Human activities in recent decades have severely affected environmental quality, and CO2 emissions have irreparable consequences on human health and the survival of the earth. Moreover, achieving sustainable development goals requires the expansion of environmental literature to accelerate the performing of critical actions. With this in mind, this study evaluates the impact of foreign direct investment, economic complexity, and the utilization of renewable energy on CO2 emission in N-11 countries from 1995 to 2019 by Panel Quantile Regression. As a novelty, the interaction between economic complexity and foreign direct investment is considered to get a better comprehension. Given the results, Environmental Kuznetz Curve is validated in N-11 countries through economic complexity. Notably, the impact of economic complexity is more substantial and robust in the incipient stages of industrialization. Furthermore, foreign direct investment is a destructive factor for environmental quality, and Pollution Haven Hypothesis is not rejected. Interestingly, the interaction of economic complexity and foreign direct investment mitigates the trend of CO2 emissions. Eventually, the utilization of renewable energy reduces CO2 emissions. Thereby, applying more strict environmental regulations and standards, developing green energy infrastructure and technologies, improving institutional quality, and supporting knowledge-based and technology-intensive exports are the main policy recommendations of this study.

• Keywords
• CO(2) emissions, Economic complexity, Environmental Kuznetz curve, Pollution haven hypothesis, Renewable energy,
• MeSH
• Economic Development * MeSH
• Internationality MeSH
• Investments MeSH
• Humans MeSH
• Renewable Energy MeSH
• Carbon Dioxide * analysis   MeSH
• Environmental Pollution analysis   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Carbon Dioxide * MeSH

Modeling the interrelationships between the input parameters and outputs (responses) in any machining processes is essential to understand the process behavior and material removal mechanism. The developed models can also act as effective prediction tools in envisaging the tentative values of the responses for given sets of input parameters. In this paper, the application potentialities of nine different regression models, such as linear regression (LR), polynomial regression (PR), support vector regression (SVR), principal component regression (PCR), quantile regression, median regression, ridge regression, lasso regression and elastic net regression are explored in accurately predicting response values during turning and drilling operations of composite materials. Their prediction performance is also contrasted using four statistical metrics, i.e., mean absolute percentage error, root mean squared percentage error, root mean squared logarithmic error and root relative squared error. Based on the lower values of those metrics and Friedman rank and aligned rank tests, SVR emerges out as the best performing model, whereas the prediction performance of median regression is worst. The results of the Wilcoxon test based on the drilling dataset identify the existence of statistically significant differences between the performances of LR and PCR, and PR and median regression models.

• Keywords
• composite material, drilling, model, regression, turning,
• Publication type
• Journal Article MeSH

Implementing policy combinations that neither negatively impact economic performance nor create the least amount of harm is the most crucial factor to consider in policy practices that promote environmental quality. In this regard, green growth, which harmonises both environmental and economic performance, gains importance. Based on this, this study analyses the effects of foreign direct investments, financial development, and financial globalisation on green growth for BRICS countries for the period 1990-2021. For this purpose, the effects of these factors on green growth are investigated using novel wavelet quantile regression and wavelet quantile correlation techniques. The findings show that while foreign direct investment inflow harms green growth in countries other than South Africa, there is a positive effect for South Africa. On the other hand, financial development and financial globalisation have adverse effects on green growth only in South Africa but have an increasing effect on green growth in other countries.

• Keywords
• Financial development, Financial globalization, Foreign direct investments, Green growth, Wavelet techniques,
• MeSH
• Economic Development * MeSH
• Internationality MeSH
• Investments * MeSH
• Publication type
• Journal Article MeSH
• Geographicals
• South Africa MeSH

The recent progress report of Sustainable Development Goals (SDG) 2023 highlighted the extreme reactions of environmental degradation. This report also shows that the current efforts for achieving environmental sustainability (SDG 13) are inadequate and a comprehensive policy agenda is needed. However, the present literature has highlighted several determinants of environmental degradation but the influence of geopolitical risk on environmental quality (EQ) is relatively ignored. To fill this research gap and propose a inclusive policy structure for achieving the sustainable development goals. This study is the earliest attempt that delve into the effects o of geopolitical risk (GPR), financial development (FD), and renewable energy consumption (REC) on load capacity factor (LCF) under the framework of load capacity curve (LCC) hypothesis for selected Asian countries during 1990-2020. In this regard, we use several preliminary sensitivity tests to check the features and reliability of the dataset. Similarly, we use panel quantile regression for investigating long-run relationships. The factual results affirm the existence of the LCC hypothesis in selected Asian countries. Our findings also show that geopolitical risk reduces environmental quality whereas financial development and REC increase environmental quality. Drawing from the empirical findings, this study suggests a holistic policy approach for achieving the targets of SDG 13 (climate change).

