Information Processing Capabilities Research Articles

Entangled photon pair generation in an integrated SiC platform

Entanglement plays a vital role in quantum information processing. Owing to its unique material properties, silicon carbide recently emerged as a promising candidate for the scalable implementation of advanced quantum information processing capabilities. To date, however, only entanglement of nuclear spins has been reported in silicon carbide, while an entangled photon source, whether it is based on bulk or chip-scale technologies, has remained elusive. Here, we report the demonstration of an entangled photon source in an integrated silicon carbide platform for the first time. Specifically, strongly correlated photon pairs are efficiently generated at the telecom C-band wavelength through implementing spontaneous four-wave mixing in a compact microring resonator in the 4H-silicon-carbide-on-insulator platform. The maximum coincidence-to-accidental ratio exceeds 600 at a pump power of 0.17 mW, corresponding to a pair generation rate of (9 ± 1) × 103 pairs/s. Energy-time entanglement is created and verified for such signal-idler photon pairs, with the two-photon interference fringes exhibiting a visibility larger than 99%. The heralded single-photon properties are also measured, with the heralded g(2)(0) on the order of 10−3, demonstrating the SiC platform as a prospective fully integrated, complementary metal-oxide-semiconductor compatible single-photon source for quantum applications.

Assessing the Operational Capability of Disaster and Emergency Management Resources: Using Analytic Hierarchy Process

This study aims to assess the operational capability of disaster and emergency management resources (DEMRs), which is not only critical for effective loss reduction and resilience, but also facilitates the sharing and optimal use of resources for the more effective achievement of sustainable development. This study constructs an evaluation index system of the operational capability of DEMRs, encompassing four key aspects: resource planning, organizational management capability, resource support capability, and information processing capability. It focuses on identifying the factors that influence the operational capability of DEMRs in China and Korea, comparing and analyzing the relative importance and priority of each evaluation domain and indicator within these countries. The results show that the organizational management capability is most significant in China, whereas the resource support capability is prioritized in Korea. A comparative analysis of the local weight of indicators within each domain revealed the largest discrepancy between China and Korea in the information processing capability domain. This study concludes by calculating global weights, identifying the fast response capability and resource allocation capability as the most impactful factors on the operational capability of DEMRs, and highlighting their critical role in disaster and emergency management.

A general neural membrane computing model

Neural P systems are a class of distributed parallel computing models inspired by the way neurons cooperate in biological neural networks. Artificial neural networks integrate biological neural networks with mathematical models, which have powerful information processing capabilities. This work adopts the structure and data processing method of neural networks and integrates them with membrane computing to improve the neural P systems in data processing. A general neural membrane computing (GNMC) model is developed to realize the flow and manipulation of data in the form of objects and rules in the neuron. Glial cells are introduced into the GNMC model at the cellular level to modulate the states of the connected neurons. The trigger conditions, the activation mechanisms and the dissolve-reconnect rules make the GNMC model more efficient in handling objects. The multiple objects and multiple topological structures enable the GNMC model to handle different practical problems. The Turing universality of the GNMC model is proved as number generating and accepting devices. Furthermore, a small universal GNMC model with 71 neurons, fewer than that of the existing state-of-the-art neural P systems, is constructed for computing functions.

Information processing style and institutional trust as factors of COVID vaccine hesitancy

This study investigates the factors contributing to COVID vaccine hesitancy. Vaccine hesitancy has commonly been attributed to susceptibility to misinformation and linked to particular socio-demographic factors and personality traits. We present a new perspective, emphasizing the interplay between individual cognitive styles and perceptions of public health institutions. In January 2020, before the COVID-19 pandemic, 318 participants underwent a comprehensive assessment, including self-report measures of personality and clinical characteristics, as well as a behavioral task that assessed information processing styles. During 2021, attitudes towards vaccines, scientists, and the CDC were measured at three time points (February–October). Panel data analysis and structural equation modeling revealed nuanced relationships between these measures and information processing styles over time. Trust in public health institutions, authoritarian submission, and lower information processing capabilities together contribute to vaccine acceptance. Information processing capacities influenced vaccination decisions independently from the trust level, but their impact was partially mediated by authoritarian tendencies. These findings underscore the multifactorial nature of vaccine hesitancy, which emerges as a product of interactions between individual cognitive styles and perceptions of public health institutions. This novel perspective provides valuable insights into the underlying mechanisms that drive this complex phenomenon.

