Concept Of Superposition Research Articles

Selective Plasmonic Responses of Chiral Metamirrors.

The properties of circularly polarized light has recently been used to selectively reflect chiral metasurfaces. Here we report the more complete basic functionalities of reflectors and absorbers that display various optical phenomena under circularly polarized light at normal incidence as before. For the chiral metamirrors we designed, the circular dichroism in about 0.4 reflection is experimentally observed in visible wavelengths. The experimental results also show high reflectance for right-handed circular polarization with preserved handedness and strongly absorbed left-handed circular polarization at chiroptical resonant wavelengths. By combining a nanobrick and wire grating for our design, we find and offer a new structure to demonstrate the superposition concept of the phase in the same plane that is helpful in effectively designing chiral metamirrors, and could advance development of their ultracompact optical components.

Analytical prediction and numerical verification of stress concentration profile around an in-situ tow break in resin-impregnated filament-wound composites

A new empirical analytical approach is developed for predicting the stress concentration profile around an in-situ tow break in filament-wound composites. A shear-lag analysis is firstly performed to solve for the perturbational axial displacement of the broken tow and resultant debonding lengths. Solution of stress field caused by tangential load on the surface of an elastic half space is then utilized in combination with superposition principle to obtain the overload magnitudes in the neighboring tows. Subsequently, high-fidelity finite element analysis on a representative uni-directional laminate model under different stress states is performed, and excellent overall agreement is observed between analytical and numerical predictions. The proposed method takes into account essential aspects such as transversely isotropic material properties, in-situ stress states and their effect on the interfacial frictional forces in the debonded interfaces, and thus provides a convenient way to evaluate the stress concentration factors in damaged filament-wound composites. In addition, this approach can be applied to yield auxiliary failure evaluation criteria for statistical strength prediction or finite element modeling of filament-wound composites or similar structures.

Quantum-based privacy-preserving techniques for secure and trustworthy internet of medical things an extensive analysis

The advent of Quantum Key Distribution (QKD) and quantum-based privacy-preserving techniques has ushered in a new era for securing communication channels within the Internet of Medical Things (IoMT) systems. We examine the fundamentals, uses, and ramifications of quantum cryptography in relation to healthcare data security in this thorough investigation. The journey commences with an in-depth overview of quantum cryptography, unraveling the concepts of superposition, entanglement, and quantum gates that form the bedrock of quantum computing. As we proceed, we investigate the uses of quantum cryptography, highlighting its contribution to resolving the particular issues brought about by the data-intensive and networked character of IoMT systems. The relevance of QKD in ensuring secure communication within IoMT is meticulously dissected, with case studies and experiments demonstrating the practicality and effectiveness of quantum-based privacy-preserving techniques. From telemedicine networks to wearable health devices, each case study offers valuable insights into the application of quantum-safe cryptography, showcasing its ability to fortify data integrity and confidentiality. A meticulous survey of existing research, coupled with an analysis of advancements in quantum cryptography, provides a panoramic view of the current landscape. From hardware limitations to distance constraints, the study navigates through challenges and breakthroughs, offering a roadmap for the future integration of quantum technologies into IoMT systems.Comparing quantum-based approaches with classical cryptography unveils a nuanced landscape of computational complexity, key distribution methodologies, and real-time encryption considerations. This comparative analysis serves as a guide for healthcare practitioners and technologists in making informed decisions regarding the adoption of quantum-based privacy-preserving techniques in IoMT environments. The case studies and experiments collectively paint a picture of the practicality and promise of quantum-based privacy-preserving techniques in IoMT scenarios. Anticipating future advancements, the exploration extends into quantum hardware improvements, standardization efforts, and the integration of quantum technologies with emerging trends like edge computing and blockchain. As the healthcare industry stands on the cusp of a quantum revolution, the comprehensive insights provided herein offer a foundation for understanding, implementing, and shaping the future of secure healthcare communication through the lens of quantum cryptography.

