Search Results (383)

This study utilises the Open Source Energy Modelling System (OSeMOSYS) in conjunction with the Climate, Land, Energy, and Water systems (CLEWs) framework to investigate Thailand’s energy transition, which is designed to achieve carbon neutrality by 2050. Two scenarios have been devised to evaluate the long-term trade-offs among energy, water, and land systems. Data were sourced from esteemed international organisations (e.g., the IEA, FAO, and OECD) and national agencies and organised into a tailored OSeMOSYS Starter Data Kit for Thailand, comprising a baseline and a carbon neutral trajectory. The baseline scenario, primarily reliant on fossil fuels, is projected to generate annual CO₂ emissions exceeding 400 million tons and water consumption surpassing 85 billion cubic meters by 2025. By the mid-century, the carbon neutral scenario will have approximately 40% lower water use and a 90% reduction in power sector emissions. Under the carbon neutral path, renewable energy takes the front stage; the share of renewable electricity goes from under 20% in the baseline scenario to almost 80% by 2050. This transition and large reforestation initiatives call for consistent investment in solar energy (solar energy expenditures exceeding 20 billion USD annually by 2025). Still, it provides notable co-benefits, including greater resource sustainability and better alignment with international climate targets. The results provide strategic insights aligned with Thailand’s National Energy Plan (NEP) and offer modelling evidence toward achieving international climate goals under COP29. Full article

The performance of optoelectronic devices is affected by various noise sources. A notable factor is the 4.2% lattice mismatch at the Ge/Si interface, which significantly influences the efficiency of Ge-on-Si photodetectors. These noise sources can be analyzed by examining the impact of the Ge/Si interface and deep traps on dark and photocurrents. This study evaluates the impact of these charge traps on key photodetector performance metrics, including responsivity, photo-to-dark current ratio, noise equivalent power (NEP), and specific detectivity (D^*). The trapping effects on charge transport under both forward and reverse bias conditions are monitored through hysteresis analysis. When illuminated with an unmodulated 1550 nm laser, all the key performance metrics exhibit maximum variations at a specific reverse bias. This critical bias marks the transition from saturated to exponential charge transport regimes, where intensified electric fields enhance trap-assisted recombination and thus maximize metric fluctuations. Full article

The presence of sea ice threatens low ice-class vessels’ navigation safety in the Arctic, and traditional Navigation Risk Assessment Models based on sea ice parameters have been widely used to guide safe passages for ships operating in ice regions. However, these models mainly rely on empirical coefficients, and the accuracy of these models in identifying sea ice navigation risk remains insufficiently validated. Therefore, under the binary classification framework, this study used Automatic Identification System (AIS) data along the Northeast Passage (NEP) as positive samples, manual interpretation non-navigable data as negative samples, a total of 10 machine learning (ML) models were employed to capture the complex relationships between ice conditions and navigation risk for Polar Class (PC) 6 and Open Water (OW) vessels. The results showed that compared to traditional Navigation Risk Assessment Models, most of the 10 ML models exhibited significantly improved classification accuracy, which was especially pronounced when classifying samples of PC6 vessel. This study also revealed that the navigability of the East Siberian Sea (ESS) and the Vilkitsky Strait along the NEP is relatively poor, particularly during the month when sea ice melts and reforms, requiring special attention. The navigation risk output by ML models is strongly determined by sea ice thickness. These findings offer valuable insights for enhancing the safety and efficiency of Arctic maritime transport. Full article