Multiplexed manipulation of orbital angular momentum and wavelength in metasurfaces based on arbitrary complex-amplitude control

Due to its unbounded and orthogonal modes, the orbital angular momentum (OAM) is regarded as a key optical degree of freedom (DoF) for future information processing with ultra-high capacity and speed. Although the manipulation of OAM based on metasurfaces has brought about great achievements in various fields, such manipulation currently remains at single-DoF level, which means the multiplexed manipulation of OAM with other optical DoFs is still lacking, greatly hampering the application of OAM beams and advancement of metasurfaces. In order to overcome this challenge, we propose the idea of multiplexed coherent pixel (MCP) for metasurfaces. This approach enables the manipulation of arbitrary complex-amplitude under incident lights of both plane and OAM waves, on the basis of which we have realized the multiplexed DoF control of OAM and wavelength. As a result, the MCP method expands the types of incident lights which can be simultaneously responded by metasurfaces, enriches the information processing capability of metasurfaces, and creates applications of information encryption and OAM demultiplexer. Our findings not only provide means for the design of high-security and high-capacity metasurfaces, but also raise the control and application level of OAM, offering great potential for multifunctional nanophotonic devices in the future.

Low-dimensional criticality embedded in high-dimensional awake brain dynamics.

Whether cortical neurons operate in a strongly or weakly correlated dynamical regime determines fundamental information processing capabilities and has fueled decades of debate. We offer a resolution of this debate; we show that two important dynamical regimes, typically considered incompatible, can coexist in the same local cortical circuit by separating them into two different subspaces. In awake mouse motor cortex, we find a low-dimensional subspace with large fluctuations consistent with criticality-a dynamical regime with moderate correlations and multi-scale information capacity and transmission. Orthogonal to this critical subspace, we find a high-dimensional subspace containing a desynchronized dynamical regime, which may optimize input discrimination. The critical subspace is apparent only at long timescales, which explains discrepancies among some previous studies. Using a computational model, we show that the emergence of a low-dimensional critical subspace at large timescales agrees with established theory of critical dynamics. Our results suggest that the cortex leverages its high dimensionality to multiplex dynamical regimes across different subspaces.

Brain-inspired computing with fluidic iontronic nanochannels

The brain's remarkable and efficient information processing capability is driving research into brain-inspired (neuromorphic) computing paradigms. Artificial aqueous ion channels are emerging as an exciting platform for neuromorphic computing, representing a departure from conventional solid-state devices by directly mimicking the brain's fluidic ion transport. Supported by a quantitative theoretical model, we present easy-to-fabricate tapered microchannels that embed a conducting network of fluidic nanochannels between a colloidal structure. Due to transient salt concentration polarization, our devices are volatile memristors (memory resistors) that are remarkably stable. The voltage-driven net salt flux and accumulation, that underpin the concentration polarization, surprisingly combine into a diffusionlike quadratic dependence of the memory retention time on the channel length, allowing channel design for a specific timescale. We implement our device as a synaptic element for neuromorphic reservoir computing. Individual channels distinguish various time series, that together represent (handwritten) numbers, for subsequent in silico classification with a simple readout function. Our results represent a significant step toward realizing the promise of fluidic ion channels as a platform to emulate the rich aqueous dynamics of the brain.

