Mean Log Research Articles

Recent CO2 emission and projections in Chinese provinces: New drivers and ensemble forecasting

Although extensive studies focused on the driver of changing CO2 emission, the roles of labor and capital were largely ignored in shaping spatiotemporal change in CO2 emission and forecasting differences on CO2 emission was few considered, hindering relevant policymaking towards sustainable development in both climate change mitigation and economic growth for developing countries in particular. To fill the gap above, the study explored the roles of capital and labor in contributing to recent CO2 emission in a case of China over 2010–2019 and projecting provincial CO2 emissions to 2030, by proposing two new spatiotemporal logarithmic mean Divisia index models with Cobb-Douglas production function and developing an ensemble forecasting model including machine learning. We found, first, the effects of capital and labor inputs and carbon factor were the positive drivers affecting aggregate CO2 emissions, while the effects of the total-factor productivity and energy intensity were negative drivers. Second, the effects of capital and labor inputs were the negative drivers for narrowing the emission gap. Third, the ensemble forecasting model can improve the generalization ability of CO2 emission predictions. Therefore, we recommend that policymakers focus on optimizing the carbon reduction effects of capital and labor inputs while promoting the development of a circular economy to achieve sustainable economic growth.

Evaluating the carbon emissions of Hong Kong's building sector from the life cycle perspective

The building sector is a typical end-use sector that involves multiple stages associated with carbon emissions (CE). Accurately quantifying the CE of the building sector from the life cycle perspective can establish a foundation for its decarbonisation. This study focuses on Hong Kong to develop a six-stage CE calculation methodology with the process-based life cycle assessment framework. Specifically, the datasets of newly-constructed buildings are analysed to classify the buildings into 11 archetypes. Furthermore, the logarithmic mean Divisia index is used to identify the primary driving factors of the CE. Results show that the CE of Hong Kong's building sector ranged from 34.75 to 40.23 Mt per annum during the ten-year period 2012–2021. Electricity consumption accounted for over 90 % of the operational CE, while the production of major building materials, such as steel, cement, and aluminium, took up over 80 % of the embodied CE. Moreover, energy efficiency and CE efficiency factors were revealed to impose significant impacts on CE decrease by 1.94 and 1.68 Mt cumulatively in 2012–2021, whereas the population factor was found to increase the CE by 1.43 Mt. The findings should inform Hong Kong building sector's decarbonisation plan, and the study provides an innovative approach for tracking the climate change impacts of building sectors in cities.

Decomposition and scenario analysis of agricultural carbon emissions in Heilongjiang, China.

As a key agricultural region in China, Heilongjiang Province has experienced significant carbon emissions over the past few decades. To understand the underlying factors and future trends in these emissions, a comprehensive analysis was conducted from 1993 to 2030. The agricultural carbon emissions from 1993 to 2020 were estimated using the emission factor method. To analyze the influencing factors and future trends of these emissions, the study employed the Logarithmic Mean Divisia Index (LMDI) and integrated it with the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. Results showed that (1) the agricultural carbon emissions in Heilongjiang were primarily driven by rice cultivation, followed by fertilizer production and irrigation electricity. (2) The economic and labor structure effects were the main driving factors of agricultural carbon emissions, while the population, demographic, and intensity effects were the main inhibitors. (3) Agricultural carbon emissions in Heilongjiang Province peaked in 2016 with 69.6 Mt CO2-eq and could subsequently decline by -3.92% to -4.52% between 2020 and 2030 in different scenario simulations. In the future, Heilongjiang Province should prioritize the reduction of agricultural carbon emissions from rice production. Adjusting the planting structure, managing the layout of rice paddies, and promoting the cultivation of dry rice varieties would significantly contribute to mitigating agricultural carbon emissions.

Characteristics of myelinated retinal nerve fibres in associated straatsma syndrome.