Operator Space Manifold Theory: Modeling Quantum Operators with a Riemannian Manifold

The Half-Transform Ansatz (HTA) is a proposed method to solve hyper-geometric equations in Quantum Phase Space by transforming a differential operator to an algebraic variable and including a specific exponential factor in the wave function, but the mechanism which provides this solution scheme is not known. Analysis of the HTA’s application to the Hydrogen atom suggests an underlying mechanism which the HTA is a part of. Observations of exponential factors that act on the wave function naturally suggest modeling quantum operator definitions as a point on a Riemannian manifold in the 4D Operator Space, a novel idea we call the Operator Space Manifold Theory. On this manifold, we explore the concepts of superposition, regions of unique energy eigenvalues, and translation operators. We also find the theoretical backing to derive the HTA and how Operator Space Manifold Theory can be used to describe and solve quantum systems by manipulating how a quantum state perceives position and momentum.

Self-Centering Precast Unit as Energy Dissipation Members in Precast Segmental Bridge Columns

This research aims to present a new generation of seismic-resisting systems designed for precast reinforced concrete (RC) bridge piers in modern sustainable cities to withstand moderate to high seismic activity. The proposed system consists of two self-centering (SC) systems operating in parallel to bring together all features of the required resiliency during a seismic action. The first/main system is a hollow core precast segmental bridge column, and the second is composed of an SC precast unit and energy dissipation (ED) steel reinforcements positioned in the main pier segment’s hollow core. To study the performance of the proposed system, a finite element model was first developed to capture the behavior of experimentally tested precast bridge columns. After validation, the created model was systematically studied to investigate the performance of the entire proposed system under cyclic loading. The effects of three parameters related to the ED system were investigated, including the reinforcement ratio, the unbonded length of ED bars, and the SC post-tensioned force ratio. Furthermore, the impact of FRP wrapping on the lower part of the core column of the ED system was also investigated. An analytical model predicting the characteristic points of the lateral response of the proposed system based on the superposition concept is also proposed. The FE results showed that the entire proposed system is a new design-based resilient system with the ability to dissipate energy without compromising the SC capacity of the main resisting system. Compared to the typical precast hollow core segmental column, a 6% reinforcement ratio of the ED unit can cause a 60% increase in lateral resistance and a 220% increase in the ED capacity. The analytical model can successfully be applied in the design of the proposed system to provide customized ED capabilities and controlled lateral resistance.

An Interdisciplinary interpretation of literature through the lens of Quantum Theory

This paper explores the interdisciplinary intersection between Quantum Theory and literary analysis, particularly focusing on William Shakespeare’s "Hamlet." By examining the character of Hamlet through the principles of quantum mechanics, specifically Schrödinger's wave function and the concept of superposition, we aim to demonstrate how scientific metaphors can provide novel insights into literary texts. This approach not only deepens our understanding of characters and narratives but also bridges the gap between the sciences and the humanities, highlighting their interconnectedness and mutual relevance. The paper concludes by discussing the implications of this interdisciplinary methodology, its limitations, and future directions for research. By employing quantum theory and the Schrödinger equation as metaphors, we gain deeper insights into the intricate layers of Hamlet’s character and the overarching themes of the play. This interdisciplinary approach enriches our understanding of Shakespeare’s work, highlighting the profound connections between the uncertainties and probabilities in quantum mechanics and the existential dilemmas faced by Hamlet. This perspective not only offers a unique lens for literary analysis but also demonstrates the potential for cross-disciplinary interpretations to enhance our appreciation of classic literature.

Exploring Quantum Computing's Potential Breakthroughs and Challenges

Recent years have seen the rise of quantum computing as a game-changing technology that might alter the face of many industries, from optimization to cryptography. From theory to practice, this article covers quantum computing's journey. We review the quantum computing foundational concepts of superposition and entanglement and examine their consequences for the paradigm of computation. We emphasize the concrete advances in quantum hardware, error correction methods, and quantum algorithm creation through a thorough survey of recent discoveries. Nevertheless, significant obstacles accompany these advancements. An ever-present problem, quantum de coherence endangers both the consistency of quantum states and the accuracy of calculations. The effectiveness of quantum error correcting approaches in reducing de coherence is examined in our paper. We highlight the need for programming languages, compilers, and simulators that are customized to quantum hardware, as well as the increasing demands for quantum software infrastructure. Questions of security and ethics arise in light of the many possible uses of quantum computing in fields as diverse as optimization, cryptography, and materials research. Error correction and the execution of algorithms containing both classical and quantum logic require the classical part. We provide a comprehensive system stack outlining the various components of a quantum computer. We wrap up by talking about design decisions on the quantum plane and show the control logic and data flow that must be applied when quantum instructions are executed.

