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The Normalized Difference Vegetation Index (NDVI) is a fundamental metric for monitoring terrestrial ecosystem dynamics and assessing ecological responses to climate change. However, uncertainties persist across NDVI products, and a comprehensive assessment of their consistency is lacking. This study conducts a multi-faceted evaluation of three NDVI products, GIMMS V1.2 NDVI (NDVI3g+), PKU GIMMS NDVI (NDVIpku), and MODIS NDVI (NDVImod), to elucidate their performance across ecosystem applications. Our analysis encompasses a comparative analysis of NDVI values, trends, sensitivity to root-zone soil moisture (RSM), and performance in tracking photosynthesis benchmarked against solar-induced chlorophyll fluorescence (SIF). Our results reveal that NDVI3g+ deviates notably from NDVIpku and NDVImod over cold climates and Evergreen Broadleaf Forest (EBF). Additionally, NDVI3g+ exhibits significant global browning, in contrast to the significant greening observed for NDVIpku and NDVImod. Although their responses to RSM are generally uncertain, consistent positive responses appear in Drylands, with NDVImod showing the highest sensitivity. Additionally, the three NDVI products have high seasonality consistency with SIF, except over EBF, and NDVIpku and NDVI3g+ achieve the highest and lowest overall anomaly consistency with SIF, respectively. Furthermore, converting NDVI3g+, NDVIpku, and NDVImod to the corresponding kernel NDVIs improves seasonality consistency with SIF across 85% of the globe.

The effects of three kinds of oxygenated fuel blends—i.e., ethanol-gasoline, n-butanol-gasoline, and 2,5-dimethylfuran (DMF)-gasoline-on fuel consumption, emissions, and acceleration performance were investigated in a passenger car with a chassis dynamometer. The engine mounted in the vehicle was a four-cylinder, four-stroke, turbocharging gasoline direct injection (GDI) engine with a displacement of 1.395 L. The test fuels include ethanol-gasoline, n-butanol-gasoline, and DMF-gasoline with four blending ratios of 20%, 50%, 75%, and 100%, and pure gasoline was also tested for comparison. The original contribution of this article is to systemically study the steady-state, transient-state, cold-start, and acceleration performance of the tested fuels under a wide range of blending ratios, especially at high blending ratios. It provides new insight and knowledge of the emission alleviation technique in terms of tailoring the biofuels in GDI turbocharged engines. The results of our works showed that operation with ethanol–gasoline, n-butanol–gasoline, and DMF–gasoline at high blending ratios could be realized in the GDI vehicle without any modification to its engine and the control system at the steady state. At steady-state operation, as compared with pure gasoline, the results indicated that blending n-butanol could reduce CO2, CO, total hydrocarbon (THC), and NOX emissions, which were also decreased by employing a higher blending ratio of n-butanol. However, a high fraction of n-butanol increased the volumetric fuel consumption, and so did the DMF–gasoline and ethanol–gasoline blends. A large fraction of DMF reduced THC emissions, but increased CO2 and NOX emissions. Blending n-butanol can improve the equivalent fuel consumption. Moreover, the particle number (PN) emissions were significantly decreased when using the high blending ratios of the three kinds of oxygenated fuels. According to the results of the New European Drive Cycle (NEDC) cycle, blending 20% of n-butanol with gasoline decreased CO2 emissions by 5.7% compared with pure gasoline and simultaneously reduced CO, THC, NOX emissions, while blending ethanol only reduced NOX emissions. PN and particulate matter (PM) emissions decreased significantly in all stages of the NEDC cycle with the oxygenated fuel blends; the highest reduction ratio in PN was 72.87% upon blending 20% ethanol at the NEDC cycle. The high proportion of n-butanol and DMF improved the acceleration performance of the vehicle.

Ultralong organic room-temperature phosphorescence (RTP) materials have attracted tremendous attention recently due to their diverse applications. Several ultralong organic RTP materials mimicking the host-guest architecture of inorganic systems have been exploited successfully. However, complicated synthesis and high expenditure are still inevitable in these studies. Herein, we develop a series of novel host-guest organic phosphorescence systems, in which all luminophores are electron-rich, commercially available and halogen-atom-free. The maximum phosphorescence efficiency and the longest lifetime could reach 23.6% and 362 ms, respectively. Experimental results and theoretical calculation indicate that the host molecules not only play a vital role in providing a rigid environment to suppress non-radiative decay of the guest, but also show a synergistic effect to the guest through Förster resonance energy transfer (FRET). The commercial availability, facile preparation and unique properties also make these new host-guest materials an excellent candidate for the anti-counterfeiting application. This work will inspire researchers to develop new RTP systems with different wavelengths from commercially available luminophores.