To study cases diagnosed of myelinated retinal nerve fibres (MRNF) continuous with the optic disc and describe the plausible pathogenic mechanism for the ocular features in Straatsma syndrome. This retrospective observational study includes clinically diagnosed MRNF cases. MRNF, myopia, and amblyopia defined Straatsma syndrome. MRNF were classified into three types based on location: type 1 in the superior retina, type 2 in the superior and inferior retina, and type 3 in the inferior retina. MRNF size was measured on Optomap® (Optos, Daytona, UK) images and posterior staphyloma location was noted. Demographics and best-corrected refractive error in logMAR units was recorded. Descriptive statistics and Spearman's corelation test were used to analyse MRNF size's relationship to refractive error and logMAR visual acuity. The study included a total of 19 MRNF eyes from 18 patients. Seventeen (89%) eyes had Straatsma syndrome. Median age was 23.50 [range: 4-75] years. One (6%) patient exhibited bilateral presentation. The median logMAR visual acuity was 0.4 log units (interquartile range: 0.18-1.20) and mean refractive error was -7.21 ± 5.32. Type 2 MRNF (n = 14,74%) was the commonest. Average MRNF size was 34.37 ± 40.73 sq.mm. Posterior staphyloma was noted in 17 eyes, all in close MRNF association. Significant positive corelation was noted between logMAR visual acuity and MRNF size (r = 0.5, p = 0.028). Large size MRNF corelated with poor visual acuity in the study. The paper explains the possible pathogenetic mechanisms for the ocular findings seen in MRNF.

The Carbon Footprint and Influencing Factors of the Main Grain Crops in the North China Plain

The North China Plain (NCP) serves as a critical grain-producing region in China, playing a pivotal role in ensuring the nation’s food security. A comprehensive analysis of the carbon footprint (CF) related to the cultivation of major grain crops within this region and the proposal of strategies to reduce emissions through low-carbon production methods are crucial for advancing sustainable agricultural practices in China. This study employed the lifecycle assessment (LCA) method to estimate the CF of wheat, maize, and rice crops over a period from 2013 to 2022, based on statistical data collected from five key provinces and cities in the NCP: Beijing, Tianjin, Hebei, Shandong, and Henan. Additionally, the Logarithmic Mean Divisia Index (LMDI) model was utilized to analyze the influencing factors. The results indicated that the carbon footprints per unit area (CFA) of maize, wheat, and rice increased between 2013 and 2022. Rice had the highest carbon footprint per unit yield (CFY), averaging 1.1 kg CO2-eq kg−1, with significant fluctuations over time. In contrast, the CFY of wheat and maize remained relatively stable from 2013 to 2022. Fertilizers contributed the most to CF composition, accounting for 48.8%, 48.0%, and 25.9% of the total carbon inputs for wheat, maize, and rice, respectively. The electricity used for irrigation in rice production was 31.8%, which was much higher than that of wheat (6.8%) and maize (7.1%). The LMDI model showed that the labor effect was a common suppressing factor for the carbon emissions of maize, wheat, and rice in the NCP, while the agricultural structure effect and the economic development effect were common driving factors. By improving the efficiency of fertilizer and pesticide utilization, cultivating new varieties, increasing the mechanical operation efficiency, the irrigation efficiency, and policy support, the CF of grain crop production in the NCP can be effectively reduced. These efforts will contribute to the sustainable development of agricultural practices in the NCP and support China’s efforts to achieve its “double carbon” target.

An Efficiency Evaluation and Driving Effect Analysis of the Green Transformation of the Thermal Power Industrial Chain: Evidence Based on Impacts and Challenges in China

The high carbon emissions and pollution of China’s thermal power industry chain have exacerbated environmental and climate degradation. Therefore, accelerating the green transformation process is of great significance in promoting the sustainable development of enterprises. This study selected 30 listed thermal power enterprises in China as research objects, analyzed their data from 2018 to 2022, set targeted input–output indicators for different stages, and used a two-stage dynamic data envelopment analysis (DEA) model to evaluate and measure the efficiency of the green transformation of Chinese thermal power enterprises. In addition, this study also uses the logarithmic mean Divisia index (LMDI) method to analyze the driving effects of green transformation. The results indicate that in terms of overall efficiency, there is a significant difference in the overall performance of these 30 thermal power enterprises, with a large difference in average efficiency values. Efficiency values are related to enterprise size. In terms of stage efficiency, the average efficiency value of thermal power enterprises in the profit stage was significantly higher than that in the transformation stage, and the profitability of Chinese thermal power enterprises was better. In terms of sub-indicator efficiency, the efficiency of each indicator shows a “U”-shaped trend, and there is a certain correlation between the operating costs and revenue of thermal power enterprises, the market value of green transformation, and related indicators. In addition, the most important factor affecting the efficiency of green transformation is the sewage cost they face, whereas their operational capabilities have the least impact on their green transformation. In this regard, thermal power enterprises should increase their investment in the research and development of key technologies for thermal power transformation and continuously optimize their energy structure. The government will increase financial support for thermal power green transformation enterprises and correspondingly increase emission costs.

