Decomposition Procedure Research Articles

Global warming induced spread of the highest human fascioliasis hyperendemic area

BackgroundClimate change is driving the occurrence of several infectious diseases. Within a One Health action to complement the ongoing preventive chemotherapy initiative against human fascioliasis in the Northern Bolivian Altiplano hyperendemic area, field surveys showed a geographical expansion of its lymnaeid snail vector. To assess whether climate change underlies this spread of the infection risk area, an in-depth analysis of the long-term evolution of climatic factors relevant for Fasciola hepatica development was imperative.MethodsWe used monthly climatic data covering at least a 30-year period and applied two climatic risk indices, the water-budget-based system and the wet–day index, both of verified usefulness for forecasting fascioliasis transmission in this endemic area. To reveal the long-term trends of the climatic factors and forecast indices, we applied procedures of seasonal-trend decomposition based on locally weighed regression and trend analysis on the basis of linear models. To further demonstrate the changes detected, we depicted selected variables in the form of anomalies.ResultsThis study revealed a notorious climatic change affecting most of the hyperendemic area, with a strong impact on crucial aspects of the fascioliasis transmission. Trends in maximum and mean temperatures show significant increases throughout the endemic area, while trends in minimum temperatures are more variable. Precipitation annual trends are negative in most of the localities. Trends in climatic risk indices show negative trends at lower altitudes or when farther from the eastern Andean chain. However, monthly and yearly values of climatic risk indices indicate a permanent transmission feasibility in almost every location.ConclusionsWarmer temperatures have enabled lymnaeids to colonize formerly unsuitable higher altitudes, outside the endemicity area verified in the 1990s. Further, drier conditions might lead to an overexploitation of permanent water collections where lymnaeids inhabit, favoring fascioliasis transmission. Therefore, the present preventive chemotherapy by annual mass treatments is in need to widen the area of implementation. This study emphasizes the convenience for continuous monitoring of nearby zones for quick reaction and appropriate action modification.Graphical

Bouncing behaviour of a particle settling through a density transition layer

The present work focuses on a specific bouncing behaviour as a spherical particle settles through a density interface in the absence of a neutral buoyant position. This behaviour was initially discovered by Abaid et al. (Phys. Fluids, vol. 16, issue 5, 2004, pp. 1567–1580) in salinity-induced stratification. Both experimental and numerical investigations are conducted to understand this phenomenon. In our experiments, we employ particle image velocimetry (PIV) to measure the velocity distribution around the particle and to capture the transient wake structure. Our findings reveal that the bouncing process begins after the wake detaches from the particle. The PIV results indicate that an upward jet forms at the central axis behind the particle following wake detachment. By performing a force decomposition procedure, we quantify the contributions from the buoyancy of the wake ( $F_{sb}$ ) and the flow structure ( $F_{sj}$ ) to the enhanced drag. It is observed that $F_{sb}$ contributes primarily to the enhanced drag at the early stage, whereas $F_{sj}$ plays a critical role in reversing the particle's motion. Furthermore, our results indicate that the jet is a necessary condition for the occurrence of the bouncing motion. We also explore the minimum velocities (where negative values denote the occurrence of bouncing) of the particle, while varying the lower Reynolds number $Re_l$ , the Froude number $Fr$ , and the upper Reynolds number $Re_u$ , within the ranges $1 \leqslant Re_l\leqslant 125$ , $115 \leqslant Re_u\leqslant 356$ and $2 \leqslant Fr\leqslant 7$ . Our findings suggest that the bouncing behaviour is influenced primarily by $Re_l$ . Specifically, we observe that the bouncing motion occurs below a critical lower Reynolds number $Re^\ast _{l}=30$ in our experiments. In the numerical simulations, the highest value for this critical number is $Re^\ast _{l}=46.2$ , which is limited to the parametric ranges studied in this work.

