Mean Log Research Articles

Effect of aspirin on biomarker profile in women at high-risk for preeclampsia.

There is limited evidence in the literature regarding the temporal changes of preeclampsia-related biomarkers during pregnancy in high-risk women who develop preeclampsia despite the administration of aspirin prophylaxis. To compare the temporal changes in mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI), placental growth factor (PlGF), and soluble fms-like tyrosine kinase-1 (sFlt-1) across gestation in women screened high-risk for preterm preeclampsia receiving aspirin prophylaxis and low-risk women without aspirin treatment. This was a prospective longitudinal nested case-control study of 2007 women with singleton pregnancies who participated in the first-trimester screen-and-prevent program for preeclampsia at the Prince of Wales Hospital, Hong Kong SAR, China, between January 2020 and May 2023. The risk of developing preterm preeclampsia was determined using the Fetal Medicine Foundation triple test (maternal factors, MAP, UtA-PI and PlGF). High-risk women (adjusted risk ≥1:100) were administered a daily dose of aspirin at either 100 or 160 mg according to maternal weight, starting before 16 weeks until 36 weeks or until delivery or the onset of preeclampsia before 36 weeks. Low-risk women were matched based on maternal age, weight, and the date of the scan. The participants were followed up at 12-15+6, 20-24+6 and 30-37+6 weeks to measure MAP, UtA-PI, PlGF and sFlt-1 at each visit. The level of biomarker was expressed as multiple of median (MoM). Log10 transformation was applied to make the data Gaussian distribution prior to statistical analysis. A linear mixed-effects analysis was performed to compare the longitudinal changes of these biomarkers across gestation between the study groups. Our study involved 403 low-risk women without preeclampsia, 1471 high-risk women without preeclampsia, and 133 high-risk women who developed preeclampsia. The low-risk group had significantly lower estimated marginal mean log10 MAP MoM, log10 UtA-PI MoM, log10 sFlt-1 MoM and higher estimated marginal mean log10 PlGF MoM across gestation than the high-risk groups (p values <0.001). Among high-risk women, those who developed preeclampsia experienced a significantly higher estimated marginal mean log10 MAP MoM (0.06378 vs 0.02985; p<0.001), log10 UtA-PI MoM (0.08651 vs 0.02226; p<0.001), log10 sFlt-1 MoM (0.13204 vs 0.01234; p<0.001) and lower estimated marginal mean log10 PlGF MoM (-0.33504 vs -0.16388; p<0.001) across gestation in comparison to those without preeclampsia. In the individual gestational timepoint analysis, compared to high-risk women without preeclampsia, those who developed preeclampsia experienced higher log10 MAP MoM in all three trimesters, higher log10 UtA-PI MoM and lower log10 PlGF MoM in the second- and third-trimesters, and higher log10 sFlt-1 MoM in the third-trimester. The study has demonstrated that high-risk women who develop preeclampsia experience consistently high MAP level from the first-trimester and remain unchanged during pregnancy, high UtA-PI level and low PlGF level starting from the second-trimester, and high sFlt-1 level in the third-trimester compared to those who do not develop preeclampsia despite the administration of low-dose aspirin. These findings underscore the role of these biomarkers in further risk stratification for the development of preeclampsia among high-risk women following aspirin administration.

Analysis of Energy-Related-CO2-Emission Decoupling from Economic Expansion and CO2 Drivers: The Tianjin Experience in China

Cities are key areas for carbon control and reduction. The study of the decoupling between CO2 emissions and gross domestic product (GDP) and the drivers of CO2 emissions in cities facilitates the reduction of CO2 emissions to safeguard the development of the economy. This paper first calculates the CO2 emissions in Tianjin, China, from 2005 to 2022, then uses the Tapio decoupling index to quantify the decoupling status, and, finally, explores the energy-CO2-emission drivers through the Logarithmic Mean Divisia Index (LMDI) model. The findings indicate that (1) the decrease in CO2 emissions from industrial products and transport is the main reason for the decline. (2) During the period under investigation, the predominant condition observed was a state of weak decoupling. (3) Given the economic-output effect is the primary and substantial driver of energy CO2 emissions, it is essential to harmonize the interplay between economic-development approach and CO2 emissions to foster sustainable development in Tianjin. The industrial structure plays the most critical role in hindering the reduction of CO2 emissions; therefore, optimizing industrial structure can help achieve carbon reduction and control targets. These findings enrich the study of CO2 emission factors and can also interest urban policymakers.

