Stacking Technique Research Articles

Hybrid ensemble-based machine learning model for predicting phosphorus concentrations in hydroponic solution

Accurate, label-free, and rapid methods for measuring phosphorus concentrations are essential in a hydroponic system, as excessive or insufficient phosphorus levels can adversely affect plant growth, human health, and environmental sustainability. In this study, we demonstrate the advantages of hybrid machine learning models compared to single machine learning models in predicting phosphorus concentration based on the absorbance dataset. Three machine learning classifiers- Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)- were employed as bases for single and hybrid machine learning models. Three ensemble techniques (voting, bagging, and stacking) were used to hybridize the classifiers. Among the single models, KNN demonstrated the fastest computational time of 18.07 s, while SVM achieved the highest accuracy of 99.6%. The hybrid SVM/KNN model using a voting classifier showed a significant increase in accuracy for KNN with only a slight increase in computational time. Bagging techniques increased the accuracy but at a longer computational time. The stacking technique, which combined SVM, KNN, and RF, achieved the highest accuracy of 99.73% with a short computational time of 36.18 s compared to the bagging and voting technique. This study demonstrates that the machine learning method can effectively distinguish phosphorus concentrations. In contrast, hybrid machine learning techniques can improve accuracy for predicting phosphorus without using labels, despite requiring longer computational time.

ECG and EEG based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study.

Mental health, especially stress, plays a crucial role in the quality of life. During different phases (luteal and follicular phases) of the menstrual cycle, women may exhibit different responses to stress from men. This, therefore, may have an impact on the stress detection and classification accuracy of machine learning models if genders are not taken into account. However, this has never been investigated before. In addition, only a handful of stress detection devices are scientifically validated. To this end, this work proposes stress detection and multilevel stress classification models for unspecified and specified genders through ECG and EEG signals. Models for stress detection are achieved through developing and evaluating multiple individual classifiers. On the other hand, the stacking technique is employed to obtain models for multilevel stress classification. ECG and EEG features extracted from 40 subjects (21 females and 19 males) were used to train and validate the models. In the low&high combined stress conditions, RBF-SVM and kNN yielded the highest average classification accuracy for females (79.81%) and males (73.77%), respectively. Combining ECG and EEG, the average classification accuracy increased to at least 87.58% (male, high stress) and up to 92.70% (female, high stress). For multilevel stress classification from ECG and EEG, the accuracy for females was 62.60% and for males was 71.57%. This study shows that the difference in genders influences the classification performance for both the detection and multilevel classification of stress. The developed models can be used for both personal (through ECG) and clinical (through ECG and EEG) stress monitoring, with and without taking genders into account.

Aromatic cycles are widespread in cold clouds

We report the detection of large hydrocarbon cycles toward several cold dense clouds. We observed four sources (L1495B, Lupus-1A, L483, and L1527) in the Q band (31−50 GHz) using the Yebes 40 m radiotelescope. Using the line stack technique, we find statistically significant evidence of benzonitrile (C6H5CN) in L1495B, Lupus-1A, and L483 at levels of 31.8σ, 15.0σ, and 17.2σ, respectively, while there is no hint of C6H5CN in the fourth source, L1527. The column densities derived are in the range (1.7−3.8) × 1011 cm−2, which is somewhat below the value derived toward the cold dense cloud TMC-1. When we simultaneously analyze all the benzonitrile abundances derived toward cold clouds in this study and in the literature, a clear trend emerges in that the higher the abundance of HC7N, the more abundant C6H5CN is. This indicates that aromatic cycles are especially favored in those interstellar clouds where long carbon chains are abundant, which suggests that the chemical processes that are responsible for the formation of linear carbon chains are also behind the synthesis of aromatic rings. We also searched for cycles other than benzonitrile, and found evidence of indene (C9H8), cyclopentadiene (C5H6), and 1-cyano cyclopentadiene (1-C5H5CN) at levels of 9.3σ, 7.5σ, and 8.4σ, respectively, toward L1495B, which shows the strongest signal from C6H5CN. The relative abundances between the various cycles detected in L1495B are consistent – within a factor of three – with those previously found in TMC-1. It is therefore likely that not only C6H5CN but also other large aromatic cycles are abundant in clouds rich in carbon chains.