Decomposing the Gender Wage Gap in the Urban Labor Market in Kenya

Abstract Legislation and regulation have been effective in reducing the gender wage gap in developed countries; however, the gap still exists globally, and progress towards narrowing the gap has been unacceptably slow even in regions where it is improving. This study presents the analysis of gender wage gap in Kenya’s urban labor market by using the World Bank Skills Towards Employability and Productivity Survey (WBSTEPS). This study employed Mincer earnings regressions with Heckman selection correction and the Blinder-Oaxaca and Neumark decomposition procedures to answer the research questions. The results of the wage determination and participation in the labor market show that there is no selectivity-bias problem. Personal characteristics such as education and age, as well as work-related characteristics, are important factors in determining earnings. The magnitude of the gender wage gap varies across the wage distribution, and the results of the wage decomposition reveal that women in urban Kenya earn 84.5-to-86% of men’s earnings. The earnings gap is overwhelmingly due to differences in returns to endowments, which account for between 70% and 94.7% of the total earnings gap. Admittedly, the study found evidence of discrimination against women in the returns to endowments, but also observed pronounced favoritism towards men. However, discrimination against women is more pronounced than favoritism towards men. Addressing the gender wage gap in Kenya requires a multifaceted approach that tackles both systemic biases against women and structural barriers that hinder women from accessing equal opportunities in education, training, and career advancement and government policies that minimize favoritism towards men.

Assessment and prediction of Water Quality Index (WQI) by seasonal key water parameters in a coastal city: application of machine learning models.

The Water Quality Index (WQI) provides comprehensive assessments in river systems; however, its calculation involves numerous water quality parameters, costly in sample collection and laboratory analysis. The study aimed to determine key water parameters and the most reliable models, considering seasonal variations in the water environment, to maximize the precision of WQI prediction by a minimal set of water parameters. Ten statistical or machine learning models were developed to predict the WQI over four seasons using water quality dataset collected in a coastal city adjacent to the Yellow Sea in China, based on which the key water parameters were identified and the variations were assessed by the Seasonal-Trend decomposition procedure based on Loess (STL). Results indicated that model performance generally improved with adding more input variables except Self-Organizing Map (SOM). Tree-based ensemble methods like Extreme Gradient Boosting (XGB) and Random Forest (RF) demonstrated the highest accuracy, particularly in winter. Nutrients (Ammonia Nitrogen (AN) and Total Phosphorus (TP)), Dissolved Oxygen (DO), and turbidity were determined as key water parameters, based on which, the prediction accuracy for Medium and Low grades was perfect while it was over 80% for the Good grade in spring and winter and dropped to around 70% in summer and autumn. Nutrient concentrations were higher at inland stations; however, it worsened at coastal stations, especially in summer. The study underscores the importance of reliable WQI prediction models in water quality assessment, especially when data is limited, which are crucial for managing water resources effectively.

Point and interval forecasting approach for short-term urban subway passenger flow based on residual term decomposition and fuzzy information granulation

Accurate forecasting information of short-term subway passenger flow is an important scientific reference for daily operations and urban management. The rapid time-varying nature of subway passenger flow caused by various factors that affect travel behavior poses a huge challenge to accurate forecasting. The complexity and uncertainty of data mainly focus on residual terms that deeply reflect the fluctuations. Reasonably mining the residual terms has become the key to achieving accurate forecasting. Therefore, this paper proposes a sophisticated decomposition strategy to extract and analyze the useful information hidden in the residual terms. Firstly, the potential trend, seasonal and residual terms from the original data are extracted by seasonal-trend decomposition procedure based on loess. Secondly, the residual term is decomposed into a series of subcomponents with different frequency features by symplectic geometric mode decomposition. Thirdly, these subcomponents are classified into three clusters based on fuzzy C-means clustering (FCM), and then the corresponding forecasting models are matched to the three obtained clusters and trend and seasonal terms. Finally, based on a decomposition-ensemble framework and information granulation for high-frequency components, we have established point and interval forecasting approach, respectively. Three experiments on real data sets in Beijing, Guangzhou and Shenzhen are conducted to verify the performance of the proposed approach, and the experimental results show that our approach is superior to all benchmark models and contributes to improving operational management and service quality.