Long-term monitoring of citizen science: driving factors and pandemic impacts

ABSTRACT In recent years, the importance of citizen science in biodiversity monitoring has become increasingly evident. Citizen science provides valuable data to understand the dynamic changes in biodiversity across different cities. Although long-term monitoring is crucial for understanding these dynamic changes, previous research has primarily focused on short-term data analysis. Therefore, this study analysed data from the iNaturalist platform for 11 Japanese cities from 2016 to 2023, focusing on the driving factors of observation changes and the impact of the COVID-19 pandemic using the Logarithmic Mean Divisia Index (LMDI) method. The results revealed that the population (total number of active users) metric had the greatest impact, followed by intensity (the number of observations per active day), frequency (active days per active user), and structure (proportion of regular users). To ensure data continuity and reliability, optimizing user structure by increasing the proportion of regular users is essential. Although observation volumes fluctuated during the pandemic, they rebounded significantly post-pandemic, demonstrating the resilience, and adaptability of citizen science projects. The iNaturalist platform provides a stable foundation for long-term data collection, aiding in the comprehensive understanding of biodiversity dynamics. Our study also highlighted differences in citizen science participation and biodiversity monitoring across cities, indicating the need for tailored biodiversity conservation policies. To effectively address similar crises in the future, developing contingency plans is recommended to provide continuous support for biodiversity conservation and public engagement in environmental management.

Treatment for essential tremor: a systematic review and Bayesian Model-based Network Meta-analysis of RCTs

Essential tremor (ET) significantly impacts patients' daily lives and quality of life, presenting a considerable challenge in clinical practice. In recent years, novel therapeutic regimens have been investigated in randomized controlled trials (RCTs). This study aims to investigate and evaluate the relative efficacy and safety of various therapeutic interventions for ET. We did a systematic review and Bayesian Model-based Network Meta-analysis (NMA) of RCTs. Following PRISMA-NMA guidelines, a comprehensive database search was conducted up to April 1, 2024 to identify RCTs focused on ET treatments. The Bayesian Markov Chain Monte Carlo (MCMC) method was utilized for the analysis, evaluating the relative efficacy and safety of treatments using standardized mean difference (SMD) and log odds ratios (log ORs), respectively. Additionally, the Surface Under the Cumulative Ranking Curve (SUCRA) was applied to assess the relative efficacy of the treatment modalities. PROSPERO registration: CRD42023415752. This study included 33 RCTs involving 1251 patients, covering 19 oral medication treatments and six non-oral medication treatments. NMA showed that deep brain stimulation (DBS) (SMD=-4.93; 95% CI: [-7.73,-2.13]), CX-8998 (SMD=-2.69; 95% CI: [-5.26,-0.14]), atenolol (SMD=-2.36; 95% CI: [-4.70,-0.10]), and propranolol (SMD=-1.59; 95% CI: [-2.25,-0.67]) showed relative efficacy compared to placebo, with DBS demonstrating relative efficacy compared to 15 other treatment methods. However, GRADE assessment indicated that the evidence level for these conclusions was "low" or "very low." According to SUCRA rankings, DBS (0.97) ranked first in relative efficacy, followed by CX-8998 (0.80), thalamotomy (0.79), atenolol (0.76), metoprolol (0.66), propranolol (0.64), magnetic resonance guided focus ultrasound (MR-FUS) (0.624), ICI-118551 (0.620), nimodipine (0.61) and phenobarbitone (0.59). In terms of safety, as a network graph could not be constructed, DBS and thalamotomy were excluded from the NMA, while other effective treatments showed no significant differences in safety compared to placebo. Our study results indicate that CX-8998, propranolol, and atenolol demonstrate relative efficacy and safety in treating ET. DBS is effective for medication-resistant ET and ranks first in relative efficacy, though our NMA lacks safety data for DBS. Given the low overall grade of evidence, these results should be applied cautiously in clinical practice. Further large-scale, head-to-head RCTs are needed. This work was supported by grants from the National Nature Science Foundation of China (Grant No. 82271459).

