Heterogeneous Hybrid Research Articles

Synergetic effect of in-situ TiB2 reinforcement and nano precipitation on wear behavior of ZE41 magnesium matrix composite

The rise in carbon dioxide pollution and energy consumption has increased the demand for lightweight materials such as magnesium in automotive and aerospace industries. However, magnesium alloys face challenges like poor wear resistance and mechanical strength. To overcome these limitations, a promising approach involves developing heterogeneous hybrid microstructures through reinforcement addition and microstructural engineering. This study focuses on the tribological performance of a newly developed in-situ sub-micron sized TiB2/ZE41 composite under various microstructural conditions, comparing it to the unreinforced ZE41 Mg alloy. Wear experiments were conducted using a pin-on-disc tribometer under normal loads of 10, 20, 40, and 60 N at a sliding velocity of 1 m/s. Scanning electron microscopy (SEM) coupled with energy dispersive spectroscopy (EDS) was used to analyze the worn surfaces in order to identify damage types and surface distortions. By correlating worn surface microstructures with test parameters, predominant wear mechanisms for each material condition under specific loads were determined. Results consistently showed that the presence of in-situ TiB2 reinforcements, β-phase, and rare-earth precipitates enhanced wear resistance regardless of the load conditions. Additionally, the study established a scientific understanding of the wear behavior of ZE41 Mg alloy, with and without in-situ TiB2 particles and precipitates, through analysis of dominant wear mechanisms, wear-induced subsurface deformation mechanisms, kernel average misorientation, grain orientation spread, and microhardness evaluation.

Enhanced Carrier Selectivity and Superior Passivation in Cost‐Effective Heterogeneous Hybrid Transport Layers for Next Generation Silicon Solar Cells

AbstractHybrid hole transport layers (HHTLs) consisting of‐a low‐cost, low‐temperature, dopant‐free, organic, hole‐selective poly(3,4‐ethylenedioxythiophene):polystyrene sulphonate (PEDOT:PSS) and an inorganic homogenous tunnel oxides (HoTOs), such as SiOx, TiOx, and AlOx have allowed significant improvements in carrier selectivity as well as in power and cost efficiency. These key parameters can be further improved by introducing heterogeneous tunnel oxides (HeTOs), formed of two different inorganic oxides. This work examines the potential of heterogeneous HHTLs and their impact on passivation as well as on the interfacial properties in silicon solar cells. HeTOs of SiOx/TiOx and SiOx/AlOx with negative fixed charge are studied and optimized. A heterogeneous HHTL with AlOx (>1.5 nm) resulted in a high lifetime (1.2 ms), low surface recombination velocity, and improved surface band bending at the interface due to the presence of higher negative fixed charge (1012 cm−2) within these layers. The passivation properties further improved on light soaking and annealing due to oxidation of PSS and AlOx/SiOx. Overall, these HHTLs demonstrated a hole selectivity (S10) of 12.5 and very high efficiencies up to of 26.1%, surpassing homogenous‐SiOx/PEDOT:PSS by 1.4%.

Lot Sizing and Scheduling Problem in Distributed Heterogeneous Hybrid Flow Shop and Learning-Driven Iterated Local Search Algorithm

Lot Sizing and Scheduling Problem in Distributed Heterogeneous Hybrid Flow Shop and Learning-Driven Iterated Local Search Algorithm

Ultrathin Ionic Diodes with Electrostatically Heterogeneous Hybrid Interfaces of Nanoporous SiO2 Nanofilms and Polymer Layer-by-Layer Multilayers.

