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Abstract Background Postoperative nausea and vomiting (PONV) is a common and distressing complication of laparoscopic bariatric surgery (LBS). Penehyclidine hydrochloride has been reported to be effective in preventing PONV. Considering the potential preventive effects of penehyclidine against PONV, we hypothesized that intravenous infusion of penehyclidine may alleviate PONV within the first 48 h in patients scheduled for LBS. Methods Patients who underwent LBS were randomly assigned (1:2) to receive saline (Control group, n = 113) or a single intravenous dose of penehyclidine 0.5 mg (PHC group, n = 221). The primary outcome was incidence of PONV within the first 48 h postoperatively. Secondary endpoints included severity of PONV, need for rescue antiemetic therapy, volume of water intake, and time to first flatus. Results PONV occurred in 159 (48%) patients within the first 48 h postoperatively, including 51% in the Control group and 46% in the PHC group. There was no significant difference in the incidence or severity of PONV between the two groups (P > 0.05). Within the first 24 h and 24–48 h, no significant difference was found in incidence or severity of PONV, postoperative nausea, postoperative vomiting, need for rescue antiemetic therapy, or volume of water intake (P > 0.05). Kaplan–Meier curves showed that penehyclidine was significantly associated with a prolonged time to first flatus (median onset time: 22 h vs. 21 h, P = 0.036). Conclusions Penehyclidine did not decrease incidence and severity of PONV in patients undergoing LBS. However, a single intravenous dose of penehyclidine (0.5 mg) was associated with a slightly prolonged time to first flatus. Trial registration Chinese Clinical Trial Registry (ChiCTR2100052418, http://www.chictr.org.cn/showprojen.aspx?proj=134893 , date of registration: 25/10/2021).

Anesthesiologists are often faced with patients combined with a series of organ injuries, such as acute lung injury, myocardial ischemia-reperfusion injury, and neurodegenerative diseases. With the in-depth study of these diseases, we are more aware of the choice and rational use of anesthetics for the prognosis of these patients. Ferroptosis is a new type of programmed cell death. This unique pattern of cell death, driven by an imbalance between oxides and antioxidants, is regulated by multiple cellular metabolic events, including redox homeostasis, iron handling, mitochondrial activity, and lipids peroxidation. Numerous studies confirmed that anesthetics modulate ferroptosis by interfering its machineries such as cystine-import-glutathione-glutathione peroxidase 4 axis, Heme oxygenase 1, nuclear factor erythroid 2-related factor 2, and iron homeostasis system. In this literature review, we systemically illustrated possible involvement of ferroptosis in effects of anesthetics and adjuvant drugs on multiple organ diseases, hoping our work may serve as a basis for further studies on regulating ferroptosis through anesthetics related pharmacological modulation and promoting the rational use of anesthetics.

Data processing and integrated forecasting strategy has always been a major obstacle to the development of wind power forecasting system. In view of this, a novel decomposition method with SSA and CEEMDAN is constructed to decompose the original data, and also a sample entropy parsimonious integration model is applied to achieve ultra-short term wind speed prediction. Considering the respective data characteristics of each subsequence, we divide the decomposed multiple subsequences into three parts: high complexity group, low complexity group and residual group. PSO-ELM, IHOA-LSSVR, and IHOA-LSTM are applied to predict them respectively. Compared with other models with high accuracy in this paper, our model has higher prediction accuracy. [ABSTRACT FROM AUTHOR]

Abstract Lithium-ion batteries (LIBs) and plastics are pivotal components of modern society; nevertheless, their escalating production poses formidable challenges to resource sustainability and ecosystem integrity. Here, we showcase the transformation of spent lithium cobalt oxide (LCO) cathodes into photothermal catalysts capable of catalyzing the upcycling of diverse waste polyesters into high-value monomers. The distinctive Li deficiency in spent LCO induces a contraction in the Co−O6 unit cell, boosting the monomer yield exceeding that of pristine LCO by a factor of 10.24. A comprehensive life-cycle assessment underscores the economic viability of utilizing spent LCO as a photothermal catalyst, yielding returns of 129.6 $·kgLCO −1, surpassing traditional battery recycling returns (13–17 $·kgLCO −1). Solar-driven recycling 100,000 tons of PET can reduce 3.459 × 1011 kJ of electric energy and decrease 38,716 tons of greenhouse gas emissions. This work unveils a sustainable solution for the management of spent LIBs and plastics.

