Low-Complexity Tracking Control of Unknown Strict-Feedback Systems With Quantitative Performance Guarantees.

This article is concerned with the prescribed performance tracking control problem for the strict-feedback systems with unknown nonlinearities and unmatched disturbances. The challenge lies in the realization of a complete performance specification for trajectory tracking in the sense of quantitatively regulating the peak value, overshoot, settling time, and accuracy while ensuring that the initial condition holds naturally. To this end, an error transformation, equipped with a shifting function, is introduced and incorporated with a new-type barrier function. Then, a class of performance functions is exploited to quantify the settling times and steady-state bounds of the intermediate errors. Moreover, to improve the flexibility of formulating performance specifications for the tracking error, a pair of asymmetric performance boundaries are further designed. With their combination, a novel robust prescribed performance control (PPC) approach is proposed in this article. It not only achieves the quantitative performance guarantees but also preserves the unique simplicity of PPC, evading the needs for function approximation, parameter identification, disturbance estimation, derivative calculation, or command filtering. The above theoretical findings are confirmed via three simulation studies.

Manipulation of Charge Transport in MoS2/MoTe2 Field Effect Transistors and Heterostructure by Propagating the Surface Acoustic Wave.

Two-dimensional (2D) materials with atomic-scale thickness are promising candidates to develop next-generation electronic and optoelectronic devices with multiple functions due to their widely tunable physical properties by various stimuli. The surface acoustic wave (SAW) produced at the surface of the piezoelectrical substrate can generate electrical and strain fields simultaneously with micro/nanometer resolution during propagation. It provides a stable and wireless platform to manipulate the rich and fascinating properties of 2D materials. However, the interaction mechanisms between the SAW and 2D materials remain unclear, preventing further development and potential applications of SAW-integrated 2D devices. This work studied the acoustoelectric (AE) charge transport mechanism in 2D materials thoroughly by characterizing the performances of the n-type MoS2 and p-type MoTe2 field effect transistors (FETs) and the MoS2/MoTe2 p-n junction driven by the SAW. As compared to the case driven by the static electrical field alone, the SAW drove the electron and hole transport along the same direction as its propagation, and the generated AE current always had the opposite direction to the AE voltage. In the device level, the 2D FETs showed a significantly reduced subthreshold swing up to around 67% when the SAW was used to drive the channel carriers, indicating that the SAW enhanced the on/off switching speed. Moreover, the MoTe2/MoS2 p-n junction showed a tunable photoresponsivity by the power and propagation direction of the SAW. These findings provide a solid foundation to promote future research and potential applications of SAW-driven multifunctional devices based on 2D materials.

Fault-tolerant prescribed performance control of nonlinear systems with process faults and actuator failures.

This paper investigates the fault-tolerant prescribed performance control problem for a class of multiple-input single-output unknown nonlinear systems subject to process faults and actuator failures. In contrast to the related works, we consider a general class of nonlinear systems with both multiplicative nonlinearities and additive nonlinearities corrupted by the process faults; only the boundedness of the process faults and the continuity of the nonlinear functions are required, without the explicit or fixed structures of the fault functions. To conquer this problem, a less-demanding and low-complexity fault-tolerant prescribed performance control approach is proposed. The controller is independent of the specific information of faults or the system model and does not invoke fault diagnosis or neural/fuzzy approximation to acquire such knowledge. It achieves the reference tracking with the predefined rate and accuracy. A comparative simulation on a single-link robot is conducted to illustrate the effectiveness and superiority of the proposed approach.

Comparison of different prediction models for estimation of walking and running energy expenditure based on a wristwear three-axis accelerometer.

