A robust system model for the photovoltaic in industrial parks considering photovoltaic uncertainties and low-carbon demand response

Against the backdrop of carbon peaking and carbon neutrality initiatives, industrial parks have the potential to mitigate external electricity procurement and reduce carbon emissions by incorporating photovoltaic and energy storage systems. However, the inherent unpredictability in photovoltaic power generation poses notable challenges to the optimal planning of industrial parks. In light of this, the present study proposes a robust planning model for the distribution of photovoltaic and energy storage systems within industrial estates, taking into account uncertainties in photovoltaic output and low-carbon demand response. The primary objective of the model is to minimize the yearly comprehensive cost of the industrial park. It is grounded in the carbon emission flow theory, utilizing dynamic carbon emission factors calculated throughout the year as the pricing basis for real-time electricity rates informed by demand response. Subsequently, historical photovoltaic output data are employed to formulate typical output scenarios and their probability distributions through scenario clustering. These norms and constraints serve to bind the associated uncertainty probabilities. Consequently, a two-stage distribution robust model for the photovoltaic and energy storage system is established, employing a data-driven methodology. The efficacy of the proposed model is substantiated through a case simulation of an industrial park utilizing the CPLEX commercial solver. This approach not only underscores the importance of addressing uncertainties in photovoltaic power generation for industrial park planning but also showcases a practical application of the developed model

Global oceanic mesoscale eddies trajectories prediction with knowledge-fused neural network

Efficient eddy trajectory prediction driven by multiinformation fusion can facilitate the scientific research of oceanography, while the complicated dynamics mechanism makes this issue challenging. Benefiting from ocean observing technology, the eddy trajectory dataset can be qualified for data-intensive research paradigms. In this article, the dynamics mechanism is used to inspire the design idea of the eddy trajectory prediction neural network (termed EddyTPNet) and is also transformed into prior knowledge to guide the learning process. This study is among the first to implement eddy trajectory prediction with physics informed neural network. First, an in-depth analysis of the kinematic characteristics indicates that the longitude and latitude of the trajectory should be decoupled; second, the directional dispersion prior knowledge of global eddy propagation is embedded into the decoder of the EddyTPNet to improve the performance; finally, EddyTPNet predicts global eddy trajectories through pretraining and adapts to complex local regions via model transfer. Extensive experimental results demonstrate that EddyTPNet can reliably forecast the motion of eddies for the next seven days, ensuring a low daily mean geodetic error. This exploratory study provides valuable insights into solving the prediction problem of ocean phenomena by using knowledge-based time-series neural networks

Berberine Protects Human Umbilical Vein Endothelial Cells against LPS-Induced Apoptosis by Blocking JNK-Mediated Signaling

Endothelial dysfunction is a critical factor during the initiation of atherosclerosis. Berberine has a beneficial effect on endothelial function; however, the underlying mechanisms remain unclear. In this study, we investigated the effects of berberine on lipopolysaccharide- (LPS-) induced apoptosis in human umbilical vein endothelial cells (HUVECs) and the molecular mechanisms mediating the effect. The effects of berberine on LPS-induced cell apoptosis and viability were measured with 5-ethynyl-2′-deoxyuridine staining, flow cytometry, and Cell Counting Kit-8 assays. The expression and/or activation of proapoptotic and antiapoptotic proteins or signaling pathways, including caspase-3, poly(ADP-ribose) polymerase, myeloid cell leukemia-1 (MCL-1), p38 mitogen-activated protein kinase, C-Jun N-terminal kinase (JNK), and extracellular signal-regulated kinase, were determined with western blotting. The malondialdehyde levels, superoxide dismutase (SOD) activity, and production of proinflammatory cytokines were measured with enzyme-linked immunosorbent assays. The results demonstrated that berberine pretreatment protected HUVECs from LPS-induced apoptosis, attenuated LPS-induced injury, inhibited LPS-induced JNK phosphorylation, increased MCL-1 expression and SOD activity, and decreased proinflammatory cytokine production. The effects of berberine on LPS-treated HUVECs were prevented by SP600125, a JNK-specific inhibitor. Thus, berberine might be a potential candidate in the treatment of endothelial cell injury-related vascular diseases

Progress on the Co-Pyrolysis of Coal and Biomass

In this chapter, the synergistic mechanism and the resulting influence during co-pyrolysis of coal and biomass, are summarized. The properties of coal and biomass, the release and migration of alkali and alkaline earth metals (AAEMs), the interaction between volatile and char, the characteristics of the resulting volatiles, and the physicochemical structure and reactivity of co-pyrolysis char, are also analyzed. In addition, the influence of AAEMs on the properties of the co-pyrolysis products is reviewed. Moreover, the analysis of the co-pyrolysis industry demonstration is also mentioned. Finally, this chapter also proposes some additional possibilities, based on further literature research

