Mixing Operation Research Articles

A parallel recursive framework for modelling time series

Abstract Time series modelling is of significance to several scientific fields. Several approaches based on statistics, machine learning or combinations have been utilized. In order to model and forecast time series a novel parallel framework based on recursive pseudoinverse matrices is proposed. This framework enables the design of arbitrary statistical and machine learning models, adaptively, from a set of potential basis functions. This unification enables compact definition of existing and new models as well as easy implementation for new massively parallel architectures. The choice of appropriate basis functions is analysed and the fitting accuracy, termination criteria and model update operations are presented. A block variant for multivariate time series is also proposed. Parallel GPU implementation and performance optimization of the framework are provided, based on mixed precision arithmetic and matrix operations. The use of different basis functions is showcased with respect to various model univariate and multivariate time series for applications such as regression, frequency estimation and automatic trend detection. Discussions on limitations and future directions of research are also provided.

DLCH-YOLO: An Object Detection Algorithm for Monitoring the Operation Status of Circuit Breakers in Power Scenarios

In the scenario of power system monitoring, detecting the operating status of circuit breakers is often inaccurate due to variable object scales and background interference. This paper introduces DLCH-YOLO, an object detection algorithm aimed at identifying the operating status of circuit breakers. Firstly, we propose a novel C2f_DLKA module based on Deformable Large Kernel Attention. This module adapts to objects of varying scales within a large receptive field, thereby more effectively extracting multi-scale features. Secondly, we propose a Semantic Screening Feature Pyramid Network designed to fuse multi-scale features. By filtering low-level semantic information, it effectively suppresses background interference to enhance localization accuracy. Finally, the feature extraction network incorporates Generalized-Sparse Convolution, which combines depth-wise separable convolution and channel mixing operations, reducing computational load. The DLCH-YOLO algorithm achieved a 91.8% mAP on our self-built power equipment dataset, representing a 4.7% improvement over the baseline network Yolov8. With its superior detection accuracy and real-time performance, DLCH-YOLO outperforms mainstream detection algorithms. This algorithm provides an efficient and viable solution for circuit breaker status detection.

Nonideal mixing effect based on Cholette’s model on Van de Vusse reaction selectivity dynamics in an isothermal continuous stirred-tank reactor

Abstract The nonideal mixing effect on the Van de Vusse reaction dynamic in a continuous stirred-tank reactor (CSTR) has rarely been studied in literature. This work presents the mixing operation condition and kinetic reaction constant effects on the Van de Vusse reaction selectivity dynamic in an isothermal CSTR based on Cholette’s model. The mixing condition effect is defined using the parameter μ = n/m, where n is the flow fraction to the reactor and m is the dead space fraction in the reactor. In Cholette’s model, μ = 1 and μ ≠ 1 represent, respectively, the ideal and nonideal mixing operations. Herein, the final desired product yield steady state in the phase plane is first constructed under reaction constant and mixing condition combinations, indicating that the larger the μ values, the higher the desired product steady-state yield. In the selectivity dynamic simulations, the phase plane is divided into transient extreme yield region and region of monotonic variation in yield. It is found for the first time that a higher μ value leads to a decrease in the transient maximum selectivity region area and an increase in the transient minimum selectivity region area in the phase plane. Moreover, the trajectory from the start up to the final steady state will be changed due to nonideal mixing. These results have not been published and have direct applications in real reactor design and operation. Since a CSTR with ideal mixing is hardly attainable in practice, our results benefit the real reactor design and startup policy development in the desired product transient maximum yield.

