Information Processing Approach Research Articles

Ferroelectric Domain Wall Warp Memristor.

Domain walls are quasi-one-dimensional topological defects in ferroic materials, which can harbor emergent functionalities. In the case of ferroelectric domain wall (FEDW) devices, an exciting frontier has emerged: memristor-based information storage and processing approaches. Memristor solid-state FEDW devices presented thus far, however predominantly utilize a complex network of domain walls to achieve the desired regulation of density and charge state. Here, using epitaxial bismuth ferrite thin films as a prototype ferroelectric and advanced scanning probe microscopy and stroboscopic methods, we demonstrate controlled single-wall memristive behavior through deterministic electric field-driven conformal changes. Our design exploits memristive functionality through surface pinning of topological domain walls. That is, although the FEDW is constricted by the surface, it is free to twist in the thickness direction. The resulting vertical variation of FEDW morphology creates a complex interplay between surface-induced pinning and field-induced wall bending. This gives rise to metastable electronic transitions, and hence memristive attributes. Moreover, being a single FEDW memristor, once injected there is no need for repeated injection or erasure. Microscopic insight into device operation from phase field modeling indicates controllable warping of the wall, causing memristive conductivity changes. Our results reaffirm the promise of FEDWS for brain-inspired neuromorphic and in-memory computing applications based on integrated ferroelectric devices.

Emergency Response Inference Mapping (ERIMap): A Bayesian network-based method for dynamic observation processing

In emergencies, high stake decisions often have to be made under time pressure and strain. In order to support such decisions, information from various sources needs to be collected and processed rapidly. The information available tends to be temporally and spatially variable, uncertain, and sometimes conflicting, leading to potential biases in decisions. Currently, there is a lack of systematic approaches for information processing and situation assessment which meet the particular demands of emergency situations. To address this gap, we present a Bayesian network-based method called ERIMap that is tailored to the complex information-scape during emergencies. The method enables the systematic and rapid processing of heterogeneous and potentially uncertain observations and draws inferences about key variables of an emergency. It thereby reduces complexity and cognitive load for decision makers. The output of the ERIMap method is a dynamically evolving and spatially resolved map of beliefs about key variables of an emergency that is updated each time a new observation becomes available. The method is illustrated in a case study in which an emergency response is triggered by an accident causing a gas leakage on a chemical plant site.

Environmental uncertainty and entrepreneurial orientation in collectivist and individualist cultures: evidence from Brazil and Belgium

ABSTRACT A fundamental premise of the information processing approach in entrepreneurship theory is that the distribution and processing of information is key to opportunity identification. We propose that collectivist-individualist cultural values influence the efficacy of information processing leading to different levels of entrepreneurial orientation (EO). Specifically, we submit that collectivism enhances shared information processing and goal setting in a dynamic environment, while individualism facilitates creativity and divergent thinking that can help EO if sufficient informational input is available. To empirically verify our theory, we study the relationships using survey and archival data from a sample of 487 firms in six industries in Brazil and Belgium, two countries that contrast in collectivism-individualism. Consistent with our predictions, we find higher EO in the collectivist culture of Brazil under high environmental dynamism and low information availability, and this difference is increased by strategic planning.

Designing an Instructional Model to improve Reading Abilities of Technical Documents and Learning Attitudes for Vocational Students in Shenzhen Institute of Technology

The purpose of this study is to develop an instructional model to improve the reading abilities of technical documents, and to enhance students’ positive learning attitudes for TVE college students in China. This study developed a teaching model based on the Information Processing Approach, Vygotsky's Scaffolding, and Motivation. This teaching mode has the characteristics and technical attributes of TVE, which can (a) improve the reading ability of TVE college students on technical documentation, and (b) enhance the students' positive learning attitude (enthusiasm). This teaching model includes (1) Principles of the model, (2) Objectives, (3) Syntax, (4) Social system, (5) Principles of reaction, and (6) Support system. Through the application of two classes at Shenzhen Institute Technology in China, it can be seen that three-information input and output activities can be formed in both applications, and there is a progressive relationship between the three activities, and the three activities can fully stimulate students' learning enthusiasm.