Key effects contributing to changes in energy imports in the EU-27 between 2000 and 2020: A decomposition analysis based on the Sankey diagram

The aim of this paper is to analyse the key effects contributing to changes in energy imports in the European Union (EU-27) in the period 2000–2020. Using the Logarithmic Mean Divisia Index (LMDI), the analysis examines the effect of changes in six factors—energy structure, energy dependence, energy transformation efficiency, energy yield after transformation, energy efficiency and activity—on imports of oil, natural gas and other sources of energy. The results of the analysis reveal that the decarbonisation process has fostered the abandonment of the most polluting fossil fuel sources; however, there has been an increase in energy dependence on less polluting sources that are not produced domestically. In contrast, there has been an opportunity for change through improved energy efficiency, which has made it possible to reduce energy requirements per unit produced. In short, in order to achieve a sustainable and secure energy future, it is crucial to implement policies and actions that promote both the diversification of the energy mix—particularly renewable energies—and efficiency in consumption. Doing so will enable countries to move towards true decarbonisation and minimise vulnerability in their energy supply.

Agricultural carbon emissions in China: measurement, spatiotemporal evolution, and influencing factors analysis

IntroductionThe agricultural sector is the second largest emitter of greenhouse gases, accounting for 23% of global anthropogenic carbon emissions. Analysis of the basic state of carbon emissions from China's agriculture is helpful to achieve carbon reduction targets.MethodsAgricultural carbon emissions were calculated using the emission factor method, based on data from the China Rural Statistical Yearbook and various provincial statistical yearbooks. To analyze spatial patterns, the standard deviation ellipse method and the center of gravity migration model were employed, uncovering the migration path of agricultural carbon emissions. Regional disparities and the driving factors of agricultural carbon emissions were further examined using the Theil index and the Logarithmic Mean Divisia Index (LMDI) model.ResultsThe analysis indicated that the emissions center has gradually shifted towards the central and western regions, reflecting changes in agricultural production activity areas. Intraregional differences are the primary contributors to the imbalance in agricultural carbon emissions, with pronounced disparities in grain production and consumption balance regions. Key influencing factors include agricultural production efficiency, adjustments in agricultural industrial structure, economic structure and output, and urbanization levels. The economic output effect and urbanization effect are identified as the main drivers of increased carbon emissions, while declining production efficiency has hindered emission reduction efforts.ConclusionThe findings provide valuable insights for regional management and policymaking in China's agricultural sector, highlighting the need to enhance production efficiency and optimize agricultural structure to reduce emissions.

Impact of Dolutegravir plus Lamivudine as First-Line Antiretroviral Treatment on HIV-1 Reservoir and Inflammatory Markers in Peripheral Blood.

To compare the effects of first-line antiretroviral treatment (ART) with dolutegravir plus lamivudine (DTG+3TC) versus DTG plus emtricitabine/tenofovir alafenamide (FTC/TAF) on the evolution of the HIV-1 reservoir and immune activation biomarkers in people with HIV (PWH). DUALITY was a 48-week, single-center, randomized, open-label clinical trial in ART-naïve PWH. Participants were randomized (1:1) to receive ART with DTG+3TC (2DR group) or DTG+FTC/TAF (3DR group). Total and intact proviral HIV-1 DNA, cell-associated RNA in CD4+ T cells, the frequency of HIV-infected CD4+ T cells able to produce p24, plasma soluble inflammatory markers (IL-6, sCD14, TRAIL, IP-10, FABP2, CRP and D-dimer), and activation and exhaustion markers in CD4+ and CD8+ T cells were longitudinally determined. Forty-four participants (22 per study arm) were enrolled. Baseline mean (SD) log10 plasma viral load (pVL) and CD4+ T cell counts were 4.4 (0.7) copies/mL and 493 (221) cells/mm3, respectively. All participants completing the study (2DR n=20; 3DR n=21) had pVL <50 copies/mL at week 48, except for one in the 2DR group who was resuppressed after treating syphilis. Changes from baseline to week 48 in all reservoir parameters or in levels of soluble inflammatory biomarkers and activated or exhausted CD4+ and CD8+ T cells were similar between 2DR and 3DR groups. First-line ART with DTG+3TC resulted in a similar reduction of HIV-1 persistence parameters in peripheral blood, and comparable changes in immune-associated soluble and T-cell markers compared with DTG+FTC/TAF. These findings support recommendation of DTG/3TC among preferred options for first-line ART in PWH.