Plexcitonic system for high photonic spin Hall effect

The photonic spin Hall effect (PSHE) has been intensively studied and widely applied, especially in spin photonics. In this paper, we present the analysis of PSHE in the Plexcitonic system. The signature of the Plexciton is studied by implanting an organic semiconductor layer over the plasmon active metal. We report a high spin-dependent transverse shift of ∼ 60 μm, which is ∼200-fold larger than that of the pure Plasmonic counterpart. The underlying mechanism of PSHE is explained in the circular-polarization basis using the concept of superposition of abnormal and normal modes. It is observed that the transverse shift is highly sensitive to the alterations in the environmental refractive indices leading to its application as a highly sensitive PSHE-based refractometer, with a high sensitivity of 81°/RIU. We believe these results may open pathways for modulating the PSHE and developing nanophotonic applications such as advanced sensors and next-generation optical devices.

3D geological modelling of the Bologna urban area (Italy)

Urban geological modelling plays a crucial role in facilitating the sustainable utilization of the urban subsurface and the development of effective strategies for geohazard mitigation. Traditionally, geological models of urban areas rely on lithological correlations of boreholes and well data. However, in complex depositional settings such as alluvial environments, where the standard concepts of superposition and lateral continuity of strata are often violated, lithostratigraphic correlation can be challenging even over short distances, leading to considerable uncertainty in the final geological model. Therefore, in such cases, lithofacies analysis can provide a more robust categorisation of subsurface geological units as lithofacies mainly reflect the depositional process, while their associations provide a reliable picture of sedimentary bodies and their geometries. In this work, we used the lithofacies correlation approach to develop a 3D geological model of the urban area of Bologna (Italy) where a complex, thick alluvial succession makes lithological correlations very difficult. To minimize subjectivity, we re-interpreted 940 existing borehole logs in terms of depositional facies and combined stratigraphic data with geophysical HSVR (Horizontal to Vertical Spectral Ratio) measurements and surface analysis (morphology and river network) based on a DTM (Digital Terrain Model). This led to the detailed reconstruction of the subsurface depositional architecture, forming the basis for the 3D geological model. The model revealed the presence of three distinct depositional domains with different stratigraphic architectures. From west to east: Domain A (Reno River) corresponds to the gravel-dominated fill of a river valley abruptly passing to Domain B (Bologna urban area) that represents a morphological and stratigraphical divide, topographically elevated with respect to the surrounding areas and characterised by fine-grained deposits with frequent paleosols. Lastly, Domain C (Savena River) exhibits the typical alluvial fan pattern, marked by a gentle convex-up surface morphology with gravelly-sandy deposits with a low degree of lateral amalgamation. The geological model effectively explains the distribution of ground subsidence in Bologna since the 1960s, which is attributable to significant groundwater pumping. This is evident from the comparison of the model with both the ground displacement contours resulting from the 1983–1987 topographic levelling campaign and the subsidence rate map derived from the 2006–2011 Radarsat interferometric survey. Subsidence shows strong variations at the domains' boundaries, especially at the limit between Domain A (Reno River) where high values prevail, and Domain B (Bologna city centre) characterised by the lowest values.

Investigating the Effects of Hyperparameters in Quantum-Enhanced Deep Reinforcement Learning

Quantum machine learning uses quantum mechanical concepts of superposition of states to make the decision. In this work, we used these quantum advantages to enhance deep reinforcement learning (DRL). Our primary and foremost goal is to investigate and elucidate a way of representing and solving the frozen lake problems by using PennyLane which contains Xanadu’s back-end quantum processing unit. This paper specifically discusses how to enhance classical deep reinforcement learning algorithms with quantum computing technology, making quantum agents get a maximum reward after a fixed number of epochs and realizing the effect of a number of variational quantum layers on the trainability of enhanced framework. We have analyzed that, as the number of layers increases, the ability of the quantum agent to converge to the optimal state also increases. For this work, we have trained the framework agent with 2, 3, and 5 variational quantum layers. An agent with 2 layers converges to a total reward of 0.95 after the training episode of 526. The other agent with layers converges to a total reward of 0.95 after the training episode of 397 and the agent which uses 5 quantum variational layers converges to a total reward of 0.95 after the training episode of 72. From this, we can understand that the agent with a more variational layer exploits more and converges to the optimal state before the other agent. We also analyzed our work in terms of different learning rate hyperparameters. We recorded every single learning epoch to demonstrate the outcomes of enhanced DRL algorithms with selected 0.1, 0.2, 0.3, and 0.4 learning rates or alpha values. From this result, we can conclude that the greater the learning rate values in quantum deep reinforcement learning, the fewer timesteps it takes to move from the start point to the goal state.

