Stacking Method Research Articles

Clinical efficacy of the fractional ablative CO2 laser stacking method for depressed scars.

Depressed scars, which are unsightly outcomes of trauma or skin lesions, can lead to significant physical and mental stress. Various treatments have been developed to alleviate these burdens, among which the laser stacking method has proven effective. In this study, we compared the outcomes of laser stacking treatments with those of single-pulse ablative laser treatments for depressed scars. Eighty patients with depressed scars were divided into two therapeutic groups: the ablative laser stacking therapy group (Group 1, n = 40) and the single pulse ablative laser therapy group (Group 2, n = 40). The scars were evaluated using the modified Vancouver Scar Scale (mVSS) before and after treatment. Patient-completed questionnaires were also utilized. Comparative analyses were conducted both before and after treatment, as well as between the two groups. Eighty scars from each patient underwent the complete laser treatment protocols. Group 1 showed significantly greater improvement in the mVSS indices for pigmentation, vascularity, pliability, and height than group 2 (P < 0.05). In terms of patient-reported outcomes, group 1 rated better than group 2 across three indices: appearance, alleviation of concavity, and overall improvement (P < 0.05). Ablative laser stacking therapy effectively alleviated depressed scars, demonstrating significant improvement in both observer- and patient-reported outcomes. An energy level of 20-30mJ was employed, resulting in more favorable outcomes compared to a single pulse laser application of 10-15mJ. Our findings highlight the benefits of the stacking method, which shows notable improvements while minimizing complications. Further research involving various protocols may optimize clinical outcomes and patient satisfaction.

The Lyman Continuum Escape Fraction of Star-forming Galaxies at 2.4 ≲ z ≲ 3.0 from UVCANDELS

Abstract The UltraViolet Imaging of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey Fields (UVCANDELS) survey is a Hubble Space Telescope (HST) Cycle-26 Treasury Program, allocated in total 164 orbits of primary Wide-Field Camera 3 Ultraviolet and VISible light F275W imaging with coordinated parallel Advanced Camera for Surveys F435W imaging, on four of the five premier extragalactic survey fields: GOODS-N, GOODS-S, EGS, and COSMOS. We introduce this survey by presenting a comprehensive analysis of the absolute escape fraction ( f esc abs ) of Lyman continuum radiation through stacking the UV images of a population of star-forming galaxies with secure redshifts at 2.4 ≤ z ≤ 3.0. Our stacking benefits from the catalogs of high-quality spectroscopic redshifts compiled from archival ground-based data and HST slitless spectroscopy, carefully vetted by dedicated visual inspection efforts. We develop a robust stacking method to apply to 10 samples of in total 56 galaxies, and perform detailed Monte Carlo simulations of the intergalactic medium (IGM) attenuation, to take into account the sample variance of the mean IGM transmission when measuring f esc abs . The full stack at z ≈ 2.44 from 28 galaxies places a stringent 1σ upper limit of f esc abs ≲ 5 % , whereas the full stack at z ≈ 2.72 of an equal number of galaxies gives an upper limit of f esc abs ≲ 26 % at 1σ confidence level. These new F275W and F435W imaging mosaics from UVCANDELS have been made publicly available on the Barbara A. Mikulski Archive for Space Telescopes.

P-1517. Assessing the novel combinations against Carbapenem resistant Acinetobacter baunmannii (CRAB): ceftazidime-avibactam and ampicillin-sulbactam