Multimodal learning of heat capacity based on transformers and crystallography pretraining

Thermal properties of materials are essential to many applications of thermal electronic devices. Density functional theory (DFT) has shown capability in obtaining an accurate calculation. However, the expensive computational cost limits the application of the DFT method for high-throughput screening of materials. Recently, machine learning models, especially graph neural networks (GNNs), have demonstrated high accuracy in many material properties’ prediction, such as bandgap and formation energy, but fail to accurately predict heat capacity(CV) due to the limitation in capturing crystallographic features. In our study, we have implemented the material informatics transformer (MatInFormer) framework, which has been pretrained on lattice reconstruction tasks. This approach has shown proficiency in capturing essential crystallographic features. By concatenating these features with human-designed descriptors, we achieved a mean absolute error of 4.893 and 4.505 J/(mol K) in our predictions. Our findings underscore the efficacy of the MatInFormer framework in leveraging crystallography, augmented with additional information processing capabilities.

Synaptic Plasticity and Visual Memory in a Neuromorphic 2D Memitter Based on WS2 Monolayers

AbstractNeuromorphic computing aims to leverage physical phenomena of adaptive materials for emulating information processing capabilities and effectiveness of biological neuronal circuits. In this framework, memristors (resistors with memory) based on 2D materials are demonstrated for the hardware implementation of highly integrated artificial neural networks. All the works reported thus far exploited electrical properties of 2D materials to emulate neuromorphic functionalities. Here, a 2D memitter (emitter with memory) is reported on that exploits the stimuli‐responsive photoluminescence of a monolayer WS2 for neuromorphic‐type of data processing. A combined experimental and modeling approach reveals that photoluminescent dynamics triggered by optical stimulation emulates Short‐Term synaptic Plasticity and Visual Short‐Term Memory typical of biological systems. While spatio‐temporal processing capabilities of input signals can be used for information processing in the context of reservoir computing, the capability of the 2D memitter of sensing, processing, and memorizing‐forgetting optical inputs in the same physical substrate can be utilized for in‐sensor computing.

Rare-earth doped yttrium silicate (Y2SiO5) thin films grown by chemical vapour deposition for quantum technologies

Yttrium orthosilicate (Y2SiO5 - YSO) is one of the most promising crystals to host rare-earth (RE) ions for quantum technologies applications. In this matrix, they indeed exhibit narrow optical and spin linewidths that can be exploited to develop quantum memories or quantum information processing capabilities. In this paper, we propose a new method to grow RE doped silicate thin films on silicon wafers based on direct liquid injection chemical vapour deposition (DLI-CVD). We optimize the deposition and annealing conditions to achieve formation of the high temperature X2-YSO phase. The phase purity and crystalline quality of the films are assessed by evaluating the optical properties of Eu3+ ions embedded in this oxide matrix. In view of the results, we discuss the possible phase formation mechanisms, and the potential of this new wafer-compatible form of YSO for quantum technologies applications.

Evaluating edge artificial intelligence-driven supply chain management platforms using collaborative large-scale fuzzy information fusion

The rapid development of edge artificial intelligence (AI) has brought about revolutionary changes in supply chain management (SCM). It not only provides real-time data processing capabilities but also accelerates the decision-making process, injecting more innovative elements into SCM. In this context, SCM platforms require new technological developments and effective evaluations, as they collectively drive the efficient coordination of complex business processes. SCM platforms enable the seamless coordination of various supply chain elements, facilitating streamlined operations and enhanced decision-making processes. The evaluation of these platforms not only serves to validate their performance and effectiveness, but also contributes to continual improvements in design and application, and addressing the evolving demands within the realms of global commerce and dynamic market conditions. The ongoing assessment and enhancement of SCM platforms play a crucial role in optimizing resource allocation, improving production efficiency, and fostering adaptability to changing market dynamics. However, the acquisition of assessment data often introduces imprecise data. Additionally, the large-scale nature and bounded rationality of decision-makers (DMs) significantly impact the evaluation of SCM platforms. This article aims to explore a collaborative large-scale information fusion approach and provide a robust fuzzy framework for evaluating SCM platforms. This approach employs a combination of spherical fuzzy sets (SFSs), large-scale group decision-making (LSGDM), behavioral theories and three-way decisions (TWD) to thoroughly explore collaborative large-scale information fusion and its practical application in assessing SCM platforms. It introduces an innovative spherical fuzzy (SF) LSGDM technique and integrates TWD with the inclusion of prospect theory (PT) and regret theory (RT) to mitigate potential decision risks. The developed collaborative large-scale information fusion approach is assessed for validity, effectiveness and practicality in the context of evaluating SCM platforms using online data. Experimental results demonstrate that this approach provides reasonable evaluation outcomes, considering uncertain information processing capabilities, large-scale characteristics, bounded rationality and decision risks.