Wind energy, as new energy, becomes important support in the low-carbon transformation of the power industry. However, in wind farms, wind turbine fault diagnosis based on small-scale data is a thorny problem. To this end, this paper advances a wind turbine fault diagnosis method given feature selection and stacking model fusion. For unbalanced data, smote oversampling method is used to the effective instance of small class data and increase its proportion. RFECV is used to rank the importance of features, and then the features with high correlation are deleted according to the thermal map to reduce the dimension and obtain the feature subset. Then, XGBoost and LightGBM models based on 6-fold cross-validation is used to train the filtered data. To further improve the stability and generalization ability, a stacking fusion model based on logistic regression is trained using logistic regression. The results of this experiment are compared to other traditional methods by accuracy, ROC, and other indicators. The experimental results show that the strategy used in this paper has more satisfactory accuracy results than the traditional methods and can be used in wind turbine fault diagnosis engineering. [ABSTRACT FROM AUTHOR]

The chaotic nature of wind speed will damage power system seriously, and cause economic losses. Therefore, timely wind prediction is crucial for the safety of power system. However, the traditional prediction method is hard to fully learn the characteristic of wind speed. This paper proposes an optimal component IGSCV-SVR ensemble model to predict ultra-short-term wind speed. It changes the traditional single parameter optimization method of time series prediction. Firstly, the VMD based component correlation is applied to decomposing the original wind speed dataset to obtain multiple subsequences. Our model can find the dissimilarity of each subsequence, and then the model fully learns the feature of each subsequence. It can help improve the overall efficiency of ultra-short-term wind speed prediction accuracy. Finally, estimates are obtained by summing the prediction of all components. The case study proves the feasibility of our method through the comparative experiments with some previous prediction models in MSE, MAE, MAPE and running time in the experimental part of this paper. [ABSTRACT FROM AUTHOR]

Accurate load forecasting is of critical importance for the smart grid. However, power load forecasting accuracy is limited in the traditional ARIMA model due to the non-stationary power grid data. In this research, a combined forecasting model based on wavelet transform and ARIMA is proposed to improve the forecasting accuracy and increase the predictability of power load data. The non-stationarity of the load data is reduced, and the predictability of the load data is increased by decomposing the original data. Finally, the prediction result is a linear superposition of each subsequence's predicted value. The feasibility of the proposed comprehensive forecasting model is verified by an experiment with the actual load data in a county in Jiangxi Province. The experimental results demonstrate that the proposed WT-ARIMA model has good performance in terms of MAPE and RMSE. Compared with the traditional ARIMA model, the prediction accuracy of the WT-ARIMA model is more stable. [ABSTRACT FROM AUTHOR]

In recent years, with computer and Internet technology developing at a breakneck speed, ensuring the fair dispatching and stable operation of the smart grid has increasingly become the focus of power companies. The emergence of the smart grid makes it difficult for a single model to accurately predict the complex power data, and the combined prediction model has become the main target of research by experts and scholars. To this end, a new combination prediction model is proposed in this research. The missing values in the data set are filled by the forecasts of the Random Forest (RF) approach, then the significance of input attributes is computed. After that, the feature selection is performed, and the peak load forecasting is modeled by using support vector regression based on particle swarm optimization (PSO-SVR). The experimental results using real data from a county in Jiangxi Province indicate that the model's prediction ability with feature selection is better than that of the model without feature selection. [ABSTRACT FROM AUTHOR]

Abstract Gamma radiation is progressively emerging as an effective method to enhance the sterilization efficiency of medical plastics including Polyvinyl chloride (PVC). The parameters of the radiation facility will affect the efficiency of radiation sterilization. To investigate these effects, we simulate the gamma radiation sterilization performance of PVC material sample using Monte Carlo Method. The simulation results indicated that compared with the sterilization time of 20–90 min from high-temperature steam sterilization of medical waste, by optimizing the parameters of the model radiation facility, the radiation sterilization time can be reduced to 6.61 min. The optimized model facility parameters are as follows: the gamma photon energy is 1.25 MeV, the model space is 300 × 300 × 300 cm3, the reflective layer material is concrete and its thickness is 8 cm, the PVC sample layer area is 100 × 100 cm2, the distance between the radiation source and the PVC sample layer is 150 cm, the energy deposition in the bottom layer of the PVC sample layer is 1.31315 × 10–6 MeV/g. This study offers a potentially feasible way for PVC sterilization, while also providing a crucial reference for the further promotion and application of radiation sterilization technology.