An experiential program on the foundations of quantum mechanics for final-year high-school students

Teaching and learning quantum mechanics is one of the most demanding educational and conceptual challenges, in particular in secondary schools where students do not possess an adequate mathematical background to effectively support the description of quantum behaviour. Educational research shows that traditional approaches, generally based on historical and narrative perspectives, are only partially effective. The reason is that they do not address in depth those basic quantum concepts that radically question the fundamentals of classical physics. A research-based educational program has been proposed to two final-year classes of an Italian scientific high school. In order to build the main concepts of quantum mechanics and their formal basic representation via real and simulated experiments, the program uses the light polarization as a context. A quantum game was then integrated in the educational program, to support students’ learning. Their conceptual paths, monitored by means of tutorials and questionnaires, show significant student learning especially on the concept of state and on appropriating the formalism meaning, whereas students more frequently referred to the geometrical vector representation instead of the algebraic-analytic formula. The quantum game has emerged to support intuition and operative experience in distinguishing the foundational concepts of superposition and entanglement.

Abrupt moving target tracking based on quantum enhanced particle filter

Abrupt-motion tracking is challenging due to the target’s unpredictable action. Although particle filter (PF) is suitable for target tracking of nonlinear non-Gaussian systems, it suffers from the problems of particle impoverishment and sample-size dependency. This paper proposed a quantum-inspired particle filter for abrupt-motion tracking. We apply the concept of quantum superposition to transform classical particles into quantum particles. Quantum representation and corresponding quantum operations are addressed to utilize quantum particles. The superposition property of quantum particles avoids the concerns of particle impoverishment and sample-size dependency. The proposed diversity-preserving quantum-enhanced particle filter (DQPF) obtains better accuracy and stability with fewer particles. A smaller sample size also helps to reduce computational complexity. Moreover, it has significant advantages for abrupt-motion tracking. The quantum particles are propagated at the prediction stage. They will exist at possible places when abrupt motion occurs, which reduces the tracking delay and enhances the tracking accuracy. This paper conducted experiments compared to state-of-the-art particle filter algorithms. The numerical results demonstrate that the DQPF is not susceptible to motion mode and particle number. Meanwhile, DQPF maintains excellent accuracy and stability.

Quantum Machine Learning: Scope for real-world problems

Quantum computing with its inherent parallelism provides a quantum advantage over classical computing. Its potential to offer breakthrough advances in various areas of science and engineering is foreseen. Machine learning is one of the key areas where the power of quantum computing can be utilized. Though many machine learning algorithms have been successfully developed to solve a variety of problems in the past decades, these algorithms take a long time to train. Also working on today's colossal datasets makes these algorithms computationally intensive. Quantum machine learning by utilizing the concepts of superposition and entanglement promises a solution to this problem. Quantum machine learning algorithms are in surface for the past few years and majority of the current research has dealt with the two machine learning problems namely classification and clustering. In this paper, a brief review of the recent techniques and algorithms of quantum machine learning and its scope in solving real world problems is studied.

Photonic spin Hall effect using hybrid Tamm plasmon polariton

Here, we report a photonic spin Hall effect (PSHE), i.e., splitting of opposite spin in a transverse direction using a multi-layered metallo-dielectric heterostructure by hybridization of a Tamm plasmon polariton and a surface plasmon polariton. The underlying mechanism of PSHE is also explained using the concept of superposition of normal and abnormal modes in the circular-polarization basis. It is revealed that the spin-dependent transverse shift for the proposed hybrid structure is enormously high owing to the transverse shift of ∼18 μm. The PSHE is found to be strongly dependent on the number of bi-layers of the structure and the analyte refractive index, which provides an additional degree of freedom for tailoring the device performance. Therefore, this study opens a pathway for developing next-generation photonic spin Hall devices in the fields of spin photonics, optical sensing, and optical communications.

