternal-fetal interface. Single-cell sequencing, which unlike bulk sequencing that provides averaged data, is a robust method for interpreting the cellular and molecular landscape at single-cell resolution. With the support of single-cell sequencing, the issue of maternal-fetal interface heterogeneity during pregnancy has been more deeply elaborated and understood, which is important for a deeper understanding of physiological and pathological pregnancy. In this paper, we analyze the recent studies of single-cell transcriptomics in the maternal-fetal interface, and provide new directions for understanding and treating various pathological pregnancies.

Constrained neuro fuzzy inference methodology for explainable personalised modelling with applications on gene expression data

Interpretable machine learning models for gene expression datasets are important for understanding the decision-making process of a classifier and gaining insights on the underlying molecular processes of genetic conditions. Interpretable models can potentially support early diagnosis before full disease manifestation. This is particularly important yet, challenging for mental health. We hypothesise this is due to extreme heterogeneity issues which may be overcome and explained by personalised modelling techniques. Thus far, most machine learning methods applied to gene expression datasets, including deep neural networks, lack personalised interpretability. This paper proposes a new methodology named personalised constrained neuro fuzzy inference (PCNFI) for learning personalised rules from high dimensional datasets which are structurally and semantically interpretable. Case studies on two mental health related datasets (schizophrenia and bipolar disorders) have shown that the relatively short and simple personalised fuzzy rules provided enhanced interpretability as well as better classification performance compared to other commonly used machine learning methods. Performance test on a cancer dataset also showed that PCNFI matches previous benchmarks. Insights from our approach also indicated the importance of two genes (ATRX and TSPAN2) as possible biomarkers for early differentiation of ultra-high risk, bipolar and healthy individuals. These genes are linked to cognitive ability and impulsive behaviour. Our findings suggest a significant starting point for further research into the biological role of cognitive and impulsivity-related differences. With potential applications across bio-medical research, the proposed PCNFI method is promising for diagnosis, prognosis, and the design of personalised treatment plans for better outcomes in the future.

A multi‐objective particle swarm optimization for matching domain ontologies

As a generic conceptual model for describing domain knowledge, ontology is able to provide a formal and concrete description on the concepts in different domains. Since different ontology engineers have diverse viewpoints on the world, leading to the heterogeneity issue among different ontologies. To solve this problem, this paper investigates how to make certain the correspondences of entities between different domain ontologies, that is, domain ontology matching problem. When solving this problem, how to effectively aggregate different similarity measures to differentiate entities and obtain high‐quality alignment is the kernel issue. In this paper, we first introduce two statistical metrics for measuring ontology alignment's quality, then, an optimization model is established for the multi‐objective ontology matching problem, and a Multi‐Objective Particle Swarm Optimization algorithm (MOPSO) is proposed to optimize the quality of domain ontology alignment. The experimental results illustrate that it outperforms OAEI's participants with respect to both recall and precision.

Accounting for Heterogeneity in Performance Evaluation of Norwegian Dairy and Crop-Producing Farms

It is critical to analyze the performance of enterprises to achieve sustainable agricultural development. Several studies have been conducted to assess farm performance. However, the studies have been criticized for failing to account for farm heterogeneity (which is frequently unobserved) in their evaluation of Norwegian agricultural performance. Technically, a farm is efficient if it can produce a certain amount of output with the fewest possible inputs and no input waste. In this paper, efficiency scores are calculated using a production function with both a random intercept and a random slope parameter, addressing the issue of unobserved heterogeneity in stochastic frontier analysis. Using Norwegian dairy and crop farms as a case study, we demonstrate the viability of improving the agriculture industry and reducing resource waste. The case study was established on data collected from 5884 dairy farms and 1880 crop farms from the years 2000 to 2019. According to the empirical findings of the case study, dairy and crop producers used inefficient technologies and squandered production resources. If all farmers follow a sustainable and efficient path to produce agricultural output, they could increase output by 15–18%. Farmers must follow sustainable paths, and politicians must encourage farm experience exchange so that less efficient dairy and crop-producing farms can learn from the most efficient farms to achieve sustainable development.