A Comparative Analysis of Ensemble Learning Methods on Social Media Account Detection

Today, social media platforms usage and benefiting rate from these environments are increasing. This rapid spread of social media has also allowed the emergence of fake accounts. Fake accounts are generally created to implement malicious activities through another user account or to spread incorrect information. To prevent the detriment that this situation may cause to real individuals, an effective fake account detection was carried out by using ensemble learning methods (Bagging, Boosting, Stacking, Voting and Blending) in this study. These methods were combined with various machine learning algorithms to measure their effectiveness in detecting fake accounts. The experimental results suggested that Bagging technique attained an accuracy level of 90.441%, Stacking technique 89.706%, Voting technique 88.971% and the Blending technique attained 88.235% in the test phase. While for the Boosting methods, XGboost technique attained accuracy level of 86.765%, whereas the AdaBoost outperformed it with an accuracy level of 91.912% in the test phase. The extant results demonstrates that ensemble learning methods combined with machine learning algorithms are efficient in detecting fake social media accounts. It is considered that additional studies with larger datasets alongside the usage of different ensemble methods can further improve the accuracy of the detection process.

Imaging of crustal structure beneath the Larsemann Hills, Antarctica using scattered wave technique – First Results

The crustal structure in terms of crustal thickness, shear wave velocities and Poisson's ratio plays a crucial role in understanding and/or deciphering the tectonic setting and its evolution. The receiver function analysis, H–K stacking and inversion techniques are the widely used tools to determine these parameters. In the present study, the data obtained from the first-ever established broadband seismological observatory at Larsemann Hills, East Antarctica has been utilized for the P receiver function (PRF) analysis and H–K stacking to decipher the crustal thickness, Poisson's ratio and the intra-crustal layer. Further, the obtained PRFs were inverted using Bayesian inversion to get the crustal structure. The results obtained from H–K analysis reveal a crustal thickness of ∼37.9 km and the corresponding Poisson's ratio is 0.19. The inversion technique also yielded consistent results, indicating a Moho depth of ∼37 km with a velocity jump from 4.1 km/s to 4.6 km/s and an intra-crustal layer at around 16 km with a velocity jump from 3.95 km/s to 4.06 km/s. It indicates a Poisson's ratio of 0.20. These findings suggest that the crust beneath the Larsemann Hills is felsic in nature and has high crustal shear wave velocity. Further, the variation in the crustal thickness along the coast of the Princess Elizabeth Land (PEL) region, ranging from 37.9 km to 36 km with the intra-crustal layer variation from 16 km to 13 km distinguishes the Neoproterozoic Larsemann Hills from the Archean-Mesoproterozoic Vestfold Hills.

Application of machine learning (individual vs stacking) models on MERRA-2 data to predict surface PM2.5 concentrations over India

The spatial coverage of PM2.5 monitoring is non-uniform across India due to the limited number of ground monitoring stations. Alternatively, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), is an atmospheric reanalysis data used for estimating PM2.5. MERRA-2 does not explicitly measure PM2.5 but rather follows an empirical model. MERRA-2 data were spatiotemporally collocated with ground observation for validation across India. Significant underestimation in MERRA-2 prediction of PM2.5 was observed over many monitoring stations ranging from −20 to 60 μg m−3. The utility of Machine Learning (ML) models to overcome this challenge was assessed. MERRA-2 aerosol and meteorological parameters were the input features used to train and test the individual ML models and compare them with the stacking technique. Initially, with 10% of randomly selected data, individual model performance was assessed to identify the best model. XGBoost (XGB) was the best model (r2 = 0.73) compared to Random Forest (RF) and LightGBM (LGBM). Stacking was then applied by keeping XGB as a meta-regressor. Stacked model results (r2 = 0.77) outperformed the best standalone estimate of XGB. Stacking technique was used to predict hourly and daily PM2.5 in different regions across India and each monitoring station. The eastern region exhibited the best hourly prediction (r2 = 0.80) and substantial reduction in Mean Bias (MB = −0.03 μg m−3), followed by the northern region (r2 = 0.63 and MB = −0.10 μg m−3), which showed better output due to the frequent observation of PM2.5 >100 μg m−3. Due to sparse data availability to train the ML models, the lowest performance was for the central region (r2 = 0.46 and MB = −0.60 μg m−3). Overall, India's PM2.5 prediction was good on an hourly basis compared to a daily basis using the ML stacking technique.