Objective: Objectively and efficiently measuring physical activity is a common issue facing the fields of medicine, public health, education, and sports worldwide. In response to the problem of low accuracy in predicting energy consumption during human motion using accelerometers, a prediction model for asynchronous energy consumption in the human body is established through various algorithms, and the accuracy of the model is evaluated. The optimal energy consumption prediction model is selected to provide theoretical reference for selecting reasonable algorithms to predict energy consumption during human motion. Methods: A total of 100 subjects aged 18-30 years participated in the study. Experimental data for all subjects are randomly divided into the modeling group (n = 70) and validation group (n = 30). Each participant wore a triaxial accelerometer, COSMED Quark pulmonary function tester (Quark PFT), and heart rate band at the same time, and completed the tasks of walking (speed range: 2 km/h, 3 km/h, 4 km/h, 5 km/h, and 6 km/h) and running (speed range: 7 km/h, 8 km/h, and 9 km/h) sequentially. The prediction models were built using accelerometer data as the independent variable and the metabolic equivalents (METs) as the dependent variable. To calculate the prediction accuracy of the models, root mean square error (RMSE) and bias were used, and the consistency of each prediction model was evaluated based on Bland-Altman analysis. Results: The linear equation, logarithmic equation, cubic equation, artificial neural network (ANN) model, and walking-and-running two-stage model were established. According to the validation results, our proposed walking-and-running two-stage model showed the smallest overall EE prediction error (RMSE = 0.76 METs, Bias = 0.02 METs) and the best performance in Bland-Altman analysis. Additionally, it had the lowest error in predicting EE during walking (RMSE = 0.66 METs, Bias = 0.03 METs) and running (RMSE = 0.90 METs, Bias < 0.01 METs) separately, as well as high accuracy in predicting EE at each single speed. Conclusion: The ANN-based walking-and-running two-stage model established by separating walking and running can better estimate the walking and running EE, the improvement of energy consumption prediction accuracy will be conducive to more accurate to monitor the energy consumption of PA.

Water Quality Degradation Due to Heavy Metal Contamination: Health Impacts and Eco-Friendly Approaches for Heavy Metal Remediation.

Water quality depends on its physicochemical and biological parameters. Changes in parameters such as pH, temperature, and essential and non-essential trace metals in water can render it unfit for human use. Moreover, the characteristics of the local environment, geological processes, geochemistry, and hydrological properties of water sources also affect water quality. Generally, groundwater is utilized for drinking purposes all over the globe. The surface is also utilized for human use and industrial purposes. There are several natural and anthropogenic activities responsible for the heavy metal contamination of water. Industrial sources, including coal washery, steel industry, food processing industry, plastic processing, metallic work, leather tanning, etc., are responsible for heavy metal contamination in water. Domestic and agricultural waste is also responsible for hazardous metallic contamination in water. Contaminated water with heavy metal ions like Cr (VI), Cd (II), Pb (II), As (V and III), Hg (II), Ni (II), and Cu (II) is responsible for several health issues in humans, like liver failure, kidney damage, gastric and skin cancer, mental disorders and harmful effects on the reproductive system. Hence, the evaluation of heavy metal contamination in water and its removal is needed. There are several physicochemical methods that are available for the removal of heavy metals from water, but these methods are expensive and generate large amounts of secondary pollutants. Biological methods are considered cost-effective and eco-friendly methods for the remediation of metallic contaminants from water. In this review, we focused on water contamination with toxic heavy metals and their toxicity and eco-friendly bioremediation approaches.

The supply and demand of water-related ecosystem services in the Asian water tower and its downstream area.

Climate change and human activities can have an impact on the supply and demand of water-related ecosystem services (WRESs) in the Asian water tower (AWT) and its downstream area, which is closely related to the production and livelihoods of billions of people. However, few studies have taken the AWT and its downstream area as a whole to assess the supply-demand relationship of WRESs. This study aims to assess the future trends of the supply-demand relationship of WRESs in the AWT and its downstream area. Here, the supply-demand relationship of WRESs in 2019 was assessed using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and socio-economic data. Then, future scenarios were selected under the framework of the Scenario Model Intercomparison Project (ScenarioMIP). Finally, trends in the supply-demand of WRESs were analysed at multiple scales from 2020 to 2050. The study found that the supply-demand imbalance of WRESs in the AWT and its downstream area will continue to intensify. The area with imbalance intensification was 2.38 × 106 km2 (61.7 %). The supply-demand ratio of WRESs will decline significantly under different scenarios (p < 0.05). The main reason for the imbalance intensification in WRESs is the constant growth of human activities, with a relative contribution of 62.8 %. Our findings suggest that in addition to the pursuit of climate mitigation and adaptation, attention should also be paid to the impact of rapid human activity growth on the supply-demand imbalance of WRESs.