Nanofluidic ionic diodes have attracted much attention due to their unique functions as unidirectional ion transportation ability and promising applications from molecular sensing, and energy harvesting to emerging neuromorphic devices. However, it remains a challenge to fabricate diode-like nanofluidic systems with ultrathin film thickness <100nm. Herein the formation of ultrathin ionic diodes from hybrid nanoassemblies of nanoporous (NP) SiO2 nanofilms and polyelectrolyte layer-by-layer (LbL) multilayers is described. Ultrathin ionic diodes are prepared by integrating polyelectrolyte multilayers onto photo-oxidized NP SiO2 nanofilms obtained from silsesquioxane-containing block copolymer thin films as a template. The obtained ultrathin ionic diodes exhibit ion current rectification (ICR) properties with high ICR factor = ≈20 under low ionic strength and asymmetric pH conditions. It is concluded that this ICR behavior arises from effective ion accumulation and depletion at the interface of NP SiO2 nanofilms and LbL multilayers attributed to high ion selectivity by combining the experimental data and theoretical calculations using finite element methods. These results demonstrate that the hybrid nano assemblies of NP SiO2 nanofilms and polyelectrolyte LbL multilayers have potential applications for (bio)sensing materials and integrated ionic circuits for seamless connection of human-machine interfaces.

Photocatalytic Conversion of CO2 to Formate/CO by an (η6-para-Cymene)Ru(II) Half-Metallocene Catalyst: Influence of Additives and TiO2 Immobilization on the Catalytic Mechanism and Product Selectivity.

The catalytic efficacy of the monobipyridyl (η6-para-Cymene)Ru(II) half-metallocene, [(p-Cym)Ru(bpy)Cl]+ was evaluated in both mixed homogeneous (dye + catalyst) and heterogeneous hybrid systems (dye/TiO2/Catalyst) for photochemical CO2 reduction. A series of homogeneous photolysis experiments revealed that the (p-Cym)Ru(II) catalyst engages in two competitive routes for CO2 reduction (CO2 to formate conversion via RuII-hydride vs CO2 to CO conversion through a RuII-COOH intermediate). The conversion activity and product selectivity were notably impacted by the pKa value and the concentration of the proton source added. When a more acidic TEOA additive was introduced, the half-metallocene Ru(II) catalyst leaned toward producing formate through the RuII-H mechanism, with a formate selectivity of 86%. On the other hand, in homogeneous catalysis with TFE additive, the CO2-to-formate conversion through RuII-H was less effective, yielding a more efficient CO2-to-CO conversion with a selectivity of >80% (TONformate of 140 and TONCO of 626 over 48 h). The preference between the two pathways was elucidated through an electrochemical mechanistic study, monitoring the fate of the metal-hydride intermediate. Compared to the homogeneous system, the TiO2-heterogenized (p-Cym)Ru(II) catalyst demonstrated enhanced and enduring performance, attaining TONs of 1000 for CO2-to-CO and 665 for CO2-to-formate.

Event-based Consensus of Double-integral Heterogeneous Hybrid Multi-agent Systems With Communication Delays

Event-based Consensus of Double-integral Heterogeneous Hybrid Multi-agent Systems With Communication Delays

Investigating for Photocatalytic Activity of Hybrid TiO2/Reduced Graphene Oxide and Application in Reducing VOCs

In this study, rGO/TiO2 hybrid nanostructures (rGO : reduced Graphene Oxide) have been successfully fabricated for the purpose of application in the treatment of volatile organic compound (VOCs) that harmful for the environment through the method of directly measuring the change in concentration of decomposed VOCs in real time under UV-excited conditions using a home-made measuring system. the results showed that hybrids TiO2 : rGONFs (reduced Graphene Oxide nano flake) samples with the weight ratio between two kind of material are 99%:1% and 98%:2% which reduced VOCs faster than 2 time intrinsic TiO2. The phenomenon of 2D materials involved in the hybrids lead to the fact that the photocatalytic activity of TiO2 had been significantly improved, this can be explained as follows: When heterogeneous hybrid was formed, because of the difference in energy levels of conduction band between two materials was negligible, heterojunction barrier is not too high this made the photogenerated electrons from TiO2 easily move through rGONFs under UV stimulation. This thing has significantly reduced the recombination of the generated carrier in TiO2 during irradiation, which lead to an increase in the lifetime of the carrier and make photocatalytic reaction of the assembly become more effective. Copyright © 2024 by Authors, Published by BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

