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Abstract Background Integrating advanced machine-learning (ML) algorithms into clinical practice is challenging and requires interdisciplinary collaboration to develop transparent, interpretable, and ethically sound clinical decision support (CDS) tools. We aimed to design a ML-driven CDS tool to predict opioid overdose risk and gather feedback for its integration into the University of Florida Health (UFHealth) electronic health record (EHR) system. Methods We used user-centered design methods to integrate the ML algorithm into the EHR system. The backend and UI design sub-teams collaborated closely, both informed by user feedback sessions. We conducted seven user feedback sessions with five UF Health primary care physicians (PCPs) to explore aspects of CDS tools, including workflow, risk display, and risk mitigation strategies. After customizing the tool based on PCPs’ feedback, we held two rounds of one-on-one usability testing sessions with 8 additional PCPs to gather feedback on prototype alerts. These sessions informed iterative UI design and backend processes, including alert frequency and reappearance circumstances. Results The backend process development identified needs and requirements from our team, information technology, UFHealth, and PCPs. Thirteen PCPs (male = 62%, White = 85%) participated across 7 user feedback sessions and 8 usability testing sessions. During the user feedback sessions, PCPs (n = 5) identified flaws such as the term “high risk” of overdose potentially leading to unintended consequences (e.g., immediate addiction services referrals), offered suggestions, and expressed trust in the tool. In the first usability testing session, PCPs (n = 4) emphasized the need for natural risk presentation (e.g., 1 in 200) and suggested displaying the alert multiple times yearly for at-risk patients. Another 4 PCPs in the second usability testing session valued the UFHealth-specific alert for managing new or unfamiliar patients, expressed concerns about PCPs’ workload when prescribing to high-risk patients, and recommended incorporating the details page into training sessions to enhance usability. Conclusions The final backend process for our CDS alert aligns with PCP needs and UFHealth standards. Integrating feedback from PCPs in the early development phase of our ML-driven CDS tool helped identify barriers and facilitators in the CDS integration process. This collaborative approach yielded a refined prototype aimed at minimizing unintended consequences and enhancing usability.

ABSTRACT Background Substantial evidence suggests an association between obesity and sleep. However, research investigating sleep patterns in relation to novel anthropometric indices is limited. Therefore, we conducted a cross‐sectional analysis of data from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2014 to examine the relationship between the body roundness index (BRI) and unhealthy sleep patterns. Objective This study aimed to investigate the association between the BRI and unhealthy sleep patterns among US adults. Methods Data were sourced from NHANES (2007–2014), including respondents aged 20 years and older. Participants were categorized into two groups based on the healthiness of their sleep patterns. The data were weighted, and multiple potential covariates were included in the analysis to provide national estimates and account for the comprehensive sampling design. A multivariable weighted logistic regression model was used, employing restricted cubic spline (RCS) curves to examine potential associations, and subgroup analyses were conducted to determine the stability of the results. Receiver operating characteristic (ROC) analysis was used to compare the diagnostic performance of BRI and body mass index (BMI) in identifying unhealthy sleep patterns. Results In the fully adjusted multivariable logistic regression model, the prevalence odds ratio (POR) for the association between BRI and unhealthy sleep patterns was 1.09, with a 95% confidence interval (CI) of 1.07–1.10. The RCS analysis found that the nonlinear association between BRI and unhealthy sleep patterns was not significant. Subgroup and sensitivity analyses indicated a consistently positive association between high BRI and unhealthy sleep patterns across most subgroups. ROC diagnostic tests showed that BRI's effectiveness in diagnosing unhealthy sleep patterns was comparable to that of BMI, and it was not inferior to BMI in assessing certain components of sleep patterns. Conclusion High BRI is positively associated with unhealthy sleep patterns significantly, indicating that BRI could be a promising metric for evaluating sleep health.

AbstractObjective The association between nutritional status and prognosis of idiopathic pulmonary fibrosis (IPF) remains unclear. This systematic review and meta-analysis aimed to explore the effect of body mass index (BMI) and weight loss on the prognosis of IPF patients.Methods We accumulated studies on IPF, BMI, and weight loss from databases including PubMed, Embase, Web of science, Scopus, Ovid and Cochrane Library up to 4 August 2023. Using Cox proportional hazard regression model for subgroup analysis, hazard ratio (HR) and 95% confidence intervals (CI) for BMI in relation to mortality, acute exacerbation (AE), and hospitalization in IPF patients were calculated, and HR, odds ratio (OR), and 95% CI for weight loss corresponding to IPF patient mortality were assessed. Sensitivity analysis was peformed by eliminating every study one by one, and publication bias was judged by Egger’s test and trim-and-fill method.Results A total of 34 eligible studies involving 18,343 IPF patients were included in the meta-analysis. The pooled results by univariate Cox regression analysis showed that baseline BMI was a predictive factor for IPF mortality (HR = 0.93, 95%CI = [0.91, 0.94]). Furthermore, the results by the multivariable regression model indicated that baseline BMI was an independent risk factor for predicting IPF mortality (HR = 0.94, 95%CI = [0.91, 0.98]). Weight loss was identified as a risk factor for IPF mortality (HR = 2.74, 95% CI = [2.12, 3.54]; OR = 4.51, 95% CI = [1.72, 11.82]) and there was no predictive value of BMI for acute exacerbation (HR = 1.00, 95% CI= [0.93, 1.07]) or hospitalization (HR = 0.95, 95% CI = [0.89, 1.02]).Conclusion Low baseline BMI and weight loss in the course of IPF may indicate a high risk of mortality in patients with IPF, so it is meaningful to monitor and manage the nutritional status of IPF patients, and early intervention should be conducted for low BMI and weight loss.

AbstractBackground Anxiety and depression are common comorbidities in idiopathic pulmonary fibrosis (IPF) that impair health-related quality of life. However, there is a lack of studies focusing on the mental disorder of IPF after antifibrotic treatment and their related predictive factors.Methods Patients with an initial diagnosis of IPF were enrolled. Data on demographics, lung function, Generalized Anxiety Disorder-7 (GAD-7) Scale, Patient Health Questionnaire 9 (PHQ-9), Patient Health Questionnaire-15 (PHQ-15), and St. George's Respiratory Questionnaire total score(SGRQ-T) were collected. Changes in anxiety, depression, somatic symptoms, and quality of life scores before and after nintedanib treatment were compared, and the related predictive factors were analyzed.Results A total of 56 patients with a first diagnosis of IPF were enrolled, with 42 and 35 patients suffering from anxiety and depression, respectively. The GAD-7, PHQ-9, PHQ-15, and SGRQ scores were higher in the anxiety and depression groups. SGRQ total score (SGRQ-T) [OR = 1.075, 95%CI= (1.011, 1.142)] was an independent predictor of IPF combined with anxiety (p

ABSTRACTMicrostructural defects in laser powder bed fusion (LPBF) metallic materials are correlated with processing parameters. A multi-physics model and a crystal plasticity framework are employed to predict microstructure growth in molten pools and assess the impact of manufacturing defects on plastic damage parameters. Criteria for optimising the LPBF process are identified, addressing microstructural defects and tensile properties of LPBF Hastelloy X at various volumetric energy densities (VED). The results show that higher VED levels foster a specific Goss texture {110} , with irregular lack of fusion defects significantly affecting plastic damage, especially near the material surface. A critical threshold emerges between manufacturing defects and grain sizes in plastic strain accumulation. The optimal processing window for superior Hastelloy X mechanical properties ranges from 43 to 53 J/mm3. This work accelerates the development of superior strength-ductility alloys via LPBF, streamlining the trial-and-error process and reducing associated costs.