Exact analytical investigation of Duffing oscillator vibration spectra under time-periodic oscillatory external force

This work introduces an analytical technique for determining solutions to a highly nonlinear Duffing-harmonic oscillator model problem. The parametric solutions of Duffing oscillator vibrations for an undamped case are achieved analytically in terms of Adomian polynomials by implementing the straightforward approach of the Laplace Transformation, known as the Laplace Decomposition Procedure (LDP). Possible plots of both numerical and analytical results sketched for various parameters are also presented to support our discussion. These graphs demonstrate variations of position-time, speed-time, and speed-position for an undamped Duffing oscillator case. When analyzed in general, they can provide vibration pattern descriptions for a variety of physical and engineering system configurations. We also examine their physical structures and behavioral characteristics within the conceptual framework of chaotic formalism.

The method of decomposition of the values of two-dimensional spectral features of remote sensing based on the analysis of the components of the correlation coef cient

A technique has been developed for decomposing the values of two-dimensional spectral features according to their components of correlation coeffi cients. A close analogue of the proposed methodology are automatic classification algorithms. The basis of the methodology is the analysis of the proposed indicator – the products of normalized values of spectral features and their probability density. A nonparametric Rosenblatt-Parzen estimate is used to reconstruct the probability density from the initial statistical data. The peculiarity of the proposed indicator and the user-selected threshold values of the indicator make it possible to form variants of the decomposition of the initial statistical data and mapping of the results obtained during the computational experiment. Using a human-machine decomposition procedure for the values of two-dimensional spectral features, it is possible to circumvent the problem of solving optimization problems when using automatic classifi cation algorithms and use information about the relationship between spectral features in the elements of the earth’s surface. The results of the application of the technique in the processing of remote sensing data of the forest area and their comparison with the initial information are considered. Spectral features have been established, which mainly determine the decomposition between dead wood and other forest conditions. The obtained results reveal their development in the formation of sets of spectral features in the assessment of the states of natural objects.

Probabilistic sunspot predictions with a gated recurrent units-based combined model guided by pinball loss

Sunspots play a crucial role in both weather forecasting and the monitoring of solar storms. In this work, we propose a novel combined model for sunspot prediction using improved gated recurrent units (GRU) guided by pinball loss for probabilistic forecasts. Specifically, we optimize the GRU parameters using the slime mould algorithm and employ a seasonal-trend decomposition procedure based on loess to tackle challenges related to sequence prediction, such as self-correlations and non-stationarity. To address prediction uncertainty, we replace the traditional l2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$l_2$$\\end{document}-norm loss with pinball loss. This modification extends the conventional GRU-based point forecasting to a probabilistic framework expressed as quantiles. We apply our proposed model to analyze a well-established historical sunspot dataset for both single- and multi-step ahead forecasting. The results demonstrate the effectiveness of our combined model in predicting sunspot values, surpassing the performance of other existing methods.

Enhanced Traffic Congestion Management with Fog Computing - A Simulation-Based Investigation using IFOG-Simulator

Accurate latency computation is essential for the Internet of Things (IoT) since the connected devices generate a vast amount of data that is processed on cloud infrastructure. However, the cloud is not an optimal solution. To overcome this issue, fog computing is used to enable processing at the edge while still allowing communication with the cloud. Many applications rely on fog computing, including traffic management. In this paper, an Intelligent Traffic Congestion Mitigation System (ITCMS) is proposed to address traffic congestion in heavily populated smart cities. The proposed system is implemented using fog computing and tested in a crowdedCairo city. The results obtained indicate that the execution time of the simulation is 4,538 seconds, and the delay in the application loop is 49.67 seconds. The paper addresses various issues, including CPU usage, heap memory usage, throughput, and the total average delay, which are essential for evaluating the performance of the ITCMS. Our system model is also compared with other models to assess its performance. A comparison is made using two parameters, namely throughput and the total average delay, between the ITCMS, IOV (Internet of Vehicle), and STL (Seasonal-Trend Decomposition Procedure based on LOESS). Consequently, the results confirm that the proposed system outperforms the others in terms of higher accuracy, lower latency, and improved traffic efficiency.