Significance of pyroptosis-related gene in the diagnosis and classification of rheumatoid arthritis.

Background: Rheumatoid arthritis (RA), a chronic autoimmune inflammatory disease, is often characterized by persistent morning stiffness, joint pain, and swelling. Early diagnosis and timely treatment of RA can effectively delay the progression of the condition and significantly reduce the incidence of disability. In the study, we explored the function of pyroptosis-related genes (PRGs) in the diagnosis and classification of rheumatoid arthritis based on Gene Expression Omnibus (GEO) datasets. Method: We downloaded the GSE93272 dataset from the GEO database, which contains 35 healthy controls and 67 RA patients. Firstly, the GSE93272 was normalized by the R software "limma" package. Then, we screened PRGs by SVM-RFE, LASSO, and RF algorithms. To further investigate the prevalence of RA, we established a nomogram model. Besides, we grouped gene expression profiles into two clusters and explored their relationship with infiltrating immune cells. Finally, we analyzed the relationship between the two clusters and the cytokines. Result: CHMP3, TP53, AIM2, NLRP1, and PLCG1 were identified as PRGs. The nomogram model revealed that decision-making based on established model might be beneficial for RA patients, and the predictive power of the nomogram model was significant. In addition, we identified two different pyroptosis patterns (pyroptosis clusters A and B) based on the 5 PRGs. We found that eosinophil, gamma delta T cell, macrophage, natural killer cell, regulatory T cell, type 17 T helper cell, and type 2 T helper cell were significant high expressed in cluster B. And, we identified gene clusters A and B based on 56 differentially expressed genes (DEGs) between pyroptosis cluster A and B. And we calculated the pyroptosis score for each sample to quantify the different patterns. The patients in pyroptosis cluster B or gene cluster B had higher pyroptosis scores than those in pyroptosis cluster A or gene cluster A. Conclusion: In summary, PRGs play vital roles in the development and occurrence of RA. Our findings might provide novel views for the immunotherapy strategies with RA.

Identification of five hub immune genes and characterization of two immune subtypes of osteoarthritis.

Background: Osteoarthritis (OA) is one of the most prevalent chronic diseases, leading to degeneration of joints, chronic pain, and disability in the elderly. Little is known about the role of immune-related genes (IRGs) and immune cells in OA. Method: Hub IRGs of OA were identified by differential expression analysis and filtered by three machine learning strategies, including random forest (RF), least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM). A diagnostic nomogram model was then constructed by using these hub IRGs, with receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA) estimating its performance and clinical impact. Hierarchical clustering analysis was then conducted by setting the hub IRGs as input information. Differences in immune cell infiltration and activities of immune pathways were revealed between different immune subtypes. Result: Five hub IRGs of OA were identified, including TNFSF11, SCD1, PGF, EDNRB, and IL1R1. Of them, TNFSF11 and SCD1 contributed the most to the diagnostic nomogram model with area under the curve (AUC) values of 0.904 and 0.864, respectively. Two immune subtypes were characterized. The immune over-activated subtype showed excessively activated cellular immunity with a higher proportion of activated B cells and activated CD8 T cells. The two phenotypes were also seen in two validation cohorts. Conclusion: The present study comprehensively investigated the role of immune genes and immune cells in OA. Five hub IRGs and two immune subtypes were identified. These findings will provide novel insights into the diagnosis and treatment of OA.

Roles of phase change materials on the morphological, physical, rheological and temperature regulating performances of high-viscosity modified asphalt.

