Multidimensional Reduction Research Articles

State of Health Estimation for Lithium-Ion Batteries Based on Fusion Health Features and Adaboost-GWO-BP Model

To accurately predict the state of health (SOH) of lithium-ion batteries and improve the safety and reliability of battery management systems, a new SOH estimation method based on fusion health features (HFs) and adaptive boosting integrated grey wolf optimizer to optimize back propagation neural network (Adaboost-GWO-BP) is proposed. First, five kinds of multi-type HFs were extracted from the battery charging process, and the correlation between the proposed HFs and SOH was verified by Pearson and Spearman correlation coefficients. Then, the indirect health feature (IHF) was obtained by multidimensional scaling dimensionality reduction to reduce data redundancy and improve the correlation between HFs and SOH. The GWO-BP model was then used to establish the nonlinear mapping relationship between IHF and SOH. In order to overcome the problem of low accuracy of battery SOH estimation in a single model, the Adaboost algorithm in ensemble learning is introduced to enhance the accuracy of the model estimation. Finally, the proposed method is verified by NASA dataset, and compared with other models. In the comparative experiments, mean absolute error and root mean square error of the proposed method for SOH estimation is less than 0.81% and 1.26%, which has higher accuracy compared to other models.

Association of crop commercialization and rural households' multidimensional poverty using targeted maximum likelihood estimation

Recent evidence shows the highest prevalence of multidimensional poverty in the world. To alleviate poverty, Ethiopia is investing in stimulating agricultural commercialization for poverty reduction, driving sustainable economic growth, and creating jobs. Therefore, this study investigated the causal relationship between crop commercialization and multidimensional poverty across Ethiopian rural households using the Ethiopia Socioeconomic Survey 2018/19 data, which included 1129 crop commercialized and 1585 completely subsistence rural households. Moreover, this study also examined the causal effect of crop commercialization on the multidimensional poverty mediated by mobile phone ownership status. The study used the double robust method of the augmented inverse probability of treatment weighted (AIPTW) and targeted maximum likelihood estimation (TMLE) methods. The result showed that crop commercialization has an association with multidimensional poverty. Specifically, the TMLE with a default super learner model estimates were the most precise and suggest that the prevalence of risk of being multidimensionally poor among rural households who were crop commercialized is 12.2 % lower than for those who were completely subsistence households. Thus, it is important to have more robust policies like crop commercialization to target rural multidimensional poverty reduction. Moreover, the controlled direct effect estimation analyses showed that crop-commercialized rural households reduced the adjusted risk of multidimensional poverty by; 12 % for those who owned mobile phones and 7.8 % for those who did not. To ensure sustainable livelihoods for rural multidimensional poor households, it is necessary to have a multidisciplinary policy intervention to promote crop commercialization through the spread of mobile phones in rural areas.

Native arbuscular mycorrhizal fungi drive ecophysiology through phenotypic integration and functional plasticity under the Sonoran desert conditions.

Knowledge is scarce to what extent environmental drivers and native symbiotic fungi in soil induce abrupt (short-term), systemic (multiple traits), or specific (a subset of traits) shifts in C3 plants' ecophysiological/mycorrhizal responses. We cultivated an emblematic native C3 species (Capsicum annuum var. glabriusculum, "Chiltepín") to look at how the extreme heat of the Sonoran desert, sunlight regimes (low = 2, intermediate = 15, high = 46 mol m2 d-1) and density of native arbuscular mycorrhizal fungi in soil (low AMF = 1% v/v, high AMF = 100% v/v), drive shifts on mycorrhizal responses through multiple functional traits (106 traits). The warming thresholds were relentlessly harsh even under intensive shade (e.g. superheat maximum thresholds reached ranged between 47-63°C), and several pivotal traits were synergistically driven by AMF (e.g. photosynthetic capacity, biomass gain/allometry, and mycorrhizal colonization traits); whereas concurrently, sunlight regimes promoted most (76%) alterations in functional acclimation traits in the short-term and opposite directions (e.g. survival, phenology, photosynthetic, carbon/nitrogen economy). Multidimensional reduction analysis suggests that the AMF promotes a synergistic impact on plants' phenotypic integration and functional plasticity in response to sunlight regimes; however, complex relationships among traits suggest that phenotypic variation determines the robustness degree of ecophysiological/mycorrhizal phenotypes between/within environments. Photosynthetic canopy surface expansion, Rubisco activity, photosynthetic nitrogen allocation, carbon gain, and differential colonization traits could be central to plants' overall ecophysiological/mycorrhizal fitness strengthening. In conclusion, we found evidence that a strong combined effect among environmental factors in which AMF are key effectors could drive important trade-offs on plants' ecophysiological/mycorrhizal fitness in the short term.

