[Multi Scenario Carbon Peak Prediction and Emission Reduction Path Analysis of Xi'an Hi-tech Zone].

The fifth session of the 13th National People's Congress proposed to be committed to promoting carbon peaking and carbon neutrality, promoting the comprehensive green and low-carbon transformation of the economy and society and achieving high-quality development. As an important scientific and technological innovation and industrial cluster in Shaanxi Province, the economic development of the Xi'an Hi-tech Zone largely relies on energy consumption, making the task of carbon reduction particularly challenging. Firstly, taking the Xi'an Hi-tech Zone as the research object, through systematic accounting of carbon emissions within the park, we analyzed the current carbon emission status of enterprises in different energy types and industries. Then, using the Kaya model, multiple independent carbon peak scenarios were set up to predict the total carbon emissions and peak time under different scenarios. Finally, based on the development characteristics of the Xi'an Hi-tech Zone, we scientifically selected corresponding carbon emission reduction paths and provided reasonable emission reduction suggestions. The results showed that the proportion of carbon emissions consumed by electricity was currently the highest, and the share was increasing yearly. Industrial carbon emissions had always been dominant, and the development of the tertiary industry was becoming increasingly prosperous. In the scenario prediction, the carbon emission factor scenario, energy intensity scenario, and economic level scenario could reach the carbon peak by 2030. Among them, the economic development level had the greatest impact on the peak and time of the future carbon peak in the Xi'an Hi-tech Zone, whereas the industrial structure scenario, energy source structure scenario, and population size scenario had no peak before 2030. The future emission reduction path mainly started from decarbonization of the power sector, stable and high-quality economic development, green upgrading of energy and industrial structure, and building a green transportation system. This can reserve more preparation time for achieving carbon neutrality and provide decision-making reference for the low-carbon development of industrial parks in China.

Global transboundary transmission path and risk of Mpox revealed with Least Cost Path model.

OBJECTIVES: The recent surge of Mpox outbreaks in multiple countries has garnered global attention. As of July 12, 2023, there have been 88,288 reported cases of Mpox worldwide. Although genetic variation was not found to be the cause of the epidemic outbreak, the reasons for its rapid spread remain unclear. METHODS: Using the niche method, this study identified high-risk regions for Mpox and determined that human factors are the primary contributors to global risks. To further investigate, a travel network resistance surface was created based on various modes of transportation and was combined with sea, airline, highway, and railway routes to construct the least cost path for human travel networks in different risk areas. RESULTS: The results indicated that high-risk regions for Mpox are mainly concentrated in Europe and the United States, with large risk ranges and high-risk values. The least cost path revealed three primary transmission paths rely on developed transportation networks, including internal transmission in North America, Europe-Africa, and Europe-Asia-Africa. These findings suggest that human activities, facilitated by developed travel networks, remain the main contributing factor to the spread. CONCLUSIONS: In summary, based on the Mpox epidemic report, this study conducted risk prediction and driving factor analysis on Mpox. The research results indicate that human use of transportation for long-distance activities is a key factor leading to the rapid spread of the virus. Subsequently, we focused on studying the global transmission pathways of Mpox and revealed several transmission pathways with high global population migration rates by constructing the LCPs between different high-risk areas. This study also emphasizes the importance of applying early monitoring data of Mpox to model risk prediction in controlling emerging infectious diseases, providing a new perspective for controlling Mpox and similar diseases.

A deep-learning-based scatter correction with water equivalent path length map for digital radiography.