The composite of phase change material (PCM) and high-viscosity modified asphalt (HVMA) is expected as a new material regulating the temperature of high-performance pavements, thereby ameliorating the urban heat island effect. This study focused on evaluating the roles of two kinds of PCMs, i.e. paraffin/expanded graphite/high-density polyethylene composite material (PHDP) and polyethylene glycol (PEG), on a series of performances of HVMA. Fluorescence microscopy observations, physical rheological properties tests and indoor temperature regulating tests were conducted to determine the morphological, physical, rheological and temperature regulating performances of PHDP/HVMA or PEG/HVMA composites with various PCM contents prepared by fusion blending. Fluorescence microscopy test results revealed that the PHDP and PEG could be uniformly distributed in HVMA, but their distribution size and morphology were obviously different. Physical test results showed an increase in the penetration values of both PHDP/HVMA and PEG/HVMA compared to the HVMA without PCM. Their softening points did not change significantly with increasing PCM content due to the presence of a high-content of polymeric spatial reticulation. Ductility test reflected that the low-temperature properties of PHDP/HVMA were improved. However, the ductility of PEG/HVMA was much reduced due to the presence of large size PEG particles especially at 15 % PEG content. Rheological results from the recovery percent and non-recoverable creep compliance at 64 °C confirmed that the PHDP/HVMA and PEG/HVMA had excellent high-temperature rutting resistance regardless of PCM contents. Notably, the phase angle results reflected that the PHDP/HVMA was more viscous at 5-30 °C and more elastic at 30-60 °C. By contrast, PEG/HVMA was more elastic at the whole temperature range of 5-60 °C. Lastly but not least, compared to HVMA without PCM, the temperature regulating effect was 4 °C during heating of PHDP/HVMA containing 4 % PHDP and PEG/HVMA containing 15 % PEG, and their delay time were 456 s and 1240 s, respectively.

Mechano/acousto-electric coupling between ReS2and surface acoustic wave.

Two-dimensional (2D) materials are promising candidates for developing next generation electronic/optoelectronic devices with programmable multi functions, due to their widely tunable properties by various physical stimuli. Mechanical strain is one of the most promising means to effectively modulate the physical properties of 2D materials. Nevertheless, few studies reported micro/nano scale controllable strain application platforms, limiting the development of novel mechano-electrical/optoelectrical devices based on 2D materials. This work proposes surface acoustic wave (SAW) device as a controllable strain modulation platform for 2D materials with sub-micro scale resolution. The platform uses the piezoelectric material (LiNbO3) as the substrate, which is deposited with interdigitated transducers (IDT) to generate SAW on the surface. The propagation of SAW causes surface deformation, which is then transferred to the 2D materials on the substrate. The period of the surface deformation/strain is related with that of SAW, which is determined by the period of IDT with nano meter scale. It is demonstrated that the photo luminescence spectrum of a 2D ReS2flake on this platform gradually shifts with the SAW excitation power, which reaches a shift of 3 nm as the SAW excitation power achieves 26 dBm, corresponding to a band gap increase of 5 meV. Meanwhile, the platform is also capable to provide acousto-electric coupling between SAW and 2D materials, which is demonstrated by the shift of the SAW resonant frequency due to the re-distribution of photo-generated carriers in ReS2upon light illumination.

The role of construction industry and construction policy on sustainable rural development in China.

Recently, rural development has depended on the construction industry's success due to the high employment rate in the construction industry and its development role in the rural areas, and this phenomenon needs research focus. Hence, the current article examines the impact of the construction industry (construction industry revenue and growth) and construction policy (construction industry subsidies) on sustainable rural development in China. The study also used the control variable of gross domestic product (GDP) and industrialization. The article has collected secondary data from the Ministry of Housing and Urban-Rural Development and World Development Indicators (WDI) from 1991 to 2020. The article has applied the Augmented Dickey-Fuller (ADF) test to examine stationarity and quantile autoregressive distributed lag (QARDL) model to investigate the association among variables. The results revealed that the construction industry revenue, growth, construction policy GDP, and industrialization positively link sustainable rural development in China. Thus, the findings exposed that if the country's construction industry improved, rural development also increased accordingly. This study guides the policy development authorities to develop effective policies related to improvement in the construction industry that will enhance sustainable rural development.

Anterior Gradient 2 is a Significant Prognostic Biomarker in Bone Metastasis of Breast Cancer.