A parallel deep adaptive large neighbourhood search algorithm for distributed heterogeneous hybrid flow shops with mixed-model assembly scheduling

ABSTRACT Nowadays, manufacturing enterprises must have fast response and flexible production capabilities to meet personalized and diversified market demands. Mixed-model production and distributed production have become the preferred production methods for enterprises. This article studies a distributed heterogeneous hybrid flow shop scheduling problem with a mixed-model assembly line (DHHFSP-MMAL), which consists of manufacturing and assembly stages. The DHHFSP-MMAL is modelled by a mixed integer linear programming (MILP) model. Three constructive heuristics and a parallel deep adaptive large neighbourhood search (PDALNS) problem are presented. A constructive heuristic with a group strategy is employed to obtain an initial solution. Several deep destroy-and-repair operators are proposed where problem-specific greedy local search methods are applied. The PDALNS assigns weights to destroy-and-repair operators to guide the selection of operators. The parallel computing technique is introduced to increase the efficiency of training. The experiments demonstrate that the PDALNS algorithm is an efficient and effective algorithm for solving the DHHFSP-MMAL problem.

J-MISFET Hybrid Dual-Gate Switching Device for Multifunctional Optoelectronic Logic Gate Applications.

High-performance and low operating voltage are becoming increasingly significant device parameters to meet the needs of future integrated circuit (IC) processors and ensure their energy-efficient use in upcoming mobile devices. In this study, we suggest a hybrid dual-gate switching device consisting of the vertically stacked junction and metal-insulator-semiconductor (MIS) gate structure, named J-MISFET. It shows excellent device performances of low operating voltage (<0.5 V), drain current ON/OFF ratio (∼4.7 × 105), negligible hysteresis window (<0.5 mV), and near-ideal subthreshold slope (SS) (60 mV/dec), making it suitable for low-power switching operation. Furthermore, we investigated the switchable NAND/NOR logic gate operations and the photoresponse characteristics of the J-MISFET under the small supply voltage (0.5 V). To advance the applications further, we successfully demonstrated an integrated optoelectronic security logic system comprising 2-electric inputs (for encrypted data) and 1-photonic input signal (for password key) as a hardware security device for data protection. Thus, we believe that our J-MISFET, with its heterogeneous hybrid gate structures, will illuminate the path toward future device configurations for next-generation low-power electronics and multifunctional security logic systems in a data-driven society.

Streamlining trajectory map-matching: a framework leveraging spark and GPU-based stream processing

Real-time online trajectory map-matching has emerged as a critical component in the era of location-based services (LBS) and intelligent transportation systems (ITS). It refers to the process of aligning a user’s GPS trajectory data with the corresponding road network in real-time. This technology has significant implications for various industries and applications. As our reliance on LBS and ITS continues to grow, the demand for faster, more accurate, and more reliable trajectory map-matching methods becomes increasingly important. Contemporary online map-matching predominantly employs stream processing techniques. Based on stream processing frameworks, we propose a heterogeneous hybrid architecture for map-matching. The architecture integrates Spark Streaming and graphics processing unit (GPU) heterogeneous computing for the first time. The hidden Markov model is employed as the map-matching algorithm, and Spark Streaming serves as the distributed processing platform. We conduct map-matching experiments using a GPS taxi trajectory dataset in Beijing’s Haidian District. The results demonstrate that in comparison to other analogous research, our framework’s performance has increased by over ten times, possessing a superior data processing capability and lower latency. This research provides a novel approach of stream-based heterogeneous computation for processing large-scale geographic data.

High-performance computing: Transitioning from Instruction-Level Parallelism to heterogeneous hybrid architectures

This paper delves into the shift from Instruction-Level Parallelism (ILP) to Heterogeneous Hybrid Parallel Computing in the quest for optimized performance processing. It sheds light on the constraints of ILP, emphasizing how these shortcomings have catalyzed a move toward the more adaptable and proficient framework of heterogeneous hybrid computing. This transformation's advantages are explored across diverse applications, notably in deep learning, cloud computing, data centers, and mobile SoCs. Additionally, the study underscores emerging architectures and innovations of this era, including many-core processors, FPGA-driven accelerators, and an assortment of software tools and libraries. While heterogeneous hybrid computing offers a promising horizon, it isn't without challenges. This paper brings to the fore issues like restricted adaptability, steep development costs, software compatibility hurdles, the absence of a standardized programming model, and vendor reliance. Through this in-depth exploration, our aim is to present a holistic snapshot of the present and potential future of high-performance processing.

