Search Results (7,244)

To develop sustainable strategies for mitigating ruminal methanogenesis and improving nitrogen efficiency in dairy systems, this study investigated how low-dose tannic acid (T), tea polyphenols (TP), and their combination (T+TP; 50:50) modulate rumen microbiota and function. A sample of Holstein cows were given four dietary treatments: (1) control (basal diet); (2) T (basal diet + 0.4% DM tannic acid); (3) TP (basal diet + 0.4% DM tea polyphenols); and (4) T+TP (basal diet + 0.2% DM tannic acid + 0.2% DM tea polyphenols). We comprehensively analyzed rumen fermentation, methane production, nutrient digestibility, milk parameters, and microbiota dynamics. Compared with the control group, all diets supplemented with additives significantly reduced enteric methane production (13.68% for T, 11.40% for TP, and 10.89% for T+TP) and significantly increased milk protein yield. The crude protein digestibility significantly increased in the T group versus control. The results did not impair rumen health or fiber digestion. Critically, microbiota analysis revealed treatment-specific modulation: the T group showed decreased Ruminococcus flavefaciens abundance, while all tannin treatments reduced abundances of Ruminococcus albus and total methanogens. These microbial shifts corresponded with functional outcomes—most notably, the T+TP synergy drove the largest reductions in rumen ammonia-N (34.5%) and milk urea nitrogen (21.1%). Supplementation at 0.4% DM, particularly the T+TP combination, effectively enhances nitrogen efficiency and milk protein synthesis while reducing methane emissions through targeted modulation of key rumen microbiota populations, suggesting potential sustainability benefits linked to altered rumen fermentation. Full article

This study assesses the projected impacts of climate change on sediment retention and soil loss in Savannakhet Province, Lao PDR, through the application of the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Sediment Delivery Ratio (SDR) model. Using climate projections under SSP2-4.5 and SSP5-8.5 scenarios for the mid- and late-21st century (2050 and 2080), compared against a 2015 baseline, the analysis quantifies changes in sediment dynamics and ecosystem service provision. Results reveal a substantial increase in sediment retention, particularly in forested and flooded vegetation areas, under moderate and high-emission pathways. However, an overall rise in soil loss is observed across croplands and urbanized zones, driven by intensified high-risk areas, which requires conservative management. This study advocates for ecosystem-based adaptation (EbA) strategies—including afforestation, intercropping, and riparian restoration—to enhance watershed resilience. These nature-based solutions align with national adaptation goals and offer co-benefits for biodiversity, climate regulation, and rural livelihoods. Full article

Interactions between the increased penetration of the power system by renewable energy sources (RESs) and the energy pricing mechanism in the EU (day-ahead market) can lead to many unexpected and paradoxical consequences. This article analyses the case of the long-term maintenance of prices around zero on the day-ahead market in south-western Europe at a certain time of a day. This is an important case since, at the same time, this area generates electricity from a similar source mix as it is in the target for the EU. Zero or very low energy prices are becoming increasingly common across the EU. This can pose a problem for the stability of the electricity supply, as it translates into a lower power of used disposable power sources, which can be used as a reserve when the majority of the energy supply comes from renewable energy sources. Furthermore, this work refutes the most frequently proposed solution to the problem of excessively low prices based on energy storage systems. This work attempts to analyze the long-term low-price situation in Spain and extrapolate the expected consequences based on it; however, it is difficult to find all the factors that occur in the power system and influence the price market and vice versa. The issue is multidimensional and complex, and the analyzed situation revealed a number of trends. Therefore, a multifaceted problem remains. A constant electricity supply must be ensured at a reasonable price, thus avoiding the exposure of individual consumers to energy shortages or significant price increases, while, at the same time, the EU must reduce dependence on fossil fuels, and its legislation must push for reduced CO₂ emissions. On the other hand, the EU must provide some type of market mechanism to support the achievement of these goals because the current pricing mechanism based on the day-ahead market does not seem to be effective. This article aims to spark a discussion about this problem; it does not provide any simple solutions to it. Full article

Fintech and government effectiveness are encouraged to be considered in the campaign towards renewable energy transition. However, the literature on these factors is tilted towards their impact on carbon emissions and less on fintech and energy transition. To address this significant gap in the literature, this current study employs the Cross-Sectional Autoregressive Distributed Lag (CS-ARDL) to estimate the influence of fintech and government effectiveness on renewable energy transition and carbon emissions in selected Asian countries. The results reveal that in the long and short terms, government effectiveness encourages the transition to renewable energy; however, government effectiveness effect on carbon emissions is insignificant in both terms. Nevertheless, fintech is statistically not significant in affecting the renewable energy transition and carbon emissions. Based on the study findings, it is recommended that a strong governance system is required to achieve a clean energy transition. Full article