Deep ensemble learning for high-dimensional subsurface fluid flow modeling

The accuracy of Deep Learning (DL) algorithms can be improved by combining several deep learners into an ensemble. This avoids the continuous endeavor required to adjust the architecture of individual networks or the nature of the propagation. This study investigates prediction improvements possible using Deep Ensemble Learning (DEL) to determine four distinct multiscale basis functions in the mixed Generalized Multiscale Finite Element Method (GMsFEM), involving the permeability field as the only input. 376,250 samples were initially generated, filtered down to 367,811 after data pre-processing. A standard Convolutional Neural Network (CNN) named SkiplessCNN and three skip connection-based CNNs named FirstSkipCNN, MidSkipCNN, and DualSkipCNN were developed for the base learners. For each basis function, these four CNNs were combined into an ensemble model using linear regression and ridge regression, separately, as part of the stacking technique. A comparison of the coefficient of determination (R2) and Mean Squared Error (MSE) confirms the effectiveness of all three skip connections in enhancing the performance of the standard CNN, with DualSkip being the most effective among them. Additionally, as evaluated on the testing subset, the combined models meaningfully outperform the individual models for all basis functions. The case that applies linear regression delivers R2 ranging from 0.8456 to 0.9191 and MSE ranging from 0.0092 to 0.0369. The ridge regression case achieves marginally better predictions with R2 ranging from 0.8539 to 0.922, and MSE ranging from 0.009 to 0.0349 because its solution involves more evenly distributed weights.

Prominent Josephson tunneling between twisted single copper oxide planes of Bi2Sr2-xLaxCuO6+y

Josephson tunneling in twisted cuprate junctions provides a litmus test for the pairing symmetry, which is fundamental for understanding the microscopic mechanism of high temperature superconductivity. This issue is rekindled by experimental advances in van der Waals stacking and the proposal of an emergent d+id-wave. So far, all experiments have been carried out on Bi2Sr2CaCu2O8+x (Bi-2212) with double CuO2 planes but show controversial results. Here, we investigate junctions made of Bi2Sr2-xLaxCuO6+y (Bi-2201) with single CuO2 planes. Our on-site cold stacking technique ensures uncompromised crystalline quality and stoichiometry at the interface. Junctions with carefully calibrated twist angles around 45° show strong Josephson tunneling and conventional temperature dependence. Furthermore, we observe standard Fraunhofer diffraction patterns and integer Fiske steps in a junction with a twist angle of 45.0±0.2°. Together, these results pose strong constraints on the d or d+id-wave pairing and suggest an indispensable isotropic pairing component.

Novel recycling of plastic waste into high impact resistant ultralightweight thermally insulated hybrid composite

This research discusses the development of a high performance composite out of waste plastic that is ultralightweight, thermally insulated, and high impact resistant. Polycarbonate sheet and Cross-linked Polyethylene (XLPE) foam were combined to create the composite, and silicon adhesive was used to fabricate the composite. Polycarbonate sheets are high impact resistant material, high thermal insulation is provided by XLPE foam, and silicon adhesive is used because of its low thermal conductivity. The composite was fabricated using a stacking technique. The composite prototype was then evaluated by experimenting it to dry ice at a temperature of −78.6°C. Drop tests, compression tests, density measurements was carried out, and thermal analysis was carried out using thermogravimetric analysis. The sample can endure low temperatures while maintaining room temperature on the other side, according to a 24 h dry ice test, whilst a drop test demonstrated that it can withstand significant impact loads. A numerical simulation using Abaqus/Explicit was also used to validate the variations in temperature between the outside surface at −20°C and the inside surface maintained at 30°C of the composites when exposed to cold temperature. The result reveals that the hybrid composite are highly promising in terms of thermal insulation.