WADED Al-Mg and Al-Mg-Sc-Zr aluminium alloy thin-wall components were produced. Heat treatment (350°C aging for 4 h) was further conducted. The effects of Sc/Zr addition on porosity, microstructure, mechanical properties and corrosion behaviour were investigated. With Sc/Zr adding, pore area fraction decreased from 0.208 to 0.005% and grain size decreased from 84.34 to 19.54 μm. In WADED Al-Mg-Sc-Zr component, microstructure showed refined equiaxed grains. Mechanical properties were improved with 360 MPa ultimate tensile strength (UTS), 185 MPa yield strength, and 23.31% elongation. After heat treatment, UTS increased to 388 MPa with good ductility (22.53%) maintained. The grain refinement and Al3(Sc, Zr) particles were the key factors for mechanical property improvement. The corrosion behaviour was improved with introduced Sc/Zr and further optimised after heat treatment. The corrosion exhibited layered characteristics due to the distribution of primary Al3(Sc, Zr) while minor-size secondary Al3(Sc, Zr) along grain boundaries improved the corrosion behaviour.

Alarmin cytokines including IL-25, IL-33, and thymic stromal lymphopoietin (TSLP) function as danger signals to trigger host immunity in response to tissue injury caused by pathogenic factors such as parasitic infections. Parasitic diseases also provide an excellent context to study their functions and mechanisms. Numerous studies have indicated that alarmin cytokine released by non-immune cells such as epithelial and stromal cells induce the hosts to initiate a type 2 immunity that drives parasite expulsion but also host pathology such as tissue injury and fibrosis. By contrast, alarmin cytokines especially IL-33 derived from immune cells such as dendritic cells may elicit an immuno-suppressive milieu that promotes host tolerance to parasites. Additionally, the role of alarmin cytokines in parasite infections is reported to depend on species of parasites, cellular source of alarmin cytokines, and immune microenvironment, all of which is relevant to the parasitic sites or organs. This narrative review aims to provide information on the crucial and diverse role of alarmin cytokines in parasitic infections involved in different organs including intestine, lung, liver and brain.

Trifluoromethyl (CF3) and other fluoroalkyl groups are of great significance in the fields of pharmaceutical chemistry and agricultural chemicals. Fluoroalkyl acids, especially trifluoroacetic acid (TFA) is considered the most ideal fluoroalkylation reagent due to its low cost and easy availability. However, the extremely high oxidation potential requirement of TFA limits its wide application. In recent years, since visible-light-induced fluoroalkylation through the ligand-to-metal charge transfer (LMCT) process can overcome the above limitations, it has become an effective synthetic tool for the construction of fluorinated compounds with complex molecules and structures. In this review, according to the classification of different metal catalysts, we summarize the trifluoromethylation and fluoroalkylation of olefins, heteroaromatics, and terminal alkynes in different metal catalytic systems and their corresponding reaction mechanisms. The photocatalytic fluoroalkylation via LMCT is believed to expedite the development of fluoro-containing drugs, and more novel fluoroalkylation methologies using this strategy will be disclosed.

Spiral-channel holes are utilized on the turbine endwall to enhance cooling effectiveness. Numerical simulations and experimental verification are conducted to analyze the cooling performance and energy loss of spiral-channel holes, as well as the influence of parameters and flow field structure on the vane. Results reveal that the energy loss due to the spiral-channel hole is slightly higher than that of the cylindrical hole at low blowing ratio (M), with the opposite being true for M exceeds 1.5. Increasing M heightens the film effectiveness around the hole but reduces coolant coverage downstream of the hole. The cooling capacity of the spiral-channel hole outperforms cylindrical hole around the hole, resulting in more uniform transverse film effectiveness and greatly improved the cooling effect on the pressure section, particularly at high M. The introduction of compound angle further enhances the cooling performance of spiral-channel hole, but increases the energy loss. Upstream the hole, the laterally-averaged film effectiveness of spiral-channel hole with −28° compound angle is 16.9 % higher than that of cylindrical hole, and 20.1 % higher downstream the hole. The spiral-channel hole with −28° compound angle exhibits the best cooling performance on the pressure side, while the spiral-channel hole with 48° compound angle provides optimal cooling effect on the suction side. Moreover, the pressure section exhibits a larger vortex scale and intensity compared to the suction section. The hole with positive and negative compound angle exhibits minimal difference in the flow field structure between the pressure and suction sections, and form better protection on the endwall with a smaller zero coverage zone.

Abstract Background Electronic health records (EHRs) are digital records of individual health information. However, their adoption and utilization remain low. This study explores the factors influencing the implementation of EHRs through a questionnaire survey to enhance individual awareness and adoption of EHRs. Methods A questionnaire and an expert rating scale were developed sequentially, and the consistency of the scores from five experts was calculated using Kendall’s W to generate a final questionnaire. A non-parametric test was utilized to analyze differences in continuous data that did not follow a normal distribution. Categorical variables were expressed as percentages (%), the chi-square test was employed for group comparisons, and multiple logistic regression was implemented to assess individuals’ awareness and adoption of EHRs. Results In total, 1,341 survey questionnaires were distributed between January and December 2022, with 1,337 valid responses (99.7%). The results indicated that the proportion of participants who were aware of EHRs and had a bachelor’s degree or higher education, an income of ≥$700 per month, residence in urban areas, possessed self-care abilities, and underwent annual physical examinations was significantly higher than that without awareness of EHRs (P

In this paper, a multi-strategy enhanced chimpanzee optimization algorithm (MSEChOA) acting on path planning for delivery vehicles is proposed to achieve the goal of shortening global path lengths for the delivery unmanned vehicles and obtaining safer paths. In the initialization phase, the algorithm introduces a hybrid good point set and chaos initialization strategy, combining the advantages of both to enhance the randomness and homogeneity of the initial population. After that, it incorporates a benchmark weight strategy and Gaussian-modulated cosine factor to adaptively adjust algorithm parameters, thus balancing the global and local search capabilities and improving the search efficiency. In the end, the algorithm incorporates a global search enhancer (GEE) to further enhance the global search capability in the later phases, thereby avoiding local optima. Experiments on several benchmark test functions show that MSEChOA outperforms traditional ChOA and other optimization algorithms in optimization accuracy and convergence speed. In simulation experiments, MSEChOA shows stronger path planning ability and good computational efficiency in both simple and complex environments, proving its feasibility and superiority in the field of path planning.