Prediction of sloshing pressure and structural response of LNG CCS

Sloshing pressure that has a short rising time and a large peak value can cause structural damage to the primary membrane or insulation panel of the LNG cargo containment system (LNG CCS). Several experimental and numerical approaches have been suggested to estimate the design sloshing pressures used in the LNG CCS design stage. In this paper, in order to improve the management quality of LNG CCS during the operation of a vessel, methods for prediction of LNG CCS sloshing pressures and hot-spot stresses in real-time from measured inner-deck stresses (the latter not actually measured in the present study but calculated from the numerical simulation) are suggested. The proposed methods are based on the impulse superposition method and mode superposition method. A space superposition concept was introduced to consider the characteristic that the sloshing pressure appears differently at each position of the primary membrane. For prediction of sloshing pressure from inner-deck stresses, the inverse impulse/space superposition method was established, and the accuracy of the overall sloshing pressure prediction was enhanced by minimizing the sloshing pressure prediction error at a specific duration time through the least square method. By applying the impulse/space superposition method to the hot-spot stresses for the unit impulse, the hot-spot stresses for the sloshing pressure were calculated. The inverse process of the mode superposition method was also studied. In order to increase the accuracy of modal amplitude estimation, an additional mode selection process was devised to remove highly correlated modes. The prediction of LNG CCS hot-spot stresses calculated from the modal amplitude only was well performed, whereas the uncertainty of sloshing pressure prediction requiring numerical difference of the modal amplitude was quite high. From a series of numerical simulations, the validity of the proposed methods was verified. Using the real-time sloshing pressure time histories predicted from the proposed methods, the level of sloshing pressures generated in the actual LNG tank can be identified and the validity of the design sloshing pressures used in LNG CCS design can be judged. In addition, from the estimated real-time LNG CCS hot-spot stresses under sloshing pressures, LNG CCS structural health monitoring can be carried out during the lifetime of vessel.

Modelling steady-state convective heat transfer in different periodic open cellular structures (POCS) – A superposition approach

Additively manufactured periodic open cellular structures (POCS) offer promising attributes for the intensification of heat transfer processes. Their geometry and consequently thermal transport properties are highly customizable resulting in a vast design freedom. To fully exploit their potential, a detailed understanding of the relationship between their geometric and thermal transport properties is crucial. Therefore, in this contribution the convective heat transfer in four different unit cell geometries and varying cell dimensions during fully developed laminar steady-state flow is investigated. Numerical simulations are used to get a detailed insight of the local flow structures and heat flux density distributions. A correlation between the heat transfer coefficient of each unit cell and the heat transfer capability of unconnected strut arrangements with similar flow paths is introduced. On this basis, a new modelling approach is established in combination with the concept of superposition. It takes into account the detailed geometry of the porous medium and shows good agreement with the simulation data of all unit cell geometries studied in this work with porosities above 79.1%. Thus, it is the first model applicable to different types of unit cells.

Investigation of hot spot stress for welded tubular K-joints with stiffeners

Steel stiffeners can be utilized to reinforce the welded hollow section K-joints, possibly bringing about a different magnitude and distribution of hot spot stress from the traditional joints without stiffeners. There is a lack of knowledge on the hot spot stress and its calculation method for the welded tubular K-joints with stiffeners, especially under complex load cases, which has hindered the fatigue design. With regard to an engineering practice of a bridge under construction, this paper investigates the distribution and magnitude of hot spot stress and its calculation method. Experimental tests including two load cases are conducted to provide a benchmark for the finite element (FE) modelling. FE analysis is carried out to investigate the influence of stiffeners on the hot spot stress ranges at several critical locations, covering the parameters including number, thickness and position of internal ring stiffeners. The hot spot stress calculation method is then developed based on the superposition concept. It is found that the hot spot stress ranges near the stiffeners are relatively large, in addition to those at crowns and saddles. The locations of interest include not only crowns and saddles but also the locations at the intersection of stiffener contours and intersection lines. The maximum hot spot stress ranges can be reduced by 55.7% in chords and 37.4% in braces by setting stiffeners. The developed hot spot stress calculation method is verified for the welded tubular K-joints with stiffeners. In addition, a fatigue test on the welded tubular K-joints with stiffeners is performed. • Investigate the hot spot stress distribution and magnitude of the welded tubular K-joints with stiffeners. • Conduct experimental tests of the specimens in two combined load cases. • Analyze the effects of steel stiffeners on the hot spot stress ranges of welded tubular K-joints using FE modelling method. • Extend the hot spot stress calculation method for the welded tubular K-joints with stiffeners. • Perform a fatigue test on the welded tubular K-joints with stiffeners.

