Intermediate Parameters Research Articles

Instance segmentation models for detecting floating macroplastic debris from river surface images

Quantifying the transport of floating macroplastic debris (FMPD) in waterways is essential for understanding the plastic emission from land. However, no robust tool has been developed to monitor FMPD. Here, to detect FMPD on river surfaces, we developed five instance segmentation models based on state-of-the-art You Only Look Once (YOLOv8) architecture using 7,356 training images collected via fixed-camera monitoring of seven rivers. Our models could detect FMPD using object detection and image segmentation approaches with accuracies similar to those of the pretrained YOLOv8 model. Our model performances were tested using 3,802 images generated from 107 frames obtained by a novel camera system embedded in an ultrasonic water level gauge (WLGCAM) installed in three rivers. Interestingly, the model with intermediate weight parameters most accurately detected FMPD, whereas the model with the most parameters exhibited poor performance due to overfitting. Additionally, we assessed the dependence of the detection performance on the ground sampling distance (GSD) and found that a smaller GSD for image segmentation approach and larger GSD for object detection approach are capable of accurately detecting FMPD. Based on the results from our study, more appropriate category selections need to be determined to improve the model performance and reduce the number of false positives. Our study can aid in the development of guidelines for monitoring FMPD and the establishment of an algorithm for quantifying the transport of FMPD.

A cache-aware congestion control mechanism using deep reinforcement learning for wireless sensor networks

In Wireless Sensor Networks (WSN) communication protocols, rule-based approaches have been traditionally used for managing caching and congestion control. These approaches rely on explicitly defined, unchanging models. Recently, a trend has been toward incorporating adaptive methods that leverage machine learning (ML), including its subset deep learning (DL), during network congestion conditions. However, an adaptive cache-aware congestion control mechanism using Deep Reinforcement Learning (DRL) in WSN has not yet been explored. Therefore, this study developed a DRL-based adaptive cache-aware congestion control mechanism called DRL-CaCC to alleviate WSN during congestion scenarios. The DRL-CaCC uses intermediate caching parameters as its state space and adaptively moves the congestion window as its action space through the Rapid Start and DRL algorithms. The mechanism aims to find the optimal congestion window movement to avoid further network congestion while ensuring maximum cache utilization. Results show that DRL-CaCC achieved an average improvement gain between 20% and 40% compared to its baseline protocol, RT-CaCC. Finally, DRL-CaCC outperformed other caching-based and DRL-based congestion control protocols in terms of cache utilization, throughput, end-to-end delay, and packet loss metrics, with improvement gains between 10% and 30% in various congestion scenarios in WSN.

A Lab-Scale Evaluation of Parameters Influencing the Mechanical Activation of Kaolin Using the Design of Experiments.

This research investigates the mechanical activation of kaolin as a supplementary cementitious material at the laboratory scale, aiming to optimize milling parameters using the response surface methodology. The study evaluated the effects of rotation speed and milling time on the amorphous phase content, the reduction in crystalline kaolinite, and impurity incorporation into the activated clay through the Rietveld method. The results demonstrated that adjusting milling parameters effectively enhanced clay activation, which is crucial for its use in low-carbon cements. High rotation speeds (300/350 rpm) and prolonged grinding times (90/120 min) in a planetary ball mill increased the pozzolanic activity by boosting the formation of amorphous phases from kaolinite and illite and reducing the particle size. However, the results evidenced that intermediate milling parameters are sufficient for reaching substantial degrees of amorphization and pozzolanic activity, avoiding the need for intensive grinding. Exceedingly aggressive milling introduced impurities like ZrO2 from the milling equipment wear, underscoring the need for a balanced approach to optimizing reactivity while minimizing impurities, energy consumption, and equipment wear. Achieving this balance is essential for efficient mechanical activation, ensuring the prepared clay's suitability as supplementary cementitious materials without excessive costs or compromised equipment integrity.

Exploring Distributed Scatterers Interferometric Synthetic Aperture Radar Attributes for Synthetic Aperture Radar Image Classification

Land cover classification of Synthetic Aperture Radar (SAR) imagery is a significant research direction in SAR image interpretation. However, due to the unique imaging methodology of SAR, interpreting SAR images presents numerous challenges, and land cover classification using SAR imagery often lacks innovative features. Distributed scatterers interferometric synthetic aperture radar (DS-InSAR), a common technique for deformation extraction, generates several intermediate parameters during its processing, which have a close relationship with land features. Therefore, this paper utilizes the coherence matrix, the number of statistically homogeneous pixels (SHPs), and ensemble coherence, which are involved in DS-InSAR as classification features, combined with the backscatter intensity of multi-temporal SAR imagery, to explore the impact of these features on the discernibility of land objects in SAR images. The results indicate that the adopted features improve the accuracy of land cover classification. SHPs and ensemble coherence demonstrate significant importance in distinguishing land features, proving that these proposed features can serve as new attributes for land cover classification in SAR imagery.