Tracking China's methane emissions from wastewater: Inventories, driving forces, and mitigation potential

China's rapid urbanization and burgeoning economy drove significant growth in sewage discharge, necessitating an examination of factors influencing wastewater methane emissions and mitigation measures. This study presented a comprehensive inventory of methane emissions in 31 provinces of China over a significant period. We developed a logarithmic mean divisor index (LMDI) model to reveal the driving forces behind emission changes. Methane emissions surged from approximately 1.4 Tg in 2000 to about 2.7 Tg in 2020, with treated domestic sewage being the fastest-growing source (49 Gg to 810 Gg). In 2020, the eastern, central, and southern regions contributed 61.7 % to total emissions. The increase in emissions was primarily driven by economic output, urbanization and population growth. This study forecasted future emission reduction potential, highlighting that reducing emission intensity (52.7 %) offered the most significant mitigation opportunities, followed by enhancing methane recovery (35.2 %). We recommended strengthening sewage discharge supervision and promoting biogas production.

Effect of water Resource utilization in Poyang lake area on carbon emissions based on decoupling theory

The utilization of regional water resources has the potential to impact carbon emissions. Maintaining a decoupled relationship between water resources and carbon emissions facilitates harmonious regional development. Understanding the mechanism of their coordination is conducive to achieving the "Double Carbon" goal and control of regional carbon emissions and water resource consumption. This study examines the decoupling relationship between water resource utilization and carbon emissions in Poyang Lake area, China, employing the Tapio decoupling model and the LMDI(logarithmic mean divisia index) decomposition model. The results indicate that carbon emissions in Poyang Lake area exhibited a gradual increase, accompanied by an annual growth rate of 5.99 %. The water supply exhibited a slow expansion. They have exhibited state of affairs strong negative decoupling and expansive negative decoupling over the past 15 years. Moreover, this situation is most acute and worsening in the secondary industry. The water use structure effect and water economic benefit effect are the primary factors affecting carbon emission increases, contributing 57.93 % and 65.66 %, respectively. Carbon emissions intensity is the largest inhibiting factor, which accounts for a maximum contribution of 42.96 %. The order of potency of the driving factors is as follows: water economic benefit > carbon emission intensity > water use structure > water use efficiency. In summary, this research recognised the enhancement of the water economic efficiency index not only facilitates the decoupling phenomenon but also improves the water-carbon relationship, especially in the secondary industry. It serves as a compelling illustration of the significance of elucidating the interrelationship between regional water and carbon dynamics, and charting the course for the formulation of regional policies that would facilitate the advancement of environmentally conscious and carbon-neutral development, as well as water conservation.

Ensuring China's food security in a geographical shift of its grain production: Driving factors, threats, and solutions

Grain production in China has increased over time despite limited access to natural resources. However, this has led to China's grain supply system becoming increasingly vulnerable and unsustainable due to a northward shift of its grain production. Here, we establish a framework to guide future grain policy formulation in response to previous practices and lessons learned from China's past grain production. This framework integrated the spatial gravity model, spatial mismatch index, logarithmic mean Divisia index (LMDI), and Monte Carlo simulation to identify the key drivers behind grain growth, analyze their impacts on food security, and propose countermeasures. We found that grain production increase in China is not universally attributable to increased input of farmland and water resources, but rather, socio-economic factors including agricultural technology and irrigation facilities have improved efficiency. The spatial heterogeneity in their driving effects in different major grain producing regions has led grain production to shifted to the northern regions that have few natural resources, posing new challenges for food security: without new policies, China will not be poised to meet increased grain demand projected from 2025 to 2030, especially for rice and soybeans. From a comparison across four grain policies, it is found that region-specific policies will most effectively accelerate transitions to sustainable food production systems. We urge decision-makers to reach beyond natural resource inputs and focus on socio-economic factors, particularly inputs for replanting abandoned farmland and irrigation facilities that can ensure food security in the future.

