Water used for water supply undergoes numerous changes that affect its composition prior to entering the water supply system (WSS). Once it enters the WSS, it is subject to numerous influences altering its physical and chemical composition, redox potential, and microbial quality. Observations of water quality parameters at different locations within the WSS indicate that it is justified to assume that these processes take place from the source to the end user. In this study, we used the results of routine everyday analyses (EC, T, pH, ORP, chloride, nitrate, nitrite, ammonium, and bacteria) supplemented by experimental data from a one-year sampling campaign assessing the main cations and anions and stable isotopes δ²H and δ¹⁸O. Through these data, the statistical significance of the differences between the concentrations of the basic water quality parameters among different WSS locations was determined, together with the water retention time in the system. The results indicate minor changes in water chemical composition within the observed WSS, remaining below the prescribed Maximum Contaminant Level (MCL) for human consumption. However, factors such as water retention time, CaCO₃ deposition, pH fluctuations, and bacterial growth may influence its suitability, which necessitates further investigation into potential risks affecting water quality. Full article

Electric vehicle (EV) charging facilities are essential to their development and deployment. These days, autonomous microgrids that use renewable energy resources to energize charging stations for electric vehicles alleviate pressure on the public electricity grid. Nevertheless, controlling and managing such charging stations’ energy is difficult due to the nonlinearity and irregular character of renewable energy sources. The current research recommends using a Brain Emotional Learning Intelligent Control (BELBIC) controller to enhance an autonomous EV charging station’s performance and power management. The charging station uses a battery to store energy and is primarily powered by photovoltaic (PV) solar energy. The principles of BELBIC are dependent on emotional cues and sensory inputs, and they are based on an emotion processing system in the brain. Noise and parameter variations do not affect this kind of controller. In this study, the performance of a conventional proportional–integral (PI) controller and the suggested BELBIC controller is evaluated for variations in solar insolation. The various parts of an EV charging station are simulated and modelled by the MATLAB/Simulink framework. The findings show that, in comparison to the conventional PI controller, the suggested BELBIC controller greatly enhances the transient responsiveness of the EV charging station’s performance. The EV keeps charging while the storage battery perfectly saves and keeps steady variations in PV power, even in the face of any PV insolation disturbances. The suggested system’s simulation results are provided and scrutinized to confirm the concept’s suitability. The findings validate the robustness of the suggested BELBIC control versus parameter variations. Full article

Particle image velocimetry (PIV) measurements were performed inside a low-pressure turbine cascade operating at engine-relevant high-speed and low-Re conditions to investigate the near-endwall flow. Of particular research interest was the dominant periodic disturbance of the flow field by incoming wakes, which were generated by moving cylindrical bars at a frequency of 500 Hz. Two PIV setups were utilized to resolve both (1) a large blade-to-blade plane close to the endwall as well as midspan and (2) the wake effects in an axial flow field downstream of the blade passage. The measurements were performed using a phase-locked approach in order to align and compare the results with comprehensive CFD data that are also available for this test case. The experimental results not only support a better understanding and even a quantification of the wake-induced over/under-turning inside and downstream of the passage, they also enable the tracing of the `negative-jet-effect’, which is widely known in the CFD branch of the turbomachinery community but is seldom visualized in experiments. The results also reveal that the bar wake periodically widens the blade wake by up to 165%, while the secondary flow is less affected and exhibits a phase lag with respect to the 2D-flow effects. The results presented here are an essential basis for the subsequent investigation of the near-endwall blade suction surface effects using unsteady pressure-sensitive paint in the second part of this two-part publication. Full article