Abstract Background Carbapenem-resistant Acinetobacter baumannii (CRAB) poses a serious threat to public health, as treatment options are becoming limited. IDSA Guidance has advocated combination therapies to combat CRAB. Sulbactam (SUL), a β-lactamase inhibitor with inherent antibacterial activity against CRAB, is recognized as an important component of combination therapy. Pairing avibactam (AVI) inhibitor with ceftazidime (CAZ), has also been successful in restoring its antibiotic efficacy against many Gram-negative pathogens but has not proven effective against CRAB when used alone. In vitro assays combining SUL and AVI have shown promise against CRAB with different genetic backgrounds. Our aim is to assess the in vitro efficacy of combining ampicillin-SUL (AMS) and CAZ-AVI (CZA) against CRAB. Figure 1 Methods Three different methodological strategies were used. 89 strains underwent MIC evaluation (agar dilution) against SUL plus AVI, AMS (2:1) plus AVI, and AMS plus CZA, with a constant concentration of AVI (4 mg/L) and CAZ (8 mg/L). Additionally, 30 CRAB strains with different genetic backgrounds were used to assess AMS/CZA (14 μg and 50 μg) synergy by the disk stacking method and MTS synergy. Results AVI reduced SUL MIC values to &amp;lt; =4 mg/L in 96.8% of the tested isolates, excluding NDM producers. For MBLs producers, SUL-AVI MIC values less &amp;lt; =4 mg/L were tested in 14.3% strains (Fig. 1A). The inclusion of AMP or CAZ in the SUL-AVI combination did not result in any alterations in MIC values (98% of strains with identical values). Using stacking assay, 59% of isolates showed &amp;gt;3 mm increase in halo diameter for AMS with both CZA disks (Fig. 1B). Additionally, we calculated the FICI by using gradient strips (MTS) obtaining a synergistic or additive effect in 69% and 2% of strains, respectively. Antagonism was only seen in two tested NDM-1 positive isolates. Conclusion Our results demonstrated synergy between AMS and CZA in geographically diverse CRAB, including extensively drug-resistant isolates, such as those producing widespread oxacillinases. Some methods, mainly disk-based, may underestimate the true extent of AMS-CZA synergy. These findings could facilitate the implementation AMS/CZA in clinical settings and provide a potential alternative in regions where the latest approved drugs are not accessible. Disclosures David Paterson, Infectious Diseases Physician, AMR Action Fund: Board Member|Aurobac: Advisor/Consultant|bioMerieux: Grant/Research Support|bioMerieux: Honoraria|CARB-X: Advisor/Consultant|Entasis: Expert Testimony|Menarini: Honoraria|Merck: Grant/Research Support|Pfizer: Advisor/Consultant|Pfizer: Grant/Research Support|Shionogi: Grant/Research Support|Shionogi: Honoraria

Controllable synthesis of nonlayered high-κ Mn3O4 single-crystal thin films for 2D electronics

Two-dimensional (2D) materials have been identified as promising candidates for future electronic devices. However, high dielectric constant (κ) materials, which can be integrated with 2D semiconductors, are still rare. Here, we report a hydrate-assisted thinning chemical vapor deposition (CVD) technique to grow manganese oxide (Mn3O4) single crystal nanosheets, enabled by a strategy to minimize the substrate lattice mismatch and control the growth kinetics. The material demonstrated a dielectric constant up to 135, an equivalent oxide thickness (EOT) as low as 0.8 nm, and a breakdown field strength (Ebd) exceeding 10 MV/cm. MoS2 field-effect transistors (FETs) integrated with Mn3O4 thin films through mechanical stacking method operate under low voltages (<1 V), achieving a near 108 Ion/Ioff ratio and a subthreshold swing (SS) as low as 84 mV/dec. The MoS2 FET exhibit nearly zero hysteresis (<2 mV/MV cm⁻¹) and a low drain-induced barrier lowering (~20 mV/V). This work further expands the family of 2D high-κ dielectric materials and provides a feasible exploration for the epitaxial growth of single-crystal thin films of non-layered materials.

Artificial Fine-Tuned van Hove Singularity in Twisted Bilayer and Double-Twist Trilayer Graphene with Enhanced Absorption for Photodetection and Photoemission in the Near-Infrared II Range.

Optical responses of twisted bilayer graphene at targeted wavelengths can be amplified by leveraging energy levels of van Hove singularities (VHS) via tuning periods of moiré superlattices. Therefore, precise control of twist angles as well as the moiré superlattices is necessary for fabricating integrated optoelectronic devices such as photodetectors and emitters. Although recent advances in twist angle control help the observation of correlated states in twisted magic-angle graphene structures, the impact of such precise control on enhanced optical absorption is still under investigation. Here, we employ a cut and stack method to construct tBLG with twist angles finely tuned between 4.6° and 6.6°, aligning the VHS near the telecom band with optimized optical absorption. The effective enhanced optical absorption and interlayer interaction uniformity are confirmed through Raman and reflection contrast spectroscopy. Additionally, we demonstrate increased photodetection responsivity and broadband upconversion photothermal emission, both enhanced by VHS, under resonant laser excitation. The study further explores a double-twist trilayer graphene configuration, resulting in better enhanced absorption due to the generation of additional VHS points. Our results underscore the potential of precisely engineered twisted structures in advancing the performance of optoelectronic devices.