Effect of Chevron Angles on the Thermal Performance of Corrugated Plate Heat Exchanger (CPHEs)

Corrugated plate heat exchangers (CPHEs) possess a greater surface area for heat transfer and a heightened level of turbulence as a result of the corrugations. This study presents an experimental analysis comparing the thermal performance of a flat plate heat exchanger and a corrugated plate heat exchanger (CPHE) with varied corrugation angles. The analysis was conducted using water as the test fluid. The comprehensive testing has been carried out on CPHEs with different chevron angles (β) of 30°/30°, 45°/45°, and 60°/60°. Experimental data is collected under steady- state conditions, with a single phase (water-water) system, covering Reynolds numbers (Re) ranging from 900 to 1250. The test fluid's mass flow rate, ranging from 0.05 to 0.16 kilograms per second, is measured together with the accompanying steady state temperatures. Each plate was equipped with four thermocouple sensors to detect the bulk temperature of the cold and hot fluids at the intake and output. The performance was evaluated using both parallel and counterflow configurations. Experimental observations are utilized to estimate the temperature difference (∆T) between the inlet and output streams, the logarithmic mean temperature difference (LMTD), and the exergy (E). The measured ∆T and E values for corrugation angles (30°, 45°, and 60°) of CPHE were compared to those of flat plate heat exchangers. For corrugation angles of 30°, 45°, and 60°, the values of ∆T and E (efficiency) increase as the mass flow rate of the fluid increases. An increase in the corrugation angle leads to an increase in turbulence in the flow, which in turn enhances heat transmission. In addition, the thermal effectiveness (ε) was calculated using the NTU method and compared for all of the plates. Key Words: Corrugated plate heat exchanger, Thermal performance, Chevron angle, heat transfer coefficient.

Decomposition of carbon emissions and companies’ willingness-to-pay for Green Fund: evidence from the manufacturing sector in Busan, South Korea

ABSTRACT The impact of carbon neutrality on the manufacturing sector, through regulatory and carbon pricing mechanisms, pushes companies toward environmental-friendly management. This paper seeks to examine the manufacturing sector’s willingness-to-pay (WTP) for the Busan Green Fund, an initiative designed to incentivize companies to adopt green management practices in Busan Metropolitan City. Three industries with the highest level of CO2 emissions are selected utilizing the Logarithmic Mean Divisia Index (LMDI) method. Companies in these three key industries are surveyed regarding their WTP, beyond their energy costs, for the Busan Green Fund. The study also investigates the characteristics of these companies. The average WTP is an 11.1% premium on energy costs. The regression results show that WTP is significantly influenced by the level of investment in energy-saving technology and company size. Based on our analysis of CO2 emission trends within the manufacturing sector, policy implications are suggested for sustainable development via the operation of a dedicated fund.

Antidepressant class and concurrent rTMS outcomes in major depressive disorder: a systematic review and meta-analysis

Repetitive transcranial magnetic stimulation (rTMS) is frequently used as an adjunctive treatment with antidepressants for depression. We aimed to evaluate the clinical efficacy and safety of antidepressant classes when administered concurrently with rTMS for the management of major depressive disorder (MDD). In this systematic review and meta-analysis, MEDLINE, Embase, PsycINFO, and the Cochrane Library were searched from inception to April 12th 2024 for terms relating to medication, depression, and rTMS and appraised by 2 independent screeners. All randomized clinical trials that prospectively evaluated a specific antidepressant adjunctively with sham rTMS as a control in MDD were included. The study was registered with PROSPERO (CRD42023418435). The primary outcome measure assessed symptomatic improvement measured by formal depression scales. We used a random-effects model with pooled Standardized Mean Differences (SMDs) and log odds ratios (OR). All studies were assessed for their methodological quality and bias using the Cochrane Collaboration Risk of Bias tool version 2 (RoB2). 14 articles from 5376 identified studies were included in the systematic review and meta-analysis. There was only sufficient trial data to evaluate the effects of rTMS and combination therapy with selective serotonin reuptake inhibitors (SSRIs) and selective norepinephrine reuptake inhibitors (SNRIs). Across studies, 848 participants (mean [SD] age:41.1 [18.7] years for SSRIs, 51.8 [3.8] years for SNRIs) prospectively examined the efficacy of antidepressant medication with rTMS. Combining rTMS with SSRIs led to significantly lower depression scores, (SMD [CI] of-0.65 [-0.98,-0.31], p=0.0002, I2=66.1%), higher response (OR=0.97 [0.50, 1.44], p<0.0001, I2=25.33%) and remission rates (OR=1.04 [0.55, 1.52], p<0.0001, I2=0.00%) than medication with sham rTMS. No additive benefit was found for SNRIs with rTMS (SMD of 0.10 [-0.14, 0.34], p=0.42, I2=0.00%; OR=0.12 [-0.39, 0.62], p=0.64, I2=0.00%; OR=-0.31 [-0.90, 0.28], p=0.86, I2=39.9%). The overall risk of bias for the included studies ranged from low to high, with 1 study having a high risk of bias. The combination of rTMS with SSRIs, but not SNRIs, significantly reduced depression severity, increasing response and remission rates. Some analyses demonstrated high heterogeneity, which was influenced by an SSRI trial with a high effect size. Overall, these results suggest that not all antidepressant combination therapies are alike, and SSRIs should be considered when initiating rTMS. Donald T. Stuss Young Investigator Research Innovation Award from the Sandra Black Centre for Brain Resilience & Recovery and the Harquail Centre for Neuromodulation through the Sunnybrook Foundation.

