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In the applications of polymer film production and lubrication systems, ternary hybrid nanofluids can significantly enhance product performance by improving thermal conduction. This study focuses on investigating a nanofluid consisting of alumina (Al2O3), zincite (ZnO), and magnetite (Fe3O4) dispersed in water to optimize its thermal and flow properties. By integrating magnetohydrodynamic (MHD) flow dynamics over a stretching/shrinking sheet with velocity slip and employing both analytical solutions and numerical analysis through MATLAB’s BVP4C, the research investigates critical parameters such as suction/injection, magnetic strength, and slip effects on surface properties and thermal transfer. The results show that increased nanoparticle hybridity, suction/injection, and magnetic strength enhance skin friction and the local Nusselt number in the primary solution, while the secondary solution shows reductions. Velocity accelerates with increased suction/injection and magnetic parameters, while temperature decreases with higher velocity slip, suction/injection, and magnetic strength. Stability analysis highlights the robustness of the primary solution and the complexities of secondary solutions, with ternary and binary nanofluids outperforming mono nanofluids under shrinking sheet conditions (λ = −6) and achieving thermal efficiency gains of 0.43% and 0.2%, respectively. This study’s novelty lies in determining the critical conditions for one, two, or no solutions and integrating analytical and numerical methods for comprehensive insights. The contribution of this work extends to optimizing heat transfer in advanced industrial applications using ternary hybrid nanofluids.

The industrial significance of stability analysis for dual solutions and heat transfer sets the stage for this research. Focusing on Maxwell ternary nanofluid flow, the study aims to enhance thermal conductivity and stability by delving into viscous dissipation and velocity slip effects on a stretching/shrinking sheet. Employing a mathematical model, refined with nondimensional transformations and MATLAB’s BVP4C solver, the research identifies dual solutions and examines the influence of key parameters on fluid dynamics and heat transfer. Results showcase a progressive improvement in convective heat transfer and skin friction from mono nanofluid (NF) to binary hybrid nanofluid (HNF), culminating with ternary hybrid nanofluid (THNF). These improvements are significantly associated with the suction/injection parameter (S), whereas the slip (σ) and elastic (K) parameters enhance thermal transfer but negatively affect skin friction efficiency at elevated levels. The robustness of the upper branch solutions underscores the reliability of these findings. Remarkably, at λ=−1.25 with a nanoparticle volume fraction of 0.04, ternary nanofluids achieve a 2.9% thermal efficiency leap over HNF, which itself surpasses NF by 0.46%. These findings hold potential for significant advancements in sectors such as electronics, manufacturing, energy, biomedical, environmental engineering, aerospace, and automotive, aiming at elevating thermal efficiency.

Abstract Background Evidence suggests that the circadian clock (CIC) is among the important factors for tumorigenesis. We aimed to provide new insights into CIC-mediated molecular subtypes and gene prognostic indexes for prostate cancer (PCa) patients undergoing radical prostatectomy (RP) or radical radiotherapy (RT). Methods PCa data from TCGA was analyzed to identify differentially expressed genes (DEGs) with significant fold changes and p-values. A prognostic index called CIC-related gene prognostic index (CICGPI) was developed through clustering methods and survival analysis and validated on multiple data sets. The diagnostic accuracy of CICGPI for resistance to chemotherapy and radiotherapy was confirmed. Additionally, the interaction between tumor immune environment and CICGPI score was explored, along with their correlation with prognosis. Results TOP2A, APOE, and ALDH2 were used to classify the PCa patients into two subtypes. Cluster 2 had a higher risk of biochemical recurrence (BCR) than cluster 1 for PCa patients undergoing RP or RT. A CIC-related gene prognostic index (CICGPI) was constructed using the above three genes for PCa patents in the TCGA database. The CICGPI score showed good prognostic value in the TCGA database and was externally confirmed by PCa patients in GSE116918, MSKCC2010 and GSE46602. In addition, the CICGPI score had a certain and high diagnostic accuracy for tumor chemoresistance (AUC: 0.781) and radioresistance (AUC: 0.988). For gene set variation analysis, we observed that both beta alanine metabolism and limonene and pinene degradation were upregulated in cluster 1 for PCa patients undergoing RP or RT. For PCa patients undergoing RP, cell cycle, homologous recombination, mismatch repair, and DNA replication were upregulated in cluster 2. A strongly positive relationship between cancer-related fibroblasts and CICGPI score was observed in PCa patients undergoing RP or RT. Moreover, a high density of CAFs was highly closely associated with poorer BCR-free survival of PCa patients. Conclusions In this study, we established CIC-related immunological prognostic index and molecular subtypes, which might be useful for the clinical practice.