This paper proposes the Open Competency Optimization (OCO) approach, based on adaptive combinatorial operators, to solve the Dynamic Green Traveling Salesman Problem (DG-TSP), which extends the classical TSP by incorporating dynamic travel conditions, realistic road gradients, and energy consumption considerations. The objective is to minimize fuel consumption and emissions by reducing the total tour length under varying conditions. Unlike conventional metaheuristics based on real-coded representations, our method directly operates on combinatorial structures, ensuring efficient adaptation without costly transformations. Embedded within a dynamic metaheuristic framework, our operators continuously refine the routing decisions in response to environmental and demand changes. Experimental assessments conducted in practical contexts reveal that our algorithm attains a tour length of 21,059, which is indicative of a 36.16% reduction in fuel consumption relative to Ant Colony Optimization (ACO) (32,994), a 4.06% decrease when compared to Grey Wolf Optimizer (GWO) (21,949), a 2.95% reduction in relation to Particle Swarm Optimization (PSO) (21,701), and a 0.90% decline when juxtaposed with Genetic Algorithm (GA) (21,251). In terms of overall offline performance, our approach achieves the best score (21,290.9), significantly outperforming ACO (36,957.6), GWO (122,881.04), GA (59,296.5), and PSO (36,744.29), confirming both solution quality and stability over time. These findings underscore the resilience and scalability of the proposed approach for sustainable logistics, presenting a pragmatic resolution to enhance transportation operations within dynamic and ecologically sensitive environments. Full article

In the context of both domestic and international economic landscapes, regional policy has emerged as an increasingly influential factor shaping the developmental trajectories of Chinese enterprises. Despite its growing significance, the extant literature lacks a comprehensive and systematically visualized synthesis that encapsulates the scope and trends of research in this domain. This study addresses this critical gap by conducting an integrative bibliometric and qualitative review of the academic output related to regional policy and Chinese firm growth. Drawing on a final dataset comprising 3428 validated academic publications—selected from an initial pool of 3604 records retrieved from the Web of Science Core Collection between 1991 and 2022, the research employs a two-stage methodological framework. In the first phase, advanced bibliometric tools, and software applications, including RStudio, Bibliometrix, VOSviewer, and CitNetExplorer, are utilized to implement techniques such as keyword co-occurrence analysis, thematic clustering, and the tracing of thematic evolution over time. These methods facilitate rigorous data cleansing, breakpoint identification, and the visualization of intellectual structures and emerging research patterns. In the second phase, a targeted qualitative review is conducted to evaluate the influence of regional policies on Chinese firms across three critical stages of business development: start-up, expansion, and maturity. The findings reveal that regional policy interventions generally exert a positive influence on firm performance throughout all stages of development. Notably, a significant concentration of citation activity occurred prior to 2017; however, post-2017, the volume of scholarly publications, journal-level impact (as measured by h-index), and author-level influence experienced a marked increase. Among the 3428 analyzed publications, a substantial portion—2259 articles—originated from Chinese academic institutions, highlighting the strong domestic research interest in the subject. Furthermore, since 2015, there has been a discernible shift in keyword co-occurrence trends, with increasing scholarly attention directed towards sustainable development issues, particularly those related to carbon dioxide emissions and green innovation, reflecting evolving policy priorities and environmental imperatives. Full article

The transportation sector is a major consumer of primary energy and is a significant contributor to greenhouse gas emissions. Sustainable transportation requires identifying and quantifying factors influencing transport-related CO₂ emissions. This research aims to establish an adaptable, precise, and transparent forecasting structure for transport CO₂ emissions of the United States. For this reason, we proposed a multi-stage method that incorporates explainable Machine Learning (ML) and Feature Selection (FS), guaranteeing interpretability in comparison to conventional black-box models. Due to high multicollinearity among 24 initial variables, hierarchical feature clustering and multi-step FS were applied, resulting in five key predictors: Total Primary Energy Imports (TPEI), Total Fossil Fuels Consumed (FFT), Annual Vehicle Miles Traveled (AVMT), Air Passengers-Domestic and International (APDI), and Unemployment Rate (UR). Four ML methods—Support Vector Regression, eXtreme Gradient Boosting, ElasticNet, and Multilayer Perceptron—were employed, with ElasticNet outperforming the others with RMSE = 45.53, MAE = 30.6, and MAPE = 0.016. SHAP analysis revealed AVMT, FFT, and APDI as the top contributors to CO₂ emissions. This framework aids policymakers in making informed decisions and setting precise investments. Full article