CP factor model for dynamic tensors

Abstract Observations in various applications are frequently represented as a time series of multidimensional arrays, called tensor time series, preserving the inherent multidimensional structure. In this paper, we present a factor model approach, in a form similar to tensor CANDECOMP/PARAFAC (CP) decomposition, to the analysis of high-dimensional dynamic tensor time series. As the loading vectors are uniquely defined but not necessarily orthogonal, it is significantly different from the existing tensor factor models based on Tucker-type tensor decomposition. The model structure allows for a set of uncorrelated one-dimensional latent dynamic factor processes, making it much more convenient to study the underlying dynamics of the time series. A new high-order projection estimator is proposed for such a factor model, utilizing the special structure and the idea of the higher order orthogonal iteration procedures commonly used in Tucker-type tensor factor model and general tensor CP decomposition procedures. Theoretical investigation provides statistical error bounds for the proposed methods, which shows the significant advantage of utilizing the special model structure. Simulation study is conducted to further demonstrate the finite sample properties of the estimators. Real data application is used to illustrate the model and its interpretations.

M-band wavelet network for machine anomaly detection from a frequency perspective

The autoencoder (AE) is widely utilized in deep anomaly detection, but it lacks explainability due to the complexity of nonlinear mapping. One approach to address this issue is incorporating wavelet theory, which shares similarities in decomposition and reconstruction procedures. However, the perfect reconstruction property of wavelet theory conflicts with AE-based anomaly detection. To tackle this problem, we introduce a novel deep anomaly detection method from a frequency perspective. A learnable M-band wavelet network (MWNet) is designed to offer a flexible frequency band structure for signal representation. Subsequently, with the aid of sparsity constraint, MWNet dynamically focuses on key components within each frequency band. A learnable hard threshold function with a threshold maximization constraint is proposed to retain the essential frequency band of normal signals. After training, the MWNet is exclusively capable of well reconstructing normal signals, thereby producing a noticeable reconstruction error difference between normal and abnormal signals. Extensive experiments on both simulated and experimental datasets validate the effectiveness of the proposed method. The corresponding Python codes are available at https://github.com/albertszg/MbandWaveletNet.

Pseudograin decomposition of short fiber-reinforced plastics for two-step homogenization using machine learning approach

Decomposing the representative volume element (RVE) of short fiber-reinforced plastics (SFRPs) into several pseudograins (PGs) is essential for understanding its effective mechanical behavior. However, conventional PG decomposition methodologies are limited by their high computational costs due to iteration-based algorithms. To address this, we propose a machine learning-assisted PG decomposition procedure that utilizes a series–parallel artificial neural network (ANN) system to facilitate the time-consuming decomposition process. To validate the effectiveness of our proposal, we implemented a two-step homogenization framework of SFRP that consists of the series–parallel ANN system, Mori-Tanaka model, and Voigt model into ABAQUS user material subroutine (UMAT). The elastic modulus values predicted by the UMAT are found to be in good agreement with both DIGIMAT-MF and experimental values, while also maintaining low computational time.

Novel method for chlorine determination in biofuel lipid feedstock by coupling a total sample introduction system (hTISIS) to an inductively coupled plasma tandem mass spectrometry (ICP-MS/MS)