Accurate short-term load forecasting (STLF) is of positive significance to the effective management of power companies and the stable operation of society. In spite of many studies conducted in this field, there are few to consider the inherent disadvantages of an individual module, which results in sub-optimal forecasting accuracy. Therefore, by integrating data preprocessing module and optimization module into support vector regression (SVR) forecasting module, this paper successfully presents a novel model (AHO-Lasso-SVR). The data preprocessing module, which is comprised of feature construction and Lasso feature selection, is used to construct and select meaningful features. An adaptive hybrid optimization (AHO) algorithm is proposed by introducing two strategies on the basis of standard particle swarm optimization (PSO). The AHO algorithm inevitably increases the computational complexity of model learning, thus, this paper proposes a subsampling technology to improve the optimization efficiency of the algorithm based on the sparsity of SVR. The proposed model is used to forecast the load at 48 points in the next day. To verify the properties of the proposed model, power load data from New South Wales, Australia are adopted as a case study. The results reveal that our model positively exceeds all comparison models in terms of forecasting accuracy and stability. [ABSTRACT FROM AUTHOR]

Short-term power load forecasting is an important factor affecting the security and economic operation of modern power systems. In order to avoid the limitation of a single model, this paper proposes a novel variable weight combined model that combines three single models of support vector regression (SVR), linear regression (LR) and random forest (RF). In the proposed model, a weight table is created based on time similarity, which contains weight information of 336 timestamps for determining the weight of each model. Then, particle swarm optimization (PSO) is employed to complete the modeling of the variable weight combined model. To evaluate the forecasting ability of the combined model, this paper takes the half-hourly power data of New South Wales as an example. Experimental analysis shows that all the evaluation metrics of the proposed model are better than the three single models and combined model based on average weight. [ABSTRACT FROM AUTHOR]

Precipitation occurs in individual events, but the event characteristics of precipitation are often neglected. This work seeks to identify the precipitation events on both spatial and temporal scales, explore the event characteristics of precipitation, and reveal the relationships between the different characteristics of precipitation events. To do this, we combined the Forward-in-Time (FiT) algorithm with the gridded hourly precipitation product to detect precipitation events in time and space over China. The identified precipitation events were analyzed to determine their characteristics. The results indicate that precipitation events can be detected and identified in time and space scales based on the FiT algorithm and the gridded hourly precipitation product. The precipitation total, duration, and intensity of these events decrease gradually from the southern (eastern) coastal regions to northern (western) inland areas of China. The event precipitation totals are strongly correlated with event duration and event maximum intensity; the totals are more strongly correlated with event maximum intensity and event intensity in the regions with lower precipitation than the regions with higher precipitation. More than 90% of precipitation events are shorter than 6 h, and events with long duration normally occur in temperate monsoon (TM) and subtropical/tropical monsoon (ST) climate zones. Heavy precipitation events with a duration longer than 7 h generally occur more than seven times per year in TM and ST climate zones. Our results suggest that precipitation analyses should sufficiently consider the characteristics of events across different regions.

Supercritical anti-solvent fluidized bed (SAS-FB) coating technology has the advantages of reducing particle size, preventing high surface energy particle aggregation, improving the dissolution performance and bioavailability of insoluble drugs. The poor solubility of Biopharmaceutics Classification System (BCS) class IV drugs poses challenges in achieving optimal bioavailability. Numerous anti-cancer drugs including paclitaxel (PTX) belong to the BCS class IV, hindering their therapeutic efficacy. To address this concern, our study explored SAS-FB technology to coat PTX with D-α-tocopherol polyethylene glycol 1000 succinate (TPGS) onto lactose. Under our optimized conditions, we achieved a PTX coating efficiency of 96.8%. Further characterization confirmed the crystalline state of PTX in the lactose surface coating by scanning electron microscopy and X-ray powder diffraction. Dissolution studies indicated that SAS-FB processed samples release over 95% of the drug within 1 min. Moreover, cell transmembrane transport assays demonstrated that SAS-FB processed PTX samples co-coated with TPGS had an enhanced PTX internalization into cells and a higher permeability coefficient compared to those without TPGS. Finally, compared to unprocessed PTX, SAS-FB (TPGS) and SAS-FB processed samples showed a 2.66- and 1.49-fold increase in oral bioavailability in vivo, respectively. Our study highlights the efficacy of SAS-FB co-coating for PTX and TPGS as a promising strategy to overcome bioavailability challenges inherent in BCS class IV drugs. Our approach holds broader implications for enhancing the performance of similarly classified medications.

