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This paper presents a low-carbon optimal scheduling model for distribution networks with wind and photovoltaic (PV), accounting for supply and demand uncertainties. The model optimizes thermal generation costs, wind and PV maintenance costs, and carbon emissions using a chance-constrained approach with fuzzy variables. The clear equivalent class method simplifies these constraints for easier problem-solving. Validation on the IEEE-30 node system shows the model reduces costs by 32.9% and carbon emissions by 19.2% compared to traditional scheduling, effectively lowering both costs and the carbon footprint. This real-world optimization approach tackles uncertainty in renewable energy supply and improves system efficiency and sustainability.

A risk-aware scheduling method of multienergy virtual power plant (MEVPP) is proposed to measure the uncertainty in MEVPP. First, a novel day-ahead uncertainty scenario generation method based on denoising diffusion probabilistic model is proposed, and historical data are employed to learn the error relationship between real power curves and predict power curves. The probability distribution of the prediction error which describes the day-ahead output power curve of renewable energy source is learned by parameter training. Subsequently, the effect of risk aversion on decision-making is investigated by implementing conditional value-at-risk in the optimization model, MEVPP operation mode under the carbon trading and green certificate trading mechanism is analyzed. Finally, the proposed scheme is implemented on a test MEVPP with carbon trading, and green certificate trading is addressed in detail through a numerical study. Moreover, the effects of the operator’s risk-averse behavior on the MEVPP are investigated.

Abstract Background Metabolites in spent embryo culture medium correlate with the embryo’s viability. However, there is no widely accepted method using metabolite dada to predict successful implantation. We sought to combine metabolomic profiling of spent embryo culture medium and clinical variables to create an implantation prediction model as an adjunct to morphological screening of day 3 embryos. Methods This investigation was a prospective, nested case-control study. Forty-two day 3 embryos from 34 patients were transferred, and the spent embryo culture medium was collected. Twenty-two embryos implanted successfully, and the others failed. Metabolites in the medium relevant to implantation were detected and measured by Liquid Chromatography-Mass Spectrometry. Clinical signatures relevant to embryo implantation were subjected to univariate analysis to select candidates for a prediction model. Multivariate logistical regression of the clinical and metabolomic candidates was used to construct a prediction model for embryo implantation potential. Results The levels of 13 metabolites were significantly different between the successful and failed groups, among which five were most relevant and interpretable selected by Least Absolute Shrinkage and Selection Operator regression analysis. None of the clinical variables significantly affected day 3 embryo implantation. The most relevant and interpretable set of metabolites was used to construct a prediction model for day 3 embryo implantation potential with an accuracy of 0.88. Conclusions Day 3 embryos’implantation potential could be noninvasively predicted by the spent embryo culture medium’s metabolites measured by LC-MS. This approach may become a useful adjunct to morphological evaluation of day 3 embryos.

With the development of urbanization and industrialization, more and more chemicals were produced in large quantities and widely used. As a result, they entered the water environment through sewage discharge and agricultural drainage. Currently, the analytical methods of contaminants in water include three main strategies, including target screening, suspect analysis and nontarget screening. The number of compounds identified using target screening methods has increased to hundreds, which is far from meeting the current demand for emerging contaminants (ECs) detection in water environment. To comprehensively screen potential emerging contaminants in the Yangtze River in Jiangsu Province, a nontarget screening strategy combined spectral library search with a molecular network was proposed in Jiangsu-Anhui provincial boundary cross section and 22 drinking water sources in Jiangsu section of Yangtze River. Under this strategy, through spectral library screening using a in-house library, a total of 118 emerging contaminants were identified by accurate mass, isotope distribution and MS/MS fragment ions. These contaminants involved pesticides, herbicides, fungicides, drugs, antibiotics, persistent organic pollutants, flame retardants, plasticizers, indicated active agents, chemical intermediates, and other categories. Since traditional targeted screening relies on commercial standards and screening lists, nontarget screening is a more favorable approach to study ECs and their transformation products in water. Therefore, unannotated chemical substances with similar structures to known contaminants were identified through molecular network technology based on the premise that structurally similar compounds have similar fragment ions. Through using the Global Natural Products Society molecular network (GNPS) to extract molecular families, another contaminant with similar toxicity to amantadine, N-methyl-1-adamantaneamine was found. Furthermore, twenty-three contaminants were confirmed through commercial standard, and 14 of them were detected at a rate of 100%. The quantitative analysis revealed the prevalence of perfluorinated compounds, pharmaceuticals, and personal care products in water bodies, and the total concentrations of the 23 contaminants in each sample ranged from 6.46×102 ng/L to 1.72×103 ng/L. Seven drugs and personal care products were detected. Amantadine was detected at the highest concentration among all locations, and perfluorooctanoic acid (PFOA), amantadine and 2,4-dinitrophenol were detected with the highest mean concentration levels in each location. The nontarget screening strategy using spectral library search and molecular network enabled high-throughput identification of contaminants in water without prior information and standards, in which the structures of unannotated characteristic peaks were inferred from annotated substances with spectral libraries. This strategy will accelerate the systematic discovery of emerging and unknown contaminants in the aqueous environment.

Existing cross-lingual summarization (CLS) datasets consist of inconsistent sample quality and low scale. To address these problems, we propose a method that jointly supervises quality and scale to build CLS datasets. In terms of quality supervision, the method adopts a multi-strategy filtering algorithm to remove low-quality samples of monolingual summarization (MS) from the perspectives of character and semantics, thereby improving the quality of the MS dataset. In terms of scale supervision, the method adopts a text augmentation algorithm based on the pretrained model to increase the size of CLS datasets with quality assurance. This method was used to build an English-Chinese CLS dataset and evaluate it with a reasonable data quality evaluation framework. The evaluation results show that the dataset is of good quality and large size. These outcomes show that the proposed method may comprehensively improve quality and scale, thereby resulting in a high-quality and large-scale CLS dataset at a lower cost.