Research on ecosystem carbon sequestration services and ecological compensation is crucial for advancing carbon neutrality. As a public good, ecosystem carbon sequestration services inherently lead to externalities. Therefore, it is essential to consider externalities in the flow of sequestration services. However, few studies have examined intra- and inter-regional ecosystem carbon sequestration flows, making regional ecosystem carbon sequestration flows less comprehensive. Against this background, the research objectives of this paper are as follows. The flow of carbon sequestration services between Henan Province and out-of-province regions is studied. In addition, this study clarifies the beneficiary and supply areas of carbon sink services in Henan Province and the neighboring regions at the prefecture-level city scale to obtain a more systematic, comprehensive, and actual flow of carbon sequestration services for scientific and effective eco-compensation and to promote regional synergistic emission reductions. The research methodologies used in this paper are as follows. First, this study adopts a meta-coupling framework, designating Henan Province as the focal system, the Central Urban Agglomeration as the adjacent system, and eight surrounding provinces as remote systems. Regional carbon sequestration was assessed using net primary productivity (NEP), while carbon emissions were evaluated based on per capita carbon emissions and population density. A carbon balance analysis integrated carbon sequestration and emissions. Hotspot analysis identified areas of carbon sequestration service supply and associated benefits. Ecological radiation force formulas were used to quantify service flows, and compensation values were estimated considering the government’s payment capacity and willingness. A three-dimensional evaluation system—incorporating technology, talent, and fiscal capacity—was developed to propose a diversified ecological compensation scheme by comparing supply and beneficiary areas. By modeling the ecosystem carbon sequestration service flow, the main results of this paper are as follows: (1) Within Henan Province, Luoyang and Nanyang provided 521,300 tons and 515,600 tons of carbon sinks to eight cities (e.g., Jiaozuo, Zhengzhou, and Kaifeng), warranting an ecological compensation of CNY 262.817 million and CNY 263.259 million, respectively. (2) Henan exported 3.0739 million tons of carbon sinks to external provinces, corresponding to a compensation value of CNY 1756.079 million. Conversely, regions such as Changzhi, Xiangyang, and Jinzhong contributed 657,200 tons of carbon sinks to Henan, requiring a compensation of CNY 189.921 million. (3) Henan thus achieved a net ecological compensation of CNY 1566.158 million through carbon sink flows. (4) In addition to monetary compensation, beneficiary areas may also contribute through technology transfer, financial investment, and talent support. The findings support the following conclusions: (1) it is necessary to consider the externalities of ecosystem services, and (2) the meta-coupling framework enables a comprehensive assessment of carbon sequestration service flows, providing actionable insights for improving ecosystem governance in Henan Province and comparable regions. Full article

Background/Objectives: Obesity is associated with various skin complications, yet its impact on dermal elastic fibers—key components maintaining skin elasticity—remains unclear, particularly in cases of mild obesity prevalent in East Asian populations. The aim of this study was to investigate whether mild obesity is associated with the early structural deterioration of dermal elastic fibers and alterations in elastin-related gene expression in Japanese individuals. Methods: Abdominal skin samples from 31 Japanese women (the mean body mass index [BMI] 23.9 ± 3.2 kg/m², mean age 49.5 ± 4.8) undergoing breast reconstruction surgery were analyzed. Gene expression levels of elastin-regenerative and -degradative molecules were assessed by quantitative polymerase chain reaction in the epidermis, dermis, and subcutaneous fat. Dermal elastic fiber content was evaluated histologically using Elastica van Gieson staining. Results: No statistically significant correlations between the BMI and elastin-degrading gene expression (NE, MMP2, MMP9, and NEP) were observed. ELN expression in the dermis showed a significant positive correlation with the BMI (ρ = 0.517, p = 0.003), potentially reflecting a compensatory response. Histological analysis revealed a significant inverse correlation between dermal elastic fiber content and the BMI (r = −0.572, p = 0.001), independent of age or smoking history. Conclusions: Even mild obesity is associated with the early degradation of dermal elastic fibers despite limited transcriptional alterations. These findings underscore the need for early skin care interventions to mitigate obesity-related skin fragility, especially in populations with predominantly mild obesity. Full article

Research on forest carbon sinks is crucial for mitigating global climate change and achieving carbon peaking and neutrality. However, studies at the county level remain relatively limited. This study utilized multi-source remote sensing data and the Carnegie–Ames-Stanford Approach (CASA) and soil respiration models to estimate the forest net ecosystem productivity (NEP) in Taoyuan County from 2000 to 2023. The spatiotemporal differentiation was analyzed using seasonal Mann–Kendall tests, Theil–Sen slope estimation, and standard deviation ellipses. The forest NEP for 2035 was predicted under multiple climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) by applying a discrete coupling of the Patch-generating Land Use Simulation (PLUS) model, incorporating territorial spatial planning policy, and using the CASA model. The results indicated that the Taoyuan County forest NEP exhibited a fluctuating upward trend from 2000 to 2023, with higher (lower) values in the west/south (east/north). Under future warming and humidification, the overall forest NEP in Taoyuan County was projected to decrease by 2035, with predicted NEP values across scenarios ranking as SSP5-8.5 > SSP1-2.6 > SSP2-4.5. The findings offer practical insights for improving local forest management, optimizing forest configuration, and guiding county-level “dual-carbon” policies under future climate and land use change, thereby contributing to ecological sustainability. Full article