Exploring the efficiency of employing Fe3O4-MWCNT Nanofluids in a heat sink equipped with circular micro pin-fins

This work investigates the effects of adding different turbulence-inducing elements and varying the concentration of Fe3O4-MWCNT hybrid nanofluid to evaluate the efficiency of a micro pin-fin heat sink. To achieve this objective, a multiphase Lagrangian-Eulerian method that includes gravity, thermophoresis, drag force, Saffman lift, virtual mass, and pressure gradient-induced force to model will employ hybrid nanofluids. The evaluation of heat sink efficiency includes the analysis of quantitative variables like surface Nusselt number, average Nusselt number, and the logarithmic mean temperature difference. Furthermore, a qualitative representation of flow and thermal distributions is illustrated by visualizing flow streamlines, vortex contours, and thermal contours. This topic is addressed in the current work by using the finite volume approach with Ansys Fluent 18.1. Using the multiphase Lagrangian-Eulerian approach, the hydrothermal performance of the Fe3O4-MWCNT hybrid nanofluid in a pin-fin heat sink is investigated. This article's novelty lies in its utilization of four distinct turbulator models, each with unique geometries and widths. These models are positioned within a heat sink with micro fins and are tested in the presence of Fe3O4-MWCNT nanofluids. According to the findings, using the hybrid nanofluid at a concentration of 5 % rather than 1 % causes an average Nusselt number rise of 7.3 %, 9.4 %, and 12.5 % in various heat sink segments. This study's most significant thermal enhancement is associated with case D, which demonstrated a 7 % improvement over the baseline scenario by employing Fe3O4-MWCNT at a concentration of 5 % and a Reynolds number of 2400. It has been demonstrated that hybrid nanofluids have thermal efficiency between 600 and 2400 Reynolds numbers. The thermal efficiency criteria represent the base model, denoted by η = 1. A higher thermal efficiency denotes a more cost-effective design that offers superior heat transmission at a reduced pressure drop. It has been discovered that raising the concentration of hybrid nanofluids improves the micro pin fin heat sink's thermal performance by increasing the heat transfer coefficient.

Field versus laboratory measurements of PFAS sorption by soils and sediments

Sorption by soils and sediments is an important process that influences the distribution, transport, and fate of per and polyfluoroalkyl substances (PFAS) in the environment. Many laboratory studies have been conducted to quantify magnitudes of PFAS sorption as a function of the properties of the PFAS, solutions, and porous media. A critical question is how representative are laboratory-measured sorption magnitudes for field applications. To address this question, log Koc data were compiled from the literature for a range of PFAS of different chain lengths and functional groups. The aggregated field-based data consisted of two types, in-situ measurements comprising paired soil/sediment and water samples and ex-situ measurements obtained from laboratory desorption experiments employing field-contaminated media. These two sets of field-based measurements were compared to a comprehensive data set of standard laboratory batch-type measurements. The compiled data sets represent an extremely wide range of soil and sediment properties. One important novel outcome is the observation that the enhanced sorption of short-chain PFAS observed in several prior laboratory studies is also observed for the field data. This differential enhanced sorption should be accounted for in site investigations and modeling studies. Another relevant outcome is the observation of consistency between measurements obtained with standard batch adsorption experiments and those from desorption studies employing either field-contaminated or artificially-contaminated media. The mean log Koc values determined for the in-situ measured field data were generally larger than the mean laboratory-determined values, particularly for the short-chain PFAS. Notably, however, values for subsets of the laboratory data representing measurements for lower aqueous concentrations or for soils with low organic-carbon contents (<1 %) compared well to the in-situ field data. Measurements compiled for anionic-polyfluoroalkyl, neutral, and cationic PFAS compared well to those for PFCAs and PFSAs. Overall, the results indicate that the laboratory measurements of PFAS sorption were generally representative of the field-derived magnitudes when compared on a consistent basis.