Background: The study aimed to detect DEGs associated with BRCA bone metastasis, filter prognosis biomarkers, and explore possible pathways. Methods: GSE175692 dataset was used to detect DEGs between BRCA bone metastatic cases and non-bone metastatic cases, followed by the construction of a PPI network among DEGs. The main module among the PPI network was then determined and pathway analysis on genes within the module was performed. Through performing Cox regression, Kaplan-Meier, nomogram, and ROC curve analyses using GSE175692 and GSE124647 datasets at the same time, the most significant prognostic biomarker was gradually filtered. Finally, important pathways associated with prognostic biomarkers were explored by GSEA analysis. Results: The 74 DEGs were detected between bone metastasis and non-bone metastasis groups. A total of 15 nodes were included in the main module among the whole PPI network and they mainly correlated with the IL-17 signaling pathway. We then performed Cox analysis on 15 genes using two datasets and only enrolled the genes with p < 0.05 in Cox analysis into the further analyses. Kaplan-Meier analyses using two datasets showed that the common biomarker AGR2 expression was related to the survival time of BRCA metastatic cases. Further, the nomogram determined the greatest contribution of AGR2 on the survival probability and the ROC curve revealed its optimal prognostic performance. More importantly, high expression of AGR2 prolonged the survival time of BRCA bone metastatic patients. These results all suggested the importance of AGR2 in metastatic BRCA. Finally, we performed the GSEA analysis and found that AGR2 was negatively related to IL-17 and NF-kß signaling pathways. Conclusion: AGR2 was finally determined as the most important prognostic biomarker in BRCA bone metastasis, and it may play a vital role in cancer progression by regulating IL-17 and NF-kB signaling pathways.

Engineering of Oxidized Line Defects on CVD-Grown MoS2 Flakes.

Defect engineering is a promising means to create patterns on two-dimensional (2D) materials to enable unconventional properties. However, defects usually exist abundantly and randomly on 2D materials, which makes it difficult to tune the properties in a controllable manner. Therefore, it is highly desirable to find out the formation mechanism and controllable fabrication method of defects on 2D materials. In this report, we systematically investigated the line defects on monolayer MoS2 formed by introducing oxygen during the CVD growth. The line defects were formed due to the overoxidation of the MoS2 flake along crystal boundaries, which bulged out of the surface and had the same surface potential as the basal plane. Therefore, the MoS2 flake with line defects maintained the optical and electrical integrity but exhibited distinct properties as compared to the pristine one. By controlling the oxygen concentration during CVD growth, the density of the line defects can be precisely controlled to implement controllable property tuning. Moreover, during the transfer process, the MoS2 flake was easily broken along the line defects, which increased the active sites to achieve enhanced hydrogen evolution reaction performance. This work is expected to inspire the development of patterned functional 2D materials by defect engineering.

Typing characteristics of metabolism-related genes in osteoporosis.

Objective: Osteoporosis is a common musculoskeletal disease. Fractures caused by osteoporosis place a huge burden on global healthcare. At present, the mechanism of metabolic-related etiological heterogeneity of osteoporosis has not been explored, and no research has been conducted to analyze the metabolic-related phenotype of osteoporosis. This study aimed to identify different types of osteoporosis metabolic correlates associated with underlying pathogenesis by machine learning. Methods: In this study, the gene expression profiles GSE56814 and GSE56815 of osteoporosis patients were downloaded from the GEO database, and unsupervised clustering analysis was used to identify osteoporosis metabolic gene subtypes and machine learning to screen osteoporosis metabolism-related characteristic genes. Meanwhile, multi-omics enrichment was performed using the online Proteomaps tool, and the results were validated using external datasets GSE35959 and GSE7429. Finally, the immune and stromal cell types of the signature genes were inferred by the xCell method. Results: Based on unsupervised cluster analysis, osteoporosis metabolic genotyping can be divided into three distinct subtypes: lipid and steroid metabolism subtypes, glycolysis-related subtypes, and polysaccharide subtypes. In addition, machine learning SVM identified 10 potentially metabolically related genes, GPR31, GATM, DDB2, ARMCX1, RPS6, BTBD3, ADAMTSL4, COQ6, B3GNT2, and CD9. Conclusion: Based on the clustering analysis of gene expression in patients with osteoporosis and machine learning, we identified different metabolism-related subtypes and characteristic genes of osteoporosis, which will help to provide new ideas for the metabolism-related pathogenesis of osteoporosis and provide a new direction for follow-up research.