Intermediate parameter based distributed sensor fault-tolerant estimation for a class of nonlinear systems

This paper investigates the estimation problem of a class of nonlinear systems with actuator and sensor faults. The primary objective is to design a distributed fault-tolerant observer which can estimate system states and actuator faults. Firstly, a distributed observer network with intermediate parameters is constructed to compensate the missing information of unobservable nodes of the system. Next, a class of redundant sensors is set up for each distributed observer node to obtain more output measurement samples. More importantly, when some of the redundant sensors occur faults, all sensor signals will be further processed and classified by a new algorithm. An index of sensor health level is constructed to characterize the quality of the fault-free or faulty sensor signals. By using the proposed algorithm, unhealthy sensor signals will be automatically filtered out, while healthy ones will be retained. Based on the healthy sensor signals, the system states and actuator faults are estimated. Finally, an example demonstrates that the proposed method is effective.

Stress–strain Behavior of Silt Sand Soil Under Isotropically Consolidated Drained Conditions by Principal Stress Direction and Intermediate Stress Coefficient Parameters

Stress–strain Behavior of Silt Sand Soil Under Isotropically Consolidated Drained Conditions by Principal Stress Direction and Intermediate Stress Coefficient Parameters

Technological properties of aromatic Basmati rice accessions

The objective of this study was to characterize the paste profile of basmati-style aromatic rice accessions. The evaluation of the 56 rice accessions by the Mean of Distances Method yielded seven groups of rice accessions based on the evaluated parameters (elongation, apparent amylose content (AAC), aroma, and paste properties). Group 1 was represented by the control sample IRGA417, a non-aromatic cultivar with the highest AAC (22.5%). The aromatic cultivars Jasmine, Empasc 104, IAC500, and Jasmine85 constituted the third group. Group 4 consisted of 41 Basmati accessions and exhibited the highest average elongation ratio (greater than 2.0). Groups 3 and 4 exhibited comparable aroma scores (2.9 and 2.8, respectively) and retrogradation processes (final viscosity: 285.37 and 291.26 RVU, and setback: 136.84 and 166.01 RVU, respectively). Group 5 was constituted with seven Basmati accessions, while groups 6 and 7 were formed with one and two Basmati accessions, respectively. Groups 5 and 6 exhibited comparable paste properties, with peak viscosities of 186.06 and 153.1 RVU, respectively, and final viscosities of 210.27 and 236.7 RVU, respectively, which were lower than those observed in the other groups. In contrast, group 7 exhibited intermediate parameters between the control IRGA417 and groups 3 and 4. Groups 3, 4, 5, 6, and 7 were classified as low amylose grains, with an AAC of 12-20%. The Basmati accession (group 4) exhibited a comparable paste profile, with a notable distinction in its high elongation, aroma, and low AAC. Consequently, these cultivars may be well accepted by consumers of aromatic rice.

CuFastTucker: A Novel Sparse FastTucker Decomposition For HHLST on Multi-GPUs

High-order, high-dimension, and large-scale sparse tensors (HHLST) have found their origin in various real industrial applications, such as social networks, recommender systems, bioinformatics, and traffic information. To handle these complex tensors, sparse tensor decomposition techniques are employed to project the HHLST into a low-rank space. In this paper, we propose a novel sparse tensor decomposition model called Sparse FastTucker Decomposition (SFTD), which is a variant of Sparse Tucker Decomposition (STD). The SFTD utilizes Kruskal approximation for the core tensor, and we present a theorem that reduces the exponential space and computational overhead to a polynomial one. Additionally, we reduce the space overhead of intermediate parameters in the algorithmic process by sampling the intermediate matrix. Furthermore, this method guarantees convergence. To enhance the speed of SFTD, we leverage the compactness of matrix multiplication and parallel access through a stochastic strategy, resulting in GPU-accelerated cuFastTucker. Moreover, we propose a data division and communication strategy for cuFastTucker to accommodate data on Multi-GPU setups. Our proposed cuFastTucker demonstrates faster calculation and convergence speeds, as well as significantly lower space and computational overhead compared to state-of-the-art (SOTA) algorithms such as P-Tucker, Vest, GTA, Bigtensor and SGD_Tucker.