Co-pyrolysis of furniture wood with mixed plastics and waste tyres: assessment of synergistic effect on biofuel yield and product characterization under different blend ratio

Pyrolysis of waste furniture wood, mixed plastics and waste tyres was examined separately and in different combinations from the perspective of improved value products and energy production. The effect of different combinations of furniture wood, plastics and tyres on the product distribution during co-pyrolysis was analyzed. The experimental work throughout this study was performed at a temperature of 500 °C. Prior to the pyrolysis experiments, thermogravimetric analysis was done to assess the thermal degradation behavior of all selected feedstocks. During individual pyrolysis, mixed plastic wastes produced 70.6 wt% of pyrolysis liquid, which is 37.9% and 33.4% more than furniture wood and waste tyres. During co-pyrolysis, a binary blend of mixed plastics and waste tyres produced 63.3 wt% of pyrolysis liquid, which is 7.65% more than the logarithmic mean value, indicating a positive synergistic interaction on liquid production. The liquid yield of the ternary blend was observed to be as low as 54.4 wt% due to the lower volatiles present in the blend. The presence of volatiles in the feedstock is correlated with the production of liquids by individual and co-pyrolysis. For feed-flexible, more efficient, and cleaner operating systems, the increased liquid production offers crucial information. With the use of Fourier transform infrared spectroscopy (FT-IR) and gas chromatography/mass spectroscopy (GC–MS), the impact of different combinations on product characterization was investigated. The characterization study of the pyrolysis liquid obtained from mixed plastics showed the presence of different aromatic and aliphatic compounds. The findings offered viable options for efficiently utilizing waste materials, particularly plastics and tyres to improve the quality and substance of products.

Spatiotemporal Evolution and Driving Factors of Land Use Carbon Emissions in Jiangxi Province, China

Analyzing the spatiotemporal changes and influencing factors of carbon emissions generated by land use is of great importance for improving land use structure and promoting regional low-carbon economic development. This study, based on remote sensing and statistical yearbook data from 1995 to 2020, calculated the carbon emissions from land use in Jiangxi Province, China. Multiple spatial analysis methods and the logarithmic mean Divisia index were used to elucidate the spatiotemporal evolution and driving factors of carbon emissions, and the findings revealed the following: (1) The spatiotemporal changes in land use in Jiangxi Province during 1995–2020 were substantial as forest land accounted for 65% of the entire land area, while construction land increased by 98.1%. Cultivated land decreased the most, followed by forest land. (2) There was a fourfold rise in carbon emissions in Jiangxi Province, driven primarily by construction land, and northern areas produced higher carbon emissions compared with central and southern regions. Forest land was the main carbon sink. (3) Economic development (257.36%) and the impact of the proportion of construction land (211.31%) were the primary factors contributing to the increase in carbon emissions from land use, while other factors had inhibitory effects. This study transformed the macroscale low-carbon development strategy of cities into targeted local policies, and the research theories and methods adopted could provide scientific reference for other regions in urgent need of carbon reduction worldwide.

Research on the Z.S. technique for 8 − 0 polypropylene suture in scleral fixation of one-piece intraocular lens

BackgroundTo introduce a new flange technique (called the Z.S. fixation technique) for 8 − 0 polypropylene suture in scleral fixation of a one-piece intraocular lens (IOL) and evaluate its effectiveness and safety.MethodsAll surgeries were performed using two strategies. First, the Z.S. fixation technique was used to fix 8 − 0 polypropylene sutures to the sclera and the two haptics of a one-piece IOL, respectively. The one-piece IOL previously implanted in the anterior chamber was then relocated to the posterior chamber. Finally, the suture ends outside the sclera were cauterized into flanges and buried in the sclera. All effectiveness and safety data related to the Z.S. technique were recorded and compared.ResultsThe Z.S. fixation technique was employed in 16 eyes of 13 patients, with a follow-up duration of at least 6 months. Postoperative uncorrected distance visual acuity (UDVA) was greatly improved in most of the operated eyes (11/16). The mean log MAR UDVA after surgery was significantly improved compared with that before surgery (0.93 ± 0.72 vs. 1.53 ± 0.73, P = 0.002). IOLs in the vast majority of operated eyes (15/16) remained stable during half-year follow-up. No severe intra-/post-operative complications were observed in any of the operated eyes.ConclusionThe Z.S. fixation technique for 8 − 0 polypropylene suture in scleral fixation of one-piece IOL reduced surgical injuries and had ideal surgical effectiveness and safety.