Improving carbon emission efficiency (CEE) is crucial for coordinating economic development and reducing carbon emissions. Drawing on panel data for 30 provinces in China from 2013 to 2022, this paper selects six key antecedent conditions guided by the Technology–Organization–Environment (TOE) framework. Then the dynamic qualitative comparative analysis (DQCA) is employed to explore CEE improvement pathways from a configurational perspective, and regression analysis is used to compare the driving effects of different pathways. The findings reveal that (1) single factors cannot independently achieve high CEE; instead, multiple factors must work synergistically to form various improvement pathways, including “technology–organization dual-driven”, “environment-dominated”, and “multi-equilibrium” pathways, with industrial structure upgrading as the core factor for improving CEE; (2) temporally, these improvement pathways demonstrate universality, while, spatially, they exhibit significant provincial heterogeneity; and (3) in terms of marginal effects, the “multi-equilibrium” pathway has the strongest driving effect on CEE. The findings provide valuable policy implications for developing targeted CEE enhancement strategies across provinces at different developmental stages. Full article

A new generation of compact radon detectors with high sensitivity and fine spatial resolution (1–2 cm scale) was used to investigate indoor radon distribution and identify potential entry pathways. Solid-state nuclear track detectors (Kodak-Pathe LR-115 type II, Dosirad, France), combined with activated carbon fabric (ACC-5092-10), enabled sensitive, spatially resolved radon measurements. Two case studies were conducted: Case 1 involves a room with elevated radon levels suspected to originate from the floor. Case 2 involves a house with persistently high indoor radon concentrations despite active basement ventilation. In the first case, radon emission from the floor was found to be highly inhomogeneous, with concentrations varying by more than a factor of four. In the second, unexpectedly high radon levels were detected at electrical switches and outlets on walls in the living space, suggesting radon transport through wall voids and entry via non-hermetic electrical fittings. These novel detectors facilitate fine-scale mapping of indoor radon concentrations, revealing ingress routes that were previously undetectable. Their use can significantly enhance radon diagnostics and support the development of more effective mitigation strategies. Full article

We report the development of highly luminescent, bovine serum albumin (BSA)-stabilized gold–silver bimetallic nanoclusters (Au-AgNCs@BSA) as a novel platform for high-sensitivity, ratiometric intracellular temperature sensing. Precise and non-invasive temperature sensing at the nanoscale is crucial for applications ranging from intracellular thermogenesis monitoring to localized hyperthermia therapies. Traditional luminescent thermometric platforms often suffer from limitations such as high cytotoxicity and low photostability. Here, we synthesized Au-AgNCs@BSA via a one-pot aqueous reaction, achieving significantly enhanced photoluminescence quantum yields (PL QYs, up to 18%) and superior thermal responsiveness compared to monometallic counterparts. The dual-emissive Au-AgNCs@BSA exhibit a linear ratiometric fluorescence response to temperature fluctuations within the physiological range (20–50 °C), enabling accurate and concentration-independent thermometry in live cells. Time-resolved PL and Arrhenius analyses reveal two distinct emissive states and a high thermal activation energy (E_a = 199 meV), indicating strong temperature dependence. Silver doping increases radiative decay rates while maintaining low non-radiative losses, thus amplifying fluorescence intensity and thermal sensitivity. Owing to their small size, excellent photostability, and low cytotoxicity, these nanoclusters were applied to non-invasive intracellular temperature mapping, presenting a promising luminescent nanothermometer for real-time cellular thermogenesis monitoring and advanced bioimaging applications. Full article

Amidst global climate change, carbon emissions across the edible vegetable oil supply chain are critical for sustainable development. This paper systematically reviews the existing literature, employing life cycle assessment (LCA) to analyze key factors influencing carbon footprints at stages including cultivation, processing, and transportation. It reveals the differential impacts of fertilizer application, energy structures, and regional policies. Unlike previous reviews that focus on single crops or regions, this study uniquely integrates global data across major edible oils, identifying three critical gaps: methodological inconsistency (60% of studies deviate from the requirements and guidelines for LCA); data imbalance (80% concentrated on soybean/rapeseed); weak policy-technical linkage. Key findings: fertilizer emissions dominate cultivation (40–60% of total footprint), while renewable energy substitution in processing reduces emissions by 35%. Future efforts should prioritize multidisciplinary integration, enhanced data infrastructure, and policy scenario analysis to provide scientific insights for the low-carbon transformation of the global edible oil industry. Full article