We proposed a new deep learning (DL) model for accurate scatter correction in digital radiography. The proposed network featured a pixel-wise water equivalent path length (WEPL) map of subjects with diverse sizes and 3D inner structures. The proposed U-Net model comprises two concatenated modules: one for generating a WEPL map and the other for predicting scatter using the WEPL map as auxiliary information. First, 3D CT images were used as numerical phantoms for training and validation, generating observed and scattered images by Monte Carlo simulation, and WEPL maps using Siddon's algorithm. Then, we optimised the model without overfitting. Next, we validated the proposed model's performance by comparing it with other DL models. The proposed model obtained scatter-corrected images with a peak signal-to-noise ratio of 44.24 ± 2.89 dB and a structural similarity index measure of 0.9987 ± 0.0004, which were higher than other DL models. Finally, scatter fractions (SFs) were compared with other DL models using an actual phantom to confirm practicality. Among DL models, the proposed model showed the smallest deviation from measured SF values. Furthermore, using an actual radiograph containing an acrylic object, the contrast-to-noise ratio (CNR) of the proposed model and the anti-scatter grid were compared. The CNR of the images corrected using the proposed model are 16% and 82% higher than those of the raw and grid-applied images, respectively. The advantage of the proposed method is that no actual radiography system is required for collecting training dataset, as the dataset is created from CT images using Monte Carlo simulation.

The economic consequence of large-scale epidemic outbreak: The path and loss evaluation of COVID-19 in China based on input-output analysis.

The COVID-19 pandemic has significantly impacted China's economic and social development. Understanding the direct and indirect effects of the epidemic on the economy is vital for formulating scientifically grounded epidemic management policies. This study assesses the economic losses and influence paths of a large-scale epidemic in China. We proposed three COVID-19 scenarios - serious, normal, and mild - to evaluate the direct economic impact on China's GDP from a demand perspective. An input-output model was used to estimate the indirect impact. Our findings show that China's GDP could lose 94,206, 75,365, and 56,524 hundred million yuan under serious, normal, and mild scenarios, respectively, with corresponding GDP decline rates of 9.27%, 7.42%, and 5.56%. Under the normal scenario, indirect economic loss and total loss are projected at 75,364 and 489,386 hundred million yuan, respectively. Additionally, the pandemic led to a reduction in carbon emissions: direct emissions decreased by 1,218.69 million tons, indirect emissions by 9,594.32 million tons, and total emissions by 10,813.01 million tons across various industries. This study provides a comprehensive analysis of the economic and environmental impacts of the pandemic.

Use of quantitative in vitro to in vivo extrapolation (QIVIVE) for the assessment of non-combustible next-generation product aerosols.

With the use of in vitro new approach methodologies (NAMs) for the assessment of non-combustible next-generation nicotine delivery products, new extrapolation methods will also be required to interpret and contextualize the physiological relevance of these results. Quantitative in vitro to in vivo extrapolation (QIVIVE) can translate in vitro concentrations into in-life exposures with physiologically-based pharmacokinetic (PBPK) modelling and provide estimates of the likelihood of harmful effects from expected exposures. A major challenge for evaluating inhalation toxicology is an accurate assessment of the delivered dose to the surface of the cells and the internalized dose. To estimate this, we ran the multiple-path particle dosimetry (MPPD) model to characterize particle deposition in the respiratory tract and developed a PBPK model for nicotine that was validated with human clinical trial data for cigarettes. Finally, we estimated a Human Equivalent Concentration (HEC) and predicted plasma concentrations based on the minimum effective concentration (MEC) derived after acute exposure of BEAS-2B cells to cigarette smoke (1R6F), or heated tobacco product (HTP) aerosol at the air liquid interface (ALI). The MPPD-PBPK model predicted the in vivo data from clinical studies within a factor of two, indicating good agreement as noted by WHO International Programme on Chemical Safety (2010) guidance. We then used QIVIVE to derive the exposure concentration (HEC) that matched the estimated in vitro deposition point of departure (POD) (MEC cigarette = 0.38 puffs or 11.6 µg nicotine, HTP = 22.9 puffs or 125.6 µg nicotine) and subsequently derived the equivalent human plasma concentrations. Results indicate that for the 1R6F cigarette, inhaling 1/6th of a stick would be required to induce the same effects observed in vitro, in vivo. Whereas, for HTP it would be necessary to consume 3 sticks simultaneously to induce in vivo the effects observed in vitro. This data further demonstrates the reduced physiological potency potential of HTP aerosol compared to cigarette smoke. The QIVIVE approach demonstrates great promise in assisting human health risk assessments, however, further optimization and standardization are required for the substantiation of a meaningful contribution to tobacco harm reduction by alternative nicotine delivery products.