Conditional-not Displacement: Fast Multioscillator Control with a Single Qubit

Bosonic encoding is an approach for quantum information processing, promising lower hardware overhead by encoding in the many levels of a harmonic-oscillator mode. Scaling to multiple modes requires weak interaction for independent control, yet strong interaction for fast control. Applying fast and efficient universal control on multiple modes remains an open problem. Surprisingly, we find that displacements conditioned on the state of a single-qubit ancilla coupled to multiple harmonic oscillators are sufficient for universal control. We present the conditional- operation concept, which can be used to reduce the duration of entangling gates. Within this guiding concept, we develop the conditional- displacement control method which enables fast generation and control of bosonic states in multimode systems weakly coupled to a single-ancilla qubit. Our method is fast despite the weak ancilla coupling. The weak coupling in turn allows for excellent separability and thus independent control. We demonstrate our control on a superconducting transmon qubit weakly coupled to a multimode superconducting cavity. We create both entangled and separable cat states in different modes of the multimode cavity, showing entangling operations at low crosstalk while maintaining independent control of the different modes. We show that the operation time is not limited by the inverse of the coupling rate, which is the typical timescale, and we exceed it by almost 2 orders of magnitude. We verify our results with an efficient method for measurement of the multimode characteristic function which employs our conditional- displacement. Our results inspire a new approach toward general entangling operations and allow for fast and efficient multimode bosonic encoding and measurement. Published by the American Physical Society 2024

Phase-encoding of loosely bound soliton molecules

Dissipative soliton molecules (DSMs) are of great interest for studying the complexity of nonlinear optical problems as they can map with the matter molecules for making interdisciplinary analogies. In contrast to strongly bound DSMs that have a short time separation between the bound solitons, the complex dynamics and underlying binding mechanism of loosely bound soliton molecules (LBSMs) with orders of magnitude longer time separation remain open questions. To this end, here, we explore real-time spectroscopy using a dispersive temporal interferometer (DTI) to visualize the dynamics of LBSMs in a mode-locked fiber laser and unveil their underlying phase-evolving mechanism. The DTI enables fringe-resolved spectroscopy in real time of the LBSM’s evolution by creating duplicates of the LBSM that results in a much closer time separation between the individual solitons of the LBSM. The real-time evolution of the LBSM’s phase exhibits a diverging sliding landscape, which is theoretically and experimentally proved to be closely associated with gain dynamics. Based on the understanding of its phase dynamics, we finally demonstrate programmable phase-encoding modulation of the LBSM through gain control. These efforts not only shed light on understanding the mechanism of long-range interactions in LBSMs but also provide an alternative approach for all-optical information processing.

Valley-selective carrier transfer in SnS-based van der Waals heterostructures.

Valleytronics, i.e., use of the valley degree of freedom in semiconductors as an information carrier, is a promising alternative to conventional approaches for information processing. Transition metal dichalcogenides with degenerate K/K' valleys have received attention as prototype 2D/layered semiconductors for valleytronics, but these systems rely on exotic effects such as the valley-Hall effect for electrical readout of the valley occupancy. Non-traditional valleytronic systems hosting sets of addressable non-degenerate valleys could overcome this limitation. In the van der Waals semiconductor Sn(II) sulfide (SnS), for instance, different bandgaps and band edges may allow manipulating the population of the X- and Y-valleys via charge transfer across interfaces to other layered semiconductors. Here, we establish this concept by comparing SnS flakes and SnS-based heterostructures. Cathodoluminescence spectroscopy shows a striking reversal of the luminescence intensity of the two valleys in SnS-GeS van der Waals stacks, which stems from a selective electron transfer from the Y-valley into GeS while X-valley electrons remain confined to SnS. Our results suggest that non-traditional systems, embodied here by SnS-based van der Waals heterostructures, open avenues for valley-selective readout relying on design parameters such as heterostructure band offsets that are among the core concepts of semiconductor technology.

An integrated three-way decision methodology for sustainability of wastewater circularity in thermal power plants

The presence of multiple criteria for evaluating wastewater reuse applications indicates the potential usage of Multi-Criteria Decision-Making (MCDM) methods for this purpose. However, there is currently a scarcity of studies in the domain literature that utilize MCDM approaches in this application topic. This paper therefore advances the domain literature in two distinctive ways. Firstly, it analyzes and advances the reuse agenda of wastewater from thermal power plants, recognized as large-scale users of water, thus promoting greater water circularity. Secondly, it provides a methodological advance by integrating the notion of Three-Way Decision (3WD) into the computational structure of MCDM methods by introducing a middle reference point. Such an initiative results in a novel 3WD extension of the Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) method. Additionally, this work provides a proof that the MARCOS method utilizes a compromise solution in identifying priority alternatives, along with the integration of a Weighted Aggregated Sum Product ASsessment (WASPAS) metric. An initial hypothetical example illustrates how the proposed approach augments the canonical MARCOS method, particularly in promoting the “thinking in threes” as a more natural information processing approach and the high degree of distinguishability of priorities between decision alternatives. An actual case study in a thermal power plant then demonstrates the contributions of this work. With the best-worst method used to determine the priorities of the decision attributes, the findings reveal that wastewater reuse applications achieving reduced costs for needed infrastructures, operational simplicity, technological compatibility, consumer safety and household savings are preferred by stakeholders. The 3WD-MARCOS approach identifies industrial and commercial use, municipal use, environmental restoration, and household use as the high-priority alternatives, with cooking and drinking as least preferred. These insights guide stakeholders in their design of initiatives that allocate resources for greater wastewater reuse. A comparative analysis yields high consistency of these findings with similar MCDM methods. In addition, the efficacy of the novel 3WD-MARCOS method highlights its potential in handling MCDM problems, including those promoting water circularity.