Advances and Applications of Capillary Electrophoresis Mass Spectrometry in Food Analysis: Strategies for Online and Offline Preconcentration.

Advancements in food technology have increased the need for thorough analysis to ensure food safety, quality, and compliance with regulatory requirements. Capillary electrophoresis-mass spectrometry (CE-MS) has emerged as a powerful tool in food analysis due to its high separation efficiency, low sample consumption, and ability to handle complex matrices. However, challenges such as the use of volatile running buffers and maintaining the stability of the electrical circuit connecting the CE and MS systems have been addressed through advancements in interface designs, such as sheathless systems and optimized sheath-liquid compositions. Online and offline preconcentration techniques have significantly enhanced CE-MS sensitivity (up to 1000-fold) through stacking methods such as large volume sample stacking (LVSS) and dynamic pH junction stacking. Meanwhile, offline sample preparation techniques, such as solid-phase extraction (SPE) and liquid-based methods, are essential for removing matrix interferences and preconcentrating targeted analytes. This review explores both online and offline preconcentration methods and emphasizes the importance of CE-MS in helping researchers develop effective strategies for selecting the best preconcentration methods for food analysis.

A machine learning-based model for predicting the risk of cognitive frailty in elderly patients on maintenance hemodialysis

Elderly patients undergoing maintenance hemodialysis (MHD) face a heightened risk of cognitive frailty (CF), which significantly compromises quality of life. Early identification of at-risk individuals and timely intervention are essential. Nevertheless, current CF risk prediction models fall short in accuracy to adequately fulfill clinical requirements. This study aimed to examine the determinants of CF in elderly patients undergoing MHD and to develop a risk prediction model through machine learning algorithms. The objective is to furnish healthcare professionals with an early prediction tool and to offer insights for personalized CF risk management. A convenience sampling method was employed to select 1,075 elderly MHD patients from various tertiary-level hospitals in Chengdu between October 2023 and March 2024 as the modeling set, and 269 elderly MHD patients from hospitals in Chengdu, Yibin, and Zigong between September 2024 and October 2024 as the external validation set.CF was assessed using the Fried Phenotypic Scale for Frailty (FP) and the Montreal Cognitive Assessment Scale (MOCA). Data on patients’ demographics, sleep, nutrition, depression, and social support were collected. Single-factor and multi-factor logistic regression analyses were conducted to identify the factors influencing CF. Five machine learning algorithms—Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Neural Network (NNET), and Logistic Regression (LR)—were employed to develop risk prediction models. These five models served as base classifiers, and 16 ensemble models were constructed using the Stacking method. The optimal ensemble models were identified and compared with the five individual models, followed by the selection and external validation of the most effective predictive models. Finally, the optimal models were deployed on web platforms utilizing the Streamlit library. CF prevalence was 14.2%. Significant CF risk factors included age, mode of residence, medical payment method, exercise, alcohol consumption, dialysis vascular access, serum albumin classification, serum phosphorus classification, total cholesterol classification, blood urea nitrogen classification, malnutrition score and depression score. The Stacking model showed superior performance (AUC = 0.911), with external validation confirming its accuracy (AUC = 0.832). Machine learning models, particularly Stacking, effectively predict CF risk in elderly MHD patients, providing a valuable tool for clinical intervention.

Characterization of crystal quartz for QPM wavelength-conversion device.

Crystal quartz can be expected for material of intense-laser pumped wavelength conversion, by construction of periodically inverted structure for quasi phase matching. Transmission characteristics in VUV and MIR were measured for comparison of various quartz. Also, QPM-structured quartz constructed by multi stacking method was evaluated in ultraviolet 266 nm generation scheme. QPM quartz by multi stacking method is expected for intense-laser pumped THz-wave generation.