Quadriceps Activation After Anterior Cruciate Ligament Reconstruction: The Early Bird Gets the Worm!

Arthrogenic Muscle Inhibition (AMI) is a phenomenon observed in individuals with joint injury or pathology, characterized by a reflexive inhibition of surrounding musculature, altered neuromuscular control, and compromised functional performance. After anterior cruciate ligament reconstruction (ACLR) one of the most obvious consequences of AMI is the lack of quadriceps activation and strength. Understanding the underlying mechanisms of AMI is crucial for developing effective therapeutic interventions. The surgical procedure needed to reconstruct the ACL has biochemical et physiological consequences such as inflammation, pain, and altered proprioception. These alterations contribute to the development of AMI. Therapeutic interventions aimed at addressing AMI encompass a multidimensional approach targeting pain reduction, inflammation management, proprioceptive training, and quadriceps activation. Early management focusing on pain modulation through modalities like ice, compression, and pharmacological agents help mitigate the inflammatory response and alleviate pain, thereby reducing the reflexive inhibition of quadriceps. Quadriceps activation techniques such as neuromuscular electrical stimulation (NMES) and biofeedback training aid in overcoming muscle inhibition and restoring muscle strength. NMES elicits muscle contractions through electrical stimulation, bypassing the inhibitory mechanisms associated with AMI, thus facilitating muscle activation and strength gains. Comprehensive rehabilitation programs tailored to individual needs and stage of recovery are essential for optimizing outcomes in AMI. The objective of this clinical viewpoint is to delineate the significance of adopting a multimodal approach for the effective management of AMI, emphasizing the integration of pain modulation, proprioceptive training, muscle activation techniques, and manual therapy interventions. Highlighting the critical role of early intervention and targeted rehabilitation programs, this article aims to underscore their importance in restoring optimal function and mitigating long-term complications associated with AMI.

Achieving sustainable development in ECOWAS countries: the impact of trade openness, poverty and human capital

This study investigates the relationship among trade openness, poverty, and human capital development in the pursuit of sustainable development across a panel of ten ECOWAS economies over a 34-year period (1987–2020). Specifically, it examines the roles of trade openness, poverty, and human capital development in sustainable development, while also exploring the moderating role of trade openness on the poverty-sustainable development relationship within the ECOWAS region. To conduct this analysis, the study employs panel autoregressive distributed lag (ARDL) using both the Pool Mean Group (PMG) estimator and Cross-Sectional Autoregressive Distributed Lag (CS-ARDL) techniques, taking into account cross-sectional dependence, cointegration, and other relevant diagnostic tests. The findings indicate that poverty has a consistent negative long-run impact on sustainable development, while human capital is positively associated with sustainability over the long term. Trade openness lacks a significant relationship with sustainability in both the short and long run. Inflation is insignificantly related to sustainability. Exchange rates demonstrate mixed effects. In terms of moderation, trade openness positively and significantly moderates the poverty-sustainability relationship in the long run but not the short run. Robustness testing using the AMG and P-OLS models further validates the significant positive impact of human capital and the insignificant effect of trade openness on sustainable development. Given poverty’s significant negative association and human capital’s positive link with sustainable development, the findings suggest the need for integrated policy mixes prioritizing multidimensional poverty reduction and human capability enhancement to promote sustainability objectives across both short- and long-term horizons in ECOWAS countries. Furthermore, prudent management of exchange rates and well-designed trade policies should complement these efforts to mitigate potential risks and harness any benefits for sustainability.