INTEGRATING LARGE-SCALE ONTOLOGIES FOR ECONOMIC AND FINANCIAL SYSTEMS VIA ADAPTIVE CO-EVOLUTIONARY NSGA-II

The identification, prediction, management, and control of economic and financial systems render extremely challenging tasks, which require comprehensively integrating the knowledge of different expert systems. Ontology, as a state-of-the-art knowledge modeling technique, has been extensively applied in the domain of economics and finance. However, due to ontology engineers’ subjectivity, ontology suffers from the heterogeneity issue, which hampers the co-operation among the intelligent expert system based on them. To address this issue, ontology matching for finding heterogeneous concept pairs between two ontologies has been rapidly developed. It is difficult to find the perfect ontology alignment that satisfies the needs of all decision-makers. Therefore, Multi-Objective Evolutionary Algorithm, such as Non-dominated Sorting Genetic Algorithm (NSGA-II), attracts many researchers’ attention. However, when facing large-scale ontology matching problems, NSGA-II tends to fall into local optimal solutions due to the large search space. To effectively address this drawback, we model the large-scale ontology problem as a nonlinear optimization problem, and propose an Adaptive Co-Evolutionary NSGA-II (ACE-NSGA-II) to deal with it. Compared with NSGA-II, ACE-NSGA-II introduces a co-evolutionary mechanism to increase the diversity of populations in order to decrease the probability of premature convergence. In particular, ACE-NSGA-II uses an adaptive population maintenance strategy to assign more resources toward the dominant ones in order to improve the solution efficiency for solving large-scale ontology matching. The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)’s benchmark and anatomy track to test the effectiveness of ACE-NSGA-II, and the resulting experiment demonstrated that compared to NSGA-II and OAEI’s participants, ACE-NSGA-II is able to find better alignment.

Determinants of bank profitability: Evidence from the emerging economy

This study examines the determinants of bank profitability in Zimbabwe, a country that has faced severe economic challenges in the past decade. To address this issue, we use a panel data analysis of 11 commercial banks over the period 2011–2020 and apply the system generalised method of moments (GMM) estimator to control heterogeneity and endogeneity issues. We find that bank specific factors, such as non-interest income, liquidity, cost efficiency, capital adequacy, and bank stability, have a positive and significant impact on bank profitability, while the industry factor, bank concentration, has a negative and significant impact on bank profitability. We also find that macroeconomic factors, such as gross domestic product (GDP) and inflation, do not have a significant influence on bank profitability. This result is surprising given the high inflation and low growth rates experienced by Zimbabwe in recent years. Moreover, we find that regulatory capital weakens the positive effect of bank stability on bank profitability in Zimbabwe. This result suggests that higher capital requirements may reduce the risk-taking incentives or opportunities of banks, which may lower their profitability potential. Finally, we find no evidence of a moderating effect of fintech on bank performance. This result implies that fintech may not significantly impact the competitiveness and performance of banks in Zimbabwe in non interest income activities. Our study concludes that bank profitability in Zimbabwe is mainly determined by internal factors that are under the control of bank managers and regulators, rather than external factors that are beyond their influence. Based on these findings, we provide several policy implications and recommendations for enhancing bank profitability and fostering a sound and resilient banking sector in Zimbabwe.

Air Moving Target Indication in Nadir Region for Spaceborne Surveillance Radar Systems

For air moving target indication in nadir region, due to the fact that a spaceborne radar beam can illuminate the top of fuselage, the target radar cross section is usually high, which is beneficial for the detection of a low-observable target. However, due to the short slant range, specular reflection effect, and relatively low radar ground resolution, the power of clutter component from nadir region is comparatively high, leading to the insufficient clutter suppression and the degradation of target detection performance. Fortunately, when an air moving target is adequately high, the target echo is able to be separated from the main clutter echoes due to a shorter time delay, making it possible to be only mixed with low-power ambiguous clutter echoes. Based on these considerations, this paper analyzes the performance of air moving target indication in nadir region for a spaceborne surveillance radar system. It analyzes the target minimum detectable velocities with different target heights and beam center elevation angles. Also, an effective sample selection method based on adaptive range segmentation is proposed to solve the power heterogeneity issue between the main clutter area and the range ambiguous clutter area. As a conclusion, the larger the elevation angle of an air moving target is, the higher the minimum target detectable height is.