The traditional scheduling approach, which primarily considers energy oligarchs like large-scale loads and large-scale generators, is challenged by the rapid rise of distributed energy resources (DERs) in energy internet of things (EIoT). Metaverse is emerging as one of the most promising technologies considering emergence from DERs in EIoT. Our work, from a macro perspective in the virtual space, provides a metaverse framework to harness the swarm intelligence that emerges from the aggregation behavior of massive diverse DERs in EIoT. The presented framework is built upon virtual twins, data science, systems theory, and 4th-Paradigm (data-intensive scientific discovery paradigm), enabling a novel energy scheduling mode. Our goal is to achieve data empowerment and intelligence improvement through data connectivity, virtual and real interaction, which will ultimately result in a new theory on complex system scheduling.

Abstract INTRODUCTION Alzheimer's disease (AD) is often misclassified in electronic health records (EHRs) when relying solely on diagnosis codes. This study aimed to develop a more accurate, computable phenotype (CP) for identifying AD patients using structured and unstructured EHR data. METHODS We used EHRs from the University of Florida Health (UFHealth) system and created rule‐based CPs iteratively through manual chart reviews. The CPs were then validated using data from the University of Texas Health Science Center at Houston (UTHealth) and the University of Minnesota (UMN). RESULTS Our best‐performing CP was “patient has at least 2 AD diagnoses and AD‐related keywords in AD encounters,” with an F1‐score of 0.817 at UF, 0.961 at UTHealth, and 0.623 at UMN, respectively. DISCUSSION We developed and validated rule‐based CPs for AD identification with good performance, which will be crucial for studies that aim to use real‐world data like EHRs. Highlights Developed a computable phenotype (CP) to identify Alzheimer's disease (AD) patients using EHR data. Utilized both structured and unstructured EHR data to enhance CP accuracy. Achieved a high F1‐score of 0.817 at UFHealth, and 0.961 and 0.623 at UTHealth and UMN. Validated the CP across different demographics, ensuring robustness and fairness.

In this article, an innovative online adaptive high-efficiency control strategy is proposed to improve the power generation efficiency of a marine micro gas turbine under partial load. Firstly, a mathematical model of the micro-gas turbine is established, and a control strategy consisting of an on-board prediction model and an online update model is proposed. To evaluate the performance changes of the gas turbine, we applied deep learning techniques to enhance the extreme learning machine (ELM) algorithm, resulting in the development of a high-precision, high-real-time deep extreme learning machine (DL_ELM) prediction model. This model effectively monitors changes in the gas turbine’s performance. Furthermore, an online time-series deep extreme learning machine with a dynamic forgetting factor (DFF_DL_OSELM) model is designed to achieve the real-time tracking of performance variations. When the DL_ELM model detects a gas turbine’s performance change, a particle swarm optimization (PSO) algorithm is employed to iteratively calculate the DFF_DL_OSELM model, determining the optimal speed control scheme to ensure the gas turbine operates at maximum efficiency. To validate the superiority of the proposed control strategy, a comparison is made with traditional high-efficiency control strategies based on polynomial fitting and BP neural networks. The results demonstrate that although all three strategies can achieve efficient operation under constant conditions, traditional strategies fail to identify and adjust to performance changes in real time, leading to decreased control performance and potential engine damage as engine characteristics degrade. In contrast, the proposed online adaptive control strategy dynamically adjusts the speed control plan based on performance degradation, ensuring that the gas turbine operates efficiently while keeping the turbine inlet and exhaust temperatures within safe limits.

Titanium alloys are widely used in the manufacture of gas turbines’ compressor blades. Elucidating their mechanical behavior and strength under damaged conditions is the key to evaluating the equipment’s reliability. However, the conventional Johnson-Cook (J-C) constitutive model has limitations in describing the dynamic response of titanium alloy materials under the impact of a high strain rate. In order to solve this problem, the mechanical behavior of a TC4 titanium alloy under high strain rate and different temperature conditions was analyzed by combining experiments and numerical simulations. In this study, the parameters of the J-C model were analyzed in detail, and an improved J-C constitutive model is proposed, based on the new mechanism of the strain rate strengthening effect and the temperature softening effect, which improves the accuracy of the description of strain sensitivity and temperature dependence. Finally, the VUMAT subroutine of ABAQUS software was used for numerical simulation, and the predictive ability of the improved model was verified. The simulation results showed that the maximum prediction error of the traditional J-C model was 23.6%, while the maximum error of the improved model was reduced to 5.6%. This indicates that the improved J-C constitutive model can more accurately predict the mechanical response of a titanium alloy under an impact load and provides a theoretical basis for the study of the mechanical properties of titanium alloy blades under subsequent conditions of foreign object damage.

Abstract Background The effectiveness of immune checkpoint inhibitors in treating gallbladder cancer (GBC) remains unsatisfactory. Recently, several new immune checkpoints have been identified. However, investigations exploring these immune checkpoints in GBC are limited. In this study, we aim to investigate the expression patterns and clinical implications of various immune checkpoints, and further characterize the spatial and quantitative heterogeneity of immune components in GBC. Methods We employed single and multiplex immunohistochemistry to evaluate the expression of five immune checkpoint markers and four immune cell markers in the primary tumor core, hepatic invasion margin, and liver metastasis. Subsequently, we analyzed their interrelationships and their prognostic significance. Results We observed a robust positive correlation between PD1/TIM3 expression in GBC (R = 0.614, P