The natural enhanced weathering (EW) must be significantly accelerated by optimizing the local triple-phase environment prior to practical large-scale carbon dioxide removal (CDR). The implementation of stirred-slurry reactor (SSR) for enhancing mass transport and reaction rates of the EW-based CO2 capture process has not yet been reported. We conducted a hybrid modelling approach, in which mechanistic and data-driven models are integrated, for the scaled-up batch SSRs designed for EW-based CO2 capture. It is revealed that CO2 mass transport into the aqueous phase has significant impact on the overall capture performance. The scaled-up batch system is found to perform comparably to the continuous system in terms of CO2 capture rate, energy and water consumption. The energy consumption for gas enrichment in the batch system is expected to be less than 50% of that in continuous systems. Multi-objective optimisation reveals the efficacy and accuracy of the hybrid modeling within low energy consumption ranges.

Aqueous multivalent ion batteries have attracted increasing attention due to their high capacity, long cycle life, and impressive output energy. Considering that intercalation electrodes are more suitable for long cycle battery compared to the conversion ones, Cu2+ with appropriate ion radii has been chosen as intercalation carriers and bismuth sulfide with the matched lattice spacing as cathode to achieve long cycle life aqueous battery. We have synthesized the micrometer-scale flower-like Bi2S3 by regulating temperature and it is proved to be a perennial phase during Cu2+ intercalation/deintercalation of the Bi2S3 host when charge/discharge with a capacity of 104.5 mAh g–1 retention at 0.5 A g–1 after 1000 cycles. Lastly, build a hybrid ion battery with Zn and ZnSO4 electrolyte, the Bi2S3 cathode for Cu2+ intercalation can reach a maximum energy of 427.5 Wh Kg–1. The study provides a new intercalation cathode for aqueous multivalent ion batteries and will broaden the energy storage system.

This study explores using hybrid nanofluids to improve water quality by enhancing heat transfer and substance decomposition. Nanofluids effectively remove pollutants, optimise heat transfer, control pollution sources, and regulate fluid dynamics, which can lead to efficient pollution management in water systems. Thus, the present research examines the flow of an unsteady hybrid Al2O3–Cu/water nanofluid near the stagnation region in a porous medium, considering the discharge concentration and convective boundary conditions. Governing equations in ordinary differential equations are obtained using similarity transformations. The BVP4C solver in MATLAB is employed to expose dual solutions. The volume fraction of copper φa2, the suction/injection parameter (S), and the unsteadiness parameter (A), collectively contribute to the delay of the boundary layer separation. Increasing the values of φa2,A, and S enhances convective heat transfer. When the sheet shrunk between the range of −16.2 and −13, hybrid nanofluid has higher convective thermal transfer than nanofluid. Moreover, an increment in φa2 and S raises the skin friction coefficients and mass diffusion rates. Stability analysis reveals that the first solution is stable while the second one is unstable.

The dependable and efficient identification of Qin seal script characters is pivotal in the discovery, preservation, and inheritance of the distinctive cultural values embodied by these artifacts. This paper uses image histograms of oriented gradients (HOG) features and an SVM model to discuss a character recognition model for identifying partial and blurred Qin seal script characters. The model achieves accurate recognition on a small, imbalanced dataset. Firstly, a dataset of Qin seal script image samples is established, and Gaussian filtering is employed to remove image noise. Subsequently, the gamma transformation algorithm adjusts the image brightness and enhances the contrast between font structures and image backgrounds. After a series of preprocessing operations, the oriented gradient histograms (HOG) features are extracted from the images. During model training, different weights are assigned to classes with varying sample quantities to address the issue of class imbalance and improve the model's classification accuracy. Results show that the model achieves an accuracy of 95.30%. This research can help historians quickly identify and extract the text content on newly discovered Qin slip cultural relics, shortening the cycle of building a historical database.