Understanding how energy use and economic activity shape carbon emissions is pivotal for achieving global climate targets. This study quantifies the dynamic nexus between disaggregated energy consumption, economic growth, and CO₂ emissions in India and China—two economies that together account for more than one-third of global emissions. Using annual data from 1990 to 2021, we implement Long Short-Term Memory (LSTM) neural networks, which outperform traditional linear models in capturing nonlinearities and lagged effects. The dataset is split into training (1990–2013) and testing (2014–2021) intervals to ensure rigorous out-of-sample validation. Results reveal stark national differences. For India, coal, natural gas consumption, and economic growth are the strongest positive drivers of emissions, whereas renewable energy exerts a significant mitigating effect, and nuclear energy is negligible. In China, emissions are dominated by coal and petroleum use and by economic growth, while renewable and nuclear sources show weak, inconsistent impacts. We recommend retrofitting India’s coal- and gas-plants with carbon capture and storage, doubling clean-tech subsidies, and tripling annual solar-plus-storage auctions to displace fossil baseload. For China, priorities include ultra-supercritical upgrades with carbon capture, utilisation, and storage, green-bond-financed solar–wind buildouts, grid-scale storage deployments, and hydrogen-electric freight corridors. These data-driven pathways simultaneously cut flagship emitters, decouple GDP from carbon, provide replicable models for global net-zero research, and advance climate-resilient economic growth worldwide. Full article

A 51-year-old female presented to the emergency department with vertigo, visual disturbances, involuntary rapid repetitive eye movements, incoordination, and imbalance. Physical examination revealed opsoclonus, myoclonus, and bilateral limb and gait ataxia. Initial workup was negative for intracranial abnormalities, and no abnormalities were noted on blood work or cerebrospinal fluid analysis. Tumor markers were within normal limits. As part of her diagnostic workup, a positron emission tomography (PET) scan was performed, which showed a highly FDG-avid solitary 7 mm left axillary lymph node. Ultrasound-guided percutaneous biopsy revealed metastatic poorly differentiated carcinoma. Histopathological examination could not conclusively distinguish between adenocarcinoma and squamous cell carcinoma. She was diagnosed with seronegative opsoclonus-myoclonus ataxia syndrome of paraneoplastic origin from an occult primary malignancy and started on pulsatile corticosteroids and intravenous immunoglobulin (IVIG), with only moderate symptomatic improvement. Given the anatomic location and immunohistochemical staining pattern of the lymph node, the malignancy was considered as being of primary breast origin. A left axillary lymph node dissection was performed, with 1/12 nodes testing positive for poorly differentiated carcinoma. The patient experienced significant improvement in her neurological symptoms 2–3 days following resection of the solitary malignant lymph node, largely regaining her functional independence. She went on to receive adjuvant radiotherapy to the breast and axilla, as well as adjuvant hormonal therapy. Full article

This study explores the impact of driving behaviors, specifically cautious and aggressive, on the performance of human-driven vehicles (HDVs) and autonomous vehicles (AVs) in traffic flow dynamics. It focuses on various metrics, including level of service (LOS), average speed, traffic volume, queue delays, carbon emissions, and fuel consumption, to assess their effects on overall performance. The findings reveal significant differences between cautious and aggressive AVs, particularly at varying market penetration rates (MPRs). Aggressive autonomous vehicles demonstrate greater traffic efficiency compared to their cautious counterparts. They achieve higher levels of service, improving from poor performance at low MPRs to significantly better performance at higher MPRs and in fully autonomous scenarios. In contrast, cautious AVs often experience poor service ratings at low MPRs, with an improvement in performance only at higher MPRs. Regarding environmental performance, aggressive AVs outperform cautious ones in terms of reduced emissions and fuel consumption. The emissions produced by aggressive AVs are significantly lower than those from cautious AVs, and they further decrease as the MPRs increases. Additionally, aggressive AVs show a considerable reduction in fuel usage compared to cautious AVs. While cautious AVs improve slightly at higher MPRs, they continue to generate higher emissions and consume more fuel than their aggressive counterparts. In conclusion, aggressive AVs offer better traffic efficiency and environmental performance than both cautious AVs. Their ability to improve road efficiency and reduce congestion positions them as a valuable asset for sustainable transportation. Strategically incorporating aggressive AVs into transportation systems could lead to significant advancements in traffic management and environmental sustainability. Full article

Hydrotreated ester and fatty acids to jet (HEFA-tJ) is presently the most developed and economically attractive pathway to produce sustainable aviation fuel (SAF). An ongoing systematic study of the critical variables of different pathways to SAF has revealed significantly lower greenhouse gas (GHG) reduction potential for the HEFA-tJ pathway compared to competing markets using the same resources for road diesel production. Moderate yield variations between air and road pathways lead to several hundred thousand tons less GHG reduction per project, which is generally not evaluated thoroughly in standard environmental assessments. This work demonstrates that, although the HEFA-tJ market seems to have more attractive features than biodiesel/renewable diesel, considerable viability risks might manifest as HEFA-tJ fuel market integration rises. The need for more transparent data and effort in this regard, before envisaging making decisions regarding the volume of HEFA-tJ production, is emphasized. Overall, reducing the carbon intensity of road diesel appears to be less capital-intensive, less risky, and several times more efficient in reducing GHG emissions. Full article