[Energy-saving and Emission Reduction Path for Road Traffic in Key Coastal Cities of Guangdong, Fujian and Zhejiang].

The rapid development of society and economy has resulted in a substantial increase in energy consumption, consequently exacerbating pollution issues. Current research predominantly focuses on energy-saving and emission reduction in road transportation within individual cities or the three major economic regions of China:the Yangtze River Delta, the Pearl River Delta, and the Beijing-Tianjin-Hebei Region. However, there is a dearth of studies addressing the southeastern coastal economic region. Located at the heart of China's southeastern coastal economic development, the provinces of Guangdong, Fujian, and Zhejiang unavoidably face challenges associated with energy consumption and emissions while pursuing economic growth. To address these challenges, this study employed a LEAP model to construct various scenarios for road transportation in the key coastal cities of Guangdong, Fujian, and Zhejiang from 2015 to 2035. These scenarios included a baseline scenario (BAU), an existing policy scenario (EPS), and an improved policy scenario (MPS). The MPS and EPS encompassed vehicle structure optimization (VSO), improved fuel economy (IFE), and reduced annual average mileage (RDM). By simulating and evaluating these scenarios, the energy-saving and emission reduction potentials of road transportation in the key coastal cities were assessed. The results indicated that, in the primary scenario, the MPS exhibited the most significant improvements in energy-saving, carbon reduction, and pollutant reduction effects. By 2035, the MPS achieved a remarkable 75% energy-saving rate compared to that in the baseline scenario, accompanied by reductions of 68%, 59%, 66%, 70%, and 64% in CO2, CO, NOx, PM2.5, and SO2 emissions, respectively. In the secondary scenario, the improved scenario of enhancing fuel economy achieved a notable 30% reduction in energy consumption. Additionally, the scenarios involving vehicle structure adjustment (yielding reductions of 36%, 30%, 36%, 26%, and 40%) and annual average mileage reduction (resulting in reductions of 37%, 37%, 36%, 37%, and 36%) demonstrated significant reductions in CO2, CO, NOx, PM2.5, and SO2 emissions.

[Quantitative Assessment of the Impact of Climate Change on the Growing Season of Vegetation Gross Primary Productivity in the Middle and Lower Reaches of the Yangtze River].

Quantitatively determining the direct, indirect, and comprehensive effects of climatic factors on the growing season of the vegetation GPP (GPPGS) in the middle and lower reaches of the Yangtze River at the regional and vegetation type scales can provide a scientific basis for the management and restoration of regional vegetation resources under the background of global climate change. Using MODIS GPP data, meteorological data, and vegetation type data, combined with Theil-Sen Median trend analysis and the Mann-Kendall significance test, the spatiotemporal characteristics of the GPPGS in the middle and lower reaches of the Yangtze River were investigated at different temporal and spatial scales. Path analysis was used to further reveal the direct, indirect, and comprehensive effects of climate factors on GPPGS variation in different vegetation types. The results showed that:① from 2000 to 2021, the vegetation GPPGS in the middle and lower reaches of the Yangtze River showed a fluctuating upward trend, with a rising rate (in terms of C, same below) of 2.70 g·(m2·a)-1 (P<0.01). The GPPGS of different vegetation types all showed a significant upward trend (P<0.01), with shrubs having the highest upward rate of 3.31 g·(m2·a)-1 and cultivated vegetation having the lowest upward rate of 2.54 g·(m2·a)-1. ② The proportion of the area with an upward trend in GPPGS in the middle and lower reaches of the Yangtze River was 88.11%. The proportion of the area with an upward trend in GPPGS was greater than 84% for all different vegetation types, with shrubs (49.76%) and cultivated vegetation (44.36%) having significantly higher proportions of the area with an upward trend than that in other vegetation types. ③ The path analysis results showed that precipitation and the maximum temperature had a significant positive direct effect on vegetation GPPGS (P<0.05), whereas solar radiation had a non-significant positive effect (P ≥ 0.05). The indirect effects of maximum temperature, precipitation, and solar radiation on vegetation GPPGS were all non-significantly negative (P ≥ 0.05). Under the combined effects of direct and indirect influences, precipitation and maximum temperature had a non-significant positive effect on vegetation GPPGS (P ≥ 0.05), whereas solar radiation had a non-significant negative effect on vegetation GPPGS (P ≥ 0.05). Among different vegetation types, precipitation was the main climate factor affecting the changes in GPPGS of cultivated vegetation, whereas the maximum temperature was the main climate factor affecting the changes in GPPGS of coniferous forests, broad-leaved forests, shrubs, and grasslands. ④ The changes in vegetation GPPGS in the middle and lower reaches of the Yangtze River were mainly influenced by the direct effects of maximum temperature, precipitation, and solar radiation, with the direct effect of precipitation dominating 56.72% of the changes in GPPGS. The research results can provide a reference for quantifying the carbon sequestration potential of vegetation in the middle and lower reaches of the Yangtze River and formulating ecological restoration governance policies tailored to local conditions under the background of global climate change.

