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As the growing demand for energy as well as the strengthening of environmental awareness, photovoltaic power generation, as a clean and renewable energy source, has gradually attracted people's attention and attention. To facilitate the dispatching and planning of power system, this study uses historical data and meteorological data to build a photovoltaic power generation prediction and configuration optimization model on the ground of Gaussian process regression and improved particle swarm optimization algorithm. The simulation results show that the regression prediction curve of the Gaussian process regression prediction model is the closest to the real curve, and the prediction curve is stable and not easily disturbed by noise data. The Root-mean-square deviation and the average absolute proportional error of the model are small, and the disparity in the predicted value and the true value of the model is small; The integration of multi factor data has improved the accuracy of prediction data, and the regression prediction effect is good. The improved Particle swarm optimization algorithm could continuously enhance in the search for the optimal solution, and the Rate of convergence is fast. The Pareto solution can provide different solutions suitable for photovoltaic power generation optimization. Reasonable optimization configuration can effectively reduce active power line loss and voltage deviation, with the maximum reduction values reaching 132kW and 0.028, respectively. The research and design of predictive models and optimized configuration models can promote the formation of smart grids.

Abstract Electromagnetic ion cyclotron (EMIC) instabilities in the inner magnetosphere are investigated with considering the effect of temperature anisotropic electrons. The perpendicular anisotropic electrons (Ae > 1) impose an inhibiting effect on EMIC instabilities, whereas parallel anisotropic electrons (Ae

Abstract Constitutive activation of RAS-RAF-MEK-ERK signaling pathway (MAPK pathway) frequently occurs in many cancers harboring RAS or RAF oncogenic mutations. Because of the paradoxical activation induced by a single use of BRAF or MEK inhibitors, dual-target RAF and MEK treatment is thought to be a promising strategy. In this work, we evaluated erianin is a novel inhibitor of CRAF and MEK1/2 kinases, thus suppressing constitutive activation of the MAPK signaling pathway induced by BRAF V600E or RAS mutations. KinaseProfiler enzyme profiling, surface plasmon resonance (SPR), isothermal titration calorimetry (ITC), cellular thermal shift assay, computational docking, and molecular dynamics simulations were utilized to screen and identify erianin binding to CRAF and MEK1/2. Kinase assay, luminescent ADP detection assay, and enzyme kinetics assay were investigated to identify the efficiency of erianin in CRAF and MEK1/2 kinase activity. Notably, erianin suppressed BRAF V600E or RAS mutant melanoma and colorectal cancer cell by inhibiting MEK1/2 and CRAF but not BRAF kinase activity. Moreover, erianin attenuated melanoma and colorectal cancer in vivo. Overall, we provide a promising leading compound for BRAF V600E or RAS mutant melanoma and colorectal cancer through dual targeting of CRAF and MEK1/2.

Lithium–sulfur batteries offer the potential for significantly higher energy density and cost-effectiveness. However, their progress has been hindered by challenges such as the “shuttle effect” caused by lithium polysulfides and the volume expansion of sulfur during the lithiation process. These limitations have impeded the widespread adoption of lithium–sulfur batteries in various applications. It is urgent to explore the high-performance sulfur host to improve the electrochemical performance of the sulfur electrode. Herein, bimetallic NiFe hydroxide (NiFe-LDH)-modified carbon nanotubes (CNTs) are prepared as the sulfur host materials (NiFe-CNT@S) for loading of sulfur. On the one hand, the crosslinked CNTs can increase the electron conductivity of the sulfur host as well as disperse NiFe-LDHs nanosheets. On the other hand, NiFe-LDHs command the capability of strongly adsorbing lithium polysulfides and also accelerate their conversion, which effectively suppresses the shuttle effect problem in lithium polysulfides. Hence, the electrochemical properties of NiFe-CNT@S exhibit significant enhancements when compared with those of the sulfur-supported pure NiFe-LDHs (NiFe-LDH@S). The initial capacity of NiFe-CNT@S is reported to be 1010 mAh g−1. This value represents the maximum amount of charge that the material can store per gram when it is first synthesized or used in a battery. After undergoing 500 cycles at a rate of 2 C (1 C = 1675 mA g−1), the NiFe-CNT@S composite demonstrates a sustained capacity of 876 mAh g−1. Capacity retention is a measure of how well a battery or electrode material can maintain its capacity over repeated charge–discharge cycles, and a higher retention percentage indicates better durability and stability of the material.