Analytical modelling is an effective approach to obtaining a gear dynamic response or vibration pattern for health monitoring and useful life prediction. Many researchers have modelled this response with various fault conditions commonly observed in gears. The outcome of such models provides a good idea about the changes in the dynamic response available between different gear health states. Hence, a catalogue of the responses is currently available, which ought to aid predictions of the health of actual gears by their vibration patterns. However, these analytical models are limited in providing solutions to useful life prediction. This may be because a majority of these models used single fault conditions for modelling and are not valid to predict the remaining life of gears undergoing more than one fault condition. Existing reviews related to gear faults and dynamic modelling can provide an overview of fault modes, methods for modelling and health prediction. However, these reviews are unable to provide the critical similarities and differences in the single-fault dynamic models to ascertain the possibility of developing models under combined fault modes. In this paper, existing analytical models of spur gears are reviewed with their associated challenges to predict the gear health state. Recommendations for establishing more realistic models are made especially in the context of modelling combined faults and their possible impact on gear dynamic response and health prediction. Full article

Glass waste products represent a significant environmental concern, with an estimated 1.4 billion tons being landfilled globally and 200 million tons annually. This results in a significant use of land resources. Therefore, it would be highly advantageous to develop a new method for disposing of waste glass. Waste glass can be recycled and ground into waste glass powder (WGP) for use in solidified soil applications as a sustainable resource. This study is based on solidified soil research, wherein NaOH, Ca(OH)₂, and Na₂SO₄ were incorporated as activators to enhance the reactivity of WGP. The optimal solidified soil group was determined based on unconfined compressive strength tests, which involved varying the activator concentrations and WGP content in combination with cement. X-ray diffraction (XRD) was used to study the composition of solidified soil samples. Microscopic pore characteristics were investigated using scanning electron microscopy (SEM), and the Image J software was employed to quantify the number and size of pores. Fourier-transform infrared spectroscopy (FTIR) was employed to examine the activation effect of waste glass powder. This study investigated the solidification mechanism and porosity changes. The results demonstrate that the addition of activated WGP to solidified soil enhances its strength, with a notable 12% increase in strength achieved using a 6% Ca(OH)₂ solution. The use of 2% concentration of Na₂SO₄ and NaOH also shows an increase in strength of 7.6% and 8.6%, respectively, compared to the sample without WGP. The XRD and SEM analyses indicate that activated WGP enhances the content of hydrates, reduces porosity, and fosters the formation of a more densely packed solidified soil structure. Full article

As an innovative form in the digital age, VR art exhibitions have attracted increasing attention. This study aims to explore the key factors that influence visitors’ continuance intention to VR art exhibitions using the expectation confirmation model and experience economy theory and to explore ways to enhance visitor immersion in virtual environments. We conducted a quantitative study of 235 art professionals and enthusiasts, conducted using the partial least squares structural equation modeling (PLS-SEM), to examine the complex relationship between confirmation (CON), Perceived Usefulness (PU), Aesthetic Experiences (AE), Escapist Experiences (EE), Satisfaction (SAT), and Continuance Intention (CI). The results show that confirmation plays a key role in shaping PU, AE, and EE, which in turn positively affect visitors’ SAT. Among these factors, AE positively impacts PU, but EE have no impact. A comprehensive theoretical model was then constructed based on the findings. This research provides empirical support for designing and improving VR art exhibitions. It also sheds light on the application of expectation confirmation theory and experience economy theory in the art field to improve user experience and provides theoretical guidance for the sustainable development of virtual digital art environment. Full article