What Dynamic Approaches Have Taught Us About Cognition and What They Have Not: On Values in Motion and the Importance of Replicable Forms.

Over the past several decades, research in the cognitive sciences has foregrounded the importance of active bodies and their continuous dependence on the changing environment, strengthening the relevance of dynamical models. These models have been steadily developed within the ecological psychology approach to cognition, which arguably contributes to the "ecological turn" we are witnessing today. The embodied and situated nature of cognition, regarded by some as a passing trend, is presently becoming a largely accepted assumption. In this paper, I claim that in light of these developments, ecological psychology, in alliance with related approaches, such as enactivism and interactivism, has the potential to deeply transform our perspectives on cognition and action, restoring their pertinence to humans as persons. However, an important challenge to the realization of this potential has to be noted: neither the mainstream information-processing approach nor the dynamics-oriented perspective on cognition provides an account of how the capacity of humans to use language and think "symbolically" can be derived from the continuous flow of agent-environment interaction. I will attempt to show that posing the "dynamical" and "computational" hypotheses about the nature of cognition as mutually exclusive approaches to cognition results in undesirable reductionism, which makes it difficult to meet this challenge. There are good reasons, advanced over half a century ago by, for example, Michael Polanyi or Howard Pattee, to think that we need complementary descriptions to understand cognizing systems, in order to grasp the fact that they are governed both by physical laws and by emergent historical constraints. Details of such a complementarity-based approach still await elucidation, but some proposed solutions have the potential to ease the tension between the information-processing and dynamical approaches to cognition and to lead to a better understanding of their interrelation.

Suspicion, inclusive leadership and team innovation: A motivated information processing approach

Suspicion has been linked to significant effects at work, yet its influence on teams remains largely unexplored. This gap in the literature is particularly surprising as initial evidence suggests that suspicion has the potential to engender both positive and negative behaviors. We draw on the motivated information processing theory of groups (MIP-G) to develop a model of suspicion’s effect on team innovation through the mediating role of minority dissent. Further, in recognition of the potential dysfunctional role of suspicion and dissent, we explore an important moderator of this mediated path: inclusive leadership. Survey results from a field study of healthcare teams lend support to our moderated mediation model. These results provide a valuable contribution to MIP-G, our understanding of the role of suspicion and inclusive leadership in team information-processing and innovation, and provide advice to HR managers on relevant leadership strategies.

Drivers’ Reaction Time and Mental Workload: A Driving Simulation Study

Abstract Drivers play a significant role in causing serious accidents, which underscores the need for further investigating the human element in order to improve road safety. Given the predominance of the information processing approach in driver’s behavior research field, an important psychological construct, Mental Workload (MWL), has been introduced to study the behavior of drivers. The objective of this paper is to investigate the impact of increased MWL on driver behavior and specifically the changes in driver’s Reaction Time (RT) under increased MWL. The experiment conducted in the driving simulator of the Hellenic Institute of Transport which is part of the Centre for Research and Technology Hellas, with the participation of 56 subjects from all age groups. For the simulation of the increased MWL conditions during driving, a secondary task was employed. To this end, the MIT AgeLab Delayed Digit Recall Task in the 1-back version was adapted for the needs of the present research. The driving scenario included 4 unexpected events, which further increase driver’s MWL. Driving performance was observed and relative parameters were measured as RT on the unexpected events, accidents occurred, and maneuvers performed. Appropriate statistical analysis was performed to examine the difference in the drivers’ RT in the unexpected events. Results demonstrated that higher MWL increased drivers’ RT in the majority of the participants. Furthermore, results also indicated a number of participants that probably employed adaptive control behaviors to counterbalance the increased MWL. Overall, variance on MWL proved to play an important role on driver performance, and thus further research on its consequences on driving performance, and the factors that influence its variance during driving, is imperative.

Entropy Optimization by Redesigning Organization in Hospital Operations.