Efficient progressive global-local fatigue assessment methodology for existing metallic railway bridges

This paper presents an integrated fatigue analysis methodology for existing metallic railway bridges, progressively implementing global to local fatigue assessment methods, each requiring a different level of calculation detail. The former are adopted mainly for screening the fatigue-critical connections using nominal stress-based SN curves, which often oversimplify the characteristics of the loading transference mechanisms, relying on high safety factors. On the other hand, the latter methods are innovatively proposed to refine the fatigue evaluation of connections that present unsatisfactory remaining fatigue life according to the global assessment. A multiscale modelling is adopted, leveraged by modal superposition concepts for structural analysis, which allows efficiently bridging the difference between the scale related to the overall size of the bridge and the local one associated with the fatigue damage developed at hot-spots (e.g., rivet holes). A real case study is investigated to demonstrate the added value of the innovative and efficient progressive fatigue assessment of details that would otherwise not be accurately analysed, taking into account the local characteristics of the response, if only common global procedures were implemented.

Games for Teaching/Learning Quantum Mechanics: A Pilot Study with High-School Students

The teaching of quantum physics is challenging, not the least because teachers must overcome the traditional narrative approach, students must gain a conceptual understanding of fundamentals, and citizens must become aware of quantum technologies. Quantum games are powerful tools to overcome obstacles and push one’s limits without fear of failure. We report on a pilot study involving twenty high-school student volunteers, consisting of a compact intervention module on the concepts of quantum states, properties, measurement, superposition, and entanglement within the framework of the Model of Educational Reconstruction, followed by playing a game, quantum TiqTaqToe. The outcomes of this research-based learning environment are discussed via the qualitative analysis of students’ answers to two open questionnaires. We find that students grasped the concepts of superposition and, with special awareness, entanglement, the game proving effective to help students experience their implications in quantum behavior. The informal and stimulating tournament atmosphere favored intertwining of the game with learning goals. Our central message is that the use of quantum game tools fits a teaching/learning environment in manners often not well understood in the literature; it enhances awareness of the nature of new and non-intuitive concepts, increases complementarity with other languages within the process of thinking about physics, boosts student engagement, and improves intervention efficiency and effectiveness.

Techniques for History-Matching and Forecasting Tight Oil Reservoirs Compared

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTEC 208352, “Assessment of Rate-Normalization and Pressure-Deconvolution Techniques To History-Match and Forecast Production of Tight Oil Reservoirs Using a Physics-Based Rate-Time Model,” by Leopoldo M. Ruiz Maraggi, SPE, Larry W. Lake, SPE, and Mark P. Walsh, SPE, The University of Texas at Austin. The paper has not been peer reviewed. Rate transient analysis (RTA) is used routinely to analyze the production history of unconventional wells. Specifically, it is used to determine the time at the end of linear flow (time to depletion), estimate drainage volume, and predict the estimated ultimate recovery (EUR) of wells. RTA uses a rate-normalization technique to characterize how the well rate changes with a changing bottomhole flow pressure. However, rate normalization approximates a more-general technique known as pressure deconvolution. The complete paper evaluates and compares the performance of rate normalization and pressure deconvolution for both synthetic and tight oil examples. Overview The complete paper first presents the solution of the single-phase slightly compressible fluid-diffusivity equation for a certain set of idealized conditions. Second, the concepts of time superposition, convolution, deconvolution, and rate normalization are presented. Third, a numerical regularized exponential basis function inverse scheme is proposed for use in deconvolution. Fourth, the comparison of the rate-normalization and pressure-deconvolution techniques for a synthetic case are presented. Finally, the two methods are evaluated for tight oil wells. This synopsis will not include the many equations involved in these processes or detailed discussions thereof; instead, it concentrates on the work flow that the authors have developed, specifically its application to synthetic cases.