Secure and Efficient Authentication using Linkage for permissionless Bitcoin network

The cryptocurrency’s permissionless and large-scale broadcasting requirements prohibit the traditional authentication implementation on the blockchain’s underlying peer-to-peer (P2P) networking. Thus, blockchain networking implementations remain vulnerable to networking integrity threats such as spoofing or hijacking. We design Secure and Efficient Authentication using Linkage (SEAL) to build connection security for permissionless Bitcoin networking. SEAL uses the linkage between the packets for a symmetric operation, in contrast to the traditional authentication approach relying on identity-credential-based trust. To make it appropriate for cryptocurrency networking, SEAL utilizes the packet header, protects the end-to-end connection, and separates the online process and the offline process so that the real-time overhead is minimal for greater efficiency and practicality. We implement SEAL on a functioning Bitcoin node and demonstrate that SEAL operates efficiently with minimal overhead. Specifically, it reduces the hash rate by only 1.3% compared to an unsecured node. Additionally, we use a network simulator to emulate the Bitcoin Mainnet and analyze SEAL’s impact on block propagation delay. SEAL yields 2.04 times smaller delay and 1.25 times smaller delay in block propagation than HMAC and ChaCha20-Poly1305, respectively. The key advantage of SEAL is that it requires fewer hash computations and simpler mixing operations, resulting in significantly lower computational overhead compared to traditional authentication schemes based on message authentication codes (MACs).

HSMix: Hard and soft mixing data augmentation for medical image segmentation

Due to the high cost of annotation or the rarity of some diseases, medical image segmentation is often limited by data scarcity and the resulting overfitting problem. Self-supervised learning and semi-supervised learning can mitigate the data scarcity challenge to some extent. However, both of these paradigms are complex and require either hand-crafted pretexts or well-defined pseudo-labels. In contrast, data augmentation represents a relatively simple and straightforward approach to addressing data scarcity issues. It has led to significant improvements in image recognition tasks. However, the effectiveness of local image editing augmentation techniques in the context of segmentation has been less explored. Additionally, traditional data augmentation methods for local image editing augmentation methods generally utilize square regions, which cause a loss of contour information.We propose HSMix, a novel approach to local image editing data augmentation involving hard and soft mixing for medical semantic segmentation. In our approach, a hard-augmented image is created by combining homogeneous regions (superpixels) from two source images. A soft mixing method further adjusts the brightness of these composed regions with brightness mixing based on locally aggregated pixel-wise saliency coefficients. The ground-truth segmentation masks of the two source images undergo the same mixing operations to generate the associated masks for the augmented images.Our method fully exploits both the prior contour and saliency information, thus preserving local semantic information in the augmented images while enriching the augmentation space with more diversity. Our method is a plug-and-play solution that is model agnostic and applicable to a range of medical imaging modalities. Extensive experimental evidence has demonstrated its effectiveness in a variety of medical segmentation tasks. The source code is available in https://github.com/DanielaPlusPlus/HSMix.

OStr-DARTS: Differentiable Neural Architecture Search Based on Operation Strength.

Differentiable architecture search (DARTS) has emerged as a promising technique for effective neural architecture search, and it mainly contains two steps to find the high-performance architecture. First, the DARTS supernet that consists of mixed operations will be optimized via gradient descent. Second, the final architecture will be built by the selected operations that contribute the most to the supernet. Although DARTS improves the efficiency of neural architecture search (NAS), it suffers from the well-known degeneration issue which can lead to deteriorating architectures. Existing works mainly attribute the degeneration issue to the failure of its supernet optimization, while little attention has been paid to the selection method. In this article, we cease to apply the widely-used magnitude-based selection method and propose a novel criterion based on operation strength that estimates the importance of an operation by its effect on the final loss. We show that the degeneration issue can be effectively addressed by using the proposed criterion without any modification of supernet optimization, indicating that the magnitude-based selection method can be a critical reason for the instability of DARTS. The experiments on NAS-Bench-201 and DARTS search spaces show the effectiveness of our method.