Summary: With the exponential expansion of electric vehicles (EVs), the disposal of Li-ion batteries (LIBs) is poised to increase significantly in the coming years. Effective recycling of these batteries is essential to address environmental concerns and tap into their economic value. Direct recycling has recently emerged as a promising solution at the laboratory level, offering significant environmental benefits and economic viability compared to pyrometallurgical and hydrometallurgical recycling methods. However, its commercialization has not been realized in the terms of financial feasibility. This perspective provides a comprehensive analysis of the obstacles that impede the practical implementation of direct recycling, ranging from disassembling, sorting, and separation to technological limitations. Furthermore, potential solutions are suggested to tackle these challenges in the short term. The need for long-term, collaborative endeavors among manufacturers, battery producers, and recycling companies is outlined to advance fully automated recycling of spent LIBs. Lastly, a smart direct recycling framework is proposed to achieve the full life cycle sustainability of LIBs.

Nitrogen-doped carbon quantum dots (N-CQDs) are nanocomposites that can be synthesized by the hydrothermal method. In this work, N-CQDs with the size of 3.2 ± 1.7 nm was prepared from 1 g citric acid and 2 g urea precursor in 10 mL water. Electrochemiluminescence (ECL) of the prepared N-CQDs with K2S2O8 as a coreactant was found to reach a high ECL efficiency up to 109% relative to that of the Ru(bpy)32+/K2S2O8, coreactant system, revealing their great potential for electroanalysis. It is probably because that N elements were doped well in this N-CQDs and increased presence of surface states per mass of N-CQDs. From the spooling ECL spectroscopy, it can be found that the ECL spectra exhibited both a red shift compared with their photoluminescence (PL) spectrum and a wavelength shift during the potentiodynamic scan in the ECL evolution and devolution processes, due to various emissive excited states of N-CQDs leading to higher ECL efficiency. This work gives an insight into development of high ECL efficiency N-CQDs for bioanalytical and light emitting applications.

Abstract Background Patients with critical limb ischemia (CLI) are at great risk of major amputation and cardiovascular events. Adipose-derived mesenchymal stem cell (ADSC) therapy is a promising therapeutic strategy for CLI, but the poor engraftment and insufficient angiogenic ability of ADSCs limit their regenerative potential. Herein, we explored the potential of human umbilical vein endothelial cells (HUVECs)-derived small extracellular vesicles (sEVs) for enhancing the therapeutic efficacy of ADSCs in CLI. Results sEVs derived from hypoxic HUVECs enhanced the resistance of ADSCs to reactive oxygen species (ROS) and further improved the proangiogenic ability of ADSCs in vitro. We found that the hypoxic environment altered the composition of sEVs from HUVECs and that hypoxia increased the level of miR-486-5p in sEVs. Compared to normoxic sEVs (nsEVs), hypoxic sEVs (hsEVs) of HUVECs significantly downregulated the phosphatase and tensin homolog (PTEN) via direct targeting of miR-486-5p, therefore activating the AKT/MTOR/HIF-1α pathway and influencing the survival and pro-angiogenesis ability of ADSCs. In a hindlimb ischemia model, we discovered that hsEVs-primed ADSCs exhibited superior cell engraftment, and resulted in better angiogenesis and tissue repair. Conclusion hsEVs could be used as a therapeutic booster to improve the curative potential of ADSCs in a limb ischemia model. This finding offers new insight for CLI treatment. Graphical Abstract

CdZnTe (CZT) is a new type of compound semiconductor that has emerged in recent years. Compared to other semiconductor materials, it possesses an ideal bandgap, high density, and high electron mobility, rendering it an excellent room-temperature composite semiconductor material for X-ray and γ-ray detectors. Due to the exceptional performance of CZT material, detectors manufactured using it exhibit high energy resolution, spatial resolution, and detection efficiency. They also have the advantage of operating at room temperature. CZT array detectors, furthermore, demonstrate outstanding spatial detection and three-dimensional imaging capabilities. Researchers worldwide have conducted extensive studies on this subject. This paper, building upon this foundation, provides a comprehensive analysis of CZT crystals and CZT array detectors and summarizes existing research to offer valuable insights for envisioning new detector methodologies.