Abstract Background The introduction of multiparameter MRI and novel biomarkers has greatly improved the prediction of clinically significant prostate cancer (csPCa). However, decision-making regarding prostate biopsy and prebiopsy examinations is still difficult. We aimed to establish a quick and economic tool to improve the detection of csPCa based on routinely performed clinical examinations through an automated machine learning platform (AutoML). Methods This study included a multicenter retrospective cohort and two prospective cohorts with 4747 cases from 9 hospitals across China. The multimodal data, including demographics, clinical characteristics, laboratory tests, and ultrasound reports, of consecutive participants were retrieved using extract-transform-load tools. AutoML was applied to explore potential data processing patterns and the most suitable algorithm to build the Prostate Cancer Artificial Intelligence Diagnostic System (PCAIDS). The diagnostic performance was determined by the receiver operating characteristic curve (ROC) for discriminating csPCa from insignificant prostate cancer (PCa) and benign disease. The clinical utility was evaluated by decision curve analysis (DCA) and waterfall plots. Results The random forest algorithm was applied in the feature selection, and the AutoML algorithm was applied for model establishment. The area under the curve (AUC) value in identifying csPCa was 0.853 in the training cohort, 0.820 in the validation cohort, 0.807 in the Changhai prospective cohort, and 0.850 in the Zhongda prospective cohort. DCA showed that the PCAIDS was superior to PSA or fPSA/tPSA for diagnosing csPCa with a higher net benefit for all threshold probabilities in all cohorts. Setting a fixed sensitivity of 95%, a total of 32.2%, 17.6%, and 26.3% of unnecessary biopsies could be avoided with less than 5% of csPCa missed in the validation cohort, Changhai and Zhongda prospective cohorts, respectively. Conclusions The PCAIDS was an effective tool to inform decision-making regarding the need for prostate biopsy and prebiopsy examinations such as mpMRI. Further prospective and international studies are warranted to validate the findings of this study. Trial registration Chinese Clinical Trial Registry ChiCTR2100048428. Registered on 06 July 2021.

Due to high greenhouse gas emissions, countries worldwide are stepping up their emission reduction efforts, and the global demand for new, carbon-free fuels is growing. Ammonia (NH3) fuels are popular due to their high production volume, high energy efficiency, ease of storage and transportation, and increased application in power equipment. However, their physical characteristics (e.g., unstable combustion, slow flame speed, and difficult ignition) limit their use in power equipment. Based on the structural properties of the power equipment, NH3 fuel application and emissions characteristics were analyzed in detail. Combustion of NH3 fuels and reduction measures for NOx emissions (spark plug ignition, compression ignition, and gas turbines) were analyzed from various aspects of operating conditions (e.g., mixed fuel, fuel-to-exhaust ratio, and equivalence ratio), structure and strategy (e.g., number of spark plugs, compression ratio (CR), fuel injection, and ignition mode), and auxiliary combustion techniques (e.g., preheating, humidification, exhaust gas recirculation, and secondary air supply). The performance of various NH3 fuel cell (FC) types was analyzed, with a focus on the maximum power achievable for different electrolyte systems. Additionally, the application and NOx emissions of indirect NH3 FCs were evaluated under flame and catalytic combustion conditions. The system efficiency of providing heat sources by burning pure NH3, anode tail gas, and NH3 decomposition gas was also compared. Based on a comprehensive literature review, the key factors influencing the performance and emissions of NH3-powered equipment were identified. The challenges and limitations of NH3-powered equipment were summarized, and potential strategies for improving efficiency and reducing emissions were proposed. These findings provide valuable insights for the future development and application of NH3 FCs.

The air fryer utilizes heated air rather than hot oil to achieve frying, eliminating the need for cooking oil, rendering it a healthier cooking method than traditional frying and baking. However, there is limited evidence supporting that the air fryer could effectively reduce the level of food-derived carcinogen. In this study, we compared the concentration of Benzo[a]pyrene (BaP), a typical carcinogen, in beef patties cooked using an air fryer and an oven, under different cooking conditions, including temperatures (140 °C, 160 °C, 180 °C, and 200 °C), times (9, 14, and 19 min), and oil added or not. The adjusted linear regression analysis revealed that the BaP concentration in beef cooked in the air fryer was 22.667 (95% CI: 15.984, 29.349) ng/kg lower than that in beef cooked in the oven. Regarding the air fryer, the BaP concentration in beef cooked without oil brushing was below the detection limit, and it was significantly lower than in beef cooked with oil brushing (p < 0.001). Therefore, cooking beef in the air fryer can effectively reduce BaP concentration, particularly due to the advantage of oil-free cooking, suggesting that the air fryer represents a superior option for individuals preparing meat at high temperatures.

Lipid peroxidation and redox imbalance are hallmarks of ferroptosis, an iron-dependent form of cell death. Growing evidence suggests that dysregulation in glycolipid metabolism and iron homeostasis substantially contribute to the development of hepatocellular carcinoma (HCC). However, there is still a lack of comprehensive understanding regarding the specific transcription factors that are capable of coordinating glycolipid and redox homeostasis to initiate the onset of ferroptosis. We discovered that overexpression of SOX8 leads to impaired mitochondria integrate, increased oxidative stress, and enhanced lipid peroxidation. These effects can be attributed to the inhibitory impact of SOX8 on de novo lipogenesis, glycolysis, the tricarboxylic acid cycle (TCA), and the pentose phosphate pathway (PPP). Additionally, upregulation of SOX8 results in reduced synthesis of NADPH, disturbance of redox homeostasis, disruption of mitochondrial structure, and impairment of the electron transport chain. Furthermore, the overexpression of SOX8 enhances the process of ferroptosis by upregulating the expression of genes associated with ferroptosis and elevating intracellular levels of ferrous ion. Importantly, the overexpressing of SOX8 has been observed to inhibit the proliferation of HCC in immunodeficient animal models. In conclusion, the findings suggest that SOX8 has the ability to alter glycolipid and iron metabolism of HCC cells, hence triggering the process of ferroptosis. The results of our study present a novel strategy for targeting ferroptosis in the therapy of HCC.

A spiral-channel hole is implemented to heighten the cooling performance of turbine vanes by inducing swirling flow. Numerical simulations examine and compare the film effectiveness and flow structure at four positions. The impact of various factors, such as blowing ratio, hole position, and mainstream Reynolds numbers, on film effectiveness is studied. The spiral-channel hole provides superior cooling effect compared to the cylindrical hole on the suction and pressure sections. When M is 0.5, the coverage range of the spiral-channel hole, where film cooling effectiveness exceeds 0.25, is twice as large as that of the cylindrical hole. The cooling performance difference between the two types of holes is minimal at the leading edge of the turbine vane. The optimal blowing ratio for the spiral-channel hole to achieve maximum cooling performance is 2.0. The groove structure on the inner wall of the spiral-channel hole induces numerous vortices, which deflect the streamlines inside the spiral channel, improving the adhesion of the coolant to the vane surface. The local radial pressure gradient at the outlet suppresses the coolant flow, enhancing its adhesion to the vane surface. The acceleration effect resulting from the streamwise pressure gradient reduces the mixing speed of the fluids. Collectively, these effects enhance the film effectiveness. Increasing Reynolds numbers expands the transverse coverage region of the mixed fluid. But it also lowers the temperature of the cooling film. The dominant impact of Reynolds numbers on film cooling effectiveness depends on which of the above-mentioned factors is most significant.