Flexoelectricity-Enhanced Photovoltaic Effect in Self-Polarized Flexible PZT Nanowire Array Devices.

In this investigation, we report the flexoelectricity-enhanced photovoltaic (FPV) effect in a flexible Pb(Zr0.52Ti0.48)O3 nanowire (PZT NW) array/PDMS (polydimethylsiloxane) nanocomposite. The simulation result of density functional theory (DFT) indicated that the FPV effect in PZT NWs can be greatly affected by the interactions of the strain gradients with the internal field generated by self-polarization. We found that when the nanocomposite film was curved down, the photovoltaic current of the aligned PZT-NW/PDMS composite increased by 84.6-fold and 27.6-fold compared with the PZT-nanoparticles/PDMS and randomly aligned PZT-NW/PDMS nanocomposites at the same curvature, respectively. This is mainly ascribed to the increased flexoelectricity in the aligned PZT-NW/PDMS nanocomposite. This study will contribute to a full understanding of the influence of nanoparticle shape on the flexophotovoltaic effect of nanocomposites. It will have potential use in nanocomposites for the study of the FPV effect and associated applications.

Cadmium induces apoptosis by miR-9-5p targeting PTEN and regulates the PI3K/AKT pathway in the piglet adrenal gland.

Cadmium (Cd) is an environmental pollutant that can cause endocrine organ damage. To explore the effect of subacute CdCl2 exposure on piglet adrenal gland tissue and its mechanism based on the establishment of this model, bioinformatics, TUNEL assay, western blot (WB), and qRT-PCR methods were used to detect related indicators. The results showed that after Cd exposure, antioxidant enzymes decreased, heat shock protein increased, and miR-9-5p-gene of phosphatase and tensin homolog (PTEN) upregulates the phosphatidylinositol-3-kinase (PI3K/AKT) pathway. After this pathway was activated, the expression of the apoptosis-related factors cysteinyl aspartate-specific proteinase 3 and 9 (caspase 3 and 9), B-cell lymphoma-2-associated X (BAX) was increased sharply, and the expression of B-cell lymphoma-2 (BCL2) was significantly decreased. The changes in these indicators indicate that Cd exposure induces apoptosis and causes tissue damage in the adrenal gland of piglets. This study aims to reveal the toxic effects of CdCl2 in animals and will provide new ideas for the toxicology of Cd.

Prediction and Evaluation of Machine Learning Algorithm for Prediction of Blood Transfusion during Cesarean Section and Analysis of Risk Factors of Hypothermia during Anesthesia Recovery.