Hybrid method for accurate starch estimation in adulterated turmeric using Vis-NIR spectroscopy

Turmeric is widely used as a health supplement and foodstuff in South East Asian countries because of its medicinal benefits. Like several other plants and peppers, turmeric is prone to exploitation because of its economic value, rising consumer need, and essential food element that adds colour and flavour. Due to this, quick and comprehensive testing processes are needed to detect adulterants in turmeric. In this study, pure turmeric powders were mixed with starch in proportions ranging from 0 to 50% with a 1% variation to obtain different combinations. Reflectance spectra of pure turmeric and starch mixed samples were recorded using a JASCO-V770 spectrometer from 400 to 2050 nm. The recorded spectra were pre-processed using a Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV). The Savitzky-Golay (SG) filter was initially applied to these original (X), MSC, and SNV-corrected spectra. Secondly, the Extra Tree Regressor (ETR) feature selection method was employed to select the best features. Finally, principal component analysis (PCA) was used to reduce the dimension of the selected features. The stacked generalization method was applied to improve the performance of this work. Both regressors and classifier stacking techniques have been tested with different classification and regression methods. The K-Nearest Neighbours (KNN), Decision Tree (DT), and Random Forest (RF) models were used as base learners, and Logistic Regression (LRC) was used as a meta-model for classification and Linear Regression (LR) for regression analysis. The proposed method achieved the best regression performance with r 2 of 0.999, Root Mean Square Error (RMSE) of 0.206, Ratio of Performance to Deviation (RPD) of 73.73, and Range Error Ratio (RER) of 480.58, whereas 100% F1 score and Matthew’s Correlation Coefficient (MCC) classification performance.

Preparation and characterization of silk hybridizing carbon fiber/polybutylene succinate composite

AbstractCarbon fiber‐reinforced composites (CFRPs) offer numerous benefits due to their exceptional strength and modulus, making them extensively utilized in various applications. However, the disadvantage of poor impact resistance also limits their application in some aspects. In this study, silk fabric is used to hybridize CFRPs, and the silk/carbon fiber‐reinforced polybutylene succinate (PBS) composites were fabricated by combining the film stacking technique with the hot‐pressing method. The mechanical, thermal, and thermodynamic properties of the composites were assessed using various techniques, including tensile testing, scanning electron microscope, flexural testing, Charpy impact test, low‐velocity impact test, differential scanning calorimetry, and dynamic mechanical thermal analysis, and the optimal preparation process conditions (140°C/15 min) for the composites were obtained. Additionally, the mechanical properties of silk fiber/PBS, carbon fiber/PBS, and hybrid composite materials are comprehensively compared and evaluated. This work indicates that the synergistic toughening of silk and PBS significantly improves the impact resistance of CFRPs, and the impact strength of sample 5 (140°C/15 min) (54.97 kJ/m2) is higher than many reported values for CFRPs.Highlights High strength, high modulus carbon fiber, and high toughness silk were rarely combined as reinforcing phases for PBS composites. A hybrid composite material with a balanced combination of stiffness, strength, and toughness was prepared. Compared with similar studies, it was found that the hybrid composite prepared by this work exhibited relatively balanced mechanical properties, especially, its impact strength higher than many reported values.