Neomphalida is an order of gastropod molluscs with highly diverse morphology and a global distribution across various chemosynthesis-based ecosystems from organic falls to hot vents. The phylogenetic relationships of taxa within this order remain contentious, due to the rarity of material leading to a low taxonomic coverage and few genetic markers used. Neomphalida includes three families—Melanodrymiidae, Neomphalidae, and Peltospiridae—and molecular sequences are especially lacking in Melanodrymiidae. Here, we assembled a total of 11 mitogenomes covering these three families and 14 genus-level groups to reconstruct the most complete phylogeny of Neomphalida to date. Our current result recovered the monophyly of three families with maximum support and a likely interfamilial relationship of (Melanodrymiidae + Neomphalidae) + Peltospiridae. These indicate the possibility of habitat shifting from non-chemosynthetic deep sea to hot vent and then to sunken wood, accompanied by elevated mitogenome rearrangements and amino acid substitution rates in Melanodrymiidae. By mapping species distribution on the phylogeny, our findings suggest a Pacific origin of Neomphalida and multiple historical dispersal events of Peltospiridae to the Indian Ocean and at least once to the Atlantic.

AbstractBackground: Cardiovascular disease (CVD) is widely observed in modern society. CVDs are responsible for the majority of fatalities, with heart attacks and strokes accounting for approximately 80% of these cases. Furthermore, a significant proportion of these deaths, precisely one-third, occurs in individuals under 70. Metabolic syndrome encompasses a range of diseases characterized by various physiological dysfunctions. These include increased inflammation in adipose tissue, enhanced cholesterol synthesis in the liver, impaired insulin secretion, insulin resistance, compromised vascular tone and integrity, endothelial dysfunction, and atheroma formation. These factors contribute to the development of metabolic disorders and significantly increase the likelihood of experiencing cardiovascular complications.Method: We selected studies that proposed hypotheses regarding metabolic disease syndrome and cardiovascular disease (CVD) and the role of Nrf2/HO-1 and factor regulation in CVD research investigations based on our searches of Medline and PubMed.Results: A total of 118 articles were included in the review, 16 of which exclusively addressed hypotheses about the role of Nrf2 on Glucose regulation, while 16 involved Cholesterol regulation. Likewise, 14 references were used to prove the importance of mitochondria on Nrf2. Multiple studies have provided evidence suggesting the involvement of Nrf2/HO-1 in various physiological processes, including metabolism and immune response. A total of 48 research articles and reviews have been used to highlight the role of metabolic syndrome and CVD.Conclusion: This review provides an overview of the literature on Nrf2/HO-1 and its role in metabolic disease syndrome and CVD.

The mono-exponential decay has been used to describe various physical phenomena such as cavity ring-down signal, fluorescence decay, etc. In this paper, a neural network method of extreme learning machine (ELM) is adopted to efficiently extract decay time. The theoretical extraction precision, accuracy, and computation cost are all preliminarily analyzed and quantitatively compared with the traditional Levenberg-Marquardt (LM) algorithm. The training set and the testing set for the ELM are built based on our experimental system parameters. After dataset training, the ELM model for mono-exponential decay extraction is obtained. In the dataset testing, this model gives almost the same results with the LM algorithm. The relative deviation of precision is only about ±1 nanosecond. This ELM model can also be directly used in experimental cavity ring-down system. Comparing with the LM algorithm, the relative deviations are less than ±2 nanoseconds when the decay time is in the range of 0.98 μs∼2.20 μs. The ELM method for mono-exponential decay extraction has high efficiency and fine robustness. It has the potential for the applications in cavity ring-down spectroscopy, fluorescence decay analysis, and nuclear radioactive technique, etc.

Colorectal cancer (CRC) is one of the most lethal human malignancies, and with the growth of societies and lifestyle changes, the rate of people suffering from it increases yearly. Important factors such as genetics, family history, nutrition, lifestyle, smoking, and alcohol can play a significant role in increasing susceptibility to this cancer. On the other hand, the metabolism of several macromolecules is also involved in the fate of tumors and immune cells. The evidence discloses that cholesterol and its metabolism can play a role in the pathogenesis of several cancers because there appears to be an association between cholesterol levels and CRC, and cholesterol-lowering drugs may reduce the risk. Furthermore, changes or mutations of some involved genes in cholesterol metabolism, such as CYP7A1 as well as signaling pathways, such as mitogen-activated protein kinase (MAPK), can play a role in CRC pathogenesis. This review summarized and discussed the role of cholesterol in the pathogenesis of CRC as well as available cholesterol-related therapeutic approaches in CRC.

Irregular large pores induced by laser powder bed fusion (LPBF) are inherent defects, which greatly domain the mechanical properties of as-built components. Laser remelting is an effective method to diminish the porosity and increase the energy input, while excessive heat accumulation is generally at the expense of powder spatter. In the study, LPBF-fabricated Hastelloy X alloy was built by combining laser remelting and five scanning strategies (three meander and two chessboard strategies) to investigate the scanning strategy effects on the related microstructure and mechanical properties via experiment and simulation. In comparison, results showed that the average temperature of the powder model rises from 622.02 K to 880.88 K, and the cooling rate approximately increases from 106 K/s up to 107 K/s for the chessboard strategy. The chessboard strategy with a large scanning island yields the highest density (99.34%). More homogeneous grain also nucleates due to the evident 〈111〉 micro-texture distribution. The refined microstructure achieved by the chessboard strategy with laser remelting leads to a superior combination of tensile strength (∼820 MPa) and elongation (∼30%). Grain boundary strengthening and dislocations hardening are the dominant mechanisms of as-built Hastelloy X alloy.

As one of the main threats to human life (the fourth most dangerous and prevalent cancer), colorectal cancer affects many people yearly, decreases patients’ quality of life, and causes irreparable financial and social damages. In addition, this type of cancer can metastasize and involve the liver in advanced stages. However, current treatments can’t completely eradicate this disease. Chemotherapy and subsequent surgery can be mentioned among the current main treatments for this disease. Chemotherapy has many side effects, and regarding the treatment of this type of tumor, chemotherapy can lead to liver damage, such as steatohepatitis, steatosis, and sinus damage. These damages can eventually lead to liver failure and loss of its functions. Therefore, it seems that other treatments can be used in addition to chemotherapy to increase its efficiency and reduce its side effects. Biological therapies and immunotherapy are one of the leading suggestions for combined treatment. Antibodies (immune checkpoint blockers) and cell therapy (DC and CAR-T cells) are among the immune system-based treatments used to treat tumors. Immunotherapy targets various aspects of the tumor that may lead to 1) the recruitment of immune cells, 2) increasing the immunogenicity of tumor cells, and 3) leading to the elimination of inhibitory mechanisms established by the tumor. Therefore, immunotherapy can be used as a complementary treatment along with chemotherapy. This review will discuss different chemotherapy and immunotherapy methods for colorectal cancer. Then we will talk about the studies that have dealt with combined treatment.