Evaluating the economic value of carbon sinks is fundamental to advancing carbon market mechanisms and supporting sustainable regional development. This study focuses on Fujian Province in China, aiming to assess the spatiotemporal evolution of carbon sink value and analyze the influence of socio-economic drivers. Carbon sink values from 2000 to 2020 were estimated using Net Ecosystem Productivity (NEP) simulation combined with the carbon market valuation method. Eleven socio-economic variables were selected through correlation and multicollinearity testing, and their impacts were examined using Geographically and Temporally Weighted Regression (GTWR) at the county level. The results indicate that the total carbon sink value in Fujian declined from CNY 3.212 billion in 2000 to CNY 2.837 billion in 2020, showing a spatial pattern of higher values in the southern region and lower values in the north. GTWR analysis reveals spatiotemporal heterogeneity in the effects of socio-economic factors. For example, the influence of urbanization and retail sales of consumer goods shifts direction over time, while the effects of industrial structure, population, road, and fixed asset investment vary across space. This study emphasizes the necessity of incorporating spatial and temporal dynamics into carbon sink valuation. The findings suggest that northern areas of Fujian should prioritize ecological restoration, rapidly urbanizing regions should adopt green development strategies, and counties guided by investment and consumption should focus on sustainable development pathways to maintain and enhance carbon sink capacity. Full article

Wetlands are critical components of the global carbon cycle, yet their carbon sink dynamics under hydrological fluctuations remain insufficiently understood. This study employed the Carnegie-Ames-Stanford Approach (CASA) model to estimate the net ecosystem productivity (NEP) of the Dongting Lake wetland and explored the spatiotemporal dynamics and driving mechanisms of carbon sinks from 2000 to 2022, utilizing the Theil-Sen median trend, Mann-Kendall test, and attribution based on the differentiating equation (ADE). Results showed that (1) the annual mean spatial NEP was 50.24 g C/m²/a, which first increased and then decreased, with an overall trend of −1.5 g C/m²/a. The carbon sink was strongest in spring, declined in summer, and shifted to a carbon source in autumn and winter. (2) Climate variability and human activities contributed +2.17 and −3.73 g C/m²/a to NEP, respectively. Human activities were the primary driver of carbon sink degradation (74.30%), whereas climate change mainly promoted carbon sequestration (25.70%). However, from 2000–2011 to 2011–2022, climate change shifted from enhancing to limiting carbon sequestration, mainly due to the transition from water storage and lake reclamation to ecological restoration policies and intensified climate anomalies. (3) NEP was negatively correlated with precipitation and water level. Land use adjustments, such as forest expansion and conversion of cropland and reed to sedge, alongside maintaining growing season water levels between 24.06~26.44 m, are recommended to sustain and enhance wetland carbon sinks. Despite inherent uncertainties in model parameterization and the lack of sufficient in situ flux validation, these findings could provide valuable scientific insights for wetland carbon management and policy-making. Full article

Promoting environmental stewardship among youths is crucial for inspiring collaborative, multi-generational actions to tackle long-term environmental challenges. This research study explores the impact of an environmental education (EE) field trip, which highlighted wastewater management and renewable energy technology, on high school students using the revised new ecological paradigm (NEP) scale as a key metric in a pre-post survey, which uses traditionally pro (NEP) and anti (dominant social paradigm, DSP) conservationist statements to measure beliefs towards the environment. When applying the Wilcoxon signed-rank test (null hypothesis t = 0, no change) to the series of environmental stewardship action questions “___ is an extremely important part of protecting the environment”, we identified ten out of the thirteen scale questions to show significant change, all of which were positive. Additionally, the overall impact score was positive and significant (p ≤ 0.05). This finding demonstrates that respondents felt more strongly that these variables played a role in protecting the environment after experiencing the field trip. This suggests that exposure to environmental management intervention strategies utilizing man-made infrastructure and technology may enhance human capability to positively influence the environment and mitigate environmental threats, potentially alleviating concerns about environmental issues. These results suggest that environmental stewardship in youth needs to be reconceptualized in an increasingly STEM-focused world, and a new metric should be developed to assess environmental beliefs. Full article