Genetic polymorphisms of BACH2, a key gene regulating Th2 immune response, increasing risk of allergic rhinitis

BackgroundAllergic rhinitis (AR) is an allergic disease characterized by the dominant differentiation of T helper cell 2 (Th2). BACH2 plays a key role in regulating Th2 immune response. This study aimed to explore the association between BACH2 single nucleotide polymorphism (SNPs) and susceptibility to AR. MethodsHan population from northern Shaanxi, China was chosen as subjects. After the DNA extraction from the peripheral blood of subjects, genotyping was completed through the Agena MassARRAY platform. Logistic regression analysis was used to assess the association. Multivariate dimensionality reduction (MDR) was used to evaluate the effect of the interaction between ‘SNP-SNP’ on susceptibility to AR. Using false-positive report probability (FPRP) analysis to test whether the significant results obtained in this study were noteworthy. ResultsBACH2-rs905670 and −rs2134814 were significantly associated with increased risk of AR. The mutant allele ‘A’ of rs905670 (OR = 1.36, p = 0.018) and mutant allele ‘G’ of rs2134814 (OR = 1.34, p = 0.027) were risk genetic factors for AR. The above genetic association was further observed in the stratified analysis: BACH2-rs905670 and-rs2134814 were significantly associated with an increased risk of AR in females, aging older than 43 years, and participants working and living in the loess hills (OR > 1, p < 0.05). ConclusionBACH2-rs905670 and −rs2134814 are significantly associated with increasing AR risk.

Does e-agriculture practice matter for poverty reduction among coastal farm households?

The study hypothesizes that e-agriculture practice may contribute to multidimensional poverty reduction in the coastal areas of Bangladesh. Two-stage instrumental variable (IV) Tobit and seemingly unrelated regression (SUR) analyses are used to identify the relationship. We collected data from 351 rice producing households applying a multi-stage sampling technique. Results reveal that e-agriculture adoption significantly reduces multidimensional poverty by 32 percentage points compared to non-adoption. Besides, education, farming experience, residence in the peri-urban area, and access to agricultural extension services are identified as positive predictors of e-agriculture adoption. The SUR regressions show that e-agriculture practice can help improve the standard of living of coastal farm households. In addition, multiple cropping patterns significantly contribute to the reduction of all the deprivations of multidimensional poverty. Based on the research outcomes, the study advocates broadening access to extension services to seamlessly incorporate small and marginalized farm households in remote coastal areas. Such integration may equip coastal communities with comprehensive insights into e-agriculture, thereby empowering them to fully leverage the advantages inherent in this farming technique.

Tác động của tài chính toàn diện đến nghèo đa chiều ở cấp độ hộ gia đình tại Việt Nam năm 2020

The relationship between financial inclusion and poverty is of interest to governments and researchers worldwide, including Vietnam. The research focuses on assessing the current status of financial inclusion at the household level in Vietnam in 2020 and its impact on multidimensional poverty. The study employs principal component analysis and a logistic regression model. Based on data from the Household Living Standards Survey, the study shows that the assessment of the financial inclusion differs between ecological regions, with households living in the Red River Delta and Southeast regions having the highest level of financial inclusion. An increase in financial inclusion at the household level has a statistically significant impact on reducing multidimensional poverty (relative risk reduces by 0.5 times). Based on research results, sustainable multidimensional poverty reduction policies can be implemented in combination with comprehensive financial inclusion development strategies. Furthermore, factors such as region, education, and occupation of the household head need to be considered when addressing poverty reduction and promoting financial inclusion.

Gene‒environment interaction effect of hypothalamic‒pituitary‒adrenal axis gene polymorphisms and job stress on the risk of sleep disturbances.

Studies have shown that chronic exposure to job stress may increase the risk of sleep disturbances and that hypothalamic‒pituitary‒adrenal (HPA) axis gene polymorphisms may play an important role in the psychopathologic mechanisms of sleep disturbances. However, the interactions among job stress, gene polymorphisms and sleep disturbances have not been examined from the perspective of the HPA axis. This study aimed to know whether job stress is a risk factor for sleep disturbances and to further explore the effect of the HPA axis gene × job stress interaction on sleep disturbances among railway workers. In this cross-sectional study, 671 participants (363 males and 308 females) from the China Railway Fuzhou Branch were included. Sleep disturbances were evaluated with the Pittsburgh Sleep Quality Index (PSQI), and job stress was measured with the Effort-Reward Imbalance scale (ERI). Generalized multivariate dimensionality reduction (GMDR) models were used to assess gene‒environment interactions. We found a significant positive correlation between job stress and sleep disturbances (P < 0.01). The FKBP5 rs1360780-T and rs4713916-A alleles and the CRHR1 rs110402-G allele were associated with increased sleep disturbance risk, with adjusted ORs (95% CIs) of 1.75 [1.38-2.22], 1.68 [1.30-2.18] and 1.43 [1.09-1.87], respectively. However, the FKBP5 rs9470080-T allele was a protective factor against sleep disturbances, with an OR (95% CI) of 0.65 [0.51-0.83]. GMDR analysis indicated that under job stress, individuals with the FKBP5 rs1368780-CT, rs4713916-GG, and rs9470080-CT genotypes and the CRHR1 rs110402-AA genotype had the greatest risk of sleep disturbances. Individuals carrying risk alleles who experience job stress may be at increased risk of sleep disturbances. These findings may provide new insights into stress-related sleep disturbances in occupational populations.