Decoupling analysis of economic development and human well-being: A case study of the Qinghai-Xizang Plateau, China.

Clarifying the relationship between economic development and human well-being is conducive to promoting high-quality economic development. This study focused on 16 prefecture-level cities in the Qinghai-Xizang Plateau region. The critic weighting method assessed the 2007-2018 human well-being index (HWI). The Tapio decoupling model allowed the study of the human well-being decoupling state. Finally, the drivers of decoupling between economic development and human well-being were analyzed using the Logarithmic Mean Divisia Index method. The results indicated that (1) almost all cities in the study region had an upward 2007-2018 HWI trend, but there were significant differences in growth magnitude and change trend. (2) Economic development and human well-being in the study region in 2007-2018 had expansion negative decoupling, thus, human well-being increased with economic growth, but not as fast as gross domestic product. 9 cities in 2007 showed weak decoupling, expansion connection, and expansion negative decoupling, increasing to 13 cities by 2018, indicating that human well-being development gradually improved from 2007 to 2018. (3) For most cities, the economic scale effect was the most influential factor in the decoupling of economic development and human well-being. Therefore, this study provided policy recommendations for decoupling economic development and human well-being.

Tackling carbon peak and carbon neutrality challenges: A method with long-range energy alternatives planning system and Logarithmic Mean Divisia Index Integration

China aims to carbon peak emissions before 2030 and achieve carbon neutrality by 2060 in response to climate change challenges. Studying the feasibility of this goal, identifying potential gaps, and formulating corresponding emission reduction pathways are urgent issues that need to be addressed. This study creatively integrates Logarithmic Mean Divisia Index into Long-range Energy Alternatives Planning System to assess energy consumption and carbon emissions in Gansu Province from 2001 to 2060, historical and scenario analysis was conducted from a novel perspective. Results indicate that the economic development effect is the most significant driving factor for the emission growth of 131.54 million tons from 2001 to 2020, and its contribution rate is 176.31 %. The leading negative driving factor is the energy intensity effect, contributing at a rate of −62.00 %. Additionally, the energy structure and industrial structure effects also play minor negative roles. Scenario analysis suggests achieving a carbon peak by 2030 requires a 29.53 % reduction in energy intensity from the 2020 baseline, with electricity consumption must reach 46.37 % and clean energy generating 64 %. Carbon peak emissions are projected to be 220.37 million tons, with reduction of carbon emissions by 123.84 million tons by 2060.

A modified surface to volume (SVR) method to calculate nuclear magnetic resonance (NMR) surface relaxivity: Theory and a case study in shale reservoirs