Background: Lung cancer represents a significant health concern, particularly among the elderly population. With global life expectancy increasing, the number of very elderly patients is rising. Robotic-assisted thoracic surgery (RATS) offers potential advantages over both traditional and video-assisted thoracoscopic surgery (VATS). This study aims to evaluate the feasibility and safety of RATS in very elderly patients (VEP) diagnosed with lung cancer. Methods: This retrospective study included patients who underwent major lung resections using RATS between 2015 and 2022 at two specialized centers. Patients were divided into very elderly patients (VEP, ≥80 years) and non-elderly patients (NEP, <80 years). Demographic, clinical, and surgical data were analyzed. Propensity score matching (PSM) at a 1:3 ratio was performed using clinically relevant variables that were significantly different at baseline to balance the two groups. Results: This study included 340 patients: 28 VEP and 312 NEP. Before PSM, VEP had higher ASA scores, more advanced disease stages, and increased comorbidities. Despite these differences, postoperative outcomes were comparable. Complications occurred in 42.9% of VEP and 29.8% of NEP (p = 0.16), but grade III complications were observed in 14.3% of VEP and 6.4% of NEP (p = 0.12), and grade IV complications were observed in 0% of VEP and 0.9% of NEP (p = not estimable). The mean hospital stay was 4 days in both groups (p = 0.99). Even after PSM (26 VEP vs. 71 NEP), complications, hospital stay, and 90-day mortality (3.9% in VEP, 0% in NEP) were similar. Multivariable analysis identified reduced FEV1 as a predictor of complications, while pathological stage I and lobectomy were associated with a decreased risk of complications, both before and after PSM. Conclusions: RATS is a safe and feasible option for selected very elderly patients with lung cancer, yielding outcomes comparable to younger patients. Full article

It has become increasingly important to incorporate carbon metrics in the forest harvest planning process. The Generic Carbon Budget Model (GCBM) is a well-recognized tool to evaluate the potential impact of management decisions on carbon sequestration and storage, supporting sustainable forest management planning. Although GCBM is effective in carbon budgeting and estimating carbon metrics, its computational complexity makes it difficult to integrate into forest planning with multiple scenarios. In this regard, this study proposes using machine algorithms to expedite the output generated by GCBM. XGBoost was implemented to estimate the carbon pool and NEP in managed forests of Quebec. Furthermore, polynomial regression was also implemented to serve as a validation benchmark. Datasets with total sizes of 13.53 million and 7.56 million samples were compiled for NEP and carbon pool forecasting to run the model. The results indicate that XGBoost was able to accurately replicate the performance of the GCBM model for both NEP forecasting (R² = 0.883) and carbon pool estimation (R² = 0.967 for aboveground biomass). Although machine learning approaches are comparatively faster, GCBM still offers better accuracy. Hence, the decision on which method to use, either machine learning or GCBM, should be dictated by the specific objectives and the constraints of the project. Full article

This study investigates the applicability of finite mixture models (FMMs) for accurately modeling yarn quality parameters in 28/1 Ne ring-spun polyester/viscose yarns, focusing on both yarn imperfections and mechanical properties. The research addresses the need for advanced statistical modeling techniques to better capture the inherent heterogeneity in textile production data. To this end, the Poisson mixture model is employed to represent count-based defects, such as thin places, thick places, and neps, while the gamma mixture model is used to model continuous variables, such as tenacity and elongation. Model parameters are estimated using the expectation–maximization (EM) algorithm, and model selection is guided by the Akaike and Bayesian information criteria (AIC and BIC). The results reveal that thin places are optimally modeled using a two-component Poisson mixture distribution, whereas thick places and neps require three components to reflect their variability. Similarly, a two-component gamma mixture distribution best describes the distributions of tenacity and elongation. These findings highlight the robustness of FMMs in capturing complex distributional patterns in yarn data, demonstrating their potential in enhancing quality assessment and control processes in the textile industry. Full article