A digital twin framework for innovating rural ecological landscape control

BackgroundBecause cities prioritize economic development and face ecological space and resource constraints, the development of rural areas, which have untapped potential, should receive increased attention. Consequently, rural ecological landscapes should be constructed through the control of land use types and quality to fully ensure the sustainable development of urban and rural ecosystems. The digital twin is a philosophy and a methodology that connects the digital and physical realms, facilitating realistic and dynamic mapping simulations of the real world. This capability offers valuable insights for digital decision-making, maintenance, and optimization of rural ecological landscapes. Given the digital transformation of rural ecological landscape control, this paper proposes a rural intelligent control approach based on the digital twin concept and new technology.MethodsFive components of the rural ecological landscape digital twin framework are selected to collectively facilitate the monitoring and analysis of rural conditions, formulate strategic solutions, implement management and control behaviors, and enhance participant interaction. The method includes three steps: mapping and fusing information, constructing and managing a database, and constructing a digital platform. Data mining and spatial fusion are performed through targeted mapping methods, and Oracle and ArcGIS SDE are utilized for database construction and fused data management. The twin platform is generated via HTML, desktop application development and geographic information system development technologies using a distributed system as the core.ResultsBased on multiple case studies, our platform efficiently gathers system information on rural ecological landscapes using a twin model. Through evaluation and analysis, it determines landscape governance zones, adjusting them based on land use conditions. The platform refines control schemes with feedback from diverse users, ensuring effective control in various scenarios. Its key advantages include high development efficiency, flexible access, and smooth cross-platform integration. Although implemented in rural China, the proposed digital twin framework is applicable to any rural area requiring ecological landscape digital control.ConclusionsThe value of the platform lies in its powerful information processing capability, overcoming the limitations of time and space and enabling the presentation and integration of fictional scenes Thus, the platform provides a reference for the digital transformation of rural ecological landscape control.

Emergency logistics resource scheduling algorithm in cloud computing environment

In dealing with emergency logistics, it is essential to use regional and central distribution hubs efficiently, and local and international sources of supply for automotive relief supplies are analyzed in this research. Resource scheduling algorithms employed by service providers to supply and assign resources in an environment are collectively referred to as resource scheduling. Cloud computing uses computational resources pooled and made available across a network, including information storage (cloud) and processing capabilities, requiring user participation in its deployment, operation, and maintenance. The challenging characteristics of such emergency logistics resource scheduling are inconsistencies in tracking, empty miles, and delivery delays. Hence, Emergency Supplier Distribution Mobile Edge Computing (ESD-MEC) research has been designed to improve emergency logistics resource scheduling algorithms in cloud computing. With the abovementioned requirements, ESD may address the distributed scheduling issue of vehicle emergency logistics resources. In particular, the MEC employs a specialized negotiating system to manage the scheduling of resources in impacted regions using an agent-based approach to ESD management in light of the need to do so. The proposed technology helps the decision-maker schedule resources in a dynamic environment and addresses supply demands. An ESD-MEC effectively predicts the rise in emergency logistics resources with faster vehicle strategies in cloud computing. The research concludes that ESD-MEC effectively indicates emergency logistics resource scheduling in cloud computing. The experimental analysis of vehicle logistics outperforms the method in terms of performance, accuracy, prediction ratio, mean square error, and efficiency ratio.