Surface relaxivity (ρ2) is a critical parameter for converting nuclear magnetic resonance (NMR) T2 data to pore size distribution (PSD). The surface-to-volume ratio (SVR) method, known for its simplicity and ease of operation, has been widely used for ρ2 calculation in unconventional reservoirs. However, previous studies often overlooked the equivalence of pore ranges characterized when directly applying the classical SVR model. Moreover, shale reservoirs generally develop layered fractures, whose ρ2 values are different from matrix pores. The logarithmic mean value of the T2 distribution (T2LM) is significantly influenced by layered fractures, therefore, relying solely on the T2LM value of a whole sample under fluid-saturated state will lead to inaccurate ρ2 values of matrix pores, particularly in laminated shales where fractures are well developed. However, insufficient attention has been paid to the effect of fractures on the ρ2 calculation. In this study, a modified SVR method based on the theory of NMR relaxation in partially fluid-saturated pores was proposed to characterize the ρ2 of shale matrix pores. Twenty-four shale core samples from the Shahejie Formation in the Jiyang Depression, China were selected, and subjected to series of NMR experiments at varying oil-bearing conditions, and low-temperature nitrogen adsorption (LTNA) analysis. The results indicate a strong linear correlation (R2 > 0.85) between the inverse T2LM (1/T2LM) and the inverse fluid saturation (1/f) when oil molecules across the entire surface layer participate in the exchange process. For a whole core sample, ρ2 values obtained using the modified SVR model are higher than those obtained using the classical SVR model, especially in samples with numerous fractures. The modified SVR method effectively reduces the impact of fractures on the characterization of ρ2 of matrix pores. For shale pore ρ2 characterization, the classical SVR model may be more suitable for pores smaller than 300 nm, with a recommended T2 range of <33 ms. Additionally, ρ2 values for different pore ranges (<25 nm, 25–100 nm, and >100 nm) within individual samples were estimated. It is found that the ρ2 values of smaller pores is greater than those of larger pores, which may be due to differences in mineralogy of the pores across various size ranges. The small pores are more associated with clay minerals while large pores are surrounded by quartz and rigid minerals. In addition, ρ2 is lower in larger pores and fractures that do not contain organic matter and clays, thus the underestimation of ρ2 by the classical SVR method can be corrected by modified SVR method. This study represents the first attempt to examine ρ2 variations across different pore ranges in shale reservoirs. The methodology presented can be applied to other formations, enhancing NMR data application in both laboratory settings and well logging.

Decoupling relationship between logistics growth and carbon emissions and driving factors in Chongqing: A novel decomposition framework

A comprehensive understanding of the logistics decoupling status and driving factors is of immense theoretical and practical importance for rationally formulating low-carbon logistics policies and accelerating the realization of high-quality development. Present study introduces the logarithmic mean Divisia index method (LMDI) decomposition technique into Tapio index model, extends traditional decoupling model, and establishes a new analytical framework for the decoupling relationship between logistics growth and carbon emissions. Using long-term data from Chongqing (1997–2021), this study investigated the Chongqing logistics decoupling relationship and driving factors. The study found the following: (1) Chongqing logistics carbon emissions show phased changes and face greater pressure to reduce emissions. (2) The decoupling status of logistics growth and carbon emissions is predominantly expansive and weak decoupling, with an overall evolutionary trend of “expansive decoupling–weak decoupling–strong decoupling. The average decoupling index in 2013–2021 was 0.5523, indicating a decreasing trend; however, there was still a large gap in realizing a strong decoupling goal. (3) The energy consumption intensity effect facilitates logistics carbon decoupling, the economic scale effect has a strong inhibiting effect, and the industrial structure effect and transportation intensity effect are staged. Finally, targeted policy recommendations are proposed to expedite logistics carbon emissions decoupling in Chongqing.

Decomposition of Carbon Dioxide (CO2) Emissions in Hungary

Elevated levels of global greenhouse gases (GHGs), primarily produced through fossil fuel combustion, present environmental risks such as prolonged droughts, global warming, and catastrophic floods. Despite raising awareness and international efforts to combat climate change, fossil fuel and GHGs demand persists. At this juncture, this study investigates Hungary's transportation sector, a notable CO2 emitter. Using the Kaya Identity and Log Mean Divisia Index method, this study decomposes the primary components of CO2 emissions in Hungary's transport sectors from 2001-2021. This analysis is done in two decomposition levels, annual and periodic decomposition analysis for each of the five years starting from 2001. The analysis reveals that the economic activity effect ∆CGPE is the predominant driver of CO2 emissions, while the energy intensity effect ∆CEIE improvements have substantially mitigated these increases. Population changes affect ∆CPOPE, and the carbon emission intensity effect ∆CCEIE has had minimal impacts. The study provides insights and recommendations for policy and strategic interventions to reduce emissions and promote sustainable transportation practices in Hungary.