BagStacking: An Integrated Ensemble Learning Approach for Freezing of Gait Detection in Parkinson’s Disease

This study introduces BagStacking, an innovative ensemble learning framework designed to enhance the detection of freezing of gait (FOG) in Parkinson’s disease (PD) using accelerometer data. By synergistically combining bagging’s variance reduction with stacking’s sophisticated blending mechanisms, BagStacking achieves superior predictive performance. Evaluated on a comprehensive PD dataset provided by the Michael J. Fox Foundation, BagStacking attained a mean average precision (MAP) of 0.306, surpassing standalone LightGBM and traditional stacking methods. Furthermore, BagStacking demonstrated superior area under the curve (AUC) metrics across key FOG event classes. Specifically, it achieved AUCs of 0.88 for start hesitation, 0.90 for turning, and 0.84 for walking events, outperforming multistrategy ensemble, regular stacking, and LightGBM baselines. Additionally, BagStacking exhibited reduced runtime compared to other ensemble approaches, making it suitable for real-time clinical monitoring. These results underscore BagStacking’s effectiveness in addressing the variability inherent in FOG detection, thereby contributing to improved patient care in PD.

Enhancing Disease Diagnosis: Leveraging Machine Learning Algorithms for Healthcare Data Analysis

Healthcare data analysis has emerged as one of the most promising fields of study in recent years. There are different types of data in the healthcare industry, such as medical test results, blood reports, medical reports, X-rays, CT, MRI, ultrasound, clinical data, omics data, and sensor data. One of the most important and useful techniques for analysing this complicated medical data is machine learning (ML). ML is proving to be a useful artificial intelligence (AI) technique for data analysis. To accurately predict the outcomes of healthcare data, ML employs a variety of statistical techniques and cutting-edge algorithms. In recent years, different ML approaches have been applied to a variety of medical data for disease diagnosis. The paper provides a comprehensive literature survey based on ML techniques to diagnose various diseases. ML importance in the analysis of medical data is discussed with applications. This paper will motivate advanced research in machine intelligence-driven healthcare by showing its potential in healthcare data analysis. We also discuss the challenges that arise when applying ML to healthcare data. Furthermore, this study introduces a new approach to ensemble learning through explainable stacking. By integrating explainable artificial intelligence (XAI) techniques with the stacking method, we aim to not only enhance predictive accuracy but also improve the interpretability of the stacking model. The proposed predictive model outperforms the existing categorisation models, enhancing both the performance and efficiency of the diagnostic process. In addition, we suggest several future directions for further work in this area.

Segmented Trapdoor Fault in Kita‐Ioto Caldera, Japan: Insights From Millimeter Tsunami Waves Captured by an Array Network of Ocean Bottom Pressure Gauges

AbstractSubmarine calderas with active magma supply have recently been identified as potential sources of volcanic tsunamis due to sudden meter‐scale uplift by trapdoor faulting, occurring every few years to a decade. These trapdoor uplifts are seismically recorded as non‐double‐couple earthquakes with magnitudes M &gt; 5. Kita‐Ioto Caldera, a submarine caldera in the Izu‐Bonin arc, caused such earthquakes every 2–5 years. Our previous study (Sandanbata &amp; Saito, 2024, https://doi.org/10.1029/2023jb027917) analyzed data from a single ocean bottom pressure (OBP) gauge in the Philippine Sea, confirming trapdoor uplifts during the earthquakes in 2008 and 2015. However, high temporal‐resolution data for the earthquakes in 2017 and 2019 were lost, preventing source mechanism investigation. To address this, we examine OBP data of the two recent earthquakes from the array network of Dense Oceanfloor Network system for Earthquakes and Tsunamis, deployed off the southwestern coast of Japan. Despite the poor signal‐to‐noise ratio in each record, we successfully detect clear tsunami signals associated with the earthquakes using a waveform stacking method, with sea‐surface wave amplitudes of only 1–2 mm. By analyzing the data, we propose source models that represent trapdoor uplifts in the submarine caldera and accurately reproduce the detected tsunami waveforms, confirming the recurrence of trapdoor uplifts. Notably, differences in the tsunami waveforms between the 2017 and 2019 earthquakes suggest that different segments of the intra‐caldera fault system were activated. This segmentation likely influences the recurrence characteristics of the inflation cycle in calderas, which would be a key to understanding the magma accumulation process and assessing the sizes and timings of future trapdoor uplifts.