ELViM: Exploring Biomolecular Energy Landscapes through Multidimensional Visualization.

Molecular dynamics (MD) simulations provide a powerful means of exploring the dynamic behavior of biomolecular systems at the atomic level. However, analyzing the vast data sets generated by MD simulations poses significant challenges. This article discusses the energy landscape visualization method (ELViM), a multidimensional reduction technique inspired by the energy landscape theory. ELViM transcends one-dimensional representations, offering a comprehensive analysis of the effective conformational phase space without the need for predefined reaction coordinates. We apply the ELViM to study the folding landscape of the antimicrobial peptide Polybia-MP1, showcasing its versatility in capturing complex biomolecular dynamics. Using dissimilarity matrices and a force-scheme approach, the ELViM provides intuitive visualizations, revealing structural correlations and local conformational signatures. The method is demonstrated to be adaptable, robust, and applicable to various biomolecular systems.

Impact of income diversification on multidimensional poverty: Household level evidence from tea estates in Bangladesh

Policy advocates commonly use income diversification strategies worldwide to address economic disturbances such as poverty. Realizing the importance of poverty reduction and the raging debate on whether the household should specialize or diversify their income, this study attempts to investigate the poverty and income diversification nexus in a tea estate of Bangladesh. A multistage sampling procedure was applied to select 1 tea estate and 382 households. Primary data was collected through interview schedule. The Simpson diversification index and Alkire-Foster multidimensional poverty index were used to measure income diversification and multidimensional poverty respectively. Additionally, this paper used the propensity score matching method to assess the causal impact of income diversification on multidimensional poverty. The findings revealed that the research area has a 35% household level income diversity, a 43% household level multidimensional poverty rate, and income diversification has a positive impact on multidimensional poverty reduction. The multidimensional poverty was reduced by 0.095% on average for income diversified households. Therefore, from a policy perspective, income diversification can be a good solution for reducing household-level multidimensional poverty. Government and other stakeholders should redesign working guidelines for tea workers regarding working hour, days and wage in such a way that they can engage in several income-generating activities apart from tea production-related activities in the tea estates.

How to choose a proper representation of compositional data for mineral exploration?

Regional mineral exploration is based on geochemical data of which the nature is compositional and frequently involves a large number of components. Consequently, it mostly needs multivariate dimension reduction methods such as principal component analysis (PCA) and its various robust versions. The application of such methods, defined for real random variables, require the data to be represented in coordinates supported in the real space. However, a common problem in exploration geochemistry is to select the appropriate representation. Using centered (clr) and isometric (ilr) logratio coordinates to discriminate anomalous zones for orogeny gold exploration throughout Sweden revealed that there is, as expected, no difference between the two representation methods. The main difference affects the interpretation of the coordinates used. This is observed for regional scale exploration, while it is also needed to study different ways of representing geochemical data in local scale.

Retracted: Multidimensional State Data Reduction and Evaluation of College Students’ Mental Health Based on SVM

Retracted: Multidimensional State Data Reduction and Evaluation of College Students’ Mental Health Based on SVM

Breaking the chains of poverty: examining the influence of smartphone usage on multidimensional poverty in rural settings

We analyze the impact of smartphone usage on multidimensional poverty reduction. Utilizing the A-F approach, we compute a multidimensional poverty index based on five dimensions: education, health, income, living standard, and labor force. This index categorizes multidimensional poverty into three levels: Vulnerable Multidimensional Poverty Index (VMPI), General Multidimensional Poverty Index (GMPI), and Extreme Multidimensional Poverty Index (EMPI), following MPI criteria. Furthermore, we investigate the mediating role of social capital in the smartphone-multidimensional poverty relationship through a mediating effects analysis. We used the survey data of 382 sample out-of-poverty rural households in Jiangxi, China, in 2020. Our results indicated that: (1) Education (37.80%), labor force (29.7%), and health (20.40%) were identified as the primary contributors to multidimensional poverty. (2) Increasing deprivation categories correlated with declining multidimensional poverty index, following an inverted U-shaped pattern. (3) Smartphone usage significantly reduced VMPI (57.6%), GMPI (52.6%), and EMPI (5%). (4) Social capital fully mediated EMPI reduction through smartphones (91.67%), and partially mediated VMPI (14.09%) and GMPI (20.84%) reduction. These insights inform targeted policy formulation for rural multidimensional poverty reduction.