The spatial organisation of diffuse midline glioma-H3K27M mutant (DMG) and glioblastoma (GBM) remains to be investigated. Here, the authors integrate short-read and long-read spatial profiling of DMG and GBM to identify regulatory programs and cellular ecosystems in distinct glioma niches.

The six-degree-of-freedom (6 DOF) motion of the mother-ship, which is caused by wave fluctuation, makes offshore operations difficult. To solve this problem, various heave compensation systems were proposed, as well as the law of wave motion. These heave compensation systems included passive heave compensation system, semi-active heave compensation system, and active heave compensation (AHC) system. However, the empirical formula of wave motion is not suitable for practical offshore operations, and rare AHC systems take the characteristic of compensation into consideration. To study the compensation characteristic of the AHC system based on electric-driven marine winch in the actual sea condition and improve the accuracy of compensation, this work uses the measured data of wave motion, and deduces the motion equations of multi-axial driving system. By substituting the above elements into the AHC system and designing the sliding mode controller (SMC), the simulation results show the ideal compensation effect of the system. By comparing with experimental results, it is concluded that the driving system operates in according with the specified states, and the compensation performance of system is effective. The overall work is conductive to the research on AHC system, as well as the exploitation of marine resources.

Fault diagnosis based on the expert system (ES) is still a research topic of manufacturing in Industry 4.0 because of the stronger interpretability. As the core component of the ES, fault diagnosis accuracy is positively correlated to the precision of the knowledge base. But it is difficult for users to understand the knowledge obtained from the original dataset utilizing the existing knowledge extraction method. Therefore, it is of great significance to extract easy-to-understand and exact rules from the NN framework. This paper proposes a hybrid extraction framework to perform the rule extraction for overcoming this drawback. First, an improved adaptive genetic algorithm (GA) using a logistic function, namely LAGA, is proposed to solve the traditional GA’s insufficient prediction performance issue. Compared with the other three mainstream adaptive GAs, the experiment results of optimizing six selected test functions by these GA variants show that the LAGA algorithm’s convergence accuracy and speed have been greatly improved, especially for high latitude functions. On this basis, a rule extraction method based on the symbol rule and NN, namely the LAGA-BP framework, is discussed in this manuscript to classify the real-valued attributes. This framework obtains hidden knowledge (knowledge refinement process) by NN and further transforms the acquired hidden knowledge into more easy-to-understand rule knowledge (rule extraction process). The execution of the LAGA-BP framework could be separated into two phases. The first phase is to optimize a back propagation NN (BPNN) using the LAGA and refine prediction classification knowledge over the optimized BPNN. In the second phase, an attribute reduction algorithm using multi-layered NN (SD algorithm) based on two different superposed networks is used in this framework to reduce data-set attributes and then uses the K-means clustering algorithm to extract the if-then rule from the simplified attributes. The Wisconsin breast cancer dataset is used as a case study to reveal the correctness and robustness of the proposed LAGA-BP method. Consulting relevant medical personnel and referencing relevant data shows that the rules extracted using this method help verify the diagnosis results, thus verifying the proposed framework’s feasibility and practicality.

BackgroundPhotodynamic therapy (PDT) is increasingly used for the treatment of basal cell carcinoma (BCC) and squamous cell carcinoma (SCC). However, it is unknown whether photodynamic therapy is more effective than other commonly used treatment modalities for these cancers.PurposeThe aim of this study was to determine the relative efficacy and safety of PDT compared with placebo or other interventions for the treatment of skin carcinomas.MethodsSearches were performed in PubMed, Web of Science, Embase, and the Cochrane Central Register of Controlled Trials databases. We included randomized controlled trials comparing the PDT with other interventions in adults skin BCC or SCC that reported on lesion response, recurrence, cosmetic appearance, or safety outcomes.ResultsSeventeen unique randomized controlled trials, representing 22 study arms from 21 publications were included. The included trials included 2,166 participants, comparing methyl aminolevulinic (MAL) PDT (six studies) or aminolevulinic acid (ALA) PDT (two studies). Comparators included placebo, surgery, hexaminolevulinic (HAL) PDT, erbium: yttrium-aluminum-garnet ablative factional laser (YAG-AFL) PDT, fluorouracil, and imiquimod. There were few studies available for each comparison. Mantel-Haenszel fixed effects risk ratios were calculated for response, recurrence, cosmetic outcomes, and adverse events. MAL-PDT had similar response rates to surgery, ALA-PDT, fluorouracil and imiquimod at 3- and 12 months post-intervention. The rate of recurrence was similar, showing few differences at 12 months, but at later time points (24–60 months), fewer lesions recurred with surgery and imiquimod than with PDT. PDT also caused more adverse events and pain than other interventions. However, PDT treatment was more likely to receive a “good” or “excellent” rating for cosmetic appearance than surgery or cryotherapy.ConclusionThis systematic review and meta-analysis demonstrates that the choice of treatment modality for BCC or SCC is best chosen in the context of the location and size of the lesion, the socioeconomic circumstances of the patient, as well as the patient’s preferences. We call for more high quality studies to be done, in order to enable more reliable interpretations of the data.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=368626, identifier CRD42022368626.