An Identification Method for Road Hypnosis Based on the Fusion of Human Life Parameters.

A driver in road hypnosis has two different types of characteristics. One is the external characteristics, which are distinct and can be directly observed. The other is internal characteristics, which are indistinctive and cannot be directly observed. The eye movement characteristic, as a distinct external characteristic, is one of the typical characteristics of road hypnosis identification. The electroencephalogram (EEG) characteristic, as an internal feature, is a golden parameter of drivers' life identification. This paper proposes an identification method for road hypnosis based on the fusion of human life parameters. Eye movement data and EEG data are collected through vehicle driving experiments and virtual driving experiments. The collected data are preprocessed with principal component analysis (PCA) and independent component analysis (ICA), respectively. Eye movement data can be trained with a self-attention model (SAM), and the EEG data can be trained with the deep belief network (DBN). The road hypnosis identification model can be constructed by combining the two trained models with the stacking method. Repeated Random Subsampling Cross-Validation (RRSCV) is used to validate models. The results show that road hypnosis can be effectively recognized using the constructed model. This study is of great significance to reveal the essential characteristics and mechanisms of road hypnosis. The effectiveness and accuracy of road hypnosis identification can also be improved through this study.

Stacking model framework reveals clinical biochemical data and dietary behavior features associated with type 2 diabetes: A retrospective cohort study.

Background: Type 2 diabetes mellitus (T2DM) is the most common type of diabetes, accounting for around 90% of all diabetes. Studies have found that dietary habits and biochemical metabolic changes are closely related to T2DM disease surveillance, but early surveillance tools are not specific and have lower accuracy. This paper aimed to provide a reliable artificial intelligence model with high accuracy for the clinical diagnosis of T2DM. Methods: A cross-sectional dataset comprising 8981 individuals from the First Affiliated Hospital of Guangxi Medical University was analyzed by a model fusion framework. The model includes four machine learning (ML) models, which used the stacking method. The ability to leverage the strengths of different algorithms to capture complex patterns in the data can effectively combine questionnaire data and blood test data to predict diabetes. Results: The experimental results show that the stacking model achieves significant prediction results in diabetes detection. Compared with the single machine learning algorithm, the stacking model has improved in the metrics of accuracy, recall, and F1-score. The test set accuracy is 0.90, and the precision, recall, F1-score, area under the curve, and average precision (AP) are 0.91, 0.90, 0.90, 0.90, and 0.85, respectively. Additionally, this study showed that HbA1c (P < 0.001,OR = 2.203), fasting blood glucose (FBG) (P < 0.001,OR = 1.586), Ph2BG (P < 0.001,OR = 1.190), age (P < 0.001,OR = 1.018), Han nationality (P < 0.001,OR = 1.484), and carbonate beverages (P = 0.001,OR = 1.347) were important predictors of T2DM. Conclusion: This study demonstrates that stacking models show great potential in diabetes detection, and by integrating multiple machine learning algorithms, stacking models can significantly improve the accuracy and stability of diabetes prediction and provide strong support for disease prevention, early diagnosis, and individualized treatment.

Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations

Large aperture ground-based solar telescopes allow the solar atmosphere to be resolved in unprecedented detail. However, ground-based observations are inherently limited due to Earth's turbulent atmosphere, requiring image correction techniques. Recent post-image reconstruction techniques are based on using information from bursts of short-exposure images. Shortcomings of such approaches are the limited success, in case of stronger atmospheric seeing conditions, and computational demand. Real-time post-image reconstruction is of high importance to enabling automatic processing pipelines and accelerating scientific research. In an attempt to overcome these limitations, we provide a deep learning approach to reconstruct an original image burst into a single high-resolution high-quality image in real time. We present a novel deep learning tool for image burst reconstruction based on image stacking methods. Here, an image burst of 100 short-exposure observations is reconstructed to obtain a single high-resolution image. Our approach builds on unpaired image-to-image translation. We trained our neural network with seeing degraded image bursts and used speckle reconstructed observations as a reference. With the unpaired image translation, we aim to achieve a better generalization and increased robustness in case of increased image degradations. We demonstrate that our deep learning model has the ability to effectively reconstruct an image burst in real time with an average of 0.5 s of processing time while providing similar results to standard reconstruction methods. We evaluated the results on an independent test set consisting of high- and low-quality speckle reconstructions. Our method shows an improved robustness in terms of perceptual quality, especially when speckle reconstruction methods show artifacts. An evaluation with a varying number of images per burst demonstrates that our method makes efficient use of the combined image information and achieves the best reconstructions when provided with the full-image burst.

Twin phase symmetry and multifunction features of PLZT (7/60/40) ceramics

Here, we report the effect of 7 wt% La substitution at the Pb-site of PZT (60/40), resulting in PLZT (7/60/40) prepared by solid-state reaction technique. The powder X-ray diffraction (XRD) taken at room temperature revealed the coexistence of tetragonal and rhombohedral (T+R) crystal structures having P4mm and R3c space groups. Scanning electron micrograph (SEM) showed the inhomogeneous distribution of quasi-cubical grains with a grain size range of 0.5–1.15 μm. Energy dispersive X-ray analysis (EDS) showed well-resolved peaks of all elements present, and a dielectric peak observed at 220 °C exhibited diffused ferroelectric-paraelectric phase transition of PLZT (7/60/40). The AC conductivity, impedance studies, and the conduction mechanism based on large and small polaronic movements are discussed. The semiconductor to metal transition and a ferroelectric relaxor kind feature were observed in the studied compound. The observed value of bipolar strain is ∼0.2 %, and unipolar is ∼0.18 %. The remnant polarization (Pr) and coercive field (Ec) are ∼ 20 (μC/cm2) and ∼ 13.5 (kV/cm), respectively, in both loops. We calculated large-signal piezoelectric coefficient d33* of ∼600pmV–1. A compact multilayer actuator was fabricated by a stacking method using pellets of PLZT.

Influence of stockpile design on carbonation of waste concrete: Implications for carbon management in China.

Enhancing the sequestration capacity of waste concrete is crucial for achieving carbon neutrality within the construction industry. Although existing studies primarily focus on theoretical analysis of concrete carbon sequestration, limited attention has been paid to explore the potential of waste concrete sequestration during stockpiling phase under varying environmental conditions. To fill this knowledge gap, we developed a CO2 uptake calculation model tailored for the stockpiling phase of waste concrete. This model investigates the impact of crush size, stacking method and environmental conditions on the total carbon sequestration capacity and efficiency, identifying the most advantageous approach. Our findings reveal the following: (1) Increasing the crush size of waste concrete enhances its carbon sequestration capacity, albeit extends the sequestration duration. A crush size of 5-20 mm is deemed optimal for achieving the desired sequestration efficiency. (2) The optimal stacking method involves smaller piles with reduced radii and angles. (3) High temperatures and humidity levels accelerate the sequestration rate. Practical measures such as watering and covering can be employed to enhance carbon sequestration. (4) In 2021, China's waste concrete exhibited a declining sequestration potential from the southeast to the northwest and northeast regions. The maximum sequestration potential has the capacity to neutralize up to 4% of the carbon emissions generated by the construction industry in that year. This research provides a foundation for accurate assessment and the development of effective carbon sequestration strategies for waste concrete.

Ensemble Stacking of Machine Learning Approach for Predicting Corrosion Inhibitor Performance of Pyridazine Compounds

Corrosion is a major challenge affecting various industrial sectors, leading to increased operational costs and decreased equipment efficiency. The use of organic corrosion inhibitors is one of the promising solutions. This study applies an ensemble algorithm with a stacking method to estimate pyridazine-derived compounds corrosion inhibition efficiency. This study utilized various molecular characteristics of pyridazine compounds as inputs to predict inhibition efficiency values. After evaluating several boosting models, the stacking technique was chosen as it showed the best results. Stacking Model 6, which combines XGB, LGBM, and CatBoost as the base model with Random Forest as the meta-model, produced the most accurate prediction with an RMSE of 0.055. These findings indicate that machine learning approaches can effectively and efficiently predict corrosion inhibitor performance. This method offers a faster and more economical alternative to conventional experimental methods.