Ensemble technique to predict post-earthquake damage of buildings integrating tree-based models and tabular neural networks

In this paper, we develop a novel ensemble model for seismic building damage prediction that leverages machine learning algorithms of two completely different mechanisms, tree-based models and tabular neural networks. The ensemble model can break the limitations of individual models and elevate the predictive power to a superior level. Ensemble techniques of stacking and boosting are utilized for model integration, and an optimization method is also proposed to eliminate the negative sub models based on the stacking coefficient map and the boosting process. On the database of the 2015 Nepal earthquake including 762,094 building samples, our developed ensemble model using boosting technique outperforms any other individual models and its accuracy also surpasses that of another research using the same database. Moreover, the technique of fuzzy prediction is proposed to address the inherent errors in the database caused by volunteers’ subjectivity, which preserves the uncertainty between two adjacent damage grades, and the accuracy in the testing set is increased to 76.37% by applying it. Finally, by using the Shapley additive explanations method, our ensemble model is shown to have explicit physical significance and provides valuable insights for seismic mitigation efforts.

Determination of fluoride content in infant food samples by sample stacking capillary electrophoresis-indirect UV detection and the estimation of daily-fluoride-intake

Fluoride ion plays a major role in the decline of the prevalence and severity of dental caries. However, exposure to a high level of fluoride causes dental and skeletal fluorosis and reduced levels of intelligence quotient (IQ) in children. This study reports a rapid, selective, sensitive, and economic capillary electrophoresis-sample stacking technique for fluoride analysis in infant food samples. Indirect UV detection of fluoride was performed by 10 mmol/L chromate in the separation electrolyte and 0.1 mmol/L cetyltrimethylammonium bromide (CTAB) was added to the separation medium as an electroosmotic flow modifier. The buffer pH was adjusted to 9.37. The conductivity of the separation electrolyte was increased with 10 mmol/L Na2SO4 and sample stacking was performed. The injections were administered throughout 200 s duration. The limit of detection of the method was 0.0079 mg/L. The method was applied to 7 various commercially available infant food samples which are in puree and powdered form. The fluoride concentrations ranged from 2.28 – 3.85 µg fluoride/g infant food. The proposed method is suitable for the rapid and sensitive determination of fluoride content in infant formulas, and it is recommended that fluoride content be determined by reliable methods in all commercial infant food samples.

Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity.

Contemporary advancements in wearable equipment have generated interest in continuously observing stress utilizing various physiological indicators. Early stress detection can improve healthcare by lessening the negative effects of chronic stress. Machine learning (ML) methodologies have been modified for healthcare equipment to monitor user health situations utilizing sufficient user information. Nevertheless, more data are needed to make applying Artificial Intelligence (AI) methodologies in the medical field easier. This research aimed to detect stress using a stacking model based on machine learning algorithms using chest-based features from the Wearable Stress and Affect Detection (WESAD) dataset. We converted this natural dataset into a convenient format for the suggested model by performing data visualization and preprocessing using the RESP feature and feature analysis using the Z-score, SelectKBest feature, the Synthetic Minority Over-Sampling Technique (SMOTE), and normalization. The efficiency of the proposed model was estimated regarding accuracy, precision, recall, and F1-score. The experimental outcome illustrated the efficacy of the proposed stacking technique, achieving 0.99% accuracy. The results revealed that the proposed stacking methodology performed better than traditional methodologies and previous studies.

Treatment of Benign Melanocytic Hyperplasia of Nail Apparatus with Dr. Hoon Hur’s Golden Parameter Therapy