We present THz direct detectors based on an AlGaN/GaN high electron mobility transistor (HEMT), featuring excellent optical sensitivity and low noise-equivalent power (NEP). These detectors are monolithically integrated with various antenna designs and exhibit state-of-the-art performance at room temperature. Their architecture enables straightforward scaling to two-dimensional formats, paving the way for terahertz focal plane arrays (FPAs). In particular, for one detector type, a fully realized THz FPA has been demonstrated in this paper. Theoretical and experimental characterizations are provided for both single-pixel detectors (0.1–1.5 THz) and the FPA (0.1–1.1 THz). The broadband single detectors achieve optical sensitivities exceeding 20 mA/W up to 1 THz and NEP values below 100 pW/$\sqrt{\mathrm{Hz}}$. The best optical NEP is below 10 pW/$\sqrt{\mathrm{Hz}}$ at 175 GHz. The reported sensitivity and NEP values were achieved including antenna and optical coupling losses, underlining the excellent overall performance of the detectors. Full article

With accelerating climate change and urbanization, regional carbon balance faces increasing uncertainty. Terrestrial carbon sinks play a crucial role in advancing China’s sustainable development under the dual-carbon strategy. This study quantitatively modeled China’s terrestrial carbon sink capacity and analyzed the multidimensional relationships between impact factors and carbon sinks. After preprocessing multi-source raster data, we introduced kernel normalized the difference vegetation index (kNDVI) to the Carnegie–Ames–Stanford approach (CASA) model, together with a heterotrophic respiration (R_h) empirical equation, to simulate pixel-level net ecosystem productivity (NEP) across China. A light gradient-boosting machine (LightGBM) model, optimized via Bayesian algorithms, was trained to regress NEP drivers, categorized into atmospheric components (O₃, NO₂, and SO₂) and subsurface properties (a digital elevation model (DEM), enhanced vegetation index (EVI), soil moisture (SM)), and human activities (land use/cover change (LUCC), POP, gross domestic product (GDP)). Shapley Additive Explanation (SHAP) values were used for model interpretation. The results reveal significant spatial heterogeneity in NEP across geographic and climatic contexts. The pixel-level mean and total NEP in China were 268.588 gC/m²/yr and 2.541 PgC/yr, respectively. The north tropical zone (NRZ) exhibited the highest average NEP (828.631 gC/m²/yr), while the middle subtropical zone (MSZ) and south subtropical zone (SSZ) demonstrated the most stable NEP distributions. LightGBM achieved high simulation accuracy, further enhanced by Bayesian optimization. SHAP analysis identified EVI as the most influential factor, followed by SM, NO₂, DEM, and POP. Additionally, LightGBM effectively captured nonlinear relationships and variable interactions. Full article

Due to their excellent specific strength and lightweight characteristics, Al-Cu-Li alloys are widely used in aerospace applications. The newly developed three-stage creep aging (CA) process ensures both the formability and high performance of the Al alloy. However, research at the atomic scale investigating the relationship between the microstructure and performance of ternary alloys under intricate heat treatment conditions remains scarce. This study investigates the microstructural evolution of Al-Cu-Li alloys during multi-stage low-high-low temperature CA experiments, combined with molecular dynamics (MD) simulations based on the neuroevolutionary machine learning potential (NEP) function. The simulation results indicate that the segregation state of lithium atoms at low temperatures is unstable and cannot persist at elevated temperatures. As the aging temperature in the second stage increases, the segregation of lithium atoms gradually diminishes. However, the low-temperature aging in the third stage facilitates continued atomic segregation, although the recovery is somewhat limited. Additionally, it was observed that high-temperature aging in the second stage reduces the material’s performance, while the low-temperature aging in the third stage contributes to the recovery of its properties. The experimental results indicate that the degree of precipitation phase enrichment decreases with the increase in temperature during the second stage but slightly increases with the low-temperature aging in the third stage. The excellent agreement between the experimental and simulation results validates the reliability of the MD simulations, providing a valuable reference for the performance enhancement and microstructural optimization of Al-Cu-Li alloys. Full article