Due to high salinity and low nutrient concentrations, the coastal zone is considered as one of the most vulnerable of the earth’s habitats. Thus, the effect of salt and nitrogen on growth and development of coastal halophytes has been extensively investigated in recent years, but insufficient attention has been paid to the crucial stages of plant establishment, such as seed germination and seedling growth. Thus, we carried out a field experiment to evaluate the effects of salt stress (6, 10 and 20 g/kg NaCl) and nitrogen supply (0, 6 and 12 gm−2year−1) on seed germination and seedling growth of three coastal halophytes (including two dominant herb species Glehnia littoralis and Calystegia soldanella, one constructive shrub species Vitex rotundifolia) from September 2020 to June 2021. The results of our experiment showed that seeds of G. littoralis exhibited an explosive germination strategy in the early spring of 2021 with 70% of the seeds germinating. Conversely, the seeds of V. rotundifolia exhibited slow germination in the late spring of 2021 with only 60% of the seeds germinating. C. soldanella seed germination exhibited two obvious peak periods, but only 6% of the seeds germinated, which means that most seeds may be stored in the soil by stratification or died. All three halophytes showed greater sensitivity to nitrogen than salt stress during the seed germination stage. Nitrogen supply significantly delayed seed germination and reduced the cumulative germination percentage, particularly for G. littoralis. Despite the large impact of nitrogen on seed germination, nitrogen had a larger impact on seedling growth suggesting that the seedling growth stage of halophytes is more vulnerable to changes in nitrogen supply. Moreover, nitrogen supply significantly reduced the individual biomass of G. littoralis, C. soldanella and V. rotundifolia, with greater decreases seen in the dominant species than in the constructive species. Conversely, nitrogen supply increased underground biomass allocation of G. littoralis and C. soldanella, suggesting that the constructive species were less sensitive to nitrogen and exhibited a stronger anti-interference ability than the dominant species. Therefore, increasing nitrogen supply may firstly affect the seed germination and seedling growth of the dominant species, but not the constructive species.

Plant C, N and P can reflect plant strategies of nutrient utilization and allocation. Precipitation is a key factor affecting plant nutrient uptake and utilization strategies especially in arid and semiarid regions, and it is predicted to increase in the cold deserts of Northwest China due to climate change. However, the balance of C, N and P stoichiometry in annual plants at different growth stages in response to increased precipitation is not known. Thus, we performed an experiment on the annual grass Eremopyrum distans in the Gurbantunggut Desert to determine the effect of increased precipitation on C, N and P utilization and allocation at different growth stages. In the control, organ C concentration was relatively stable during the life cycle, but leaf and root N and P concentration increased during the vegetative stage; culm N concentration decreased and P concentration increased and then decreased with plant growth. Increased precipitation significantly increased the leaf N and P concentration and decreased the C:N and C:P ratios at the vegetative growth stage, but leaf N:P was lower than 14 during the whole growth stage. Increased precipitation could improve the leaf N and P resorption efficiency to cope with the reproduction allocation, and the root nutrients are more sensitive to increased precipitation. These results indicate that increased precipitation could improve the nutrient absorption and utilization for vegetative and reproductive growth of E. distans, and the root system plays crucially important role to influence the stoichiometry under varied environment.

Background and objectives: Patient–ventilator asynchronies (PVAs) are common in mechanically ventilated patients. However, the epidemiology of PVAs and its impact on clinical outcome remains controversial. The current study aims to evaluate the epidemiology and risk factors of PVAs and their impact on clinical outcomes using big data analytics.Methods: The study was conducted in a tertiary care hospital; all patients with mechanical ventilation from June to December 2019 were included for analysis. Negative binomial regression and distributed lag non-linear models (DLNM) were used to explore risk factors for PVAs. PVAs were included as a time-varying covariate into Cox regression models to investigate its influence on the hazard of mortality and ventilator-associated events (VAEs).Results: A total of 146 patients involving 50,124 h and 51,451,138 respiratory cycles were analyzed. The overall mortality rate was 15.6%. Double triggering was less likely to occur during day hours (RR: 0.88; 95% CI: 0.85–0.90; p < 0.001) and occurred most frequently in pressure control ventilation (PCV) mode (median: 3; IQR: 1–9 per hour). Ineffective effort was more likely to occur during day time (RR: 1.09; 95% CI: 1.05–1.13; p < 0.001), and occurred most frequently in PSV mode (median: 8; IQR: 2–29 per hour). The effect of sedatives and analgesics showed temporal patterns in DLNM. PVAs were not associated mortality and VAE in Cox regression models with time-varying covariates.Conclusions: Our study showed that counts of PVAs were significantly influenced by time of the day, ventilation mode, ventilation settings (e.g., tidal volume and plateau pressure), and sedatives and analgesics. However, PVAs were not associated with the hazard of VAE or mortality after adjusting for protective ventilation strategies such as tidal volume, plateau pressure, and positive end expiratory pressure (PEEP).