Background Inflammatory bowel diseases (IBDs) are characterized by sustained inflammation and/or ulcers along the lower digestive tract, and have complications such as colorectal cancer and inflammation in other organs. The current treatments for IBDs, which affect 0.3% of the global population, mainly target immune cells and inflammatory cytokines with a success rate of less than 40%. Results Here we show that berberine, a natural plant product, is more effective than the frontline drug sulfasalazine in treating DSS (dextran sulfate sodium)-induced colitis in mice, and that berberine not only suppresses macrophage and granulocyte activation but also promotes epithelial restitution by activating Lgr5 + intestinal stem cells (ISCs). Mechanistically, berberine increases the expression of Wnt genes in resident mesenchymal stromal cells, an ISC niche, and inhibiting Wnt secretion diminishes the therapeutic effects of berberine. We further show that berberine controls the expression of many circadian rhythm genes in stromal cells, which in turn regulate the expression of Wnt molecules. Conclusions Our findings suggest that berberine acts on the resident stromal cells and ISCs to promote epithelial repair in experimental colitis and that Wnt-β-Catenin signaling may be a potential target for colitis treatment.

This study systematically evaluates the effectiveness of combining problem-based learning with the seminar teaching method and the traditional lecture-based learning model in medical education by meta-analysis. A computer-based search of major domestic and international literature databases was conducted, including PubMed, EMBASE, Web of Science Core Collection, Cochrane Library, China National Knowledge Infrastructure(CNKI), Wanfang Database, VIP Chinese Science and Technology Periodicals Database, and China Biology Medicine disk (CBMdisc). The search period spanned from the inception of the databases to 30 August 2024. Quantitative synthesis was performed using the RevMan V.5.4 software, following the Cochrane Reviewer's Handbook guidelines and the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement. A total of 13 articles involving 857 medical students were included. The meta-analysis results revealed statistically significant differences between the experimental and control groups in the following areas: theoretical knowledge scores (MD = 4.99, 95% CI: 4.29-5.69, < 0.00001); clinical skill scores (MD = 4.98,95% CI: 4.21-5.75, < 0.00001); case analysis ability (SMD = 3.07, 95% CI: 2.66-3.47, < 0.00001); Learning interest (SMD = 2.46, 95% CI: 1.89-3.03, < 0.00001); Active learning (SMD = 3.26, 95% CI: 2.66-3.85, < 0.00001); teamwork abilities (SMD = 1.66, 95% CI: 1.27-2.05, < 0.00001); students' research and academic ability (MD = 26.85, 95% CI: 24.79-28.91, < 0.00001). The experimental group demonstrated superior outcomes in all areas compared to the control group. This meta-analysis showed that the integration of problem-based learning and seminar teaching methods is an effective method for improving theoretical knowledge scores, clinical skill scores, case analysis ability, learning interest, active learning, teamwork abilities and research and academic ability.

Increasing wind power integration and coal-fired unit retirement strain the power system's spinning reserve and increase the pressure on peak regulation. With the ability to stock extra power generation and supply the peak load, the energy storage system (ESS) can alleviate the rising demand on the spinning reserve and plays an increasingly important role in the power system. In this paper, a tri-level robust ESS planning model is proposed to accommodate uncertain wind power investment as well as coal-fired unit retirement. The upper-level of this model is to determine the planning scheme of ESSs, which iteratively takes the worst-case of wind power investment and coal-fired unit retirement into consideration. The middle-level and lower-level of this model are to make the optimal daily economic dispatch under the worst-case realizations of uncertainties. We derive an equivalent reformulation of the proposed robust ESS planning model and solve it with a dual column-and-constraint generation algorithm. Case studies are conducted on the IEEE RTS-79 system. The results demonstrate the superiority of the proposed planning method in comparison with other methods. Furthermore, the effects of the capital cost of ESS, the expected proportion of wind power, and the uncertainty budget on the development of ESS are studied. Taking the uncertainties of unit retirement and wind power investment into consideration achieves a better trade-off between the ESS investment cost and the operation cost.

As uncertainty correlations confine variations of renewable energy and load demand, it possibly obstacles the worst-case incidence and alter planning schemes in the end. Meanwhile, coordinating energy storage system and transmission network trends to benefit for the reduction of imbalanced power in the presence of high-penetration renewable energy. This paper proposes an innovative robust model for joint transmission and energy storage expansion planning, which adopts the underlying idea of the self-adaptive uncertainty sets to characterize uncertainty intervals and correlation sets to eliminate unlike-happen scenarios. For constructing the correlation sets, the linear correlations between renewable energy and load demand are depicted by the first-order moving average (MA) model. Furthermore, the tri-level robust optimization problem is decoupled into a master problem and a sub-problem based on the framework of column-and-constraint generation (C&CG) algorithm. And the solution of the bilinear problem in the sub-problem can be averted through the block coordinate descent method, which takes iterations of lower-middle level problems and the approximation of first-order Taylor series into consideration. The proposed approach is implemented on a modified IEEE 24-bus test system and the results justify the efficiency of the proposed RO-based co-planning model.