According to the current guidelines, additional surgery is performed for endoscopically resected specimens of early colorectal cancer (CRC) with a high risk of lymph node metastasis (LNM). However, the rate of LNM is 2.1–25.0% in cases treated endoscopically followed by surgery, indicating a high rate of unnecessary surgeries. Therefore, this study aimed to develop an artificial intelligence (AI) model using H&E-stained whole slide images (WSIs) without handcrafted features employing surgically and endoscopically resected specimens to predict LNM in T1 CRC. To validate with an independent cohort, we developed a model with four versions comprising various combinations of training and test sets using H&E-stained WSIs from endoscopically (400 patients) and surgically resected specimens (881 patients): Version 1, Train and Test: surgical specimens; Version 2, Train and Test: endoscopic and surgically resected specimens; Version 3, Train: endoscopic and surgical specimens and Test: surgical specimens; Version 4, Train: endoscopic and surgical specimens and Test: endoscopic specimens. The area under the curve (AUC) of the receiver operating characteristic curve was used to determine the accuracy of the AI model for predicting LNM with a 5-fold cross-validation in the training set. Our AI model with H&E-stained WSIs and without annotations showed good performance power with the validation of an independent cohort in a single center. The AUC of our model was 0.758–0.830 in the training set and 0.781–0.824 in the test set, higher than that of previous AI studies with only WSI. Moreover, the AI model with Version 4, which showed the highest sensitivity (92.9%), reduced unnecessary additional surgery by 14.2% more than using the current guidelines (68.3% vs. 82.5%). This revealed the feasibility of using an AI model with only H&E-stained WSIs to predict LNM in T1 CRC. Full article

Gastric perforation refers to the complete rupture of the gastric wall, leading to the extravasation of gastric contents into the thoracic cavity or peritoneum. Without timely intervention, the expulsion of gastric contents may culminate in profound discomfort, exacerbating the inflammatory process and potentially triggering perilous sepsis. In clinical practice, surgical suturing or endoscopic closure procedures are commonly employed. Magnetic-driven microswarms have also been employed for sealing gastrointestinal perforation. However, surgical intervention entails significant risk of bleeding, while endoscopic closure poses risks of inadequate closure and the need for subsequent removal of closure clips. Moreover, the efficacy of microswarms is limited as they merely adhere to the perforated area, and their sealing effect diminishes upon removal of the magnetic field. Herein, we present a Fe&Mg@Lard-Paraffin micromotor (LPM) constructed from a mixture of lard and paraffin coated with magnesium (Mg) microspheres and iron (Fe) nanospheres for sutureless sealing gastric perforations. Under the control of a rotating magnetic field, this micromotor demonstrates precise control over its movement on gastric mucosal folds and accurately targets the gastric perforation area. The phase transition induced by the high-frequency magnetothermal effect causes the micromotor composed of a mixed oil phase of lard and paraffin to change from a solid to a liquid phase. The coated Mg microspheres are subsequently exposed to the acidic gastric acid environment to produce a magnesium protonation reaction, which in turn generates hydrogen (H₂) bubble recoil. Through a Mg-based micropower traction, part of the oil phase could be pushed into the gastric perforation, and it would then solidify to seal the gastric perforation area. Experimental results show that this can achieve long-term (>2 h) gastric perforation sealing. This innovative approach holds potential for improving outcomes in gastric perforation management. Full article

Given the widespread use of esters and polyesters in products like cosmetics, fishing nets, lubricants and adhesives, whose specific application(s) may cause their dispersion in open environments, there is a critical need for stringent eco-design criteria based on biodegradability and ecotoxicity evidence. Our approach integrates experimental and computational methods based on short oligomers, offering a screening tool for the rapid identification of sustainable monomers and oligomers, with a special focus on bio-based alternates. We provide insights into the relationships between the chemical structure and properties of bio-based oligomers in terms of biodegradability in marine environments and toxicity in benchmark organisms. The experimental results reveal that the considered aromatic monomers (terephthalic acid and 2,5-furandicarboxylic acid) accumulate under the tested conditions (OECD 306), although some slight biodegradation is observable when the inoculum derives from sites affected by industrial and urban pollution, which suggests that ecosystems adapt to non-natural chemical pollutants. While clean seas are more susceptible to toxic chemical buildup, biotic catalytic activities offer promise for plastic pollution mitigation. Without prejudice to the fact that biodegradability inherently signifies a desirable trait in plastic products, nor that it automatically grants them a sustainable “license”, this study is intended to facilitate the rational design of new polymers and materials on the basis of specific uses and applications. Full article