Influencing factors and predictions of carbon emissions for the chemical industry in China

As global warming increases the frequent occurrences of natural disasters, the reduction of carbon emissions has become an important issue around the world. The chemical industry is an important source of carbon emissions in China. The carbon emissions of the chemical industry are calculated from 2000 to 2019 by using the emission factor method. The logarithmic mean divisia index (LMDI) method is exploited to analyze the factors that influence carbon emissions, and the emissions variations are attributed to the contributions of carbon intensity, energy structure, energy intensity, industrial value-added rate, per capita industrial output value, and industrial scale. The results of decomposition show that per capita industrial output value is the main driving factor, and energy intensity is the main inhibiting factor of the chemical industry’s carbon emissions. In order to quantify the variation of carbon emissions, the extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model is constructed and examined. Using the STIRPAT model, the basic scenario and energy intensity control scenario are set, and the carbon emissions are predicted, which shows that under a strict energy intensity control scenario, carbon emissions may reach a peak around 2031. The factors influencing the decomposition and prediction of carbon emissions should be helpful in reducing the carbon emissions of the chemical industry in China.

A field comparison study of two vaccine protocols against Erysipelothrix rhusiopathiae in two types of swine breeds in Spain

Erysipelas still causes large economic losses to pig industry. Maternal immunity is critical to prevent erysipelas in young animals, thus, intensive vaccination protocols or practices focused on the improvement of the maternally derived immunity could provide substantial benefits. The present study evaluates potential changes in antibodies levels in sows and their offspring using two types of tests (commercial ELISA, Ingenasa or rSpaA415 ELISA) when two different vaccination programs (before farrowing or after farrowing) against Erysipelothrix rhusiopathiae were applied to sows from Iberian (A) or conventional Large White-Landrace (B) pig farms. The results showed a statistical correlation between titers found in sows and their one-week old piglets in both tests. The overall mean of (log) antibody titers in farm B measured by the commercial ELISA test was significantly higher in pre-farrowing vaccinated sows compared to the post-farrowing vaccine protocol (p = 0.0278). Additionally, using the rSpaA415 ELISA test, the overall mean of (log) antibody titers was significantly higher in pre-farrowing sows (p = 0.0056) compared to sows following post-farrowing vaccine protocol (p = 0.0003) or non- vaccinated sows. None of the above-mentioned differences were found in farm A. The overall mean of (log) antibody titers in piglets from the pre-farrowing vaccination protocol was significantly higher than piglets from the post-farrowing vaccination protocol in farm A (p = 0.0059; rSpaA415 ELISA) and farm B (p = 0.0168 and p = 0.0098 for the commercial and rSpaA415 ELISA data, respectively). Additionally, higher proportion of piglets from pre-farrowing vaccinated sows remained seropositive during the post-weaning period (days 42 to 84) compared to piglets from non-vaccinated or post-farrowing vaccinated groups in both farms A and B.

A Policy Proposal to Strengthen the Income Redistribution Function of the National Pension Scheme in South Korea: An Analysis of South Korea’s 5th National Pension Comprehensive Plan (Draft)

In this study, we elucidate the income redistribution effects of the proposal to incorporate the Bend Points mechanism of the U.S. OASDI into the Korean National Pension Scheme (BP-KNPS Proposal) through a micro-simulation analysis using individual data from the Korean Labor and Income Panel Study (KLIPS). In addition to examining the effects of introducing the BP-KNPS Proposal into the current National Pension Scheme (NPS), we also consider the impact of this scheme being combined with the 5th National Pension Comprehensive Plan (Draft), announced by Korea’s Ministry of Health and Welfare on 30 October 2023. When the BP-KNPS Proposal is introduced into the current NPS, the Mean Log Deviation (MLD) of the net transfer amount (lifetime pension benefits minus lifetime pension contributions) for regular employees decreases from 0.2022863 to 0.1929960. Similarly, the MLD for self-employed and irregular workers decreases from 0.2046127 to 0.1721433, indicating a reduction in income inequality. Furthermore, when the BP-KNPS Proposal is combined with the 5th National Pension Comprehensive Plan (Draft), the effects are mixed. The proposals to increase the pension contribution rate and adjust the rate increase speed via generation lead to a reduction in income inequality compared to the current NPS when combined with the BP-KNPS Proposal. However, the proposals to raise the pensionable age result in increased income inequality, similar to the outcomes under the current system. This finding suggests that the triple burden identified by literature review—the reduction in benefits due to disparities in contribution periods and life expectancy, and the raised pensionable age—has a greater impact on low-income participants than the inequality-reducing effects of the BP-KNPS Proposal.