The migration of acetochlor from feed to milk

Acetochlor has been widely used globally for its effective weed control, but the dietary intake of associated residues by people has become a major concern nowadays. Milk is regarded as the best solvent to dissolve pesticides due to its fat-rich characteristic. In this study, we aimed to evaluate the transfer of acetochlor from feed to raw milk. Twenty lactating Australian Holstein cows were randomly chosen and divided into 1 control group and 3 treatment groups, feeding acetochlor at the dosages of 0, 0.45, 1.35 and 4.05 g per day during the treatment period. The concentration of acetochlor residues in raw milk was detected by QuEChERS together with a gas chromatography-mass spectrometry (GC-MS) method. The results showed that the highest concentrations of acetochlor residues in raw milk for the three treatment groups had a positive correlation with the dosage levels and the transfer efficiency of the low dose group was only 0.080%, higher than those of the other two groups. Besides, the national estimated daily intake (NEDI) of acetochlor from milk is 1.67 × 10(−5) mg kg(−1), which is 0.08% of the ADI. Overall, we concluded that the risk of acetochlor residues in milk was low, but high-dose acetochlor had a larger impact on milk quality and low-dose acetochlor had potential risks

Genetic insights into resting heart rate and its role in cardiovascular disease

Resting heart rate is associated with cardiovascular diseases and mortality in observational and Mendelian randomization studies. The aims of this study are to extend the number of resting heart rate associated genetic variants and to obtain further insights in resting heart rate biology and its clinical consequences. A genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent genetic variants in 352 loci, including 68 genetic variants outside previously identified resting heart rate associated loci. We prioritize 670 genes and in silico annotations point to their enrichment in cardiomyocytes and provide insights in their ECG signature. Two-sample Mendelian randomization analyses indicate that higher genetically predicted resting heart rate increases risk of dilated cardiomyopathy, but decreases risk of developing atrial fibrillation, ischemic stroke, and cardio-embolic stroke. We do not find evidence for a linear or non-linear genetic association between resting heart rate and all-cause mortality in contrast to our previous Mendelian randomization study. Systematic alteration of key differences between the current and previous Mendelian randomization study indicates that the most likely cause of the discrepancy between these studies arises from false positive findings in previous one-sample MR analyses caused by weak-instrument bias at lower P-value thresholds. The results extend our understanding of resting heart rate biology and give additional insights in its role in cardiovascular disease development

Equilibrium Geometries, Adiabatic Excitation Energies and Intrinsic C=C/C–H Bond Strengths of Ethylene in Lowest Singlet Excited States Described by TDDFT

Seventeen singlet excited states of ethylene have been calculated via time-dependent density functional theory (TDDFT) with the CAM-B3LYP functional and the geometries of 11 excited states were optimized successfully. The local vibrational mode theory was employed to examine the intrinsic C=C/C–H bond strengths and their change upon excitation. The natural transition orbital (NTO) analysis was used to further analyze the C=C/C–H bond strength change in excited states versus the ground state. For the first time, three excited states including πy′ → 3s, πy′ → 3py and πy′ → 3pz were identified with stronger C=C ethylene double bonds than in the ground state

Collaborative Filtering Recommendation Using Nonnegative Matrix Factorization in GPU-Accelerated Spark Platform

Nonnegative matrix factorization (NMF) has been introduced as an efficient way to reduce the complexity of data compression and its capability of extracting highly interpretable parts from data sets, and it has also been applied to various fields, such as recommendations, image analysis, and text clustering. However, as the size of the matrix increases, the processing speed of nonnegative matrix factorization is very slow. To solve this problem, this paper proposes a parallel algorithm based on GPU for NMF in Spark platform, which makes full use of the advantages of in-memory computation mode and GPU acceleration. The new GPU-accelerated NMF on Spark platform is evaluated in a 4-node Spark heterogeneous cluster using Google Compute Engine by configuring each node a NVIDIA K80 CUDA device, and experimental results indicate that it is competitive in terms of computational time against the existing solutions on a variety of matrix orders. Furthermore, a GPU-accelerated NMF-based parallel collaborative filtering (CF) algorithm is also proposed, utilizing the advantages of data dimensionality reduction and feature extraction of NMF, as well as the multicore parallel computing mode of CUDA. Using real MovieLens data sets, experimental results have shown that the parallelization of NMF-based collaborative filtering on Spark platform effectively outperforms traditional user-based and item-based CF with a higher processing speed and higher recommendation accuracy

Nonadiabatic Dynamics Algorithms with Only Potential Energies and Gradients: Curvature-Driven Coherent Switching with Decay of Mixing and Curvature-Driven Trajectory Surface Hopping

Direct dynamics by mixed quantum–classical nonadiabatic methods is an important tool for understanding processes involving multiple electronic states. Very often, the computational bottleneck of such direct simulation comes from electronic structure theory. For example, at every time step of a trajectory, nonadiabatic dynamics requires potential energy surfaces, their gradients, and the matrix elements coupling the surfaces. The need for the couplings can be alleviated by employing the time derivatives of the wave functions, which can be evaluated from overlaps of electronic wave functions at successive timesteps. However, evaluation of overlap integrals is still expensive for large systems. In addition, for electronic structure methods for which the wave functions or the coupling matrix elements are not available, nonadiabatic dynamics algorithms become inapplicable. In this work, building on recent work by Baeck and An, we propose new nonadiabatic dynamics algorithms that only require adiabatic potential energies and their gradients. The new methods are named curvature- driven coherent switching with decay of mixing (κCSDM) and curvature-driven trajectory surface hopping (κTSH). We show how powerful these new methods are in terms of computer time and good agreement with methods employing nonadiabatic coupling vectors computed in conventional ways. The lowering of the computational cost will allow longer nonadiabatic trajectories and greater ensemble averaging to be affordable, and the ability to calculate the dynamics without electronic structure coupling matrix elements extends the dynamics capability to new classes of electronic structure methods