Dissipative stabilization of high-dimensional GHZ states for neutral atoms

High-dimensional quantum entanglement characterizes the entanglement of quantum systems within a larger Hilbert space, introducing more intricate and complex correlations among the entangled particles' states. The high-dimensional Greenberger–Horne–Zeilinger (GHZ) state, symbolic of this type of entanglement, is of significant importance in various quantum information processing applications. This study proposes integrating a neutral atom platform with quantum reservoir engineering to generate a high-dimensional GHZ state deterministically. Leveraging the advantages of neutral atoms in a modified unconventional Rydberg pumping mechanism, combined with controlled dissipation, we achieve a three-dimensional GHZ state with a fidelity surpassing 99% through multiple pump and dissipation cycles. This innovative approach paves the way for experimentally feasible, deterministic preparation of high-dimensional GHZ states in Rydberg atom systems, thereby advancing the capabilities of quantum information processing.

Few- and single-molecule reservoir computing experimentally demonstrated with surface-enhanced Raman scattering and ion gating.

Molecule-based reservoir computing (RC) is promising for achieving low power consumption neuromorphic computing, although the information-processing capability of small numbers of molecules is not clear. Here, we report a few- and single-molecule RC that uses the molecular vibration dynamics in the para-mercaptobenzoic acid (pMBA) detected by surface-enhanced Raman scattering (SERS) with tungsten oxide nanorod/silver nanoparticles. The Raman signals of the pMBA molecules, adsorbed at the SERS active site of the nanorod, were reversibly perturbated by the application of voltage-induced local pH changes near the molecules, and then used to perform time-series analysis tasks. Despite the small number of molecules used, our system achieved good performance, including >95% accuracy in various nonlinear waveform transformations, 94.3% accuracy in solving a second-order nonlinear dynamic system, and a prediction error of 25.0 milligrams per deciliter in a 15-minute-ahead blood glucose level prediction. Our work provides a concept of few-molecular computing with practical computation capabilities.

Role of information processing and digital supply chain in supply chain resilience through supply chain risk management

PurposeSupply chain (SC) management is more challenging than ever. Significantly, the pandemic has provoked global and economic destruction that appeared in the manufacturing industry as a “black swan.” Therefore, the purpose of this study was to examine the role of information processing and digital supply chain in supply chain resilience through supply chain risk management.Design/methodology/approachThis study examines SC risk management and resilience from an information processing theory perspective. The authors used data collected from 251 SC professionals in the manufacturing industry, and the authors used a quantitative method to analyze the data. The data was analyzed using partial least squares-structural equation modeling. To confirm the higher-order measurement model, the authors used SmartPLS version 4 software.FindingsThis study found that information processing capability (disruptive orientation and visibility in high-order) and digital SC significantly and positively affect SC risk management and resilience. Similarly, SC risk management positively mediates the relationship between information processing capability and digital SC. However, information processing capability was found to have a more substantial effect on SC risk management than the digital SC.Research limitations/implicationsThis study has both academic and practical contributions. It contributed to existing information processing theory, and manufacturing firms can improve their performance by proactively responding to SC disruptions by recognizing the pivotal role of study variables in risk management for a resilient SC.Originality/valueThe conceptual model of this study is based on information processing theory, which asserts that synchronizing information processing capabilities and digital SCs allows a firm to deal with unplanned events. SC disruption orientation and visibility are considered risk controllers as they allow the firms to be more proactive. An integrated model of conceptualizing the disruption orientation, visibility (higher-order) and digital SC with information processing theory makes this research novel.