BackgroundChlorine concentrations above 1 mg kg−1 in lipid feedstocks for biofuel production can generate corrosion issues in the different refining units as well as catalyst deactivation by clogging or fouling. To reach accurate analyses by inductively coupled plasma (ICP) techniques at low concentration levels, dilution in organic solvents rises as a simpler and more straightforward sample preparation methodology than conventional sample decomposition procedures (e.g., microwave-assisted acid digestion). However, matrix effects and the impact of the Cl chemical form on the signal must be overcome to obtain accurate results. ResultsIn this work, the high temperature torch integrated sample introduction system (hTISIS) operated at 350 °C is coupled to an inductively coupled plasma tandem mass spectrometry (ICP-MS/MS) for the determination of Cl in lipid biofuel feedstock samples diluted in xylene and these results are compared with those reported by a conventional sample introduction system. Under optimal conditions of the hTISIS-ICP-MS/MS configuration, matrix effects are efficiently overcome (recovery values ranged from 101 to 104%) as well as the effect of the Cl chemical form on the signal for 6 organochloride compounds. Thus, an external calibration approach can be set to carry out the quantification of this element in real samples. The method is successfully validated, obtaining a good agreement in the Cl concentration reported in a standard reference material (SRM NIST 1634c) and also by comparing the concentration results obtained by external calibration and standard addition approaches in two biofuel feedstock samples. Significance and noveltyThe hTISIS coupled to an ICP-MS/MS system is used for the first time to overcome not only matrix effects but also the impact of the Cl chemical form in biofuel feedstock samples. This novel method, with a limit of quantification (LOQ) of 7.1 μg kg−1, give access to an accurate Cl determination in all kind of lipid feedstocks for clean fuel production.

Beyond the sine wave: unveiling climate-driven baseline mortality cycles.

The relationship between temperature and mortality is well-established, with higher mortality rates occurring in moderate climates during winter. Studies on COVID-19 and influenza-related excess deaths often assume a sine-like wave pattern for baseline mortality. This study aims to assess the accuracy of this approximation in capturing the observed mortality pattern and explore its linkage with climate. Weekly mortality data from European regions (2000-2019) were modeled using the seasonal-trend decomposition procedure based on Loess. Cycles were grouped into clusters, and underlying trends were extracted using principal component analysis. Generalized linear models assuming a sine-like pattern were used to test predictive value. Cluster analysis divided the regional cycles approximately into continental and temperate climate regions, further subdivided into oceanic and Mediterranean. While the continental region exhibited a sine-like mortality pattern, it displayed modest deviations that compounded further south. The period of elevated winter mortality became shorter but more intense, while decreased summer mortality became more pronounced yet delayed. This study improves weekly estimations of excess mortality models by providing enhanced baselines. The deviation from the sine-like approximation mirrors the idealized outbreak pattern from epidemiological models with sharper surges and more gradual declines. The results point to winter infections, impacted by acquired immunity and weather conditions, as the primary drivers of fluctuations in mortality. In warmer regions, there is an apparent shift toward a lower number of overall infections within a compressed time span.

Direct Measurements of Vortex-Induced Vibrations on a Horizontal Flexible Cylinder in Water

Abstract This work presents experimental results on vortex-induced vibration (VIV) of a horizontal cylinder with 2.69 m in length, 21 mm in diameter, and a mass ratio equal to 3.9. The experiments were carried out in a towing tank, where the flexible cylinder was towed under constant current profiles. Twenty reduced velocities were tested in a range of 1.75 < Vr < 15.79, corresponding to Reynolds numbers from 1000 to 10,000. The displacements were measured directly through a submerged optical system that simultaneously acquired the in-line and cross-flow movements of 18 reflective targets along the length. The extensive monitored section of the model enabled the application of a modal decomposition technique, which is a methodology that is still relatively unexplored through direct measurements. A comprehensive analysis was applied and the VIV responses related to amplitudes, frequencies, phase angles, and trajectories are shown and discussed in depth. The overall dynamics exhibits a multimodal response behavior, and the modal decomposition procedure has conclusively confirmed that the total amplitude comprises several modes vibrating at the dominant mode frequency. Root mean square values of response amplitude were up to 0.77D and 0.23D in the cross-flow and in-line directions, respectively. The cross-flow synchronization had a significant impact on the in-line excitation modes, and the trajectories showed coherence between motions.