Background: Benign melanocytic hyperplasia (BMH) of nail apparatus may be caused by a lentigine in the nail matrix. Traditional treatment modalities including surgical excision and CO2 laser ablation may cause a severe scar of nail apparatus.There is no clear treatment for BMH of nail apparatus. Objectives: This study was performed to investigate the efficacy and safety of Dr. Hoon Hur’s Golden Paramter Therapy (GPT) with a high fluence 1064nm Q-switched Nd:YAG laser (QSNL) for treating BMH of nail apparatus . Study design: The medical records of 52 Korean patients (age range: 21 to 60 year-old, mean age: 38.6 year-old) diagnosed with BMH of nail apparatus were reviewed retrospectively. Methods: All patients were treated with a 1064nm QSNL, on a weekly basis for 10-15 treatment sessions of Dr. HoonHur’s GPT. The parameters were a spot size of 7mm, a fluence of 2.2J/cm² and a pulse rate of 10Hz with three passes by a sliding technique (sliding GPT) and one pass by a pulse stacking technique (pulse-stacking GPT) over the BMH of nail apparatus. Results: At the time of the final treatment, all patients with BMH showed the complete removal of pigmented lesions without any side effects, such as purpura, crust, post-inflammatory hyperpigmentation (PIH), nail dystrophy and scarring. No recurrences were observed in any of the patients after a follow-up of 3-12 months. Conclusion: We suggest that Dr. Hoon Hur’s GPT with a high fluence 1064nm QSNL is a safe and effective new treatment for BMH of nail apparatus without side effects and recurrences. Keywords: Benign Melanocytic Hyperplasia; Nail;1064-nm ND:YAG laser;Dr. Hoon Hur’s GPT

Seismic time-frequency masking for suppression of seismic speckle noise

Seismic speckle noise is the primary factor causing severe reflection distortions caused by small-scale near-surface scattering. As in the case of speckle noise in optics and acoustics, deterministic velocity model-building techniques cannot recover these heterogeneities which are much smaller than a wavelength. Conventional processing techniques struggle to perform when multiplicative noise remains unsuppressed. Although local and global stacking mitigates the effects of speckle noise, it leads to a severe loss of higher frequencies reducing the vertical resolution of the seismic data. The foundation for attacking speckle noise is a recently proposed mathematical model that includes two concurrent random multiplicative noise types: type 1 defining residual statics and type 2 describing random frequency-dependent phase perturbations that mimic small-scale near-surface scattering. Using this model, we have developed seismic time-frequency masking to suppress speckle noise on prestack data. The time-dependent and non-surface-consistent nature of scattering noise dictates window-based approaches borrowed from local stacking techniques. Separate manipulation of phase and amplitude spectra is achieved through modified time-frequency masking inspired by speech processing. A beamformed data set from local stacking is used as a guide for designing phase and amplitude masks that are directly applied to raw data in the time-frequency domain. The phase mask allows the restoration of coherency by repairing phase distortions caused by near-surface scattering. The amplitude mask corrects power spectrum (PS) distortions caused by multiplicative and additive noises. An amplitude mask is implemented using a data-driven minimum statistic approach that estimates the noise PS in each local window. The minimum statistics approach is adapted from speech processing using beamformed data as an initial signal estimate and as a guide to designing an improved amplitude mask. Synthetic and field data examples suggest significant improvements in coherency and signal-to-noise ratio after the suppression of multiplicative noise, which makes the data processable by conventional techniques subsequently.

A hybrid machine learning framework for forecasting house price

House price prediction is one of the most important factors affecting national real estate policies. However, developing an accurate housing price prediction model is a significant challenge for the real estate market. This study presents a framework of house price prediction models that address this issue by improving forecasting performance, explicitly demonstrating the novelty in the Hybrid Bayesian Optimization (HBO) models combined with Stacking (HBOS), Bagging (HBOB), and Transformer (HBOT) techniques. These hybrid models employ Bayesian Optimization for hyperparameter tuning, leading to superior prediction accuracy and stability. Additionally, the proposed framework can assess a statistical and accurate assessment of the predictive performance of house price forecasting models in different scenarios. Furthermore, we constructed a multi-source dataset containing 1,898,175 transactions of the Hong Kong real estate market covering a period from January 2, 1996, to May 13, 2021. This dataset, another major contribution to the field, enables comprehensive model testing and could be a valuable resource for future research. Then, the proposed hybrid models are compared with 18 benchmark models. Thirteen evaluation metrics are used to evaluate the predictive performance, while the non-parametric testing, including Friedman, Iman–Davenport, and Nemenyi post-hoc tests methods, are adopted to assess the significance of differences in the predictive performance of each model. The experimental results show that the HBOS models are superior to the other benchmark models for application in the house price prediction problem. The HBOS-CatBoost model showed superior performance in terms of RMSE compared to both the HBOB-XGBoost and HBOT-ConvLSTM models, with relative RMSE reductions of 5.11% and 25.56%, respectively. The main contributions of this work are the creation of a rich multi-source dataset, the proposal of novel hybrid models for improved house price prediction, and a comprehensive performance evaluation framework. These findings offer a significant step forward in the housing price prediction field.