Intrinsic and Extrinsic Scattering and Absorption Coefficients New Equations in Four-flux and Two-flux Models Used for Determining Light Intensity Gradients

Collimated, diffuse and total light intensity gradients, for forward and backward light senses, were determined in a three substrate layers – glass/electrolyte/glass – almost transparent sample, using optical constants and new equations for intrinsic and extrinsic scattering and absorption coefficients. These new equations were obtained for the inner electrolyte layer from the systems of differential equations of the four-flux and two-flux radiative transfer models, used for determining intrinsic and extrinsic coefficients, respectively, once knowing the optical constants of outer glass layers, from a single glass substrate sample measured in advance. Extinction coefficients were determined from optical constants and considering the wavelength compression of light when it enters into a material, decreasing its speed with respect to the vacuum. The same extinction coefficients for glass and electrolyte layers were computed in three different ways. First, from optical constants, determined using collimated transmittance and reflectance solutions of four-flux model. From them and as intermediate parameters for glass and electrolyte layers, collimated interface reflectance and attenuation due to extinction were computed since they were required for determining inner collimated light intensities at the interfaces, which were used at the collimated forward and backward differential equations, solving for the forward and backward extinction coefficients. The three-extinction matching requirement was successfully satisfied for the glass and electrolyte layers. Two average crossing parameters equations, for each sense, and four forward scattering ratios equations, for collimated and for diffuse light intensities for each sense, were used in the system of diffuse differential equations for intrinsic coefficients. For them, intuitive equations were proposed, based on collimated and diffuse light intensities at each interface. New equations for extrinsic parameters were determined by equalizing the system of total differential equations of the two-flux model to the sum of the systems of collimated and diffuse differential equations of the four-flux model.

Neural-network-based drag force model for polydisperse assemblies of irregular-shaped particles

In this study, a drag force model of irregular-shaped particles in gas-solid flows is investigated using neural network approaches. Pre-developed variational autoencoder, multi-layer perceptron model, and transpose convolutional neural-network model are utilized to obtain intermediate parameters of a pair-wise interaction point-particle (PIEP) model. These parameters are utilized to predict individual drag force coefficients of polydisperse, particulate flows. To apply the PIEP-based model to medium-concentration flows, the average drag force coefficients are obtained through an in-house particle-resolved direct numerical simulation, and a regressed model is developed to predict solid volume fraction factors. With the regression model, the PIEP-based model using the neural-network approaches shows good prediction in particulate systems from low to intermediate concentrations. This study provides computationally efficient models of practical, realistic particulate systems for large scale fluid dynamics simulation, in that it allows predicting the drag forces of polydisperse particles with a small amount of data.

Investigating the Effects of Mixing Dynamics on Twin-Screw Granule Quality Attributes via the Development of a Physics-Based Process Map.

Twin-screw granulation (TSG) is an emerging continuous wet granulation technique that has not been widely applied in the industry due to a poor mechanistic understanding of the process. This study focuses on improving this mechanistic understanding by analyzing the effects of the mixing dynamics on the granule quality attributes (PSD, content uniformity, and microstructure). Mixing is an important dynamic process that simultaneously occurs along with the granulation rate mechanisms during the wet granulation process. An improved mechanistic understanding was achieved by identifying and quantifying the physically relevant intermediate parameters that affect the mixing dynamics in TSG, and then their effects on the granule attributes were analyzed by investigating their effects on the granulation rate mechanisms. The fill level, granule liquid saturation, extent of nucleation, and powder wettability were found to be the key physically relevant intermediate parameters that affect the mixing inside the twin-screw granulator. An improved geometrical model for the fill level was developed and validated against existing experimental data. Finally, a process map was developed to depict the effects of mixing on the temporal and spatial evolution of the materials inside the twin-screw granulator. This process map illustrates the mechanism of nucleation and the growth of the granules based on the fundamental material properties of the primary powders (solubility and wettability), liquid binders (viscosity), and mixing dynamics present in the system. Furthermore, it was shown that the process map can be used to predict the granule product quality based on the granule growth mechanism.