Reverse time migration angle gathers in acoustic anisotropic media using direction vectors

The application of direction vectors in the generation of reverse time migration (RTM) angle gathers in complex acoustic anisotropic media often encounters three main challenges: not pointing to the phase-velocity direction (PVD) of the Poynting vector, inaccuracy due to overlapping wavefields, and instability due to zero points of the direction vector. In general anisotropic media, the standard Poynting vector indicates the group-velocity direction (GVD), whereas reflection and transmission phenomena rely on the PVD. Anisotropy introduces discrepancies between the GVD and the PVD. To overcome this issue, we use the so-called PVD vector to directly calculate the PVD from anisotropic wavefields, eliminating the need for an approximated conversion from the GVD to the PVD. To mitigate the inaccuracy problem, we apply the Hilbert transform-based wavefield decomposition method to separate overlapping wavefields into their up/down components, and then we calculate the PVDs using the separated wavefields. To tackle the instability problem, we incorporate the additionally simulated quadrature wavefield during the wavefield decomposition procedure. By combining the direction vector of the quadrature wavefield with that of the original wavefield, we can eliminate the zero points and thus obtain a stabilized PVD vector. With those problems solved or alleviated, we present a scheme for the generation of anisotropic RTM angle gathers in complex areas. Two numerical examples using synthetic data sets demonstrate our method’s effectiveness.

Inflation-poverty causal nexus in sub-Saharan African countries: an asymmetric panel causality approach

PurposeThis study examines the roles of cross-sectional dependence, asymmetric structure and country-to-country policy variations in the inflation-poverty reduction causal nexus in selected sub-Saharan African (SSA) countries from 1981 to 2019.Design/methodology/approachTo account for cross-sectional dependence, heterogeneity and policy variations across countries in the inflation-poverty reduction causal nexus, this study uses robust Hatemi-J data decomposition procedures and a battery of second-generation techniques. These techniques include cross-sectional dependency tests, panel unit root tests, slope homogeneity tests and the Dumitrescu-Hurlin panel Granger non-causality approach.FindingsUnlike existing studies, the panel and country-specific findings exhibit several dimensions of asymmetric causality in the inflation-poverty nexus. Positive inflationary shocks Granger-causes poverty reduction through investment and employment opportunities that benefit the impoverished in SSA. These findings align with country-specific analyses of Botswana, Cameroon, Gabon, Mauritania, South Africa and Togo. Also, a decline in poverty causes inflation to increase in the Congo Republic, Madagascar, Nigeria, Senegal and Togo. All panel and country-specific analyses reveal at least one dimension of asymmetric causality or another.Practical implicationsAll stakeholders and policymakers must pay adequate attention to issues of asymmetric structures, nonlinearities and country-to-country policy variations to address country-specific issues and the socioeconomic problems in the probable causal nexus between the high incidence of extreme poverty and double-digit inflation rates in most SSA countries.Originality/valueStudies on the inflation-poverty nexus are not uncommon in economic literature. Most existing studies focus on inflation’s effect on poverty. Existing studies that examine the inflation-poverty causal relationship covertly assume no asymmetric structure and nonlinearity. Also, the issues of cross-sectional dependence and heterogeneity are unexplored in the causal link in existing studies. All panel studies covertly impose homogeneous policies on countries in the causality. This study relaxes this supposition by allowing policies to vary across countries in the panel framework. Thus, this study makes three-dimensional contributions to increasing understanding of the inflation-poverty nexus.

Industrial Heat Source-Related PM2.5 Concentration Estimates and Analysis Using New Three-Stage Model in the Beijing–Tianjin–Hebei Region