• Keywords
• Financial development, Geopolitical risk, LCC hypothesis, Panel quantile regression,
• MeSH
• Economic Development MeSH
• Climate Change * MeSH
• Renewable Energy MeSH
• Carbon Dioxide MeSH
• Policy * MeSH
• Reproducibility of Results MeSH
• Publication type
• Journal Article MeSH
• Geographicals
• Asia MeSH
• Names of Substances
• Carbon Dioxide MeSH

Plant mycorrhizal status (a trait indicating the ability to form mycorrhizas) can be a useful plant trait for predicting changes in vegetation influenced by increased fertility. Mycorrhizal fungi enhance nutrient uptake and are expected to provide a competitive advantage for plants growing in nutrient-poor soils; while in nutrient-rich soils, mycorrhizal symbiosis may be disadvantageous. Some studies in natural systems have shown that mycorrhizal plants can be more frequent in P and N-poor soils (low nutrient availability) or Ca and Mg-high (high pH) soils, but empirical support is still not clear. Using vegetation and soil data from Scottish coastal habitats, and Latvian and Czech grasslands, we examined whether there is a link between plant mycorrhizal status and plant-available P, N, Ca and Mg. We performed the max test analysis (to examine the central tendency) and a combination of quantile regression and meta-analysis (to examine tendencies in different quantiles) on both community and plant species data combined with plant phylogenies. We consistently found no changes in mycorrhizal status at the community and species levels along the gradients of plant-available P, N, Ca and Mg in the central tendency and in almost all quantiles across all datasets. Thus, we found no support for the hypotheses that herbaceous species which are able to form mycorrhizas are more frequent in nutrient-poor and high pH environments. Obligatory, facultatively and non-mycorrhizal herbaceous species appear to assemble randomly along the gradients of nutrient availability in several European herbaceous habitats, suggesting that all these strategies perform similarly under non-extreme soil nutrient conditions.

• Keywords
• Arbuscular mycorrhiza, Community mycorrhization, Eutrophication, Meta-analysis, Nutrient availability, Quantile regression,
• MeSH
• Ecosystem MeSH
• Mycorrhizae * MeSH
• Grassland MeSH
• Soil MeSH
• Soil Microbiology MeSH
• Plants MeSH
• Nutrients MeSH
• Publication type
• Journal Article MeSH
• Meta-Analysis MeSH
• Names of Substances
• Soil MeSH

This article deals with a unique, new powertrain diagnostics platform at the level of a large number of EU25 inspection stations. Implemented method uses emission measurement data and additional data from significant sample of vehicles. An original technique using machine learning that uses 9 static testing points (defined by constant engine load and constant engine speed), volume of engine combustion chamber, EURO emission standard category, engine condition state coefficient and actual mileage is applied. An example for dysfunction detection using exhaust emission analyses is described in detail. The test setup is also described, along with the procedure for data collection using a Mindsphere cloud data processing platform. Mindsphere is a core of the new Platform as a Service (Paas) for data processing from multiple testing facilities. An evaluation on a fleet level which used quantile regression method is implemented. In this phase of the research, real data was used, as well as data defined on the basis of knowledge of the manifestation of internal combustion engine defects. As a result of the application of the platform and the evaluation method, it is possible to classify combustion engine dysfunctions. These are defects that cannot be detected by self-diagnostic procedures for cars up to the EURO 6 level.

• Keywords
• PaaS, cloud computing, exhaust emission testing and evaluation, new emission measurement methods, quantile regression,
• MeSH
• Gasoline analysis   MeSH
• Cloud Computing MeSH
• Machine Learning * MeSH
• Vehicle Emissions * analysis   MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Gasoline MeSH
• Vehicle Emissions * MeSH

The G-20 countries represent a considerable percentage of the global economy and are crucial in matters to do with support for environmental sustainability. The uniqueness of this study lies in determining the effects of forests on human well-being and environmental degradation with respect to G20, offering a unique perspective regarding the efforts to battle climate change. The study analyzed the impact of income, forest extent and education on ecological and carbon intensity of well-being following the Environmental Kuznets Curve (EKC) hypothesis. Based on annual data from 1990 to 2022 and employing the Method of Moments Quantile Regression, the results validate the presence of an inverted U-shaped relationship between GDP and environmental well-being which refers to the existence of EKC. Our results connect improved ecological and carbon intensity of well-being with expanding forest extent and improving education levels. Forest management combined with educational management work as an effective mechanism reducing environmental degradation while also positively contributing to human well-being. In addition, through these informed and rational decisions, policy makers can promote the environmental stability of forests.