Feasibility of biochar for low-emission soft clay stabilization using CO2 curing

Use of traditional lime-cement binders on stabilizing soft sensitive clays pose a significant challenge for the construction sector to reach Finland’s carbon neutrality goals by 2030. Traditional stabilization recipes consisting of cement as binders contributing significantly to CO2 emissions (≅ 500 kg CO2 eq./ton in deep mixing alone). This laboratory study explores the feasibility of achieving near carbon-negative stabilization of soft clay leveraging accelerated CO2 curing (ACC) in biochar (BC) enhanced cementitious composites. BC, a by-product of the biofuel industry, is used as partial replacement of cement (0 %, 10 %, and 50 % of binder) in developing precast cementitious piles. One non-carbonated treatment and two ACC treatments are employed to assess their uniaxial compressive strength, thermogravimetric properties and CO2 sequestration capacity. The results demonstrate that synergistic effects of using BC with ACC not only enhances the compressive strength of the composites but also promotes CO2 uptake due to formation of stable carbonates. BC due to its surface functional groups, honeycomb porous structure, and hydrophilicity facilitated uniform CO2 diffusion in the clay matrix and likely improved internal curing. In ACC treated composites, the replacement of 50 % of cement with BC resulted in sufficient load-bearing capacity (≥50 kPa as per Finnish Guidelines) for both shallow and deep clay layers, making a suitable subgrade media for many types of geotechnical applications. The measured bound CO2 increased gravimetrically from 2 % to 41 % when cement was partially replaced by BC. In case of non-carbonated samples, 10 % partial replacement of BC provided high strength (≥200kPa). Life Cycle Assessment (LCA) of a case study of utilizing BC stabilized clay in deep mixing operations can potentially reduce net carbon emissions to −50 kg CO2 eq./ton.

A 5D super-extreme-multistability hyperchaotic map based on parallel-cascaded memristors

A new 5-dimensional discrete memristors-based hyperchaotic map (5DMHM) is constructed by introducing four charge-controlled discrete memristors (DMs, coupled through parallel-cascade mixing operation) into the sine map. This 5DMHM has 4-dimensional space fixed points and its stability distributions are partitioned in coupling strength, memristor initial condition, and parameter planes. The dynamics of the map are investigated by employing 2D dynamical behavior and the first Lyapunov exponent (LE) distribution. The super extreme multistability is discovered through the basin of attraction, the initial-value-dependent LE spectra, bifurcation diagrams, the mean value of state variables, and trajectory plots. Moreover, a high level of complexity can be observed by calculating the spectral entropy complexity for various parameter planes. Subsequently, a microcontroller-based digital circuit is designed to implement the 5DMHM. The security analysis of the pseudorandom number generator (PRNG) designed by the 5DMHM is presented and the PRNG passes the NIST SP800-22 test. The comparison of the 5DMHM with the recent maps indicates a higher performance in secure communication and image encryption.

Variational Quantum Algorithm‐Preserving Feasible Space for Solving the Uncapacitated Facility Location Problem

AbstractThe Quantum Alternating Operator Ansatz (QAOA+) is one of the Variational Quantum Algorithm (VQA) specifically developed to tackle combinatorial optimization problems by exploring the feasible space in search of a target solution. For the Constrained Binary Optimization with Unconstrained Variables Problems (CBO‐UVPs), the mixed operators in the QAOA+ circuit are applied to the constrained variables, while the single‐qubit rotating gates operate on the unconstrained variables. The expressibility of this circuit is limited by the shortage of two‐qubit gates and the parameter sharing in the single‐qubit rotating gates, which consequently impacts the performance of QAOA+ for solving CBO‐UVPs. Therefore, it is crucial to develop a suitable ansatz for CBO‐UVPs. In this paper, the Variational Quantum Algorithm‐Preserving Feasible Space (VQA‐PFS) ansatz is proposed, exemplified by the Uncapacitated Facility Location Problem (UFLP), that applies mixed operators on constrained variables while employing Hardware‐Efficient Ansatz (HEA) on unconstrained variables. The numerical results demonstrate that VQA‐PFS significantly enhances the probability of success and exhibits faster convergence than QAOA+, Quantum Approximation Optimization Algorithm (QAOA), and HEA. Furthermore, VQA‐PFS reduces the circuit depth dramatically compared to QAOA+ and QAOA. The algorithm is general and instructive in tackling CBO‐UVPs.