A Dendritic Architecture‐Based Deep Learning for Tumor Detection

Brain tumor detection typically involves classifying various tumor types. Traditional classifiers, based on the McCulloch‐Pitts model, have faced criticism due to their oversimplified structure and limited capabilities in detecting brain tumor images with complex features. In this study, we propose a multiclassification model inspired by dendritic architectures in neurons, which leverages synaptic and dendritic nonlinear information processing capabilities. Experimental results using brain tumor detection datasets demonstrate that our proposed model outperforms other state‐of‐the‐art models across all evaluation metrics. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Leader-Following Consensus of Discrete-Time Nonlinear Multi-Agent Systems with Asymmetric Saturation Impulsive Control

Impulsive control is an effective approach for coordinating multi-agent systems in practical environments due to its high robustness and low cost. However, impulsive control exhibits characteristics such as high amplitude and rapid variation, potentially presenting threats to the equipment. Additionally, multi-agent systems are constrained by input saturation due to limitations in physical controller structures and information-processing capabilities. These saturation constraints may be asymmetrical. Therefore, it is necessary to consider the saturation constraint when implementing impulsive control, as it can also mitigate the threats posed by the impulse to agents. This paper investigates the leader-following consensus for a class of discrete-time nonlinear multi-agent systems, proposing an asymmetric saturation impulsive control protocol to reduce the energy consumption and damage to the equipment. Regarding the handle of asymmetric saturation, an approach is proposed that eliminates the need for transformation from the asymmetric case to the symmetric case, which retains the saturation function and directly introduces the sector condition to deal with saturation nonlinearity. Furthermore, based on Lyapunov stability theory and matrix theory, sufficient conditions for leader-following consensus in discrete-time nonlinear multi-agent systems under asymmetric saturation impulsive control are established, and the admissible region of the system is estimated. Finally, numerical simulations are provided to verify the validity of the theoretical results.

Acoustic metamaterials for realizing a scalable multiple phi-bit unitary transformation

The analogy between acoustic modes in nonlinear metamaterials and quantum computing platforms constituted of correlated two-level systems opens new frontiers in information science. We use an inductive procedure to demonstrate scalable initialization of and scalable unitary transformations on superpositions of states of multiple correlated logical phi-bits, classical nonlinear acoustic analog of qubits. A multiple phi-bit state representation as a complex vector in a high-dimensional, exponentially scaling Hilbert space is shown to correspond with the state of logical phi-bits represented in a low-dimensional linearly scaling physical space of an externally driven acoustic metamaterial. Manipulation of the phi-bits in the physical space enables the implementation of a non-trivial multiple phi-bit unitary transformation that scales exponentially. This scalable transformation operates in parallel on the components of the multiple phi-bit complex state vector, requiring only a single physical action on the metamaterial. This work demonstrates that acoustic metamaterials offer a viable path toward achieving massively parallel information processing capabilities that can challenge current quantum computing paradigms.

Simulation of cross‐pedestrian flow in intersection based on direction fuzzy visual field

AbstractPedestrian flow refers to the spatiotemporal distribution of people moving in a defined area. At crosswalks, pedestrian dynamics exhibit complex self‐organization patterns resulting from interactions between individuals. This paper proposes a novel crosswalk pedestrian flow model based on the concept of directional fuzzy visual field (DFVF) to capture pedestrian heterogeneity. The DFVF defines fuzzy distributions of personal space and information processing capabilities, enabling improved representation of diversity compared to previous models. Incorporating k‐nearest neighbour rules in the DFVF pedestrian network topology also better mimics real‐world interactions. Using a cellular automata framework, pedestrian self‐organization effects like stratification and bottleneck oscillation are simulated at intersections. The model replicates empirically observed dynamics of density, velocity, and evacuation time. Results demonstrate that controlling pedestrian conflicts can effectively enhance crosswalk flow efficiency. This research introduces new techniques for simulating pedestrian psychology and behaviour, providing a valuable contribution to pedestrian flow theory and supporting crosswalk design optimization.