• Keywords
• Carbon intensity of well-being, EKC hypothesis, Ecological intensity of well-being, Forest extent, Method of moments quantile regression,
• MeSH
• Climate Change * MeSH
• Forestry MeSH
• Forests * MeSH
• Humans MeSH
• Carbon analysis   MeSH
• Conservation of Natural Resources * methods   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Carbon MeSH

OBJECTIVES: Polychlorinated biphenyls (PCBs), a family of persistent toxic and organic environmental pollutants, were associated with multiple organ damages in humans once accumulating. However, association between PCBs exposure and circulatory immune markers were not clear. METHODS: Data was collected from participants enrolled in the National Health and Nutrition Examination Survey in 1999-2004. PCBs were categorized by latent class analysis (LCA). Weighted quantile sum (WQS) regression was used to investigate effects of PCBs exposure on circulatory immune markers including leukocyte counts, monocyte-lymphocyte ratio (MLR), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII). RESULTS: There were 3,109 participants included in the final analysis with blood PCBs levels presented as 3 classes. The high PCBs group had a higher rate of comorbidities. Leukocyte, lymphocyte and neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and system immune-inflammation index (SII) were significantly lower in the high PCBs group than in the low PCBs group (all p-values < 0.05). After adjusting for covariant variables, the low PCBs group was positively associated with SII (p = 0.021) and NLR (p = 0.006) in multivariate regression. Significantly negative correlations between PCBs classification and SII (β = -14.513, p = 0.047), and NLR (β = -0.035, p = 0.017) were found in WQS models. LBX028LA showed the most significant contribution in the associations between PCBs and SII, and LBX128LA contributed most significantly to associations with NLR. CONCLUSION: Our study adds novel evidence that exposures to PCBs may be adversely associated with the circulatory immune markers, indicating the potential toxic effect of PCBs on the human immune system.

• Keywords
• circulatory immune markers, latent class analysis, platelet-lymphocyte ratio, polychlorinated biphenyls, systemic immune-inflammation index, weighted quantile sum regression,
• MeSH
• Biomarkers * blood   MeSH
• Adult MeSH
• Environmental Pollutants * blood   MeSH
• Middle Aged MeSH
• Humans MeSH
• Polychlorinated Biphenyls * blood   toxicity   MeSH
• Aged MeSH
• Environmental Exposure adverse effects   analysis   MeSH
• Nutrition Surveys * MeSH
• Inflammation blood   chemically induced   immunology   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Biomarkers * MeSH
• Environmental Pollutants * MeSH
• Polychlorinated Biphenyls * MeSH

This article assesses the effect of energy diversity on energy risk across 64 countries over the period of 2000-2018. Employing methodologies including threshold regression, Method of Moments of Quantile Regression (MMQR), partially linear functional-coefficient (PLFC) panel models and instrumental variable (IV) estimation, our findings suggest that energy diversity plays a significant role in reducing energy risk. Panel threshold and partially linear functional-coefficient models highlight the importance of energy diversification in mitigating energy risk, particularly in countries with higher levels of economic development. These results suggest that energy diversification is beneficial for managing energy risk, but its effectiveness is contingent upon the economic condition of the country. The Panel Threshold and Method of Moments Quantile Regression results indicate that efficient governance and government expenditures may have a significant impact on reducing the countries' energy risk, whereas increasing population density is associated with an increase in energy risk.

• Keywords
• Energy diversity, Energy risk, Non-linearity,
• Publication type
• Journal Article MeSH

OBJECTIVE: Is it possible to estimate urethral mobility based on MUCP measurements? DESIGN: Retrospective study. SETTING: Department of Gynecology and Obstetrics, 1st Medical Faculty, Charles University, General Teaching Hospital, Prague. METHODS: This retrospective study included 567 patients from three prospective studies within years 2002 to 2009. Ultrasound examination was performed in 560 of them and maximal urethral closure pressure (MUCP) values were measured in 507 women. The MUCP was defined as the difference between maximum urethral pressure and bladder pressure. An ultrasound examination was performed using the transperineal approach in accordance with the recommendations of the German Urogynecology Working Group and ICS, IUGA terminology. The mobility was expressed as a distance between the position at rest and at the maximal Valsalva manoeuvre. Data were summarized as mean and median, with SD and quantile range for measures of variability. Either a matched pairs t-test or Wilcoxon test was used for statistical evaluation. RESULTS: Mean MUCP was 47.4 cm H2O (SD 22.2, first quantile 32, third quantile 62). Mean urethral descent was 20.6 mm (SD 8.2, first quantile 14.9, third quantile 25.6 mm). Using regression analysis there was an increase in urethral descent; this difference is statistically significant. For a MUCP increase of 10 cm H2O we could estimate an increase in urethral descent of 1.1 mm. CONCLUSION: We established a statistically significant relationship between urethral descent and MUCP. Unfortunately those differences are not clinically relevant, especially for MUCP over 20 cm H2O. For MUCP below 20 cm H2O low urethral descent is more likely. Clinical use of MUCP as predictor of urethral descent is limited due to the high variability involved.

• Keywords
• MUCP, descent of the urethrovesical junction, maximum urethral closure pressure, stress urinary incontinence, urodynamic study,
• MeSH
• Humans MeSH
• Urinary Bladder physiopathology   MeSH
• Prospective Studies MeSH
• Retrospective Studies MeSH
• Urinary Incontinence, Stress diagnosis   surgery   MeSH
• Pregnancy MeSH
• Urethra physiopathology   MeSH
• Urodynamics physiology   MeSH
• Urologic Surgical Procedures adverse effects   MeSH
• Treatment Outcome MeSH
• Check Tag
• Humans MeSH
• Pregnancy MeSH
• Female MeSH
• Publication type
• Journal Article MeSH