How to improve the energy-saving performance of China's transport sector? An input-output perspective.

The transport sector proves a major energy consumer in China, but improving energy-saving performance in China's provincial transport sector from the lifecycle perspective remains unresolved. Thus, this study employs the environmentally extended multi-region input-output (MRIO) method, structural path analysis, and the newest MRIO table of China from 2017, to investigate how to improve the energy-saving performance from final demand structure, supply chain, and pathway perspectives. The relevant results are threefold. (1) Regarding the final demand structure level, the embodied energy consumption of China's transport sector is predominantly driven by investment from the production side, while that of the consumption side is primarily caused by exports. (2) At the supply chain level, production-side embodied energy consumption primarily occurs along a three-echelon supply chain, while that from the consumption side mostly occurs via a two-echelon supply chain. (3) At the pathway level, the production-side energy-saving performance of China's provincial transport sector is dominated by two pathways along the construction sector, including transport sector → construction sector → final demands, and transport sector → intermediate inputs → construction sector → final demands, while that of the consumption side is chiefly determined by three pathways along internal transportation chains.

Synergizing economic growth and carbon emission reduction in China: A path to coupling the MFLP and PLUS models for optimizing the territorial spatial functional pattern.

Carbon emissions caused by economic growth are the main cause of global warming, but controlling economic growth to reduce carbon emissions does not meet China's conditions. Therefore, how to synergize economic growth and carbon emission reduction is not only a sustainable development issue for China, but also significant for mitigating global warming. The territorial spatial functional pattern (TSFP) is the spatial carrier for coordinating economic development and carbon emissions, but how to establish the TSFP of synergizing economic growth and carbon emission reduction remains unresolved. We propose a decision framework for optimizing TSFP coupled with the multi-objective fuzzy linear programming and the patch-generating land use simulation model, to provide a new path to synergize economic growth and carbon emission reduction in China. To confirm the reliability, we took Qionglai City as the demonstration. The results found a significant spatiotemporal coupling between TSFP and the synergistic states between economic growth and carbon emission reduction (q ≥ 0.8220), which resolves the theoretical uncertainty about synergizing economic growth and carbon emission reduction through the path of TSFP optimization. The urban space of Qionglai City in 2025 and 2030 obtained by the decision framework was 6497.57 hm2 and 6628.72 hm2 respectively, distributed in the central and eastern regions; the rural space was 60,132.92 hm2 and 56,084.97 hm2, concentrated in the east, with a few located in the west; and the ecological space was 71,072.52 hm2 and 74,998.31 hm2, mainly located in the western and southeastern areas. Compared with the TSFP in 2020, the carbon emission intensity of the TSFP obtained by the decision framework was reduced by 0.7 and 4.7 tons/million yuan, respectively, and realized the synergy between economic growth and carbon emission reduction (decoupling index was 0.25 and 0.21). Further confirming that TSFP optimization is an effective way to synergize economic growth and carbon emission reduction, which can provide policy implications for coordinating economic growth and carbon emissions for China and even similar developing countries.

An algorithm to create model file for Partially Observable Markov Decision Process for mobile robot path planning.