A redesign of hospitals (i.e., partitioning departments and delegating decision authority) may be needed to deal with variable demand. Uncertain demands and throughput times often need short reaction times. In this study, we develop quantitative methods to guide a redesign through an information-processing approach. To demonstrate how the methods can be used in practice, we tested them by applying them to a large perinatology care system in the Netherlands. We used the following two methods: 1. portfolio optimization and 2. efficient coordination of workload and reallocation of nurses. Our case study of a large perinatology care system showed that several designs of clustered units minimized the demand uncertainty in the perinatology care system. For the coordination strategy, the information and decision uncertainty is minimized when the decision power is positioned at the operation level and with the help of a centralized information system. When the operation decision-making power is not supplemented with the centralized and system-wide information system, hospitals can better use the hierarchy model, where the manager holds decision-making power with a system-wide overview. We also found that the speed of decision-making in real-time depends on the level of information aggregation set up by the system. We conclude that combining the correlation perspectives and the entropy theory is a way of quantifying how organizations can be (re)designed.

With Great Sensitivity Comes Great Management: How Emotional Hypersensitivity Can Be the Superpower of Emotional Intelligence.

With the goal of furthering the understanding and investigation of emotional intelligence (EI), the present paper aims to address some of the characteristics that make EI a useful skill and, ultimately, a predictor of important life outcomes. Recently, the construct of hypersensitivity has been presented as one such necessary function, suggesting that high-EI individuals are more sensitive to emotions and emotional information than low-EI individuals. In this contribution, we aim to shift the perception of hypersensitivity, which is mostly seen with a negative connotation in the literature, to the perspective that hypersensitivity has the capacity to result in both negative and positive outcomes. We advance this possibility by discussing the characteristics that distinguish hypersensitive individuals who are also emotionally intelligent from those who are not. Based on an emotion information processing approach, we posit that emotional intelligence stems from the ability to manage one's level of hypersensitivity: high-EI individuals are those who are better able to use hypersensitivity as an adaptive rather than a disabling feature. Ultimately, we propose that hypersensitivity can represent a sort of "superpower" that, when paired with regulatory processes that balance this hypersensitivity, characterizes the functioning of high-EI individuals and accounts for the positive outcomes reported in the literature.

Light-guiding-light-based temporal integration of broadband terahertz pulses in air

The next generation of all-optical computation platforms prefers the light-guiding-light (LGL) scheme inside a medium that envisions circuitry-free and rapidly reconfigurable systems powered by dynamic interactions between light beams. Currently, suitable LGL materials and corresponding mechanisms are in urgent need. In this work, we proposed ubiquitous air as a restorable LGL signal manipulation medium with transient air-plasma waveguide circuits. Briefly, by focusing femtosecond laser beams in free space, the created atmospheric plasma filament array via photoionization was able to guide terahertz (THz) pulses along its epsilon-near-zero zone with a 1/f-profile spectral response. Consequently, this achieved a time-domain integration of the THz pulse in broad bandwidth. When the pumping laser was sequentially turned off and on, this air-plasma multi-filament structure was erased and rebuilt within nano- and femto-seconds, respectively, allowing rapid and repeated rearrangements of the all-optical stage. Furthermore, this air-based LGL information processing approach is promising to pave the way toward all-optical calculations during free-space directional transmission of THz waves, in which way the delivered THz signal can be remotely controlled.

An improved TOPSIS-based rank preservation approach for hesitant fuzzy information processing

In this study, a novel approach based on the reduction of the attribution and the rank preservation is analyzed, which intends to solve the issue of multi-attribute decision making (MADM) with the hesitant fuzzy information. Firstly, several new concepts are shown to simplify the representation of hesitant fuzzy information, such as single point fuzzification estimated value, and single point fuzzification weighted Euclidean distance. Secondly, a new improved HF-TOPSIS method based on the overall situation and these new concepts are put forward, in which the positive and negative ideal solutions are fixed to calculate the complex hesitant fuzzy decision process. The proposed method in this paper achieves the purpose of compression of the complex hesitant fuzzy information, and the calculation is relatively simple and easy to operate. Finally, two examples are presented to test and verify the credibility and effectiveness of the TOPSIS-Based rank preservation approach, which can achieve the consistency of results before and after evaluation, as well as ensuring rank preservation, while other HF-TOPSIS methods may cause rank reversal problems.