Dendrite growth has been the main trouble preventing the practical application of Li metal anodes. Herein, we present how an Fe3O4-PVDF composite network prepared by using electrospinning has been designed to protect lithium metal anodes effectively. In the symmetrical cells test, the cell with the Fe3O4-PVDF composite network maintains good cycle performance after 600 h (500 cycles) at a current density of 1 mA cm−2 and a plating/stripping capacity of 1 mAh cm−2. The bulky Li dendrite is suppressed and a uniform Li deposition remains after long cycling. The characteristics of this engineered separator are further demonstrated in Li-S full cells with a good cycle performance (capacity of 419 mAh g−1 after 300 cycles at 0.5 C). This work provides a new idea for the protection of lithium metal anodes.

Objective: To investigate the duration and peak of severe acute respiratory syndrome coronavirus 2 shedding as infectivity markers for determining the isolation period. Methods: A total of 2,558 upper respiratory tract (URT) and lower respiratory tract (LRT) specimens from 138 patients with laboratory-confirmed coronavirus disease were analyzed. Measurements of sequential viral loads were aggregated using the cubic spline smoothing function of a generalized additive model. The time to negative conversion was compared between symptom groups using survival analysis. Results: In URT samples, viral RNA levels peaked on day 4 after symptom onset and rapidly decreased until day 10 for both E and RdRp genes, whereas those in LRT samples immediately peaked from symptom onset and decreased until days 15.6 and 15.0 for E and RdRp genes, respectively. Median (interquartile range) time to negative conversion was significantly longer in symptomatic (18.0 [13.0–25.0] days) patients than in asymptomatic (13.0 [9.5–17.5] days) patients. The more types of symptoms a patient had, the longer the time to negative conversion. Conclusions: The viral load rapidly changes depending on the time after symptom onset; the viral shedding period may be longer with more clinical symptoms. Different isolation policies should be applied depending on disease severity.

Environmentally friendly lead-free K1-xNaxNbO3 (KNN) ceramics possess electromechanical properties comparable to lead-based ferroelectric materials but cannot meet the needs of device miniaturization, and the corresponding thin films lack theoretical and experimental studies. To this end, we developed the nonlinear phenomenological theory for ferroelectric materials to study the effects of non-equiaxed misfit strain on the phase structure, electromechanical properties, and electrical response of K0.5Na0.5NbO3 epitaxial films. We constructed in-plane misfit strain (u1−u2) phase diagrams. The results show that K0.5Na0.5NbO3 epitaxial film under non-equiaxed in-plane strain can exhibit abundant phase structures, including orthorhombic a1c, a2c, and a1a2 phases, tetragonal a1, a2, and c phases, and monoclinic r12 phases. Moreover, in the vicinity of a2c−r12, a1c−c, and a1a2−a2 phase boundaries, K0.5Na0.5NbO3 epitaxial films exhibit excellent dielectric constant ε11, while at a2c−r12 and a1c−c phase boundaries, a significant piezoelectric coefficient d15 is observed. It was also found that high permittivity ε33 and piezoelectric coefficients d33 exist near the a2c−a2, a1a2−r12, and a1c−a1 phase boundaries due to the existence of polymorphic phase boundary (PPB) in the KNN system, which makes it easy to polarize near the phase boundaries, and the polarizability changes suddenly, leading to electromechanical enhancement. In addition, the results show that the K0.5Na0.5NbO3 thin films possess a large electrocaloric response at the phase boundary at the a1a2−r12 and a1c−a1 phase boundaries. The maximum adiabatic temperature change ΔT is about 3.62 K when the electric field change is 30 MV/m at room temperature, which is significantly enhanced compared with equiaxed strain. This study provides theoretical guidance for obtaining K1−xNaxNbO3 epitaxial thin films with excellent properties.