Objective: To explore the application of machine learning algorithm in the prediction and evaluation of cesarean section, predicting the amount of blood transfusion during cesarean section and to analyze the risk factors of hypothermia during anesthesia recovery. Methods: (1)Through the hospital electronic medical record of medical system, a total of 600 parturients who underwent cesarean section in our hospital from June 2019 to December 2020 were included. The maternal age, admission time, diagnosis, and other case data were recorded. The routine method of cesarean section was intraspinal anesthesia, and general anesthesia was only used for patients' strong demand, taboo, or failure of intraspinal anesthesia. According to the standard of intraoperative bleeding, the patients were divided into two groups: the obvious bleeding group (MH group, N = 154) and nonobvious hemorrhage group (NMH group, N = 446). The preoperative, intraoperative, and postoperative indexes of parturients in the two groups were analyzed and compared. Then, the risk factors of intraoperative bleeding were screened by logistic regression analysis with the occurrence of obvious bleeding as the dependent variable and the factors in the univariate analysis as independent variables. In order to further predict intraoperative blood transfusion, the standard cases of recesarean section and variables with possible clinical significance were included in the prediction model. Logistic regression, XGB, and ANN3 machine learning algorithms were used to construct the prediction model of intraoperative blood transfusion. The area under ROC curve (AUROC), accuracy, recall rate, and F1 value were calculated and compared. (2) According to whether hypothermia occurred in the anesthesia recovery room, the patients were divided into two groups: the hypothermia group (N = 244) and nonhypothermia group (N = 356). The incidence of hypothermia was calculated, and the relevant clinical data were collected. On the basis of consulting the literatures, the factors probably related to hypothermia were collected and analyzed by univariate statistical analysis, and the statistically significant factors were analyzed by multifactor logistic regression analysis to screen the independent risk factors of hypothermia in anesthetic convalescent patients. Results: (1) First of all, we compared the basic data of the blood transfusion group and the nontransfusion group. The gestational age of the transfusion group was lower than that of the nontransfusion group, and the times of cesarean section and pregnancy in the transfusion group were higher than those of the non-transfusion group. Secondly, we compared the incidence of complications between the blood transfusion group and the nontransfusion group. The incidence of pregnancy complications was not significantly different between the two groups (P > 0.05). The incidence of premature rupture of membranes in the nontransfusion group was higher than that in the transfusion group (P < 0.05). There was no significant difference in the fetal umbilical cord around neck, amniotic fluid index, and fetal heart rate before operation in the blood transfusion group, but the thickness of uterine anterior wall and the levels of Hb, PT, FIB, and TT in the blood transfusion group were lower than those in the nontransfusion group, while the number of placenta previa and the levels of PLT and APTT in the blood transfusion group were higher than those in the nontransfusion group. The XGB prediction model finally got the 8 most important features, in the order of importance from high to low: preoperative Hb, operation time, anterior wall thickness of the lower segment of uterus, uterine weakness, preoperative fetal heart, placenta previa, ASA grade, and uterine contractile drugs. The higher the score, the greater the impact on the model. There was a linear correlation between the 8 features (including the correlation with the target blood transfusion). The indexes with strong correlation with blood transfusion included the placenta previa, ASA grade, operation time, uterine atony, and preoperative Hb. Placenta previa, ASA grade, operation time, and uterine atony were positively correlated with blood transfusion, while preoperative Hb was negatively correlated with blood transfusion. In order to further compare the prediction ability of the three machine learning methods, all the samples are randomly divided into two parts: the first 75% training set and the last 25% test set. Then, the three models are trained again on the training set, and at this time, the model does not come into contact with the samples in any test set. After the model training, the trained model was used to predict the test set, and the real blood transfusion status was compared with the predicted value, and the F1, accuracy, recall rate, and AUROC4 indicators were checked. In terms of training samples and test samples, the AUROC of XGB was higher than that of logistic regression, and the F1, accuracy, and recall rate of XGB of ANN were also slightly higher than those of logistic regression and ANN. Therefore, the performance of XGB algorithm is slightly better than that of logistic regression and ANN. (2) According to the univariate analysis of hypothermia during the recovery period of anesthesia, there were significant differences in ASA grade, mode of anesthesia, infusion volume, blood transfusion, and operation duration between the normal body temperature group and hypothermia group (P < 0.05). Logistic regression analysis showed that ASA grade, anesthesia mode, infusion volume, blood transfusion, and operation duration were all risk factors of hypothermia during anesthesia recovery. Conclusion: In this study, three machine learning algorithms were used to analyze the large sample of clinical data and predict the results. It was found that five important predictive variables of blood transfusion during recesarean section were preoperative Hb, expected operation time, uterine weakness, placenta previa, and ASA grade. By comparing the three algorithms, the prediction effect of XGB may be more accurate than that of logistic regression and ANN. The model can provide accurate individual prediction for patients and has good prediction performance and has a good prospect of clinical application. Secondly, through the analysis of the risk factors of hypothermia during the recovery period of cesarean section, it is found that ASA grade, mode of anesthesia, amount of infusion, blood transfusion, and operation time are all risk factors of hypothermia during the recovery period of cesarean section. In line with this, the observation of this kind of patients should be strengthened during cesarean section.

Sudden respiratory and circulatory collapse after cesarean section: Amniotic fluid embolism or other reasons? - a case report.