On critical Ambrosetti–Prodi type problems involving mixed operator

On critical Ambrosetti–Prodi type problems involving mixed operator

Normalized solutions to a critical growth Choquard equation involving mixed operators

In this paper we study the existence and regularity results of normalized solutions to the following critical growth Choquard equation with mixed diffusion type operators: − Δ u + ( − Δ ) s u = λ u + g ( u ) + ( I α ∗ | u | 2 α ∗ ) | u | 2 α ∗ − 2 u in R N , ∫ R N | u | 2 d x = τ 2 , where N ⩾ 3, τ > 0, I α is the Riesz potential of order α ∈ ( 0 , N ), ( − Δ ) s is the fractional laplacian operator, 2 α ∗ = N + α N − 2 is the critical exponent with respect to the Hardy Littlewood Sobolev inequality, λ appears as a Lagrange multiplier and g is a real valued function satisfying some L 2 -supercritical conditions.

Flexible yard crane scheduling for mixed railway and road container operations in sea-rail intermodal ports with the sharing storage yard

The sea-rail intermodal port, serving as the “cornerstone” supporting the seamless connection of landside and quayside transportation, plays a crucial role in determining the service level of the container multimodal transport through port’s operational organization efficiency. With on-dock rails, the physical integration between railways and water transport is smoothly achieved. However, within the port, the diversity in container flows adds complexity to operational organization and decision-making. In this paper, we formulate a flexible crane scheduling model to reduce the mutual interference during the mixed operations of railway containers and road containers in the sharing yard. The model addresses the time window constraints of railway containers required by train schedule, and the uncertain scenarios of external truck arrivals. To solve this problem, we design a two-stage approach that encompasses a task reassignment phase and a sequential decision-making phase. Furthermore, we test the model and algorithm performance through various experiments of different scales. Our results show that the designed algorithm designed solves instances of 20 tasks in 18.7 s, compared to 3600 s for the model solver. It efficiently handles real-world instances with over 200 tasks and 100 scenarios. Finally, sensitivity analysis is carried out to capture the influence of railway container ratio, truck arrival patterns, and train time windows on yard operations. Under intensive operation, we find that road containers, following a uniform arrival pattern, are least affected by railway container operations when the proportion of railway containers in the sharing block ranges from 70% to 80%. We also observe that the flexibility of crane service increases as the time window for railway container operations becomes more lenient.

Enhancing the Performance of Electrochemical RAM (ECRAM) through Modeling Guided Device Engineering

Traditionally, the only ions allowed or welcomed by the microelectronic industry are those that act as fixed dopants. The issue of mobile ions became welcome primarily due to the memristor technology however, thin film transistor technologies benefit from them too. The electrochemical (transistor) random access memory (ECRAM) is emerging as a promising building block for multi-level neuromorphic computing. Using FAB-compatible materials we construct the ECRAM using CuOx as the gate and ions source (Cu+). The morphology of the HfOx gate insulator layer is tuned to render it ion transporting such that it can act as a uniform electrolyte layer. Lastly, the channel material is WOx with tungsten metal as the source/drain contact.While there are several reports of ECRAM devices, the operation mechanisms are not fully known/understood thus withholding progress of this field. Using the Sentaurus device simulator by Synopsis, including the hydrogen diffusion module, we simulate the mixed ionic electronic operation of the device. We have recently reported that by fitting the simulation to the device performance, we could identify the potentiation mechanism (i.e., insulator charging) and the occurrence of copper plating that takes place under high Cu+ ion flux (as in fast charging of Li batteries).[1]In the first part of the talk, we will expand on the chemical-physics details of the ECRAM device mentioned above. Next, we will present a new device architecture where we remove the WOx layer and study the ionic-electronic conduction of a modified HfOx layer. By fine-tuning the stoichiometry of the HfOx layer, it assumes both roles of ionic electrolyte and electronic conducting channel. Following detailed modelling of the measured properties, we introduce AlO2 as an ionic barrier layer at the WOx/HfOx interface to enhance the ionic retention of the HfOx layer. Using the above three device architectures in conjunction with the mixed ionic-electronic device simulation we reveal the role of the two memory mechanisms: a) Electric field-activated ion transport and b) Structure-induced trapping by ion-barrier layers.Reference[1] Nir Tessler, Nayeon Kim, Heebum Kang, Jiyong Woo; Switching mechanisms of CMOS-compatible ECRAM transistors—Electrolyte charging and ion plating. J. Appl. Phys. 21 August 2023; 134 (7): 074501. https://doi.org/10.1063/5.0154153 Figure 1