Association between Age-Adjusted Endothelial Activation and Stress Index and Intensive Care Unit Mortality in Patients with Severe COVID-19.

Endothelial activation and stress index (EASIX) reflects endothelial dysfunction or damage. Because endothelial dysfunction is one of the key mechanisms, a few studies have shown the clinical usefulness of original and age-adjusted EASIX (age-EASIX) in patients with coronavirus disease 2019 (COVID-19). We aimed to evaluate the clinical utility of age-EASIX in predicting intensive care unit (ICU) mortality in critically ill patients with COVID-19 in South Korea. Secondary analysis was performed using clinical data retrospectively collected from 22 nationwide hospitals in South Korea between January 1, 2020, and August 31, 2021. Patients were at least 19 years old and admitted to the ICU for severe COVID-19, demanding at least high-flow nasal cannula oxygen therapy. EASIX [lactate dehydrogenase (U/L)×creatinine (mg/dL)/platelet count (109 cells/L)] and age-EASIX (EASIX×age) were calculated and log2-transformed. The mean age of 908 critically ill patients with COVID-19 was 67.4 years with 59.7% male sex. The mean log2 age-EASIX was 7.38±1.45. Non-survivors (n=222, 24.4%) in the ICU had a significantly higher log2 age-EASIX than of survivors (8.2±1.52 vs. 7.1±1.32, p&lt;0.001). log2 age-EASIX was significantly associated with ICU mortality (odds ratio, 1.541; 95% confidence interval, 1.322 to 1.796; p&lt;0.001) and had a better area under the receiver operating characteristic curve than of the sequential organ failure assessment (SOFA) score in predicting ICU mortality (0.730 vs. 0.660, p=0.001). Age-EASIX is significantly associated with ICU mortality and has better discriminatory ability than the SOFA score in predicting ICU mortality.

Overlooked carbon sinks in the house building industry and the influential factors

The study aims to establish a carbon sink measurement model for the house building industry and analyze the time-varying characteristics of influential factors. China's house building industry exhibited an increasing trend in carbon sink, from 0.336 billion tons in 2000 to 234.1 million tons in 2020. This study defines the carbon sink coefficient of the house building industry to understand its carbon sink capacity. From 2000 to 2020, the average carbon dioxide absorption per square meter of floor space annually, throughout its life cycle, is 2.09 kg. Factors influencing the carbon sink were analyzed using Logarithmic Mean Divisia Index (LMDI) model. The most influential factor was resource consumption intensity, followed by value dependence level and output scale effect. Employing Time-Varying Parameter Vector Autoregression (TVP-VAR), this study examines the impact of macro factors on carbon sinks at various lead times and specific points. Resource consumption intensity exhibits significant time-varying effects. Value dependence level negatively influences, primarily in the medium and long terms, while the output scale effect consistently shows a positive influence, mainly in the long term. Scenario forecasting suggests that by 2035, under low-carbon transition scenarios, carbon sinks could increase by 8.28% and 17.26% compared to baseline and high-carbon crude scenarios, respectively. To foster this growth, prolonging the service life of houses, enhancing construction site management and waste treatment, and employing higher value-added production factors are recommended.

Biomarkers in the prediction of complications in pregnancy after assisted reproductive technology.