In South China, the large quantity of rainfall in the pre-summer rainy season can easily lead to natural disasters, which emphasizes the importance of improving the accuracy of precipitation forecasting during this period for the social and economic development of the region. In this paper, the back-propagation neural network (BPNN) is used to establish the model for precipitation forecasting. Three schemes are applied to improve the model performance: (1) predictors are selected based on individual meteorological stations within the region rather than the region as a whole; (2) the triangular irregular network (TIN) is proposed to preprocess the observed precipitation data for input of the BPNN model, while simulated/forecast precipitation is the expected output; and (3) a genetic algorithm is used for the hyperparameter optimization of the BPNN. The first scheme reduces the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the simulation by roughly 5% and more than 15 mm; the second reduces the MAPE and RMSE by more than 15% and 15 mm, respectively, while the third improves the simulation inapparently. Obviously, the second scheme raises the upper limit of the model simulation capability greatly by preprocessing the precipitation data. During the training and validation periods, the MAPE of the improved model can be controlled at approximately 35%. For precipitation hindcasting in the test period, the anomaly rate is less than 50% in only one season, and the highest is 64.5%. According to the anomaly correlation coefficient and Ps score of the hindcast precipitation, the improved model performance is slightly better than the FGOALS-f2 model. Although global climate change makes the predictors more variable, the trend of simulation is almost identical to that of the observed values over the whole period, suggesting that the model is able to capture the general characteristics of climate change. Full article

The clinical integration of prostate membrane specific antigen (PSMA) positron emission tomography and computed tomography (PET/CT) scans represents potential for advanced data analysis techniques in prostate cancer (PC) prognostication. Among these tools is the use of radiomics, a computer-based method of extracting and quantitatively analyzing subvisual features in medical imaging. Within this context, the present review seeks to summarize the current literature on the use of PSMA PET/CT-derived radiomics in PC risk stratification. A stepwise literature search of publications from 2017 to 2023 was performed. Of 23 articles on PSMA PET/CT-derived prostate radiomics, PC diagnosis, prediction of biopsy Gleason score (GS), prediction of adverse pathology, and treatment outcomes were the primary endpoints of 4 (17.4%), 5 (21.7%), 7 (30.4%), and 7 (30.4%) studies, respectively. In predicting PC diagnosis, PSMA PET/CT-derived models performed well, with receiver operator characteristic curve area under the curve (ROC-AUC) values of 0.85–0.925. Similarly, in the prediction of biopsy and surgical pathology results, ROC-AUC values had ranges of 0.719–0.84 and 0.84–0.95, respectively. Finally, prediction of recurrence, progression, or survival following treatment was explored in nine studies, with ROC-AUC ranging 0.698–0.90. Of the 23 studies included in this review, 2 (8.7%) included external validation. While explorations of PSMA PET/CT-derived radiomic models are immature in follow-up and experience, these results represent great potential for future investigation and exploration. Prior to consideration for clinical use, however, rigorous validation in feature reproducibility and biologic validation of radiomic signatures must be prioritized. Full article

A crimping and pultruding forming process for truss rods using Carbon Fiber (CF)/Polyether–Ether–Ketone (PEEK) prepreg tape as the raw material is proposed to address the problem of continuous manufacturing of space trusses on orbit. The proposed process provides material rods for continuous truss manufacturing. Through numerical simulation and experimental verification, the effects of relevant parameters on the forming process are determined, an efficient method of rod curl pultrusion, in-rail, equal material forming is proposed, and the structural configuration of the rod curl pultrusion forming mold is determined. The equivalent macroscopic mechanical properties of unidirectional CF/PEEK prepreg strips are considered, and the rod-forming process is investigated. Rod samples with different process parameters are prepared, and several tests are conducted on them. The results show that the forming load pull is negatively correlated with the temperature at the same forming speed, and forming speed is positively correlated with the forming load pull at a certain temperature. Temperature and speed affect the surface quality of the rod, the density of the material filling, and the mechanical properties of the rod. The optimal forming process parameters are determined through numerical simulation and experimental verification. The developed molding technology has the advantages of high efficiency, low energy consumption, and high integration. It reduces manufacturing costs and improves manufacturing efficiency, so it can serve as a new and effective solution for the manufacturing of high-performance truss rods in the aerospace field. Full article