Can technological innovations contribute to more overcome the issue of poverty reduction in africa?

Although recent years have shown that innovation can also thrive in economies where the challenges of absolute poverty and inequality remain long-standing problems, the effects of innovation on poverty reduction has remained a largely unexplored field of investigation. . So this study assesses the effect of technological innovations on poverty reduction using a panel of 36 sub-Saharan African countries over the period 2008 to 2019. More specifically, the study examines the influence of technological innovation on poverty reduction. Using the principal component analysis (PCA) we constructed an indicator of the use, adoption and adaptation of technological innovations and that of multidimensional poverty. After applying several estimation techniques for panel data such as fixed effects, Driscoll and Kraay, kiviet (2020), Lewbel two stage least square (Lewbel 2SLS), mediating effects, the results shows that technological innovations significantly reduce poverty in sub-Saharan Africa. Our results survive several robustness checks. Taking into account the level of urbanization, it appears that technological innovations reduce poverty in countries with low urbanisation rates and increase it in countries with high urbanization rates. Finally, the analysis of mediation effects suggests that almost 30 % of the total effect of the use, adoption and adaptation of technological innovations on multidimensional poverty reduction is through the improvement of productivity in the industrial sector while 62.5 % is through the productivity of the agricultural sector. These results imply that poverty reduction policies in sub-Saharan Africa need to refocus on capacity building in the use, adoption and adaptation of technological innovations, particularly in the agricultural and industrial sectors.

Emprego fora da fazenda e pobreza multidimensional: evidências empíricas da bacia do rio Amarelo na China

ABSTRACT: As an essential way to enhance farmers’ self-development ability, off-farm employment plays an indispensable role in farmers’ multidimensional poverty reduction in many countries. Employing a survey of 1926 farmers in five provinces of the Yellow River Basin in China, this paper examined the multi-dimensional poverty reduction effect of off-farm employment and the heterogeneous influence of different dimensions of off-farm employment (modes, levels, distances and frequency). The results showed that (1) although absolute poverty in the income dimension was largely eliminated in the Yellow River Basin, the poverty in social resources, transportation facilities, employment security were the key bottlenecks restricting farmers’ self-development. (2) The province with the best multidimensional poverty reduction effect for non-farm employment was Shaanxi, with the largest contribution to employment security. (3) Improving off-farm employment level, distance and time can significantly alleviate the multi-dimensional poverty of farmers. Therefore, to lessen the multi-dimensional poverty of farmers in the Yellow River Basin, it is necessary to focus on the governance of multi-dimensional key poverty-stricken areas, such as the middle and upper courses of the Yellow River, adopting multidimensional poverty alleviation strategy of off-farm employment according to local conditions, working on the farmers’ deficiencies in social resources, mobility, employment security, and deepening the effect of off-farm employment on benefiting farmers and helping the poor.

Multi-ensemble machine learning framework for omics data integration: A case study using breast cancer samples

Integration of voluminous omics data aids to unravel biological complexities associated with different disease phenotypes. Machine learning (ML) approaches provide insightful techniques for systematic multi-omics data integration. In this study, survival prediction of breast cancer patients was undertaken using omics data of 302 female patients from The Cancer Genome Atlas (TCGA). The data included gene expression, miRNA expression, DNA methylation and copy number variation. Three computational multi-ensemble ML pipelines were tested using Support Vector Machine (SVM), Random Forest (RF) and Partial Least Squares-Discriminant Analysis (PLS-DA) algorithms. To overcome the limitations associated with univariate feature selection criteria, the ML pipelines were built along with latent factors obtained by multivariate dimension reduction method. This facilitated investigation of background genetic networks and identification of potential hub genes. Analysis of the results obtained revealed that SVM with PLS-DA method (integrated with gene expression, DNA methylation, and miRNA expression modalities) was the best-performing model with an Area Under Curve (AUC) of 89% and an accuracy of 83% for survival prediction. This study not only corroborated previously reported breast cancer-specific prognostic biomarkers but also predicted additional potential biomarkers. The work demonstrates the effective use of a multi-ensemble ML model with efficient feature selection methods as a robust protocol for cancer genotype to phenotype correlation.