The recovery of industrial waste heat has drawn a lot of attentions, and integrating it into district heating and cooling (DHC) systems is a very attractive option for low-grade waste heat recovery. Most of the related studies used Pinch Technology to design the waste heat recovery networks for heating or refrigeration. However, district heating and cooling is a multi-period problem, and few of studies can provide specific operation plans in each different periods. In this study, a multi-period mathematical model has been established for the waste heat recovery, in which hot water is the intermediate fluid, and waste heat can be utilized in the entire year by integrating with DHC systems. The minimisation of total annual cost (TAC) of the system is regarded as the object function in the model. Operation parameters in each period including the mass flow rate and the temperature of hot water can be obtained by mathematical programming method. A case study has been conducted, and the optimal integration schemes were obtained by solving the mixed integer nonlinear programming (MINLP) model. The results show the significant potential economic benefits of the integration.

Background: Lung mechanics during invasive mechanical ventilation (IMV) for both prognostic and therapeutic implications; however, the full trajectory lung mechanics has never been described for novel coronavirus disease 2019 (COVID-19) patients requiring IMV. The study aimed to describe the full trajectory of lung mechanics of mechanically ventilated COVID-19 patients. The clinical and ventilator setting that can influence patient-ventilator asynchrony (PVA) and compliance were explored. Post-extubation spirometry test was performed to assess the pulmonary function after COVID-19 induced ARDS.Methods: This was a retrospective study conducted in a tertiary care hospital. All patients with IMV due to COVID-19 induced ARDS were included. High-granularity ventilator waveforms were analyzed with deep learning algorithm to obtain PVAs. Asynchrony index (AI) was calculated as the number of asynchronous events divided by the number of ventilator cycles and wasted efforts. Mortality was recorded as the vital status on hospital discharge.Results: A total of 3,923,450 respiratory cycles in 2,778 h were analyzed (average: 24 cycles/min) for seven patients. Higher plateau pressure (Coefficient: −0.90; 95% CI: −1.02 to −0.78) and neuromuscular blockades (Coefficient: −6.54; 95% CI: −9.92 to −3.16) were associated with lower AI. Survivors showed increasing compliance over time, whereas non-survivors showed persistently low compliance. Recruitment maneuver was not able to improve lung compliance. Patients were on supine position in 1,422 h (51%), followed by prone positioning (499 h, 18%), left positioning (453 h, 16%), and right positioning (404 h, 15%). As compared with supine positioning, prone positioning was associated with 2.31 ml/cmH2O (95% CI: 1.75 to 2.86; p < 0.001) increase in lung compliance. Spirometry tests showed that pulmonary functions were reduced to one third of the predicted values after extubation.Conclusions: The study for the first time described full trajectory of lung mechanics of patients with COVID-19. The result showed that prone positioning was associated with improved compliance; higher plateau pressure and use of neuromuscular blockades were associated with lower risk of AI.

Interannual seasonal variability in precipitation may strongly affect the life history and growth of desert annual plants. We compared the effects of dry and wet springs and dry and wet autumns on growth and F2 seed dormancy of plants from spring (SG)- and autumn (AG)-germinated seeds of the cold desert annual Erodium oxyrhinchum. Vegetative and reproductive growth and F2 seed dormancy and germination were monitored from September 2016 to November 2020 in the sandy Gurbantunggut Desert in NW China in Central Asia. Dry autumns decreased the density of AG plants, and dry springs decreased the density of SG plants and growth of SG and AG plants. In dry springs, SG plants were more sensitive to precipitation than AG plants, while in wet springs SG and AG plants had similar responses to precipitation. During growth in both dry and wet springs, most morphological characters of SG and AG plants initially increased rapidly in size/number and then plateaued or decreased, except for SG plants in dry springs. In dry springs, most morphological characters of AG plants were larger or more numerous than those of SG plants, and they were larger/more numerous for SG plants in wet than in dry springs. The percentage biomass allocated to reproduction in SG plants was slightly higher in a wet than in a dry spring. A much higher proportion of dormant seeds was produced by AG plants in a wet spring than in a dry spring. Projected changes in precipitation due to climate change in NW China are not likely to have much of an effect on the biology of this common desert annual plant.