Hypercyclic and mixing composition operators on OM(R)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathscr {O}}_M({\\mathbb {R}})$$\\end{document}

In this paper we characterize mixing composition operators acting on the space OM(R)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathscr {O}}_M({\\mathbb {R}})$$\\end{document} of slowly increasing smooth functions. Moreover we relate the mixing property of those operators with the solvability of Abel’s functional equation and we give a sufficient condition for sequential hypercyclicity of composition operators on OM(R)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathscr {O}}_M({\\mathbb {R}})$$\\end{document}. This is used to prove that many mixing composition operators are hypercyclic.

Existence of positive solutions for a class of p-Laplacian fractional differential equations with nonlocal boundary conditions

This article is devoted to proving the uniqueness of positive solutions for p-Laplacian equations with Caputo and Riemann-Liouville fractional derivative. The uniqueness result and the dependence of the solution on a parameter are established based on the fixed point point theorem of mixed monotone operators. In the end, a numerical simulation is given to verify the main results.

Energy rod based on the combination of biomasses and biochar with natural rubber binder: Mechanical, thermal, and energy releasing properties

Composite preparation from various biomasses and biochar blended natural rubber (NR) as a binder was produced for proposing an alternative fuel energy rod. Rice straw (RS), sugarcane bagasse (SC), and corncob (CC) were combined with different NR ratios. Properties of the composites, in terms of tensile, impact, and compressive strengths, together with flammability, thermal stability, and heat-releasing analyses from Higher Heating Value (HHV) and Ultimate Analysis (CHN-S), were interpreted. It was found that the increase of NR constituent improves the mechanical properties and HHV. The minimization of NR needs to be designed for the release of carbon monoxide. During the mixing operation with an internal mixer and a two-roll mill, no differences were observed in composites with RS, SC, and CC, and the optimal biomass is 80 phr. After transferring to biochar, the produced composite clarifies the usability of the energy rod using pure NR as the natural binder.

Condition monitoring for planetary journal bearings in wind turbine gearboxes by means of acoustic measurements and machine learning

The use of journal bearings instead of rolling bearings as planetary bearings in wind turbine (WT) gearboxes is advantageous for the power density of the drive train. In addition, they can increase the reliability of the gearboxes as they can be operated wear-free in hydrodynamic operation, i.e. with fluid friction. The dynamic loading conditions in wind turbines, as well as special conditions such as insufficient lubrication and particle contamination, can lead to mixed friction operation and consequently to wear in the journal bearings. If mixed friction is not detected, damage and failure of the journal bearing and consequently of the WT gearbox may occur. Therefore, it is required to develop a Condition Monitoring System (CMS) to detect mixed friction in the journal bearings during operation of the WT. Preliminary investigations have shown that various friction conditions can be detected by acoustic measurements in combination with machine learning classifiers. Current investigations on CMS methods for journal bearings using acoustic measurement methods are limited to component level applications. A condition monitoring methodology for wind turbine gearbox journal bearings does not currently exist. A major challenge for CMS development for journal bearings in WT gearboxes is the transfer of methods already proven at component level to gearbox applications. In preparation for CMS application at the gearbox level, this paper presents an approach for monitoring different friction conditions of journal bearings based on acoustic measurements at a component test rig. For the classification of the friction state, different machine learning (ML)-based approaches trained on the acquired acoustic measurement data are compared with respect to the achieved classification accuracy. Knowledge of the robustness of the classification method, e.g. with respect to the distance of the sensor to the bearing, provides the necessary basis for the use of the CMS at the gearbox level. The investigations are carried out under operating conditions typical for planetary bearings in wind turbines. Classification performance is evaluated using a validated elasto-hydrodynamic simulation model. The aim of the work is to develop a method that detects friction classes in the journal bearing based on structure-borne sound measurements. Here, simulation results are used to train the algorithms. Finally, the demonstrated method will be successfully applied to a test rig for wind turbine gearbox journal bearing. Based on the results, an ML approach will be selected for application in gearboxes.