Metal-halide perovskites (MHPs) are attracting considerable interest for optoelectronic applications, with Cs_{2}AgBiBr_{6} one of the main contenders among lead-free systems. Cs_{2}AgBiBr_{6} crystallizes in a nominally double-perovskite structure, but exhibits a soft lattice with large atomic fluctuations characteristic of MHPs. While crucial to understand electron-phonon and phonon-phonon couplings, the spatiotemporal correlations of these fluctuations remain largely unknown. Here, we reveal these correlations using comprehensive neutron and x-ray scattering measurements on Cs_{2}AgBiBr_{6} single crystals, complemented with first-principles simulations augmented with machine-learned neural-network potentials. We report the discovery of an unexpected complex modulated ground-state structure containing several hundred atoms, arising from a soft-phonon instability of the low-temperature tetragonal phase. Further, our experiments and simulations both reveal extensive correlated two-dimensional fluctuations of Br octahedra at finite temperature, arising from soft optic phonons that are strongly broadened by anhamonicity, reflecting very shallow potential wells. These results provide new insights into the atomic structure and fluctuations in MHPs, critical to understand and control their thermal and optoelectronic properties.

Improving the operating conditions can enhance the output performance of PEMFCs based on Ammonia decomposition gas as fuel (Ammonia–Hydrogen fuel cell). Therefore, in this study, a 3-dimensional non-isothermal numerical model was established to study the gas–heat–water distribution of Ammonia–Hydrogen fuel cell under different operating conditions, and the performance improved by operating conditions is analyzed through regional quantification, and then optimize the operating conditions for improving performance. The research results show that increasing the working pressure, gas humidity and nitrogen adsorption can improve the output performance of the fuel cell effectively. Under low-pressure operating conditions, increasing the unit pressure can increases the output performance by 80%; under low-humidity operating conditions, increasing the relative humidity by 0.15 can increases the output performance by 8%; under low-hydrogen content operating conditions, increasing the mole fraction of hydrogen by 5% through nitrogen adsorption can increases the output performance by 10%. Along the flow direction, the molar concentration distribution characteristics of reactant gas is opposite to the change trend of membrane water content, which is closely related to the reaction rate; the overall change trend of heat is consistent with the water content of the membrane, but the hot spot value at the outlet has a downward trend with the strengthening of the reaction conditions and the continuous increase of the load. When operating conditions changes, the variation trend of the regional total output is different, and the maximum value of the total output varies from the operating conditions. Optimizing the operating conditions can make it possible to achieve the output performance based on pure hydrogen as fuel.

Abstract An arc fault is the most common cause of charging pile fire. The series arc fault current is usually lower than the short‐circuit fault current and is challenging to detect, resulting in the inaction of the circuit breaker. Therefore, a method for detecting series arc faults of charging piles based on generalized Stockwell transform (GST) is proposed in this paper. First, an real time digital simulation system (RTDS)‐based charging pile arc experimental platform is constructed, and the point contact and the carbonization path arc fault current are measured. Then, the fault information is extracted using GST, and the 250‐Hz component is selected as the arc fault identification feature. On this basis, an improved sampling point selection method is proposed. Finally, multiple feature quantities are constructed as feature vectors, and the trained particle swarm optimization _support vector machine (PSO_SVM) is used for arc fault detection. The experimental results show that the accuracy rate reaches 98.75%; thus, the scheme proposed by this paper could detect the series arc fault current accurately and avoid the incorrect action of the circuit breaker.

SARS- CoV-2 hijacks ACE2 for cell entry. Here, the authors report that dynamic SUMOylation modulates the TOLLIP-directed selective autophagic degradation of ACE2 and suggest SUMOylation inhibition as a potential intervention against SARS-CoV-2 infection.

ObjectiveTo analyze and evaluate the role of the High-throughput Drug Sensitivity (HDS) screening strategy in identifying highly sensitive drugs against esophageal squamous cell carcinoma (ESCC).MethodsA total of 80 patients with progressive ESCC were randomly divided into the observation (40 cases) and the control groups (40 cases). In the observation group, primary ESCC cells were isolated from the tumor tissues with a gastroscope, and drug sensitivity screening was performed on cells derived from the 40 ESCC cases using the HDS method, followed by verification in a patient-derived tumor xenograft (PDX) mouse model. Finally, the differences in the therapeutic efficacy (levels of CEA, CYFRA21-1, SCCA after chemotherapy and the rates of overall survival, local progression, and distant metastasis at 12 months and 18 months time points after chemotherapy) were compared between the observation group (Screened drug-treated) and the control group (Paclitaxel combined with cisplatin regimen-treated).ResultsForty ESCC patients were screened for nine different high-sensitive chemotherapeutics, with the majority showing sensitivity to Bortezomib. Experiments on animal models revealed that the tumor tissue mass of PDX mice treated with the HDS-screened drug was significantly lower than that of the Paclitaxel-treated mice (p

Obstructive sleep apnea (OSA) is a highly prevalent type of sleep-disordered breathing, which is often comorbid with affective disorders such as anxiety. A 61-year-old woman who was diagnosed with OSA affected by anxiety disorder complained of poor sleep quality at night and anxiety symptoms, and showed chest tightness, dyspnea, snoring, and apnea events during sleep. The patient initially received treatment with positive airway pressure (PAP) combined with trazodone, and subsequently switched to auto-trilevel PAP (AtPAP) combined with trazodone therapy. The initial attempt to treat the patient’s disease by auto-adjusting PAP combined with trazodone failed because of central sleep apnea (CSA), which frequently occurred at night. After switching to AtPAP combined with trazodone therapy, CSA was effectively eliminated. In addition, sleep quality, hypoxia, and anxiety disorders were improved. The first report of successful therapy of AtPAP combined with trazodone for OSA complicated by anxiety disorder provides a new therapeutic strategy for this patient population.

Abstract In this paper, we present an edge detection scheme based on ghost imaging (GI) with a holistically-nested neural network. The so-called holistically-nested edge detection (HED) network is adopted to combine the fully convolutional neural network (CNN) with deep supervision to learn image edges effectively. Simulated data are used to train the HED network, and the unknown object’s edge information is reconstructed from the experimental data. The experiment results show that, when the compression ratio (CR) is 12.5%, this scheme can obtain a high-quality edge information with a sub-Nyquist sampling ratio and has a better performance than those using speckle-shifting GI (SSGI), compressed ghost edge imaging (CGEI) and subpixel-shifted GI (SPSGI). Indeed, the proposed scheme can have a good signal-to-noise ratio performance even if the sub-Nyquist sampling ratio is greater than 5.45%. Since the HED network is trained by numerical simulations before the experiment, this proposed method provides a promising way for achieving edge detection with small measurement times and low time cost. Graphical Abstract

Highlights 1. Circulating exosomal RNA sequencing identified novel potential biomarkers of clear cell renal cell carcinoma (ccRCC). 2. The early diagnostic signature comprising KMT2D and PREX2 showed high accuracy in distinguishing ccRCCs from healthy individuals. 3. The differential diagnostic signature comprising CUL9, KMT2D, and PREX2 showed great performance in distinguishing ccRCCs from benign renal masses. 4. These signatures could be promising and noninvasive biomarkers for ccRCC detection.