Future climate is projected to increase in the intensity and frequency of extreme precipitation events, and the resulting ecological consequences are often more serious than those of normal precipitation events. In particular, in desert ecosystems, due to the low frequency and strong fluctuation of extreme precipitation, the destructive consequences for desert plants caused by extreme precipitation have not received enough attention for some time. Based on statistics of extreme precipitation events (1965–2018) in the Gurbantunggut Desert, we investigated the effects of extreme precipitation (+0%, CK; +50%, W1; +100%, W2; +200%, W3; maintenance of field capacity, W4) on the plant life-history of the spring-germinated (SG) and autumn-germinated (AG) ephemeral plant Erodium oxyrhynchum by monitoring seedling emergence, survival, phenology, organ size, biomass accumulation, and allocation. The results showed that extreme precipitation caused about 2.5% seedling emergence of E. oxyrhynchum in autumn 2018 and 3.0% seedling emergence in early spring 2019, which means that most seeds may be stored in the soil or have died. Meanwhile, extreme precipitation significantly improved the survival, organ size, and biomass accumulation of SG and AG plants, and W3 was close to the precipitation threshold of SG (326.70 mm) and AG (560.10 mm) plants corresponding to the maximum individual biomass; thus, AG plants with a longer life cycle need more water for growth. Conversely, W4 caused AG plants to enter the leaf stage in advance and led to death in winter, which indicates that extreme precipitation may not be good for AG plants. Root and reproduction biomass allocation of SG and AG plants showed a significantly opposite trend under extreme precipitation treatments, which might be related to their different life-history strategies. Therefore, when only taking into account the changing trend of extreme precipitation from the Coupled Model Intercomparison Project 6 (CMIP6) climate projections data, we speculate that extreme precipitation may promote the growth of SG and AG plants from the beginning to the middle of this century, but extreme precipitation in autumn exceeding a certain threshold may adversely affect the survival of AG plants at the end of the century.

Precipitation change and nitrogen deposition are not only hot topics of current global change but also the main environmental factors affecting plant growth in desert ecosystems. Thus, we performed an experiment of increased precipitation, nitrogen, and precipitation plus nitrogen on the ephemeral annual species Nepeta micrantha and Eremopyrum distans in the Gurbantunggut Desert. We aimed to determine the life history responses of N. micrantha and E. distans to environment changes, and the germination percentage of the offspring (seeds) was also tested in the laboratory. The results showed that increased nitrogen and precipitation plus nitrogen increased the growth of both plant species, whereas increased precipitation inhibited the growth of N. micrantha but increased the growth of E. distans. This differential response of these two species to precipitation and nitrogen also affected the germination of their offspring. In response to increased nitrogen and precipitation plus nitrogen, the germination percentage of the offspring produced by two species decreased in conjunction with the plants exhibiting high reproduction, which may prevent overcrowding during the following year; however, the N. micrantha plants produced more nondormant offspring in conjunction with low reproduction under relatively greater amounts of precipitation, and N. micrantha offspring could occupy their habitat via rapid germination in suitable environments. Therefore, with increased precipitation and nitrogen deposition, these differences in offspring dormancy may affect their ecological niche in the community.

Mechanical ventilation is an essential life-support treatment for patients who cannot breathe independently. Patient–ventilator asynchrony (PVA) occurs when ventilatory support does not match the needs of the patient and is associated with a series of adverse clinical outcomes. Deep learning methods have shown a strong discriminative ability for PVA detection, but they require a large number of annotated data for model training, which hampers their application to this task. We developed a transfer learning architecture based on pretrained convolutional neural networks (CNN) and used it for PVA recognition based on small datasets. The one-dimensional signal was converted to a two-dimensional image, and features were extracted by the CNN using pretrained weights for classification. A partial dropping cross-validation technique was developed to evaluate model performance on small datasets. When using large datasets, the performance of the proposed method was similar to that of non-transfer learning methods. However, when the amount of data was reduced to 1%, the accuracy of transfer learning was approximately 90%, whereas the accuracy of the non-transfer learning was less than 80%. The findings suggest that the proposed transfer learning method can obtain satisfactory accuracies for PVA detection when using small datasets. Such a method can promote the application of deep learning to detect more types of PVA under various ventilation modes.

In the process industry, a large amount of low-grade waste heat is discharged into the environment. Furthermore, district heating and cooling systems require considerable low-grade energy. The integration of the two systems has great significance for energy saving. Because the energy demand of consumers varies in periods, the design and operation of an industrial waste heat recovery system need to match with the fluctuations of district energy demand. However, the impact of the periodic changes on the integration schemes are not considered enough in existing research. In this study, a framework method for solving above problem is proposed. Industrial waste heat was integrated with a district heating and cooling system through a heat recovery loop. A three-step mathematical programming method was used in design and operation optimization for multiperiod integration. A case study was conducted, and the results show that the multiperiod optimization method can bring significant benefits to the system. By solving the mixed integer nonlinear programming model, the optimal operation plans of the integration in different periods can be obtained.

This study aimed to investigate the ability of chelerythrine chloride (CHE), the main active ingredient of Macleaya cordata, to induce apoptosis in human gastric cancer BGC-823 cells. The results demonstrate that CHE inhibits cell proliferation in a time- and dose-dependent manner with accompanying S phase arrest. It also induces apoptosis by a mechanism involving a reduction in the mitochondrial membrane potential, the release of cytochrome c, activation of caspase 3 and cleavage of poly-ADP-ribose polymerase. In addition, CHE-induced apoptosis is accompanied by down-regulation of Bcl-xl and Bcl-2 proteins with no change in the levels of Bax proteins. Taken together, the results support the development of CHE as a potentially useful anticancer drug for the treatment of gastric cancer.