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 between assisted reproductive technology (ART) pregnancies complicated with great obstetrical syndromes (GOS) or gestational diabetes (GDM) ± large-for-gestational-age (LGA) fetus, and uncomplicated ART pregnancies. This was a prospective longitudinal study of 143 women with singleton pregnancies who conceived through ART at the Department of Obstetrics and Gynecology, Prince of Wales Hospital, the Chinese University of Hong Kong, Hong Kong SAR between December 2017 and January 2020. The participants were followed up at 6-6+3, 11-13+6, 20-24+6, 30-34+6, and 35-37+6 weeks for the measurement of MAP, UtA-PI, PlGF, and sFlt-1. A linear mixed-effects analysis was performed to compare the biomarkers in the GOS, GDM ± LGA, and uncomplicated groups across gestation. Thirty-three (23.1%) and fifty-five (31.5%) women were diagnosed with GOS and GDM ± LGA, respectively. The GOS group had higher estimated marginal mean log10 MAP mulitples of the median (MoM) across gestation, compared with the uncomplicated group (0.00771 vs -0.02022; P < 0.001), when adjusting for clinical visits and days of embryo transfer. The absolute mean log10 MAP MoM in the GOS group was found to be significantly higher than that of the uncomplicated group at all clinical visits from 6 weeks onwards. Furthermore, the estimated marginal mean log10 PlGF MoM was significantly lower in the GOS group across gestation, compared with the uncomplicated group (-0.04226 vs 0.05566; P = 0.010). The significant difference in log10 PlGF MoM was observed from 11-13+6 to 30-34+6 week of gestation (P < 0.05). However, no significant differences in the estimated marginal means of log10 UtA-PI MoM and log10 sFlt-1 MoM between GOS and uncomplicated groups were observed. GDM ± LGA group had a lower estimated marginal mean log10 PlGF MoM throughout pregnancy compared with the uncomplicated group (-0.01536 vs 0.05572; P = 0.032). In the individual visit analysis, the significant difference was observed at the 20-24+6 and 35-37+6 weeks visits (P < 0.05). There were no significant differences in estimated marginal mean log10 MoM of MAP, UtA-PI, and sFlt-1 between GDM ± LGA and uncomplicated groups during pregnancy. Our study has revealed that among pregnancies conceived through ART, GOS is associated with higher MAP and lower PlGF from early gestation until late third trimester, while GDM ± LGA is associated with lower PlGF during the second half of pregnancy. The same degree of differences in MAP and PlGF persists from early until late gestation in the GOS group and these findings highlight the importance of early screening during the first trimester to identify women who are at risk for developing GOS following ART procedures. Lastly, the potential of PlGF in predicting the development of GDM from the second trimester of pregnancy requires further investigation.

An Effective Soil Analysis and Crop Yield Prediction Based on Optimised Light GBM in Smart Agriculture

ABSTRACTIn the agricultural sector, crop yield prediction plays an important role as it helps farmers make decisions about the growing season and type of crops to get better yield. The main goal in the agricultural sector is to reduce operating costs and pollution by improving crop yields and quality. This paper proposes an effective method for soil analysis and crop yield prediction for intelligent agriculture. The collected data are preprocessed using missing value interpolation and data normalisation techniques. Feature selection is performed on the preprocessed data using the Aquila‐based adaptive optimisation algorithm, which selects the best trait subset for yield prediction. An improved lightweight gradient‐boosting machine based on the Battle Royale Optimisation technique is used for classification. The performance of the proposed system is evaluated using mean absolute error, root mean square error, R‐squared, mean square error, mean square logarithmic error and mean absolute percentage error, and the proposed system achieved an accuracy of 97%. The proposed system accurately predicts crop yields, improving crop production and quality.

Efficacy of dexamethasone implant in the management of chronic central serous chorioretinopathy without choroidal neovascularization.

To demonstrate the treatment efficacy of intravitreal dexamethasone (DEX) implant in chronic recurrent/persistent central serous chorioretinopathy (CSC). Prospective, non-randomized, open-label study. In this study, subjects with chronic CSC without signs of choroidal neovascularization (CNV) received intravitreal DEX implant therapy. The primary outcome measure was the change in visual acuity. Changes in central macular thickness (CMT) and change in subfoveal choroidal thickness (SFCT) on optical coherence tomography (OCT), incidence of recurrent fluid, and safety of DEX implant were secondary outcome measures. Subjects were followed up for a minimum of 3 months after DEX implantation. In total, 20 eyes of 20 subjects (mean age: 47 ± 9 years) with a median disease duration of 23.5 months were enrolled. With a single injection of DEX implant, a reduction in CMT was noted in 90% of eyes. Complete resolution of subretinal and intraretinal fluid was noted in 55% of eyes within 3 months of injection. A significant improvement in vision (mean Log MAR visual acuity 0.66 ± 0.49 vs. 0.54 ± 0.45; P = 0.020), mean CMT (338 ± 110 microns to 238 ± 73 microns; P < 0.001) and SFCT (514 ± 95 microns to 445 ± 111 microns; P < 0.001) was noted over 3 months. Recurrent fluid was noted in 50% of eyes after a mean follow-up duration of 7 ± 4 months. Elevated intraocular pressure, managed by topical therapy, was noted in six eyes. The consistent improvement in visual acuity, fluid resolution, and reduction in choroidal thickness suggests a possible role for DEX implants in managing chronic CSC. A larger randomized trial is warranted.