Spectral stacking of radio-interferometric data

Context. Mapping molecular line emission beyond the bright low-J CO transitions is still challenging in extragalactic studies, even with the latest generation of (sub-)millimetre interferometers, such as ALMA and NOEMA. Aims. We summarise and test a spectral stacking method that has been used in the literature to recover low-intensity molecular line emission, such as HCN(1−0), HCO+(1−0), and even fainter lines in external galaxies. The goal is to study the capabilities and limitations of the stacking technique when applied to imaged interferometric observations. Methods. The core idea of spectral stacking is to align spectra of the low S/N spectral lines to a known velocity field calculated from a higher S/N line expected to share the kinematics of the fainter line (e.g. CO(1−0) or 21 cm emission). Then these aligned spectra can be coherently averaged to produce potentially high S/N spectral stacks. Here we used imaged simulated interferometric and total power observations at different S/N levels, based on real CO observations. Results. For the combined interferometric and total power data, we find that the spectral stacking technique is capable of recovering the integrated intensities even at low S/N levels across most of the region where the high S/N prior is detected. However, when stacking interferometer-only data for low S/N emission, the stacks can miss up to 50% of the emission from the fainter line. Conclusions. A key result of this analysis is that the spectral stacking method is able to recover the true mean line intensities in low S/N cubes and to accurately measure the statistical significance of the recovered lines. To facilitate the application of this technique we provide a public Python package, called PYSTACKER.

Four New Techniques of Addition and Subtraction of any Two Numbers

The addition and subtraction operations are some of the most basic mathematical arithmetic operations. The arithmetic operation in the form of addition and subtraction in integer is a basic arithmetic operation that elementary school students must master from an early stage. In fact, there are some students who have difficulty in adding up numbers with existing techniques, namely the saving technique for addition and borrowing technique for subtraction. The problem happened because students forgot that they had saved or borrowed numbers. Eventhough the technique was done correctly, the answer was still incorrect. Therefore, researchers developed several addition and subtraction techniques without saving and borrowing. There were four techniques that had been developed in this research namely multi-level, box, and stacking and table techniques. These new techniques had advantages in making students easier to solve addition and subtraction problems.

The Kinematic Bimodality: Efficient Feedback and Cold Gas Deficiency in Slow-rotating Galaxies

The bimodality in the stellar spin of low-redshift (massive) galaxies, ubiquitously existing at all star formation levels and in diverse environments, suggests that galaxies grow and quench through two diverged evolutionary pathways. For spheroid-dominated galaxies of slow stellar rotation, the age composition and metallicity of their stellar populations are evidence of a fast quenching history with significant gas outflows. In this work, we measure the spin parameter , i.e., the normalized specific angular momentum of stars, out of the MaNGA integral field spectroscopy for about 10,000 galaxies. Among the two-thirds with H i follow-up observations (z ≲ 0.05), we find that, compared to fast-rotating galaxies of the same stellar mass and star formation, the galaxy populations with slower rotation are generally more H i gas-poor, robust against further environmental restriction and with nondetections properly taken into account using the stacking technique. This cold gas deficit of slow-rotating galaxies is most apparent at high mass below the star formation main sequence, supporting the pivotal role of gas outflows in their quenching history. With hints from H i velocity distributions, we suspect that massive gas outflows among the slow-rotating population are facilitated by high ejective feedback efficiency, which is a result of extensive coupling between disturbed volume-filling cold gas and (commonly) biconical feedback from central black holes. By contrast, in fast-rotating disk galaxies the feedback energy mostly goes to the hot circumgalactic medium rather than directly impacting the dense and planar cold gas, thus making the feedback mainly preventive against further gas inflow.