Combined resonant tunneling and rate equation modeling of terahertz quantum cascade lasers

Terahertz (THz) quantum cascade lasers (QCLs) are technologically important laser sources for the THz range but are complex to model. An efficient extended rate equation model is developed here by incorporating the resonant tunneling mechanism from the density matrix formalism, which permits to simulate THz QCLs with thick carrier injection barriers within the semi-classical formalism. A self-consistent solution is obtained by iteratively solving the Schrödinger–Poisson equation with this transport model. Carrier–light coupling is also included to simulate the current behavior arising from stimulated emission. As a quasi-ab initio model, intermediate parameters, such as pure dephasing time and optical linewidth, are dynamically calculated in the convergence process, and the only fitting parameters are the interface roughness correlation length and height. Good agreement has been achieved by comparing the simulation results of various designs with experiments, and other models such as density matrix Monte Carlo and non-equilibrium Green's function method that, unlike here, require important computational resources. The accuracy, compatibility, and computational efficiency of our model enable many application scenarios, such as design optimization and quantitative insights into THz QCLs. Finally, the source code of the model is also provided in the supplementary material of this article for readers to repeat the results presented here, investigate, and optimize new designs.

Interdependencies in Electrode Manufacturing: A Comprehensive Study Based on Design Augmentation and Explainable Machine Learning

AbstractElectrode manufacturing, as the core of battery cell production, is a complex process chain with a large number of interrelated parameters. An in‐depth understanding of the processes, their relevant parameters, and the resulting effects on intermediate and final product properties can accelerate the transition toward quality‐oriented, efficient battery cell production. Given the complexity of the process chain, data‐driven models have emerged as promising solutions for analyzing the existing interdependencies. The accuracy and effectiveness of these models significantly depend on the quality and comprehensiveness of the underlying data. With a low‐quality dataset, there is an increased risk of drawing inaccurate conclusions or generating misleading results. This article aimed to demonstrate a use case for the evaluation and enhancement of historical datasets to provide a statistically robust foundation for the development of machine learning models. The study was based on pilot‐scale anode manufacturing and covered variations in the coating, drying, and calendering processes. The key intermediate product and process parameters were used to predict two primary target variables: adhesion strength and discharge capacity at different C‐rates. To gain a better understanding of the analyzed interdependencies, explainable machine learning methods were adopted.

Scientific substantiation of complex extraction and processing of secondary resources for the development of alternative sources of energy

Scientific research on the integrated extraction and processing of secondary resources, such as mine water, technogenic waste, and others, in order to develop alternative energy sources in the diversification of coal mines is an urgent scientific task that will help solve the issues on the economic, social and environmental transformation of coal mining regions. In practice, it is necessary to consider several alternative options and assess the possibility of obtaining different end products from the targeted processing of secondary raw materials. The choice of a rational processing option to obtain the target product is very complicated due to the different composition of the feedstock, as well as a significant variety of primary processing methods and parameters of intermediate processes, which causes certain difficulties in forecasts and calculations. Developing a methodology for typifying actions when choosing options for integrated processing of recyclable materials of different states, defining the basic principles and its structure for comparative analysis of the input, intermediate, and output parameters in terms of composition, physical, chemical, and technological characteristics are urgent modern tasks.

Research on multi-objective parameter optimization of split flow dual flash thermodynamic cycle

In addition to considering the intermediate pressure variables, the split flow double flash thermodynamic cycle also needs to consider the operating conditions and split flow coefficient of the cycle. Traditional algorithms require too much computation and have a longer response time. Faced with sudden operating conditions or changes in production conditions, it is difficult to quickly re-optimize the intermediate parameters of the split flow double flash thermal cycle and carry out corresponding system control. This article uses intelligent optimization algorithms instead of global search methods to optimize multi-objective parameters in a split flow double flash thermodynamic cycle. By summarizing and analyzing the characteristics of algorithms such as SIO, EA, and ANN, it is believed that the GWO algorithm in the SIO class is the most suitable for multi-objective parameter optimization of the split flow double flash thermal cycle. It is combined with the SA optimization algorithm to propose the SA-GWO algorithm with faster optimization speed and higher optimization accuracy, taking the initial position of the wolf pack as the optimization objective. Combined with the cycle thermodynamic model, a multi-objective parameter optimization model for the split flow double flash thermal cycle is established.