Abstract Interfacial solar steam generation holds great promise in water desalination thanks to its high energy efficiency by heating only the top layer of water for evaporation. While three‐dimensional (3D) evaporators have been proven to increase the evaporation rate by harnessing the energy from the surroundings, further development is still required in terms of convenient fabrication with potential scalability. Herein, we propose to overcome this challenge by using a high internal phase emulsion (HIPE) to template the synthesis of 3D hierarchically porous evaporators. The HIPE‐templated synthesis combined with a molding process can efficiently fabricate the desired 3D shape without wasting any materials and generate a hierarchically porous internal structure for continuous water supply. Engineering the overall shape and internal pores produces a 3D evaporator that can suppress conduction heat loss and efficiently collect thermal energy from its surroundings, boosting the evaporation rate to 2.82 kg/(m2 h) under 1‐sun illumination, which is significantly higher than conventional 2D evaporators. HIPE‐templating synthesis is an easy but effective way to produce various porous polymers, promising for a wide range of applications where easy production, excellent shape control, and potential scalability are critical.

The k nearest neighbor (kNN) rule is known as its simplicity, effectiveness, intuitiveness and competitive classification performance. Selecting the parameter k with the highest classification accuracy is crucial for kNN. There's no doubt that the leave-one-out cross validation (LOO-CV) is the best method to do this work as its almost unbiased property. However, it is too time consuming to be used in practice especially for large data. In this paper, we propose a new algorithm for selecting an optimal neighborhood size k. We found that the classification accuracy of LOO-CV is approximate concave for the parameter k. And a search method is proposed to pick out the optimal value of k. An empirical study conducted on 8 standard databases from the UCI repository shows that the new strategy can find the optimal k with significantly less time than the LOO-CV method. [ABSTRACT FROM AUTHOR]

The electrocatalytic oxygen reduction reaction has been investigated in recent years for the production of H2O2 from O2. In this article, the authors report a single-atom rhodium catalyst, based on the Flavin-dependent oxidase, for this transformation.

Abstract The mass production of disposable polyolefin products has led to serious plastic pollution and an imbalance between manufacturing and recycling. Given these challenges, the chemical upcycling of waste polyolefins has attracted extensive attention due to its high efficiency and economic benefits. Herein, we review the development of polyolefin chemical upcycling in heterogeneous catalysis. The status quo of polyolefin recycling is first discussed. We then introduce the advanced strategies for chemical upcycling in the view of different value‐added products and discuss their challenges and prospects. Our in‐depth analysis centers on the catalytic mechanism and the design principle of heterogeneous catalysts. Finally, we outlook the promising directions to facilitate the degradation process via polymer and catalyst design and optimized catalytic engineering. Innovative strategies are expected to promote the chemical upcycling of polyolefins, bringing great promise for the sustainable development of society.

The economics and safety of reactors can be affected by the diffusion of fission products into graphite. Xenon (Xe) fission products diffusing into graphite is the most critical neutron absorber and poison that can slow down or stop the chain reaction. The transport parameters for inhibiting the xenon diffusion in graphite are therefore an important scientific problem. Self-sintered nanopore-isotropic (~40 nm) graphite (SSNG) derived from green pitch coke can decrease Xe diffusion into graphite. In this study, the surface morphology and microstructural evolution in graphite before and after irradiation, as well as after annealing, were studied with different characterization methods. A method for the measurement of diffusion coefficients of fission products’ diffusion in graphite using Rutherford backscattering spectrometry (RBS) was also reported. The SSNG substrates were implanted with Xe at a dose of 4.8 × 1015 ions/cm2 and energy of 7 MeV. The RT-implanted samples were annealed in a vacuum at 650 °C for 9 h. The implanted and annealed samples were characterized using RBS. The diffusion coefficient D (Xe, 650 °C) was 6.49 × 10−20 m2/s. The results indicate SSNG’s excellent ability to inhibit Xe diffusion and are significant for designing and evaluating the safety of nuclear reactors.