A feedback learning-based selection hyper-heuristic for distributed heterogeneous hybrid blocking flow-shop scheduling problem with flexible assembly and setup time

Distributed manufacturing is increasingly common due to economic globalization. It has important practical significance to optimize global supply chains by considering cooperative scheduling of distributed production and flexible assembly. This paper studies a distributed heterogeneous hybrid blocking flow-shop scheduling problem with flexible assembly and setup time (DHHBFSP-FAST). The objective is to minimize makespan of all products. To tackle such problem, a mixed-integer linear programming model (MILP) is presented to formulate it. Then, a feedback learning-based selection hyper-heuristic (FLS-HH) is proposed, which contains high-level control strategies and low-level heuristics. For the high-level control strategies, the transition information between each pair of low-level heuristics is collected, then a feedback learning-based selection method is presented based on such transition information, which can automatically select the appropriate low-level heuristics. For the low-level heuristics, to assign jobs/products to machines, a composite dispatch rule is proposed. To generate an initial domain solution with high quality, a constructive heuristic based on double evaluation indexes is developed. The critical jobs and products are analyzed to avoid invalid searching. Based on such analysis, several low-level heuristics are presented to search the domain solution space, which combine six problem-specific perturbation heuristics and two local searches to balances exploration and exploitation. Comprehensive numerical experiments are carried out. The effectiveness of the proposed MILP and the special designs of FLS-HH are verified. To verify the effectiveness of FLS-HH, we compare it with 10 existing high-performing approaches. The comparison results show that FLS-HH significantly outperforms its competitors in solving DHHBFSP-FAST.

OpenH: A Novel Programming Model and API for Developing Portable Parallel Programs on Heterogeneous Hybrid Servers

OpenH: A Novel Programming Model and API for Developing Portable Parallel Programs on Heterogeneous Hybrid Servers

Heterogeneous hybrid governance mechanisms of information technology and contract in supply chain quality management

AbstractThis study scrutinizes the heterogeneous hybrid governance mechanisms of information technology and contact in supply chain quality management. Drawing on governance theory and features of information technology, a conceptual model is proposed. Based on the survey data obtained from 154 manufacturing firms' managers, we use the structural equation modeling method to test our conceptual model. The method of partial least squares is used to analyze the structural equation model. We find that Information transparency and contract completeness are substitutes, and information traceability and contract completeness are complements.

Prototypes of Devices for Heterogeneous Hybrid Semiconductor Electronics with an Embedded Biomolecular Domain

Prototypes of Devices for Heterogeneous Hybrid Semiconductor Electronics with an Embedded Biomolecular Domain

Prototypes of devices for heterogeneous hybrid semiconductor electronics with an embedded biomolecular domain

A macromolecular system embedded in a semiconductor microelectronic device is considered as a biomolecular nano- or micro-sized domain that performs the functions of converting acoustic and electromagnetic signals. The issues of the choice of substances, the dynamic and structural-functional state of the domain, as well as the physical foundations of its interaction with matrix elements are discussed. The process of excitation of forced vibrations in amino acid molecules (for example, glycine, tryptophan, diphenyl-L-alanine) under the influence of short (10–100 ps) packets of electrical signals in the IR range with a frequency in the range of 1–125 THz was studied by the method of supercomputer nonequilibrium modeling of molecular dynamics. The acoustoelectric interpretation of oscillation generation was carried out using a unified equivalent circuit of the peptide group. Examples of developed prototypes of heterogeneous devices are given. It is concluded that embedded biomolecular domains, presented as a multifunctional element base, are promising for signal conversion in hybrid microelectronics.