The Partially Observable Markov Decision Process (POMDP), a mathematical framework for decision-making in uncertain environments suffers from the curse of dimensionality. There are various methods that can handle huge sizes of POMDP matrices to create approximate solutions, but no serious effort has been reported to effectively control the size of the POMDP matrices. Manually creating the high-dimension matrices of a POMDP model is a cumbersome and sometimes even impossible task. The PCMRPP (POMDP file Creator for Mobile Robot Path Planning) software package implements a novel algorithm to programmatically generate these matrices such that: •The sizes of the matrices can be controlled by configuring the granularity of discretization of the components of the state and•The sparseness of the matrices can be controlled by configuring the spread of the observation probability distribution. This kind of flexibility allows one to achieve a trade-off between time complexity and the level of robustness of the POMDP solution.

Innovation driver and county air pollution: cost-benefit analysis perspective.

Innovation is the first power to drive the county's green and low-carbon. It is crucial to explore the impact of innovation on air pollution from the perspective of counties at the bottom of the administrative division hierarchy. The article is aimed at exploring the direct impact effects, spatial spillover effects, impact mechanism pathways, non-linear relationships, and cost-benefits of innovation drive on air pollution in counties. To this end, based on the collection of county-level data from 2007 to 2020 in mainland China, the article constructs a fixed-effects model, a dynamic panel model, and a spatial Durbin model for analysis. For every 1% increase in the quantity of innovation, the county SO2 emission concentration decreases by 0.2% on average; for every 1% increase in the quality of innovation, the county SO2 emission concentration decreases by 0.3% on average. When the county innovation quantity driver increases by one standard deviation, the county SO2 concentration decreases by an average of 0.29%; when the county innovation quality driver each standard deviation increases, the county SO2 concentration is reduced by 0.33% on average. The significant entry of high-end factors, the increased frequency of regulation by the environmental protection department, and the increasing efficiency of energy use are the important mechanism pathways for innovation-driven reduction of air pollution in counties. There is no significant "(inverted) U-shaped" relationship between innovation-driven air pollution in the county samples. There is a negative spatial spillover effect of the innovation quality drive on air pollution control in all Chinese county samples. Innovation to drive the declining size of the county's sulfur dioxide can bring about one billion yuan (about 139.81 million U.S. dollars) in comprehensive economic benefits. In the coming period, county governments should build a new pattern of "blue sky and white clouds" with neighboring regions in terms of spatial agglomeration of high-end elements, green transformation and utilization of energy, and intelligent monitoring and supervision of pollution.

Benefits evaluation of ecological restoration projects based on value realization of ecological products.

Ecological restoration project is the premise to realize the value of ecological products. The current ecological product value accounting and ecological restoration project benefit evaluation methods are interlinked. The benefit evaluation of ecological restoration projects can not only analyze the completion of ecological restoration projects, but also quantify the impact of externalities on regional development. This study proposes a hypothesis that the benefit evaluation results of ecological restoration projects can affect the realization of urban ecological product value. In order to verify the above hypothesis, this study designed a benefit evaluation framework to evaluate the economic benefits, ecological benefits and project costs of ecological restoration projects. In this study, the benefit evaluation results are divided into three cases to calculate the benefit-cost ratio under different conditions. The three cases are only considering the direct economic benefits (DEB), considering the indirect economic benefits (TEB) and considering the comprehensive benefits (AEB). Taking Jiangyin City of Jiangsu Province as an example, the benefit evaluation of water source protection project in Jiangyin City is carried out. The results show that the benefit-cost ratios in the three cases are 6.68,7.60 and 9.36, respectively. The results show that the benefit-cost ratio is different in different benefit situations. Therefore, when discussing the realization path of ecological product value in ecological restoration areas, it is also necessary to discuss the situation. The evaluation system can provide reference for the benefit evaluation of ecological restoration projects, and also provide data and theoretical support for the realization of regional ecological product value.

Disentangling the link between social determinants of health and child survival in Nigeria during the Sustainable Development Goals era: a hierarchical path analysis of time-to-event outcome.