Participant movement is a major source of artifacts in functional near-infrared spectroscopy (fNIRS) experiments. Mitigating the impact of motion artifacts (MAs) is crucial to estimate brain activity robustly. Here, we suggest and evaluate a novel application of the nonlinear Hammerstein–Wiener model to estimate and mitigate MAs in fNIRS signals from direct-movement recordings through IMU sensors mounted on the participant’s head (head-IMU) and the fNIRS probe (probe-IMU). To this end, we analyzed the hemodynamic responses of single-channel oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) signals from 17 participants who performed a hand tapping task with different levels of concurrent head movement. Additionally, the tapping task was performed without head movements to estimate the ground-truth brain activation. We compared the performance of our novel approach with the probe-IMU and head-IMU to eight established methods (PCA, tPCA, spline, spline Savitzky–Golay, wavelet, CBSI, RLOESS, and WCBSI) on four quality metrics: SNR, △AUC, RMSE, and R. Our proposed nonlinear Hammerstein–Wiener method achieved the best SNR increase (p < 0.001) among all methods. Visual inspection revealed that our approach mitigated MA contaminations that other techniques could not remove effectively. MA correction quality was comparable with head- and probe-IMUs. Full article

Knowing the tolerance interval capabilities (TICs) of a manufacturing process is of prime interest, especially if specifications link the manufacturer to a customer. These TICs can be determined using the machine performance concept of ISO 22514. However, few works have applied this to Additive Manufacturing printers, while testing most of the printing area as recommended takes a very long time (nearly 1 month is common). This paper, by proposing a novel part design called COMPAQT (Component for Machine Performances Assessment in Quick Time), aims at giving the same level of printing area coverage, while keeping the manufacturing time below 24 h. The method was successfully tested on a material extrusion printer. It allowed the determination of potential and real machine tolerance interval capabilities. Independently of the feature size, those aligned with the X axis achieved lower TICs than those aligned with the Y axis, while the Z axis exhibited the best performance. The measurements specific to one part exhibited a systematic error centered around 0 mm ± 0.050 mm, while those involving two parts reached up to 0.314 mm of deviation. COMPAQT can be used in two applications: evaluating printer tolerance interval capabilities and tracking its long-term performance by incorporating it into batches of other parts. Full article

This work presents the detection of hypoxanthine (HXA), a purine derivative that is similar to nucleic acids who overconsumption can cause health issues, by using hydrothermally synthesized cerium phosphate (CePO₄) followed by a sonochemical approach for CePO₄ decorated with a functionalized carbon nanofiber (CePO₄@f-CNF) nanocomposite. The formation of the nanocomposite was confirmed with X-ray powder diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive spectroscopy (EDS). A CePO₄@f-CNF nanocomposite is used to modify a glassy carbon electrode (GCE) to analyze the electrochemical detection of HXA. Cyclic voltammetry (CV), Electrochemical impedance spectroscopy (EIS), and Differential pulse voltammetry (DPV) were used to examine the electrochemical properties of the composite. As a result, the modified electrode exhibits a larger active surface area (A = 1.39 cm²), a low limit of detection (LOD) at 0.23 µM, a wide linear range (2.05–629 µM), and significant sensitivity. Therefore, the CePO₄@f-CNF nanocomposite was used to study the real-time detection in chicken and fish samples, and it depicted significant results. Full article

Microarray experiments, a mainstay in gene expression analysis for nearly two decades, pose challenges due to their complexity. To address this, we introduce DExplore, a user-friendly web application enabling researchers to detect differentially expressed genes using data from NCBI’s GEO. Developed with R, Shiny, and Bioconductor, DExplore integrates WebGestalt for functional enrichment analysis. It also provides visualization plots for enhanced result interpretation. With a Docker image for local execution, DExplore accommodates unpublished data. To illustrate its utility, we showcase two case studies on cancer cells treated with chemotherapeutic drugs. DExplore streamlines microarray data analysis, empowering molecular biologists to focus on genes of biological significance. Full article