BACKGROUND: For a healthy parturient, a cardiopulmonary collapse that suddenly occurs shortly after an uneventful caesarean section is a relatively rare event and presents a significant challenge for the anesthesia provider. CASE PRESENTATION: Amniotic fluid embolism (AFE) is characterized by acute and rapid collapse and is well known to the obstetric team. Our patient experienced sudden cardiovascular collapse, severe respiratory difficulty and hypoxia, in the absence of other explanations for these findings at the time, and thus AFE was immediately become the focus of the consideration. However, there is no quick, standard laboratory test for AFE, therefore the diagnosis is one of exclusion based on presenting symptoms and clinical course. After given symptomatic treatment, the patient made an uneventful initial recovery in a short period and developed a rash. We recognized that the postpartum shock was associated with delayed anaphylaxis of antibiotics. CONCLUSIONS: These observations have implications for understanding whenever administering drugs in surgery, which may affect the anesthesiologist's judgment regarding the complications of anesthesia. Even though serious complications of common perioperative drugs may rarely occur, anesthesia providers should be aware of the consideration. Early recognition and effective treatment are more important than prompt diagnosis.

Multi-focus image fusion based on fractional order differentiation and closed image matting.

This paper presents a novel approach to addressing the fusion of multi-focus images in either registered or mis-registered cases. The conventional approaches often produce blurred edges of objects in the fused images due to inaccurate decision maps. On the other hand, these decision maps are sensitive to mis-registration that causes artifact in the fused images. Therefore, we propose a robust multi-focus image fusion approach with clear object edges for the registered or mis-registered source images. In this approach, a fractional order differential mask is creatively adopted to pre-process the source images, ensuring the initial decision maps both with the boundaries and fine structures of the objects and with the internal holes closed. Then, the closed matting technique, in lieu of the robust matting, is adopted to refine the initial decision maps. This significantly reduces the interaction information from the users, but still preserves the complete boundaries of the objects. Finally, the global threshold processing is skillfully adopted to form the decision maps. This not only yields the final decision maps with smooth boundaries, but also guarantees the rich gradient information from the mis-registered source images. The experimental results show that the designed algorithm provides better visual perception and higher objective evaluation than some existing representative algorithms.

Energy-Efficient Optimization for Energy-Harvesting-Enabled mmWave-UAV Heterogeneous Networks.

Energy Harvesting (EH) is a promising paradigm for 5G heterogeneous communication. EH-enabled Device-to-Device (D2D) communication can assist devices in overcoming the disadvantage of limited battery capacity and improving the Energy Efficiency (EE) by performing EH from ambient wireless signals. Although numerous research works have been conducted on EH-based D2D communication scenarios, the feature of EH-based D2D communication underlying Air-to-Ground (A2G) millimeter-Wave (mmWave) networks has not been fully studied. In this paper, we considered a scenario where multiple Unmanned Aerial Vehicles (UAVs) are deployed to provide energy for D2D Users (DUs) and data transmission for Cellular Users (CUs). We aimed to improve the network EE of EH-enabled D2D communications while reducing the time complexity of beam alignment for mmWave-enabled D2D Users (DUs). We considered a scenario where multiple EH-enabled DUs and CUs coexist, sharing the full mmWave frequency band and adopting high-directive beams for transmitting. To improve the network EE, we propose a joint beamwidth selection, power control, and EH time ratio optimization algorithm for DUs based on alternating optimization. We iteratively optimized one of the three variables, fixing the other two. During each iteration, we first used a game-theoretic approach to adjust the beamwidths of DUs to achieve the sub-optimal EE. Then, the problem with regard to power optimization was solved by the Dinkelbach method and Successive Convex Approximation (SCA). Finally, we performed the optimization of the EH time ratio using linear fractional programming to further increase the EE. By performing extensive simulation experiments, we validated the convergence and effectiveness of our algorithm. The results showed that our proposed algorithm outperformed the fixed beamwidth and fixed power strategy and could closely approach the performance of exhaustive search, particle swarm optimization, and the genetic algorithm, but with a much reduced time complexity.