Abstract Herein, we developed a near-invisible solar cell through a precise control of the contact barrier between an indium tin oxide (ITO) electrode and a monolayer tungsten disulfide (WS2), grown by chemical vapor deposition (CVD). The contact barrier between WS2 and ITO was controlled by coating various thin metals on top of ITO (Mx/ITO) and inserting a thin layer of WO3 between Mx/ITO and the monolayer WS2, which resulted in a drastic increase in the Schottky barrier height (up to 220 meV); this could increase the efficiency of the charge carrier separation in our Schottky-type solar cell. The power conversion efficiency (PCE) of the solar cell with the optimized electrode (WO3/Mx/ITO) was more than 1000 times that of a device using a normal ITO electrode. Large-scale fabrication of the solar cell was also investigated, which revealed that a simple size expansion with large WS2 crystals and parallel long electrodes could not improve the total power (PT) obtained from the complete device even with an increase in the device area; this can be explained by the percolation theory. This problem was addressed by reducing the aspect ratio (width/channel length) of the unit device structure to a value lower than a critical threshold. By repeating the experiments on this optimized unit device with an appropriate number of series and parallel connections, PT could be increased up to 420 pW from a 1-cm2 solar cell with a very high value (79%) of average visible transmission (AVT).

Nervonic acid (NA) is a very-long-chain monounsaturated fatty acid with pharmaceutical and nutraceutical functions that plays an important role in treating several neurological disorders. One major source of NA is plant seed oil. Here we report fatty acid profiles of seeds and germplasm diversity of six plant species, including three woody plants with high amounts of NA-enriched seed oil, Malania oleifera, Macaranga adenantha, and M. indica. M. oleifera had the largest seed (average 7.40 g single seed), highest oil content (58.71%), and highest NA level (42.22%). The germplasm diversity of M. oleifera is associated with its habitat but not elevation. Seeds of M. adenantha contained higher NA levels (28.41%) than M. indica (21.77%), but M. indica contained a significantly higher oil content (29.22%) and seed yield. M. adenantha germplasm varied among populations, with one population having seeds with high oil content (22.63%) and NA level (37.78%).Although M. indica grow naturally at a range of elevations, no significant differences were detected between M. indica populations. These results suggest that M. indica and M. oleifera have greater potential as a source of NA, which will contribute to constructing a germplasm resource nursery and establishing a selection and breeding program to improve the development of NA-enriched plants.

Investment in the renewable energy industry has huge market potential and economic benefits. In order to technologically upgrade, industrialize, and marketize renewable energy projects, it is necessary to build a good green policy environment and a reasonable green policy support system. This paper studies the effects of the green policy environment on renewable energy investment and conducts effect evaluation on the green policies. First, a coordinated energy planning and investment strategy was proposed against the green and low-carbon background, and the influence mechanism of the green policy environment on renewable energy investment was elaborated. Then, a multistage optimization model was constructed for renewable energy investment in the green policy environment, and the model reconstruction and solution were also given. After that, the steps to evaluate the effects of green policies for renewable energy power generation were also explained in detail. The experimental results verified the effectiveness of the constructed model. The relevant research results can enrich and improve the theoretical research framework of the green policy environment system, and fully promote the strategy transformation of energy and high-quality economic development.

Objective. To identify any concomitant complications other than bleeding (COTB) before and after endoscopic treatment of esophagogastric variceal bleeding (EGVB) in liver cirrhosis patients and explore the underlying risk factors. Materials and Methods. Cirrhotic patients complicated with EGVB, who underwent interventional endoscopic treatments in our hospital from November 2017 to August 2020, were enrolled in this study. Clinical data were retrospectively analyzed for COTB at admission and within 2 years of the first endoscopic treatment. Patients were screened for potential risk factors of COTB before and after the treatment. Univariate analysis was performed to identify clinical factors of secondary complications, and statistically significant factors were included in the multivariate Cox and logistic regression analyses. Results. Of the 547 patients with cirrhosis, 361 individuals had COTB in the first endoscopic treatment. In this cohort, the top 3 prevalent incidences were portal vein thrombosis (PVT) or spongiosis, cholelithiasis, and pathogenic infections. The COTB did not occur at admission in 171 liver cirrhosis patients but happened at the follow-up. Higher Child-Pugh scores indicated potential risks of multiple concurrent complications, including bleeding. Risk factors for concomitant PVT or cavernous changes after endoscopic treatment of EGVB, pathogenic infections, and cholelithiasis could prolong the cirrhosis symptoms, while noncholestatic cirrhosis patients might have a lower risk than posthepatitis B cirrhosis patients, in the context of a higher degree of EGV and serum level of D-D and a lower blood calcium level. Conclusions. Clinical treatment and interventions can be tailored to avoid other complications during and after EGVB treatment, which can affect the outcome and prognosis of bleeding symptoms.

In the published work [...]

Deep learning is one of the most exciting and promising techniques in the field of artificial intelligence (AI), which drives AI applications to be more intelligent and comprehensive. However, existing deep learning techniques usually require a large amount of expensive labeled data, which limit the application and development of deep learning techniques, and thus it is imperative to study unsupervised machine learning. The learning of deep representations by mutual information estimation and maximization (Deep InfoMax or DIM) method has achieved unprecedented results in the field of unsupervised learning. However, in the DIM method, to restrict the encoder to learn more normalized feature representations, an adversarial network learning method is used to make the encoder output consistent with a priori positively distributed data. As we know, the model training of the adversarial network learning method is difficult to converge, because there is a logarithmic function in the loss function of the cross-entropy measure, and the gradient of the model parameters is susceptible to the “gradient explosion” or “gradient disappearance” phenomena, which makes the training of the DIM method extremely unstable. In this regard, we propose a Wasserstein distance-based DIM method to solve the stability problem of model training, and our method is called the WDIM. Subsequently, the training stability of the WDIM method and the classification ability of unsupervised learning are verified on the CIFAR10, CIFAR100, and STL10 datasets. The experiments show that our proposed WDIM method is more stable to parameter updates, has faster model convergence, and at the same time, has almost the same accuracy as the DIM method on the classification task of unsupervised learning. Finally, we also propose a reflection of future research for the WDIM method, aiming to provide a research idea and direction for solving the image classification task with unsupervised learning.