A multiobjective memetic algorithm with particle swarm optimization and Q-learning-based local search for energy-efficient distributed heterogeneous hybrid flow-shop scheduling problem

Most existing distributed hybrid flow-shop scheduling problems (DHFSPs) assume identical shops and lack consideration of heterogeneous shops. This study focuses on energy-efficient heterogeneous DHFSP. A multiobjective memetic algorithm with particle swarm optimization and Q-learning-based local search is proposed in order to optimize both makespan and total energy consumption. Particle swarm optimization with multi-group is specifically designed as a global search strategy to improve the fast convergence performance of solutions in multi-direction of Pareto front. To improve the problem-specific knowledge search, two local search strategies are designed to further improve the quality and diversity of solutions. In addition, Q-learning is utilized to guide variable neighborhood search to better balance the exploration and exploitation of algorithms. This study investigates the effect of parameter setting and conducts extensive numerical tests. The comparative results and statistical analysis demonstrate the superior convergence and distribution performance of the proposed algorithm.

Homogeneous–Heterogeneous Hybrid Ensemble for concept-drift adaptation

Homogeneous ensembles are very effective in concept-drift adaptation. However, choosing an appropriate base learner and its hyperparameters suitable for a stream is critical for their predictive performance. Moreover, the best base learner and its hyperparameters may change over time as the stream evolves, necessitating manual reconfiguration. On the other hand, heterogeneous ensembles train multiple base learners belonging to diverse algorithmic families with different inductive biases. Though it eliminates the need to manually choose the best base learner for a stream, their size is often restricted to the number of unique base learner algorithms, limiting their scalability. We combine the strengths of homogeneous and heterogeneous ensembles into a unified scalable ensemble framework with higher predictive performance, while eliminating the need to manually specify and adapt the optimal base learner and its hyperparameters for a stream. The proposed ensemble named H3E is a single-pass hybrid algorithm which uses a genetic algorithm (GA) based optimization in combination with stacking to provide high predictive performance at a competitive computational cost. Experiments on several real and synthetic data streams affected by diverse drift types confirm the superior predictive performance and utility of our approach in comparison to popular online ensembles.

Review on the key technologies of power grid cyber‐physical systems simulation

AbstractA Cyber‐Physical System (CPS) is a spatiotemporal multi‐dimensional and heterogeneous hybrid autonomous system composed of deep integration of information resources and physical systems. With the development of digitisation and digitalisation, a large number of data acquisition equipment, computing equipment, and electrical equipment are interconnected between the power grid and the information communication network. The power grid has thus been restructured as a mature and highly complex CPS. In order to promote the development of power grid CPS technologies and provide a reference for relevant researchers in the field, the origin and concept of CPS and features in power grid CPS are introduced firstly. Then the key technologies of power grid CPS simulation are discussed and further analysed from three perspectives, including modelling theory, simulation methods, and system‐level simulation. On this basis, the application of CPS simulation technology in future power grids has been prospected.

A Heterogeneous Parallel Computing Approach Optimizing SpTTM on CPU-GPU via GCN

Sparse Tensor-Times-Matrix (SpTTM) is the core calculation in tensor analysis. The sparse distributions of different tensors vary greatly, which poses a big challenge to designing efficient and general SpTTM. In this paper, we describe SpTTM on CPU-GPU heterogeneous hybrid systems and give a parallel execution strategy for SpTTM in different sparse formats. We analyze the theoretical computer powers and estimate the number of tasks to achieve the load balancing between the CPU and the GPU of the heterogeneous systems. We discuss a method to describe tensor sparse structure by graph structure and design a new graph neural network SPT-GCN to select a suitable tensor sparse format. Furthermore, we perform extensive experiments using real datasets to demonstrate the advantages and efficiency of our proposed input-aware slice-wise SpTTM. The experimental results show that our input-aware slice-wise SpTTM can achieve an average speedup of 1.310 × compared to ParTI! library on a CPU-GPU heterogeneous system.