While social determinants of health have been perennially linked to child survival in resource-limited countries, the precise and tested pathways to effect are not clearly understood. The objective of this study was therefore to identify the critical pathways as posited a priori in a model through which social factors (at maternal, household, and community levels) determine neonatal, infant, and under-five mortalities in Nigeria. Using a novel analytic approach (hierarchical path modelling for predicting accelerated failure time) to estimate (in)direct and total effects of social determinants of child survival, we analysed 30,960 live births (weighted data for representativeness), obtained from the 2016/2017 Nigeria Multiple Indicator Cluster Survey. There were three outcome variables: time until occurrence of neonatal, infant, and under-five mortalities. The independent variables were layered factors related to child, maternal, household and community. Geographical region, rurality of residence, infrastructural development, maternal education, contraceptive use, marital status, and maternal age at birth were found to operate more indirectly on neonatal, infant, and under-five survival. Child survival is due to direct effects of child's sex (female), gestational type (singleton), birth spacing (children whose mothers delivered at least two years apart), and maternal age at delivery (20-34 years). According to the path coefficients, the indirect effects of geographical regions are the most influential determinants of child survival, accounting for 30% (neonatal), 37.1% (infant) and 39.9% (under-five) of the total effects. This study offers comprehensive set of factors, and linked pathways, at the maternal, household, and community levels that are associated with child survival in Nigeria. To accelerate progress towards Sustainable Development Goal targets for child survival and reduce geographical inequities, stakeholders should implement more impactful policies that promote maternal education, contraceptive use and improve living conditions of women (especially in rural areas of northern Nigeria). Future research should focus on identifying the most effective interventions for addressing these social determinants of child survival in Nigeria.

New treatment opportunities for older patients with acute myeloid leukemia and the increasing importance of frailty assessment - An oncogeriatric perspective.

With the introduction of targeted chemotherapy drugs, a new age of treatment for acute myeloid leukemia (AML) has begun. The promotion of the azacitidine+venetoclax combination regimen to first line of treatment in patients deemed ineligible for intensive chemotherapy marks the first of many novel combination regimens becoming part of national treatment guidelines. We review recent phase II and III clinical trials and conclude that these novel regimens offer significant increases in response rates, remission rates, and overall survival. The incidence of adverse events, the accrued time toxicity, and the healthcare costs, however, are increasing as well. Compared with clinical trials, older patients in the real world frequently present with an inferior baseline health status, which is associated with an increased risk of experiencing side effects. The key to reaping the maximum benefit of the new agents and their combination regimens therefore lies in sufficient attention being given to a patients' preexisting comorbidities, potential frailty, and quality of life. A systematic collaboration between hemato-oncologists and geriatricians can be a potent first step towards addressing the increased treatment intensity patients with AML experience under the novel regimens. In this narrative review article we provide an overview of recent and ongoing clinical trials, highlight encountered adverse events, discuss frailty assessment options, and outline an oncogeriatic care path for older patients with AML.

Multi-objective optimisation model of a low-cost path to peaking carbon dioxide emissions and carbon neutrality in China.

A low-cost path system for achieving carbon neutrality in China was modelled using multi-objective programming by integrating industrial production, electric power, heating, transportation, and forest carbon sequestration. We aimed to minimise the total system cost, CO2 emissions, and air pollutants. The constraints included China's targets of peaking CO2 emissions before 2030; achieving carbon neutrality before 2060; ensuring industry, power, heating, and transportation supplies; promoting green energy; and implementing emission control. The model accounted for industries with high coal consumption, such as steel and chemical industries. Various power sources were considered, including coal-fired, nuclear, wind, and solar energy. Forest carbon sink and carbon capture and storage technologies were employed to achieve the emission reduction goals. The model, which was validated using available research data, offered cost-effective path schemes and exhibited high validity. Our findings emphasise the importance of structural adjustments and emission control, with electric power, heating, and transportation sectors showing higher feasibility and providing greater contributions to achieving carbon neutrality than other industries. Conversely, industrial transformation in sectors such as iron and steel, chemical, and construction materials had low feasibility and limited contribution. The modelling outcomes provide valuable insights for developing low-cost, carbon emission-targeted transportation structures in China's complex system. The results presented here demonstrate the global applicability of this method in contributing to plans aimed at meeting key carbon reduction targets.