Leadership for school mindfulness: the role of school ambidexterity and leaders’ cognitive complexity

ABSTRACT Using an information-processing approach and organisational learning theory (i.e. ambidexterity), the current innovative study examines a model for the prediction of schools’ collective mindfulness and its consequences, linking principals’ cognitive complexity (CC) with school ambidexterity and mindfulness, school ambidexterity with school mindfulness, and the effect of school mindfulness and ambidexterity on a teacher’s psychological capital. The study further examines the indirect effect of principals’ CC on a teacher’s psychological capital through the effects of ambidexterity and mindfulness. Participants were 104 principals and 1002 teachers from 104 Israeli schools. Results showed that school mindfulness is affected by principals’ CC and school ambidexterity, and that school mindfulness and school ambidexterity relate directly and positively to a teacher’s psychological capital. Finally, school mindfulness and ambidexterity connected school principals’ CC to a teacher’s psychological capital. This study adds to the body of research directed at identifying leaders’ roles and work-related factors that may positively affect organisational mindfulness and a consequence of school mindfulness, i.e. a teacher’s psychological capital, and that are amenable to leadership intervention.

Team Physical Activity and Team Performance: An Information Processing Approach

Team Physical Activity and Team Performance: An Information Processing Approach

AI’s Humanoid Appearance Can Affect Human Perceptions of Its Emotional Capability: Evidence from Self-Reported Data in the U.S

As artificial intelligence (AI) integration becomes more prominent in society, the infosphere becomes increasingly complex. A better understanding of the human-AI relationship is crucial during these rapid technological and social changes. Emotions are complex responses of the human mind – a highly advanced information processing system. Thus, examining how humans perceive AI’s emotional capability may provide helpful insights. Human physiological properties such as humanoid appearance and gender are suggested to be potential influencing factors. Following the information processing approach, we employed Bayesian Mindsponge Framework (BMF) analytics on a dataset of 266 residents of the United States. Results show that interacting with an AI agent with human-like physical features is positively associated with the belief in that AI agent’s capability of experiencing emotional pain and pleasure. This effect is stronger in males than females. A possible explanation for these patterns is reliance on cognitive strategies used in interpersonal interactions, including gender-specific physiological properties. This study also demonstrates the effectiveness of using a compatible information processing analytical method such as BMF for studying humans’ perceptions of AI.

Multi-objective robust optimization strategy for community virtual cloud power plant considering different demand responses based on correlative confidence gap decision theory

To study the effect of different demand responses (DRs) on solving uncertainty, a multi-objective robust optimization model for a community virtual cloud power plant (CVCPP) is proposed based on the correlative confidence gap decision theory (CCGDT) and considering different DRs. Firstly, a CVCPP framework with multi-energy conversion equipment, multi-load DR, and a rapid energy scheduling information processing approach is proposed. Secondly, a two-layer community comprehensive demand response model is developed, which includes the CDR of CIES internal consumers and the CDR of the external energy network that connects to CIES. Thirdly, the CCGDT is presented using the confidence gap decision model and the time-varying Copula theory. A multi-objective robust economy-energy-environment optimization model is developed. Fourthly, the Pareto solution set is solved using the multi-objective grey wolf optimization algorithm. The full consistency method is employed to identify an optimal scheduling strategy for decision-makers. Finally, the model's validity is verified by a multi-scene case of a residential area. The results show that: 1) Whether uncertainty is considered or not, different DRs have varied effects on the results. Combination DR are better than single, and the same DR has different effects on different residents. 2) The multi-objective values improve after adopting the integrated energy system. The resident cost of various residents is decreased by 0.054%-12.07% when uncertainty is included. Meanwhile, the profit of energy providers is improved by 0.95%-10.98%, the carbon treatment amount is lowered by 26.85%-51.31%, and the renewable energy utilization rate is enhanced by 0.85%-7.94%. 3) The results with different significance have different robustness to uncertainty changes, and the greater the significance, the stronger the robustness. Therefore, the decision-maker can flexibly control the optimization result by setting the confidence of the target value. The model plays a good role in the economy-energy-environment aspects and provides a reference for community decision-makers to choose the comprehensive multi-objective energy scheduling strategy considering the uncertainty.

Experimental demonstration of bandwidth enhancement in photonic time delay reservoir computing.

Time delay reservoir computing (TDRC) using semiconductor lasers (SLs) has proven to be a promising photonic analog approach for information processing. One appealing property is that SLs subject to delayed optical feedback and external optical injection, allow for tuning the response bandwidth by changing the level of optical injection. Here we use strong optical injection, thereby expanding the SL's modulation response up to tens of gigahertz. Performing a nonlinear time series prediction task, we demonstrate experimentally that for appropriate operating conditions, our TDRC system can operate with sampling times as small as 11.72 ps, without sacrificing computational performance.