Accurate quantification of uncertainty in solar photovoltaic (PV) generation forecasts is imperative for the efficient and reliable operation of the power grid. In this paper, a data-driven non-parametric probabilistic method based on the Naïve Bayes (NB) classification algorithm and Dempster–Shafer theory (DST) of evidence is proposed for day-ahead probabilistic PV power forecasting. This NB-DST method extends traditional deterministic solar PV forecasting methods by quantifying the uncertainty of their forecasts by estimating the cumulative distribution functions (CDFs) of their forecast errors and forecast variables. The statistical performance of this method is compared with the analog ensemble method and the persistence ensemble method under three different weather conditions using real-world data. The study results reveal that the proposed NB-DST method coupled with an artificial neural network model outperforms the other methods in that its estimated CDFs have lower spread, higher reliability, and sharper probabilistic forecasts with better accuracy. Full article

In recent years, there has been a surge in demand for and research focus on cell therapy, driven by the tissue-regenerative and disease-treating potentials of stem cells. Among the candidates, dental pulp stem cells (DPSCs) or human exfoliated deciduous teeth (SHED) have garnered significant attention due to their easy accessibility (non-invasive), multi-lineage differentiation capability (especially neurogenesis), and low immunogenicity. Utilizing these stem cells for clinical purposes requires careful culture techniques such as excluding animal-derived supplements. Human platelet lysate (hPL) has emerged as a safer alternative to fetal bovine serum (FBS) for cell culture. In our study, we assessed the impact of hPL as a growth factor supplement for culture medium, also conducting a characterization of SHED cultured in hPL-supplemented medium (hPL-SHED). The results showed that hPL has effects in enhancing cell proliferation and migration and increasing cell survivability in oxidative stress conditions induced by H₂O₂. The morphology of hPL-SHED exhibited reduced size and elongation, with a differentiation capacity comparable to or even exceeding that of SHED cultured in a medium supplemented with fetal bovine serum (FBS-SHED). Moreover, no evidence of chromosome abnormalities or tumor formation was detected. In conclusion, hPL-SHED emerges as a promising candidate for cell therapy, exhibiting considerable potential for clinical investigation. Full article

Background: Rare diseases, predominantly caused by genetic factors and often presenting neurological manifestations, are significantly underrepresented in research. This review addresses the urgent need for advanced research in rare neurological diseases (RNDs), which suffer from a data scarcity and diagnostic challenges. Bridging the gap in RND research is the integration of machine learning (ML) and omics technologies, offering potential insights into the genetic and molecular complexities of these conditions. Methods: We employed a structured search strategy, using a combination of machine learning and omics-related keywords, alongside the names and synonyms of 1840 RNDs as identified by Orphanet. Our inclusion criteria were limited to English language articles that utilized specific ML algorithms in the analysis of omics data related to RNDs. We excluded reviews and animal studies, focusing solely on studies with the clear application of ML in omics data to ensure the relevance and specificity of our research corpus. Results: The structured search revealed the growing use of machine learning algorithms for the discovery of biomarkers and diagnosis of rare neurological diseases (RNDs), with a primary focus on genomics and radiomics because genetic factors and imaging techniques play a crucial role in determining the severity of these diseases. With AI, we can improve diagnosis and mutation detection and develop personalized treatment plans. There are, however, several challenges, including small sample sizes, data heterogeneity, model interpretability, and the need for external validation studies. Conclusions: The sparse knowledge of valid biomarkers, disease pathogenesis, and treatments for rare diseases presents a significant challenge for RND research. The integration of omics and machine learning technologies, coupled with collaboration among stakeholders, is essential to develop personalized treatment plans and improve patient outcomes in this critical medical domain. Full article