In this paper, an MMV-SWACGP algorithm is proposed, in order to solve the problem of pseudo-inverse computation in the iterative process of OMPMMV algorithm for multi-band signal reconstruction by compressed sensing. This algorithm reduces the complexity and computation of OMPMMV reconstruction algorithm. It is of great significance to the fast reconstruction of sparse multiband signals. Theoretical analysis and simulation results show that the proposed algorithm has faster computation speed and better noise stability.

The modern economic growth paradigm relies heavily on natural endowments. Renewable energy as a permanent energy source has the potential to reduce the ecological footprint (EF). We adopt the Vector Autoregressive model to examine the impact of renewable energy consumption on the energy EF and use the quantile regression method to test the heterogeneity and asymmetry between energy EF and photovoltaic, wind energy, and biomass energy. The results show that renewable energy has a long-term negative impact on the EF, and for every 1% increase in renewable energy consumption, the energy EF will decrease by 2.91%. The contribution of renewable energy consumption to reducing the EF is 1.34% on average. There is no two-way Granger causality between renewable energy consumption and energy EF. The reduction effect of wind energy consumption on the energy EF varies the most, followed by biomass energy and photovoltaic. In addition, under different energy EF distribution conditions, the impact of photovoltaic or wind energy or biomass energy consumption on the energy EF is different.

Lung ischemia–reperfusion injury is a common clinical concern. As the injury occurs, the pulmonary afferent nerves play a key role in regulating respiratory functions under pathophysiological conditions. The present study was to examine products of oxidative stress and expression of transient receptor potential A1 in the commissural nucleus of the solitary tract after lung ischemia–reperfusion injury; and further to determine molecular mediators linking to activation of oxidative stress and transient receptor potential ankyrin A1. A rat model of lung ischemia–reperfusion injury was used. Enzyme-linked immunosorbent assay and western blot analysis were employed to examine products of oxidative stress (i.e. 8-isoprostaglandin F2α and 8-hydroxy-2′-deoxyguanosine), and expression of transient receptor potential A1, Nrf2-antioxidant response element, and NADPH oxidase. 8-isoprostaglandin F2α and 8-hydroxy-2′-deoxyguanosine were amplified in the commissural nucleus of the solitary tract of lung ischemia–reperfusion injury rats, accompanied with downregulation of Nrf2-antioxidant response element, and upregulation of NOX4 and transient receptor potential A1. Blocking NADPH oxidase (subtype NOX4) decreased products of oxidative stress in the commissural nucleus of the solitary tract and attenuated upregulation of transient receptor potential A1 induced by lung ischemia–reperfusion injury. Our data revealed specific signaling pathways by which lung ischemia–reperfusion injury impairs Nrf2-antioxidant response and activates oxidative stress in the brainstem thereby leading to amplification of transient receptor potential A1 receptor likely via products of oxidative stress. Data suggest the abnormalities in the pulmonary afferent signals at the brainstem level which is likely to affect respiratory functions as lung ischemia–reperfusion injury occurs.

The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3-90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The multivariate fractional polynomial regression (MFPR) emerged as the preferred algorithm, optimised with non-linear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3000 study participants. This model can inform about the biological and behavioural implications of deviations from typical age-related neuroanatomical changes and support future study designs. The model and scripts described here are freely available through CentileBrain.

Graphene oxide (GO) and its composite membranes have exhibited great potential for application in water purification and desalination. This article reports that a novel graphene oxide membrane (GOM) of ~5 µm thickness was fabricated onto a nylon membrane by vacuum filtration and cross-linked by amino acids (L-alanine, L-phenylalanine, and serine). The GOM cross-linked by amino acids (GOM-A) exhibits excellent stability, high water flux, and high rejection to metal ions. The rejection coefficients to alkali and alkaline earth metal ions through GOM-A were over 94% and 96%, respectively. The rejection coefficients decreased with an increasing H+ concentration. Metal ions (K+, Ca2+, and Fe3+) can be inserted into GOM-A layers, which enlarges the interlayer spacing of GOM-A and neutralizes the electronegativity of the membrane, resulting in the decease in the rejection coefficients to metal ions. Meanwhile, GOM-A showed quite high antibacterial efficiency against E. coli. With the excellent performance as described above, GOM-A could be used to purify and desalt water.

Co-culture of bone marrow stromal cells (BMSCs) and vascular endothelial cells (VECs) is a promising strategy for better osteogenesis and pre-vascularization in bone tissue engineering. Recent reports have shown that mechanical stretching further promotes osteogenesis in BMSC/VEC co-culture systems, but the underlying mechanism of this process remains unclear. In this study, noncontact co-cultures of rat primary BMSCs and VECs were employed to interrogate paracrine cell-to-cell communications in response to tension. Exposure of VECs to 6% tension for 48 h elicited neither ALP activity nor mRNA expression of OCN and OPN in BMSCs incubated in a shared culture medium. Instead, BMSCs subjected to tension induced robust VEGF release, and its conditioned medium enhanced the proliferation and tubular formation of VECs with a concurrent increase in BMP-2 and IGF-1 production. Conditioned medium from activated VECs in turn promoted expression of osteogenic genes in BMSCs, followed by an increase in matrix mineralization. The addition of VEGF-R inhibitor Tivozanib to these systems abrogated the tension-induced paracrine effects on VECs and subsequently impaired BMSC osteogenesis. These results clearly demonstrate that the response of BMSCs to tension potentiates paracrine osteogenic signaling from VECs; this positive feedback loop is initiated by VEGF release.