An adaptive harmony search algorithm utilizing differential evolution and opposition-based learning (AHS-DE-OBL) is proposed to overcome the drawbacks of the harmony search (HS) algorithm, such as its low fine-tuning ability, slow convergence speed, and easily falling into a local optimum. In AHS-DE-OBL, three main innovative strategies are adopted. First, inspired by the differential evolution algorithm, the differential harmonies in the population are used to randomly perturb individuals to improve the fine-tuning ability. Then, the search domain is adaptively adjusted to accelerate the algorithm convergence. Finally, an opposition-based learning strategy is introduced to prevent the algorithm from falling into a local optimum. The experimental results show that the proposed algorithm has a better global search ability and faster convergence speed than other selected improved harmony search algorithms and selected metaheuristic approaches.

Hierarchical PbS powders doped with different contents of Bi2S3 and Cu2S were synthesized using the hydrothermal method. Subsequently, the powders were subjected to spark plasma sintering (SPS) for consolidation into bulk ceramics. X-ray photoelectron spectroscopy results showed that Bi2S3 and Cu2S were doped into PbS successfully. The effect of doping with different Bi2S3 and Cu2S contents on thermoelectric performance was investigated systematically. The results showed that pure PbS was an n-type semiconductor, and Bi2S3 doping or Bi2S3-Cu2S co-doping could decrease the thermal conductivity of PbS effectively. PbS doped with 1% Bi2S3 exhibited a moderate Seebeck coefficient, high electric conductivity, and low thermal conductivity simultaneously, thus attaining a maximum figure of merit ZT of 0.55 at 773 K. PbS doped with 1% Bi2S3-1% Cu2S exhibited an enhanced power factor and reduced thermal conductivity at an elevated temperature; the maximum ZT value obtained at 773 K was 0.83, which is more than twice that of pure PbS at 758 K (0.29), as a result.

Extracting circle information from images has always been a basic problem in computer vision. Common circle detection algorithms have some defects, such as poor noise resistance and slow computation speed. In this paper, we propose an anti-noise fast circle detection algorithm. In order to improve the anti-noise of the algorithm, we first perform curve thinning and connection on the image after edge extraction, then suppress noise interference by the irregularity of noise edges and extract circular arcs by directional filtering. In order to reduce the invalid fitting and speed up the running speed, we propose a circle fitting algorithm with five quadrants, and improve the efficiency of the algorithm by the idea of “divide and conquer”. We compare the algorithm with RCD, CACD, WANG and AS on two open datasets. The results show that we have the best performance under noise while keeping the speed of the algorithm.

It is well known that flooding brings great losses to people’s production and life, just because it is unsuspected, is extremely extensive, and has high frequency. More than half of the people in China live in flood-prone areas, and their lives and properties are threatened. Flood risk assessment is one of the measures of flood management, and it is economically and socially important to assess flood risk. The scope of the traditional monitoring and forecasting early warning system is mainly limited to the area affected by flash floods, while the objective reality of real-time monitoring, forecasting, and early warning of flood disasters in the area affected by river floods and by the scheduling of riverine terrace power stations is not possible. The multiphysics remote sensing big data fusion analysis can divide the data into grids according to the grid method in the sliding time window, filter the normal data by information entropy in each grid, judge the remaining data that may be abnormal by using local abnormality factor, and eliminate the abnormal data according to the judgment result. This paper introduces the application of remote sensing in flood control field, proposes the framework of basin flood prediction based on multiphysics field remote sensing big data fusion analysis, and designs each functional module of the system according to the object-oriented idea to realize the functions of data management, image processing, spatial analysis, and simulation output. The method can change the problems of cumbersome data processing and basic parameter rate determination in traditional hydrological methods and can find certain regularity through the connection between all related factors. Meanwhile, the use of artificial intelligence and other technical means makes the calculation speed faster and the obtained results are closer to the actual measured values, which is beneficial to guide the practical work.