Confinement of transitioning particles in bi-helical Wendelstein-type configurations

Recently, stochastic motion of 3.5-MeV alpha particles with orbits that vary between locally trapped and locally passing states (transitioning particles) in a Helias reactor was observed numerically. This validated the theoretical predictions that (i) the stochastic diffusion represents a mechanism of considerable delayed loss of fast ions in quasi-isodynamic stellarators and (ii) it is possible to prevent the escape of particles to the wall by closing the separatrix between the locally trapped and passing states. It was concluded that, in principle, the separatrix could be made closed, resulting in a reduction of fast ion losses, by compensating for the effect of the helical component of the magnetic field $(1, 1)$ with an enhanced ‘anti-helical’ harmonic $(1, -1)$ ; the enlargement of this harmonic was proposed previously for other reasons. This possibility is explored in this work. Equations of previous relevant works were generalised to include the $(1, -1)$ harmonic. Calculations were carried out for several magnitudes of the ratio of anti-helical to helical magnetic field harmonics. Positive effects were found already at the smallest anti-helical harmonic considered: when the latter ratio is 0.25, transitioning particles with the smallest and intermediate pitch parameters are confined and, moreover, their fraction decreased. When the ratio is 0.85, almost all transitioning particles are confined and their fraction is minimal; well-confined localised orbits dominate at the smallest pitch parameters.

Viable anisotropic inflation and reheating in the tachyon model

We study the intermediate tachyon inflation in an anisotropic background. By using the Friedmann equations obtained in the anisotropic geometry, we obtain the slow-roll parameters in the tachyon model. The presence of the anisotropic effects in the slow-roll parameters changes the perturbation parameters in our setup which may change its observational viability. To check this, we perform a numerical analysis and test the results with Planck2018 TT, TE, EE +lowE+lensing+BK14(18)+BAO data. We show that the intermediate anisotropic inflation in some ranges of the anisotropic and intermediate parameters is observationally viable. We also show that the equilateral amplitude of the non-gaussianity in our model is of the order of 10-2-10-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10^{-2}-10^{-1}$$\\end{document}. By studying the reheating process in our setup, we find that it is possible to have instantaneous reheating in this model. We also find that the temperature during the reheating in our setup is consistent with Big-Bang nucleosynthesis.

Federated Medical Learning Framework Based on Blockchain and Homomorphic Encryption

Federated learning-based medical data privacy sharing can promote the development of medical industry intelligence, but limited by its own security and privacy deficiencies, federated learning still suffers from a single point of failure and privacy leakage of intermediate parameters. To address these problems, this paper proposes a privacy protection framework for medical data based on blockchain and cross-silo federated learning, using cross-silo federated learning to establish a collaborative training platform for multiple medical institutions to enhance the privacy of medical data, introducing blockchain and smart contracts to realize decentralized federated learning to enhance trust between distrustful medical institutions and solve the problem of a single point of failure. In addition, a secure aggregation scheme is designed using threshold homomorphic encryption to prevent the privacy leakage problem during parameter transmission. The experimental and analytical results show that the accuracy of this paper’s scheme is consistent with the original federated learning scheme, effectively deals with the problems of single-point failure and inference attacks of federated learning, improves system robustness, and is suitable for medical scenarios with more stringent requirements on security and accuracy.

Determining the coordinates of a ground target from aircraft board using the method of multiple corner notches

The calculating of the stationary ground target coordinates using multiple angular spatial notches obtained using sighting equipment installed on board of a moving aircraft is considered. As intermediate parameters, auxiliary variables were used — slant ranges, which made it possible to reduce the problem to solving a system of linear algebraic equations obtained using the weighted least squares method. A complete algorithm for determining the geodetic coordinates of the target is presented, taking into account the positional and angular position of the aircraft at the time of measurement. Keywords spatial corner notches, multiple notches method, method of the weighted least squares

A Survey on Federated Recommendation Systems.

Federated learning has recently been applied to recommendation systems to protect user privacy. In federated learning settings, recommendation systems can train recommendation models by collecting the intermediate parameters instead of the real user data, which greatly enhances user privacy. In addition, federated recommendation systems (FedRSs) can cooperate with other data platforms to improve recommendation performance while meeting the regulation and privacy constraints. However, FedRSs face many new challenges such as privacy, security, heterogeneity, and communication costs. While significant research has been conducted in these areas, gaps in the surveying literature still exist. In this article, we: 1) summarize some common privacy mechanisms used in FedRSs and discuss the advantages and limitations of each mechanism; 2) review several novel attacks and defenses against security; 3) summarize some approaches to address heterogeneity and communication costs problems; 4) introduce some realistic applications and public benchmark datasets for FedRSs; and 5) present some prospective research directions in the future. This article can guide researchers and practitioners understand the research progress in these areas.