Red versus blue states: Inequality in energy-related CO2 emissions in the United States (1997–2021)

The partisan divide over climate change issues in the U.S. has been increasing over time, particularly after the signing of the Kyoto protocol in 1997. This divide has posed a substantial hurdle to implementing a unified climate policy in the U.S. In this paper, I employ the Shapley value decomposition approach to measure the partisan gap in state-level per capita energy-related CO2 emissions. Despite a decrease in overall carbon emissions and stable per capita carbon inequality between states (measured by the Gini index), the inequality between the so called ‘red’ and ‘blue’ states widened over the period between 1997 and 2021. This is true regardless of whether each state is treated as a unit of analysis or assigned a weight equal to its population. Throughout the period, the divide by political party color was more pronounced than that by many other dimensions, such as income, climatic condition, and geographic location. These results also hold true when Mean Log Deviation is used as the measure of inequality. Driven primarily by disparity in transportation sector and coal use for electricity generation, the partisan gap was worse in 2021 than in 2020. While the partisan gap intensified, the carbon distribution changed from being top-concentrated to bottom-concentrated between 1997 and 2019, only to be reversed in the aftermath of the coronavirus pandemic in 2020 and subsequent economic recovery in 2021.

Experimental and Comparative Heat Transfer Analysis of Heat Exchanger with Surface Modification

This research aims to enhance the heat exchanger performance by a surface modification approach. The shot peening effect on the heat transfer performance of shell and tube heat exchanger (STHE) is experimentally studied with different surface modifications of stainless steel (SS) tube-like plain SS tube, grinded surface and blast with copper, sand & iron surface. The effect of shot peening on the surface morphology of the SS outer surface is analyzed by scanning electron microscope, and it was found that the iron blast surface has a coarse grain structure. The optimum flow rate for STHE was experimentally studied by base SS tube, and its values are compared with analytical results of computational fluid dynamics and heat transfer research. The underwater medium maintained the higher and lower temperatures of 80 °C and 30 °C, respectively. Based on the logarithmic mean temperature difference, number of transfer units (NTU), overall heat transfer coefficient, effectiveness and exergy efficiency of SS and its modified surface are experimentally evaluated, and iron blasted surface offered maximum heat exchanger performance like 100% for Stanton number, 89% for Nusselt number, 89% for overall heat transfer coefficient, 89% for an NTU, 57% for effectiveness, and 148% for exergy efficiency, while compared to SS surface without modification.

Water peaking with a three‐pillar analysis in China: Exploring the interplay of resource utilization and industrial structure

AbstractThis study introduces the innovative concept of water‐peaking theory, drawing inspiration from the theories of “peak oil” and “carbon peaking.” Employing the Environmental Kuznets Curve, Tapio's decoupling theory, and Logarithmic Mean Divisia Index decomposition, we analyze data spanning from 2006 to 2020, yielding significant findings. First, China's three industries exhibit an imbalanced structure in the utilization of water resources, necessitating the reallocation of water from the primary industry to the secondary and tertiary industries. Second, although China has not yet attained absolute water peaking, it confronts a predicament in sustainable development. Third, the primary and secondary industries demonstrate favorable decoupling of water consumption from economic growth, while the tertiary industry faces challenges in achieving historical water peaking. Fourth, drivers on the production side (technological effects, structural effects, output effects) substantially reduce water consumption across all industries, contributing to progress toward water peaking. Conversely, factors on the consumer side (population effects) play a minor role with untapped potential. This research provides valuable insights for nations worldwide in identifying historical inflection points in water resource management, pursuing sustainable pathways for industrial water usage and economic growth, and achieving environmental quality and sustainable economic development objectives. China's experiences serve as a guiding exemplar in this endeavor.