The prevalent high-energy, high-pollution and high-emission economic model has led to significant air pollution challenges in recent years. The industrial sector in the Beijing–Tianjin–Hebei (BTH) region is a notable source of atmospheric pollutants, with industrial heat sources (IHSs) being primary contributors to this pollution. Effectively managing emissions from these sources is pivotal for achieving air pollution control goals in the region. A new three-stage model using multi-source long-term data was proposed to estimate atmospheric, delicate particulate matter (PM2.5) concentrations caused by IHS. In the first stage, a region-growing algorithm was used to identify the IHS radiation areas. In the second and third stages, based on a seasonal trend decomposition procedure based on Loess (STL), multiple linear regression, and U-convLSTM models, IHS-related PM2.5 concentrations caused by meteorological and anthropogenic conditions were removed using long-term data from 2012 to 2021. Finally, this study analyzed the spatial and temporal variations in IHS-related PM2.5 concentrations in the BTH region. The findings reveal that PM2.5 concentrations in IHS radiation areas were higher than in background areas, with approximately 33.16% attributable to IHS activities. A decreasing trend in IHS-related PM2.5 concentrations was observed. Seasonal and spatial analyses indicated higher concentrations in the industrially dense southern region, particularly during autumn and winter. Moreover, a case study in Handan’s She County demonstrated dynamic fluctuations in IHS-related PM2.5 concentrations, with notable reductions during periods of industrial inactivity. Our results aligned closely with previous studies and actual IHS operations, showing strong positive correlations with related industrial indices. This study’s outcomes are theoretically and practically significant for understanding and addressing the regional air quality caused by IHSs, contributing positively to regional environmental quality improvement and sustainable industrial development.

Thermal decomposition mechanism and characterization of phosphogypsum in suspension under multifactor coupling effect

Phosphogypsum (PG) is solid waste generated by the phosphate fertilizer industry. However, by high-temperature thermal treatment, it can be effectively decomposed into byproducts like SO2 and CaO, which are valuable raw materials for the production of sulfuric acid and construction materials, respectively. In this study, we investigated the thermal decomposition properties of suspended PG, as well as the factors governing the PG decomposition procedure. The results show that the PG decomposition rate was influenced by several parameters, with temperature being the most significant, followed by time, atmosphere, and charcoal powder dosing. Similarly, the desulfurization rate was primarily influenced by time, followed by temperature, atmosphere, and charcoal powder dosing. The PG exhibited maximum decomposition and desulfurization rates of 97.73% and 97.2%, respectively, at a calcination temperature of 1180 °C, a calcination period of 15 min, a carbon powder dosage of 4%, and a CO concentration of 6%. Kinetic studies revealed that the activation energy of the PG decomposition reaction decreased from 303.45 to 254.28 kJ/mol as the CO concentration increased from 4% to 6% upon addition of charcoal powder as a reducing agent. Furthermore, higher CO concentrations had a more pronounced effect on lowering the activation energy of PG pyrolysis. When the contents of SiO2 and Al2O3 impurities in the PG content were higher, they reacted with CaO in the decomposition products to produce silicate and aluminate minerals, respectively, decreasing both the CaO content in the product and the PG decomposition rate.

Non-sample fuzzy based convolutional neural network model for noise artifact in biomedical images

The use of a light-weight deep learning Convolutional Neural Network (CNN) augmented with the power of Fuzzy Non-Sample Shearlet Transformation (FNSST) has successfully solved the problem of reducing noise and artifacts in Low-Dose Computed Tomography (LDCT) pictures. Both the Normal-Dose Computed Tomography (NDCT) and the Low-Dose Computed Tomography (LDCT) images from the dataset are subjected to the FNSST decomposition procedure during the training phase, producing high-frequency sub-images that act as input for the CNN. The CNN creates a meaningful connection between the high-frequency sub-images from LDCT and their corresponding residual sub-images during the training operation. The CNN is given the capacity to distinguish between LDCT high-frequency sub-images and expected high-frequency sub-images, which frequently have varying levels of noise or artifacts, especially in a fuzzy setting. The FNSST-CNN then successfully distinguishes LDCT high-frequency sub-images from the expected high-frequency sub-images during the testing phase, thereby reducing noise and artifacts. When compared to other approaches like KSVD, BM3D, and conventional image domain CNNs, the performance of FNSST-CNN is impressive as shown by better peak signal-to-noise ratios, stronger structural similarity, and a closer likeness to NDCT pictures.