The clean energy development path and sustainable development of the ecological environment driven by big data for mining projects.

Clean energy is urgently needed to realize mining projects' sustainable development (SD). This study aims to discuss the clean energy development path and the related issues of SD in the ecological environment driven by big data for mining projects. This study adopts a comprehensive research approach, including a literature review, case analysis, and model construction. Firstly, an in-depth literature review of the development status of clean energy is carried out, and the existing research results and technology applications are explored. Secondly, some typical mining projects are selected as cases to discuss the practice and effect of their clean energy application. Finally, the corresponding clean energy development path and the SD analysis model of the ecological environment are constructed based on big data technology to evaluate the feasibility and potential benefits of promoting and applying clean energy in mining projects. (1) It is observed that under different Gross Domestic Product (GDP) growth rates, the new and cumulative installed capacities of wind energy show an increasing trend. In 2022, under the low GDP growth rate, the cumulative installed capacity of global wind energy was 370.60 Gigawatt (GW), and the new installed capacity was 45 GW. With the high GDP growth rate, the cumulative and new installed capacities were 367.83 GW and 46 GW. As the economy grows, new wind energy capacity is expected to increase significantly by 2030. In 2046, 2047, and 2050, carbon dioxide (CO2) emissions reductions are projected to be 8183.35, 8539.22, and 9842.73 Million tons (Mt) (low scenario), 8750.68, 9087.16, and 10,468.75 Mt (medium scenario), and 9083.03, 9458.86, and 10,879.58 Mt (high scenario). By 2060, it is expected that CO2 emissions reduction will continue to increase. (2) The proposed clean energy development path model has achieved a good effect. Through this study, it is hoped to provide empirical support and decision-making reference for the development of mining projects in clean energy, and promote the SD of the mining industry, thus achieving a win-win situation of economic and ecological benefits. This is of great significance for protecting the ecological environment and realizing the sustainable utilization of resources.

A study on the path of governance in health insurance fraud considering moral hazard.

Combating health insurance fraud is of utmost importance to physicians, patients, and health insurers. To delve into the mechanisms of health insurance fraud between doctors and patients, this study employed evolutionary game theory to construct a model that comprehensively considers moral hazard, fraud cost, reward, punishment, bribes from patients, and other factors. Through theoretical analysis and numerical simulation of the model, the study discovered that the evolution of governance behavior in health insurance fraud is closely linked to its initial construction of the payment matrix and the initial selection of parameters for the payment matrix. Additionally, increasing penalties for fraudulent behavior, increasing the cost of fraud for both doctors and patients, and reducing moral hazard for both can effectively drive the final strategy of the system toward a non-fraudulent state. The study aims to provide valuable insights and recommendations to doctors, patients, and medical insurance institutions in establishing a sound governance environment for managing fraud behavior in health insurance.

Racial and ethnic disparities in biomarkers of exposure and potential harm among U.S. adult exclusive e-cigarette users: 2013-2019.