Pilot-scale study of innovative mechanically-enhanced dynamic composting for treating kitchen waste

This study introduced a novel mechanically-enhanced dynamic composting (MEDC) method for treating kitchen waste (KW) through partial-mixing and stratified fermentation. A pilot test varied aeration frequencies (AF) to refine control parameters and explore the maturation mechanism. Results showed that a moderate AF (10 min/4 h) achieved optimal efficiency, with a compost germination index of 123 % within 15 d. Moderate AF enhanced the growth of Corynebacterium_1 (25.4 %) and Saccharomonospora (10.5 %) during the low-temperature stage and Bacillus growth (91.3 %) during the maturation stage. Moreover, it enhanced microbial interactions (with an average degree of 19.9) and promoted substrate degradation and transformation, expediting heating and maturation. Multivariate dimensionality reduction analysis showed the MEDC accomplished rapid composting through stratified composting, dividing the reactor into distinct functional zones: feeding, low-temperature, high-temperature, and maturation. This enabled efficient microorganism enrichment and material degradation, expediting KW decomposition and maturation. This study offers a promising alternative for accelerated KW composting.

Sex-Specific Perturbation of Systemic Lipidomic Profile in Newborn Lambs Impacted by Prenatal Testosterone Excess.

Gestational hyperandrogenism adversely impacts offspring health. Using an ovine model, we found that prenatal testosterone (T) excess adversely affects growth and cardiometabolic outcomes in female offspring and produces sex-specific effects on fetal myocardium. Since lipids are essential to cardiometabolic function, we hypothesized that prenatal T excess leads to sex-specific disruptions in lipid metabolism at birth. Shotgun lipidomics was performed on the plasma samples collected 48 hours after birth from female (F) and male (M) lambs of control (C) and (T) sheep (CF = 4, TF = 7, CM = 5, TM = 10) and data were analyzed by univariate analysis, multivariate dimensionality reduction modeling followed by functional enrichment, and pathway analyses. Biosynthesis of phosphatidylserine was the major pathway responsible for sex differences in controls. Unsupervised and supervised models showed separation between C and T in both sexes with glycerophospholipids and glycerolipids classes being responsible for the sex differences between C and T. T excess increased cholesterol in females while decreasing phosphatidylcholine levels in male lambs. Specifically, T excess: 1) suppressed the phosphatidylethanolamine N-methyltransferase (PEMT) phosphatidylcholine synthesis pathway overall and in TM lambs as opposed to suppression of carnitine levels overall and TF lambs; and 2) activated biosynthesis of ether-linked (O-)phosphatidylethanolamine and O-phosphatidylcholine from O-diacylglycerol overall and in TF lambs. Higher cholesterol levels could underlie adverse cardiometabolic outcomes in TF lambs, whereas suppressed PEMT pathway in TM lambs could lead to endoplasmic reticulum stress and defective lipid transport. These novel findings point to sex-specific effects of prenatal T excess on lipid metabolism in newborn lambs, a precocial ovine model of translational relevance.

Economic growth and quality of education: Evidence from the national high school exam in Vietnam

This study considers the impact of economic growth on the quality of K-12 education in a transitional country over the 2016–2019 period, utilizing sub-national-level data. The study employs two-stage least squares for the panel-data model, confirming that (i) a one percentage point rise in per capita income increases national test scores in Vietnam by around 0.4–1.83%, and (ii) this nexus is influenced by the quality of institutions and by poverty levels. Notably, we find a non-linear relationship between economic growth and education: economic growth exhibits a U-shaped relationship with English scores, while its association with mathematics scores follows an inverse U-shaped pattern. The current study thus adds to the body of evidence supporting the link between educational quality and economic growth. As a result, policymakers in transitional nations can use the forces of economic growth to advance social objectives, rather than concentrating primarily on changing the educational system through increased government spending. In order to maximize the benefits of economic growth for the education system, the government policies should focus on assuring institutional quality improvement and multidimensional poverty reduction.