Designing high-performance materials for the detection or removal of toxic decomposition gases of sulfur hexafluoride is crucial for both environmental monitoring and human health preservation. Based on first-principles calculations, the adsorption performance and gas-sensing properties of unsubstituted phthalocyanine (H2Pc) and H2Pc doped with 4d transition metal atoms (TM = Tc and Ru) towards five characteristic decomposition components (HF, H2S, SO2, SOF2, and SO2F2) were simulated. The findings indicate that both the TcPc and RuPc monolayers are thermodynamically and dynamically stable. The analysis of the adsorption energy indicates that H2S, SO2, SOF2, and SO2F2 underwent chemisorption on the TcPc monolayer. Conversely, the HF molecules were physisorbed through interactions with H atoms. The chemical adsorption of H2S, SO2, and SOF2 occurred on the RuPc monolayer, while the physical adsorption of HF and SO2F2 molecules was observed. Moreover, the microcosmic mechanism of the gas–adsorbent interaction was elucidated by analyzing the charge density differences, electron density distributions, Hirshfeld charges, and density of states. The TcPc and RuPc monolayers exhibited excellent sensitivity towards H2S, SO2, and SOF2, as evidenced by the substantial alterations in the band gaps and work functions of the TcPc and RuPc nanosheets. Our calculations hold significant value for exploring the potential chemical sensing applications of TcPc and RuPc monolayers in gas sensing, with a specific focus on detecting sulfur hexafluoride.

Power lines are critical infrastructure components in power grid systems. Strand breakage is a kind of serious defect of power lines that can directly impact the reliability and safety of power supply. Due to the slender morphology of power lines and the difficulty in acquiring sufficient sample data, strand breakage detection remains a challenging task. Moreover, power grid corporations prefer to detect these defects on-site during power line inspection using unmanned aerial vehicles (UAVs), rather than transmitting all of the inspection data to the central server for offline processing which causes sluggish response and huge communication burden. According to the above challenges and requirements, this paper proposes a novel method for detecting broken strands on power lines in images captured by UAVs. The method features a multi-stage light-weight pipeline that includes power line segmentation, power line local image patch cropping, and patch classification. A power line segmentation network is designed to segment power lines from the background; thus, local image patches can be cropped along the power lines which preserve the detailed features of power lines. Subsequently, the patch classification network recognizes broken strands in the image patches. Both the power line segmentation network and the patch classification network are designed to be light-weight, enabling efficient online processing. Since the power line segmentation network can be trained with normal power line images that are easy to obtain and the compact patch classification network can be trained with relatively few positive samples using a multi-task learning strategy, the proposed method is relatively data efficient. Experimental results show that, trained on limited sample data, the proposed method can achieve an F1-score of 0.8, which is superior to current state-of-the-art object detectors. The average inference speed on an embedded computer is about 11.5 images per second. Therefore, the proposed method offers a promising solution for conducting real-time on-site power line defect detection with computing sources carried by UAVs.

In order to improve the control performance of the micro gas turbine in the full range of operating conditions, this paper proposes a new control technique based on the hybridization of improved particle swarm optimization algorithm and cuckoo search algorithm (HIPSO_CS) to tune the fuzzy PID controller parameters. Firstly, the traditional particle swarm optimization algorithm is improved by linearly decreasing the number of particles and the value of the inertia weight. Secondly, the cuckoo algorithm’s local random walk strategy is introduced into the particle swarm optimization algorithm to enhance particles’ diversity. Through comparing with traditional optimization algorithms, the proposed HIPSO_CS algorithm is verified to have high convergence accuracy and fast iteration speed. To improve the dynamic response performance of the micro gas turbine, controllers with different structures are designed, and a comparative study of HIPSO_CS optimized Fuzzy PID/PID/PI is presented. The simulation results show that the micro gas turbine controlled by the fuzzy PID controller has a rapid response to fuel flow, minor speed overshoot, and shorter stabilization time during load increase or decrease. In addition, the designed control method can also achieve significant control effects under load disturbance, model parameter changes, and extreme operating condition.

Abstract The very early nucleation stage of a transition metal dichalcogenide (TMD) was directly observed with in-situ monitoring of chemical vapor deposition and automated image analysis. Unique nucleation dynamics, such as very large critical nuclei and slow to rapid growth transitions, were observed during the vapor–liquid–solid (VLS) growth of monolayer tungsten disulfide (WS2). This can be explained by two-step nucleation, also known as non-classical nucleation, in which metastable clusters are formed through the aggregation of droplets. Subsequently, nucleation of solid WS2 takes place inside the metastable cluster. Furthermore, the detailed nucleation dynamics was systematically investigated from a thermodynamic point of view, revealing that the incubation time of metastable cluster formation follows the traditional time–temperature transformation diagram. Quantitative phase field simulation, combined with Bayesian inference, was conducted to extract quantitative information on the growth dynamics and crystal anisotropy from in-situ images. A clear transition in growth dynamics and crystal anisotropy between the slow and rapid growth phases was quantitatively verified. This observation supports the existence of two-step nucleation in the VLS growth of WS2. Such detailed understanding of TMD nucleation dynamics can be useful for achieving perfect structure control of TMDs.

We reported a general transition metal-free transformation to access C3-carbamoylated 2H-indazoles via visible light-induced oxidative decarboxylation coupling, in the presence of oxamic acids as the coupling sources, 4CzIPN as the photocatalyst, and Cs2CO3 as the base. The great application potential of this mild condition is highlighted by the late-stage modification of drugs, N-terminal modification of peptides, and the good antitumor activity of the novel desired product.

The orientation construction of S-doped porous carbon fibers (SPCFs) is realized by the facile template-directed methodology using asphalt powder as carbon source. The unique fiber-like morphology without destruction can be well duplicated from the template by the developed methodology. MgSO4 fibers serve as both templates and S dopant, realizing the in-situ S doping into carbon frameworks. The effects of different reaction temperatures on the yield and S doping level of SPCFs are investigated. The S doping can not only significantly enhance the electrical conductivity, but also introduce more defects or disorders. As anode material for lithium ion batteries (LIBs), SPCFs electrode delivers better rate capability than undoped PCFs. And the capacity of SPCFs electrode retains around 90% after 300 cycles at 2 A g−1, exhibiting good cycling stability. As the electrocatalysts for fuel cells, the onset potentials of SPCFs obtained at 800 and 900 °C are concentrated at 0.863 V, and the higher kinetic current densities at 0.4 V of them are larger than that of PCFs, demonstrating the superior electrocatalytic performance. Due to the synergistic effect of abundant pore channels and S doping, SPCFs electrode exhibits superior electrochemical performances as anode for LIBs and elecctrocatalyst for fuel cells, respectively. Additionally, the oriented conversion of asphalt powder into high-performance electrode material in this work provides a new way for the high value application of asphalt.