Utilization of modified epoxidized natural rubber and zinc oxide on properties of polylactic acid/epoxidized natural rubber blends: Mechanical properties, antibacterial efficiency and biodegradability

Polylactic acid (PLA) is a biopolymer with brittleness and no antibacterial efficiency, which can be solved by the combination with epoxidized natural rubber (ENR) due to polar-polar interaction and with modified zinc oxide nanoparticles (ZnO) regarding the releasing of zinc ions (Zn2+) and reactive oxygen species (ROS). To minimize self-interaction in each PLA and ENR molecular chain, ENR with different % epoxidation was modified (mENR) by grafting with sebacic acid (SA). The addition of mENR with 50 % mol to PLA significantly improved the properties of pure PLA in terms of mechanical and impact properties, toughness, and also provided disinfectant ability. Regarding antibacterial properties, the efficiency against Staphylococcus aureus and Escherichia coli was significantly improved by adding ZnO to the blends. Combination of mENR to PLA matrix during mixing operation increases improved ZnO dispersion and distribution in PLA/mENR blends. Thus, antibacterial efficiency had been enhanced using proper mENR:ZnO concentration ratios in the PLA matrix, relative to other modified ZnO types. Also, incorporation of mENR:ZnO enhanced impact strength and retarded biodegradability of the blends due to hydrophobicity of ENR in PLA matrix. The findings of the blends' properties suggest potential for new functional applications, especially in disinfectant sheet lamination products where strength and protection against bacterial growth together with proper degradation period are required.

Design of a bioreactor as a vital biological support for testing microalgae cultures in the stratosphere.

Abstract In the present, the proposal for the design of an autonomous, light and completely closed bioreactor that will orbit in the stratosphere is developed, capable of promoting the growth of microalgae, specifically of the species: Chlorella vulgaris. The document will address the development of the relevant background information related to this proposal, which will support the bases and certain decisions described in the work, as well as the concepts that will define the theoretical framework for the identification of primary and secondary sources that allow to know, design and develop the proposed. In order to achieve a system capable of performing mixing and recirculation operations, necessary to promote the transfer of mass and energy, as well as the adequate distribution of light, the different existing bioreactor models and their most significant differences will be analyzed. For this work, only five of the nine stages of the mechatronic design methodology are covered: identification of needs, analysis of the problem, preparation of specifications, generation of possible solutions, and selection of the appropriate solution. The missing stages; detailed design, production of working drawings, prototype construction, and testing are underway.

Microfluidic mixing by magnetic particles: Progress and prospects.

Microfluidic systems have enormous potential for enabling point-of-care diagnostics due to a number of advantages, such as low sample volumes, small footprint, low energy requirements, uncomplicated setup, high surface-to-volume ratios, cost-effectiveness, etc. However, fluid mixing operations are constrained by molecular diffusion since the flow is usually in the laminar regime. The slow nature of molecular diffusion is a technological barrier to implementing fluid transformations in a reasonable time. In this context, magnetically actuated micro-mixers of different sizes, shapes, materials, and actuation techniques provide a way to enhance fluid mixing in microfluidic devices. In this paper, we review the currently existing micro-mixing technologies. From a fundamental perspective, the different magnetization models for permanent and induced dipoles are discussed. The single-particle dynamics in steady and oscillating magnetic fields is studied in order to determine the flow generated and the torque exerted on the fluid due to the magnetic particles. The effect of particle interactions, both magnetic and hydrodynamic, is examined.