OBJECTIVE: Provide evidence on racial and ethnic differences in biomarkers of exposure from rising e-cigarette use among U.S. adults. METHODS: Data were drawn from Waves 1-5 of the Population Assessment of Tobacco and Health study (September 2013-November 2019). Differences in biomarkers of exposure and potential harm (BOE/BoPH) across non-Hispanic (NH)-Whites, NH Blacks, Hispanic/Latinos, and NH others were examined using generalized estimation equations. RESULTS: Among exclusive e-cigarette users, mean concentrations of BOEs/BoPHs were not significantly different across NH Blacks (n=97), NH others (n=122), and NH Whites (n=1062), after adjustment by wave, age, sex, education, exposure to the secondhand smoke, and the number of recent puffs. Compared to NH Whites, Hispanics (n=151) had lower concentrations of nicotine equivalents (0.5[0.2-1.7] vs. 15.5 [12.5-19.1] nmol/mg creatinine, p<.0001), cotinine (33.4[9.7-114.7] vs. 1008.3 [808.3-1257.9] ng/mg creatinine, p<.0001), and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) (2.6[1.5-4.4] vs. 5.7 [4.9-6.6] pg/mg creatinine, p=.004), but similar concentrations of BOEs for heavy metals, polycyclic aromatic hydrocarbons, volatile organic compounds, and oxidative stress. Differences between Hispanics and NH Whites are expected, given different e-cigarette use profiles. Specifically, Hispanics were less likely to be daily vapers (49.4[35.1-63.8]% vs 81.3[77.7-84.5]%, p<.0001) and nicotine e-cigarette users (72.7 [64.0-79.9]% vs. 89.2 [86.4-91.5]%, p=.0002] and reported a lower number of recent puffs (mean[standard error]=16.7[3.6] vs. 28.6[2.0], p=.02] than their NH-White counterparts. Hispanic vapers were also less likely than NH Whites to have previously smoked cigarettes (49.7 [37.2-62.3]% vs. 88.5 [84.7-91.5]%, p<.0001]). CONCLUSIONS: Hispanic vapers exhibited lower exposure to nicotine metabolites and carcinogens than their non-Hispanic White counterparts. The harm reduction potential from e-cigarette use are likely to be realized across diverse racial and ethnic groups.

Hydrodynamic performance assessment of photocatalytic reactor with baffles and roughness in the flow path: A modelling approach with experimental validation.

Purification of wastewater is essential for human being as well as for the flora and fauna, and sustainable environment. Photocatalytic reactor with TiO2 coated layer can be used to degrade the pollutants but without proper pollutant mass transfer in the reactive surface, photocatalytic reactor decreases its effectiveness. The baffles and rough surface in the flow path can improve the fluid mixing to enhance pollutant mass transfer to improve the reactor's performance. In this study, a computational fluid dynamics (CFD) model has been developed to investigate the effect of four top baffles and three rough surfaces (semi-circular, triangle, and rectangle) on pressure drops, mass transfer and the hydrodynamic performance of the reactor. The experimental investigation was carried out using Formic Acid (FA) as pollutant in feed water for model validation. The simulated result varies only within 5% with the experimental data of FA concentration versus feed flow rate and fluid velocity. The model was run at fluid velocity of 0.15 m/s and 0.5 m/s (Reynolds number of 2150 (laminar flow) and 7500 (turbulent flow), respectively. The simulation result shows that the addition of baffles and roughness on the reactive surfaces increases the turbulent kinetic energy (minimum increase 8%) and consequently increases the mass transfer (maximum increase 37%) of the pollutant. The highest wall shear was observed to be 40 Pa when both square and triangular elements were used as roughness elements at turbulent flow condition. The results also shows that the highest pressure-drop of 8 kPa was found when the square roughness element was used at turbulent flow condition. Overall, the photocatalytic reactor performance is significantly enhanced by the application of combined baffles and roughness elements in the reactive surface.

Assessment of selection criteria using multi-year study for effective breeding program of Zingiber officinale L.

Background: Ginger has been an important cash crop with numerous applications since ancient times. As the demand for ginger is ever-growing and being a seasonal crop, a high-yielding variety of ginger would be economically profitable. Methods: In this study, 150 germplasm were collected from different regions of NE India and evaluated for three years in CRBD design with three replications. The present study thus focused on the variability, association, and diversity studies for the first time on 150 ginger germplasm from across North East India. The genotypic and phenotypic coefficient of variation, heritability, correlation, and path analysis were evaluated for the germplasm. Results: Analysis of variance (ANOVA) revealed considerable differences among the studied germplasm for studied characters, revealing sufficient variability in the materials. The Mahalanobis D2 and Tocher methods grouped the 150 ginger germplasm into ten clusters. Based on the results of the path coefficient analysis determined for essential oil yield and rhizome yield per plant, it can be concluded that the characters' initial rhizome weight, the weight of mother rhizome, and weight of secondary rhizome were the most important and appeared promising in improving the overall yield potential of ginger rhizome and essential oil yield. Thus, selection based on the identified traits would lead to an effective ginger breeding program for higher rhizome and essential oil yield.