10-Cell direct ammonia solid oxide fuel cell stack design development with improved efficiency and resilience to thermal shock

Hydrogen's potential as a green fuel faces a storage challenge, which has prompted recent studies to explore methods to advance solid and liquid storage, with a particular focus on utilizing ammonia. Employing hydrogen in the form of ammonia has shown considerable promise as it capitalizes on well-established and mature technologies. Nevertheless, establishing effective and resilient direct ammonia Solid Oxide Fuel Cell (SOFC) stacks demands a high degree of congruence with stack design, uniform species distribution, and temperature regulation. This research compared the conventional simple cross flow configuration with a novel approach involving two alternating fuel and air flow configurations within a 10-cell direct ammonia SOFC stack. An experimentally validated model of the direct ammonia SOFC was utilized for multi-physics modeling. The results revealed that the alternating fuel and air stacking method creates a superior ammonia decomposition profile compared to simple cross flow stacking. Moreover, the results demonstrate a lower temperature difference of 55 K within the stack. The implementation of the alternating fuel flow and air flow method enhanced the stack's efficiency by 0.74 %, primarily due to its improved thermal management capabilities within the stack. This study achieved its goal of optimizing a 10-cell direct ammonia SOFC stack by refining the design while maximizing performance and durability.

Comparison of Logistic Regression, Random Forest, SVM, KNN Algorithm for Water Quality Classification Based on Contaminant Parameters

This study compares four machine learning algorithms Logistic Regression, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) in water quality classification based on contaminant parameters. The purpose of this study is to evaluate and compare the performance of these algorithms in terms of accuracy. The methodology used includes data collection, preprocessing, and algorithm implementation with evaluation using crossvalidation techniques. The results showed that the application of the Stacking method with Gradient Boosting Meta-learner produced the highest accuracy of 96.00%, outperforming all other algorithms. In comparison, Random Forest achieved 95.75% accuracy, followed by SVM with 93.25% accuracy, and Logistic Regression and KNN each achieved 90.19% accuracy. This finding emphasizes that Stacking with Gradient Boosting provides much better performance in water quality classification compared to other models. This research provides new insights into the application of machine learning algorithms for water quality management as well as guidance for optimal algorithm selection.

High-Fidelity OC-Seislet Stacking Method for Low-SNR Seismic Data

Seismic stacking is a core technique in seismic data processing, aimed at enhancing the signal-to-noise ratio (SNR) of data by utilizing seismic data acquisition with multifold geometry. Traditional stacking methods always have certain limitations, such as the reliance on the accuracy of velocity analysis for dip moveout (DMO) in common midpoint (CMP) stacking. The seislet transform, a compression technique tailored to nonstationary seismic data, can compress and stack along the prediction direction of seismic data, which provides a new technical idea for high-fidelity seismic imaging based on the effectiveness of the compression. This paper introduces a high-order OC-seislet stacking method for low-SNR seismic data, capable of achieving the high-fidelity stacking of reflection and diffraction waves simultaneously. With the multi-scale analysis advantages of the seislet transform, this method addresses the dependency of DMO stacking on velocity analysis accuracy. In the frequency–wavenumber–scale domain, the correction compensation of the high-order CDF 9/7 basis function is used to obtain the compression coefficients of the high-order OC-seislet transform. This approach simultaneously stacks frequency–wavenumber information of reflection and diffraction waves with high fidelity while implementing DMO processing. After normalizing the weighting coefficients and applying soft thresholding for denoising, the final result is transformed back to the original time–space domain, yielding high-fidelity stacking sections. The results of applying this method to both synthetic and field data show that, compared with conventional DMO stacking methods, the high-order OC-seislet stacking technique reasonably represents dipping layers and fault amplitudes, and it can achieve a balance of a high SNR and high fidelity in stacked profiles.