Due to the long-term deformation settlement of foundations, issues such as damage and functional failure of buildings and structures have long been a concern in the engineering field. The creep of soil is one of the primary causes leading to long-term deformation of foundations. In this paper, the consolidation deformation, creep characteristics, and creep model of reconstituted saturated silty clay were studied using the isotropic consolidation creep test and triaxial compression creep test. The results show that for the isotropic consolidation creep test, although the applied load adopted different stages of loading, as long as the final applied confining pressure was the same, the number of stages applied by the confining pressure had little effect on the final isotropic consolidation deformation of the sample and the triaxial undrained shear strength after creep. However, for the triaxial shear creep test, it was found that under the same final deviatoric stress, the final deviatoric strain of the sample was closely related to the number of loading stages of deviatoric stress. The test showed that the more loading stages with the same deviatoric stress, the smaller the final deviatoric strain, and the triaxial undrained shear strength of the sample after creep increased. In addition, it was reasonable to set the pore pressure dissipation of the sample at 95% ((u₀ − u)/u₀ = 95%) as the time (t₁₀₀) at which the primary consolidation of the soil sample was completed. The isotropic consolidation creep curves and the triaxial compression creep curves showed certain non-linearity. Then, the logarithmic model and the hyperbolic model were used to fit the creep curves of the samples. It was found that the hyperbolic model had a better fitting effect than the logarithmic model, but for the triaxial compression creep test, the creep parameters of the sample changed greatly. Therefore, studying the creep characteristics of soil under different pre-loading steps is of significant engineering importance for evaluating the long-term deformation of underground structures. Full article

This research presents a new, eco-friendly, and swift method combining solid-phase extraction and hydrophobic deep eutectic solvents (DES) with high-performance liquid chromatography (SPE-DES-HPLC) for extracting and quantifying catechin and epicatechin in Shanxi aged vinegar (SAV). The parameters, such as the elution solvent type, the XAD-2 macroporous resin dosage, the DES ratio, the DES volume, the adsorption time, and the desorption time, were optimized via a one-way experiment. A central composite design using the Box–Behnken methodology was employed to investigate the effects of various factors, including 17 experimental runs and the construction of three-dimensional response surface plots to identify the optimal conditions. The results show that the optimal conditions were an HDES (tetraethylammonium chloride and octanoic acid) ratio of 1:3, an XAD-2 macroporous resin dosage of 188 mg, and an adsorption time of 11 min. Under these optimal conditions, the coefficients of determination of the method were greater than or equal to 0.9917, the precision was less than 5%, and the recoveries ranged from 98.8% to 118.8%. The environmentally friendly nature of the analytical process and sample preparation was assessed via the Analytical Eco-Scale and AGREE, demonstrating that this method is a practical and eco-friendly alternative to conventional determination techniques. In summary, this innovative approach offers a solid foundation for the assessment of flavanol compounds present in SAV samples. Full article

As a transitional flow pattern, slug–annular flow occurs over a wide range of operating conditions in micro-channels while its distribution in parallel micro-channels has not been well characterized. Herein, we conducted an experiment to study the phase distribution of slug–annular flow in parallel micro-channels. The test section consists of a header with a diameter of 0.48 mm and six branch channels with a diameter of 0.40 mm. Nitrogen and 0.03 wt% sodium dodecyl sulfate (SDS) solution were used as the test fluids. It was found that the phase distribution of the slug–annular flow was unstable and the duration of the varying process showed regularity with different inlet conditions. Increasing the liquid superficial velocity facilitated the liquid phase to flow into channels at the fore part of the header, while the channels at the rear part of the header were more supplied with liquid as the gas superficial velocity, volume fraction of gas, and volume flow rate increased. Furthermore, the results indicated that the channels located at the rear part of the header experienced a pronounced enhancement in the supply of both the liquid and gas phases, with the spacing between the branches increasing. A predictive correlation was formulated to ascertain the distribution of the liquid phase within slug–annular flow across parallel micro-channels. Full article