Real System Research Articles

Midwave infrared resonant cavity detectors with >70% quantum efficiency

We report resonant cavity infrared detectors with a peak wavelength of 4.54–4.58 μm that combine external quantum efficiency (EQE) exceeding 70% with spectral bandwidth 20–40 nm and ≤2% EQE at all non-resonance wavelengths between 4 and 5 μm. A 300-nm-thick absorber assures that most of the radiation propagating in the cavity produces photocurrent rather than parasitic loss. The cavity is formed by heterogeneously bonding a midwave infrared (MWIR) nBn detector chip to a GaAs/AlGaAs distributed Bragg reflector, etching away the GaSb substrate, forming mesas with diameter ≈100 μm, depositing a Ge spacer, and then depositing a single-period Ge-SiO2 top mirror. At all temperatures between 125 and 300 K, the responsivity at 150 mV bias exceeds 2.2 A/W and the EQE exceeds 61%. When the thermal background current for a realistic system scenario with f/4 optic that views a 300 K scene is derived from the observed EQE spectra, the resulting specific detectivity D* of 7.5 × 1012 cmHz½/W at 125 K operating temperature is 4.5 times higher than for a state-of-the-art broadband MWIR HgCdTe device. Simulations of the cavity performance indicate that EQE > 90% may be feasible following minimization of parasitic optical loss and maximization of the photocarrier collection efficiency. Potential applications include free space optical communication, chemical sensing, on-chip spectroscopy, and hyperspectral imaging.

How Accurate Are QM/MM Models?

Despite the success and widespread use of QM/MM methods in modeling (bio)chemically important processes, their accuracy is still not well understood. A key reason is because these methods are ultimately approximations to direct QM calculations of very large systems, which are impractical to perform in most cases. We highlight recent progress toward the development of realistic model systems where it is possible to obtain full QM reference data to directly and systematically evaluate the effectiveness of different QM/MM generation schemes. These model systems are highly flexible and can be tailored to probe the sensitivity of a QM/MM model to different reaction types and simulation parameters such as pairing of QM and MM potentials, QM region size, and composition. It is envisaged that this strategy could be used to directly validate different QM/MM generation schemes and spur the development of more robust models in the future.

Analytical solution to the problem of polarization drift compensation in an all-fiber QKD system

A method for simultaneous compensation of polarization drift in a fiber-optic quantum channel, and a fully fiber-optic receiver has been successfully developed, implemented, and tested using a commercial QKD system. The method is based on the exact analytical solution we derived, which gives the necessary corrective transformation for polarization. Noise and imperfections in any realistic system require iterative application of the computed corrections. In our case, polarization drift compensation was completed on average in 2-3 iterations. This is an improvement by a factor of 10-100 compared to the common methods based on hill-climbing algorithms.

Interdiffusion Induced Changes in the Absorption Spectra of III-V Quantum Dot Systems

III-V Semiconductor quantum dots (QDs) grown with growth interruption technique do not have uniform group III composition inside the dot. Modelling such as-grown structures requires in-depth knowledge of the composition and potential well profile. In this work, we have developed a three-dimensional analytical model which incorporates strain, as well as the variation of group III composition profile in both lateral and vertical directions inside an as-grown QD in order to quantify the effect of dot size variation on the interband optical absorption spectra of as-grown QD systems. Earlier reports on this subject considered the variation of either the dot height or base to account for the size deviation, which may not be sufficient enough to predict the dot size distribution in realistic systems, where all dimensions are likely to suffer from deviation. Keeping this in mind, Gaussian distribution of dot volume has been adopted here to consider the variations of height and base of QDs in a cumulative way. In our study, we considered InGaAs/GaAs and InGaN/GaN QD systems. The non-uniform distribution of indium inside the as-grown dot leads to a non-rectangular potential well profile, which eventually transforms into a rectangular one after annealing. Through the proposed model, we have investigated the effect of inhomogeneous indium composition and dot size variation on the optical absorption spectra of InGaAs/GaAs and InGaN/GaN as-grown QD systems through Gaussian distribution of dot volume. We have also discussed the effect of size deviation on the full width at half maxima (FWHM) of both as-grown and annealed QD systems. Emphasis has been given to the changes in the conduction and valence band potential profiles due to changes in the morphology inside the dot leading to subsequent changes in the energy levels caused by composition uniformity due to annealing and interdiffusion, thus modifying the nature of absorption spectra.

Evaluation of Solar Photovoltaics on University Buildings: A Case Study Toward Campus Sustainability

Renewable energy leads Brazil's energy mix at about 82% of its total, with solar photovoltaics (PV) now the second largest contribution to the electric power sector. The country's target to eliminate carbon emissions provides a significant role for higher-education institutions that shape and influence future societies through energy and education leadership. This paper focuses on the improvement of the sustainability level of the PUC Minas university campus in Belo Horizonte, Brazil, through the assessment and design of a PV system into the existing engineering building structures. The student-led case study provides an implementation roadmap that includes a coordinated methodology of evaluating the campus power and energy consumption, documenting the seasonal solar irradiance, evaluating the solar-available rooftop areas, simulating the daily and seasonal shadowing effects from existing surrounding structures, determining the panel placements following Brazil's codes and standards, and calculating the contributions of the PV to displacing grid-supplied electricity and potential lowering of the University's energy bills. The result is a realistic PV system design that meets the emission and electricity-cost reduction objectives, with a value-added aspect is that the lead team interfaced with ongoing Energy-Engineering course participants for real-time exposure to the research and photovoltaics technology. Additionally, the value analysis of the project is aimed at improving the University's Times Higher Education Ranking by addressing the United Nations Sustainability Development Goals (SDG) that correlate with the clean energy solar installation.

How Sophisticated Are Neural Networks Needed to Predict Long-Term Nonadiabatic Dynamics?

Nonadiabatic dynamics is key for understanding solar energy conversion and photochemical processes in condensed phases. This often involves the non-Markovian dynamics of the reduced density matrix in open quantum systems, where knowledge of the system's prior states is necessary to predict its future behavior. In this study, we explore time-series machine learning methods for predicting long-time nonadiabatic dynamics based on short-time input data, comparing these methods with the physics-based transfer tensor method (TTM). To understand the impact of memory time on these approaches, we demonstrate that non-Markovian dynamics can be represented as a linear map within the Nakajima-Zwanzig generalized quantum master equation framework. We further propose a practical method to estimate the effective memory time, within a given tolerance, for reduced density matrix propagation. Our predictive models are applied to various physical systems, including spin-boson models, multistate harmonic (MSH) models with Ohmic spectral densities and for a realistic organic photovoltaic system composed of a carotenoid-porphyrin-fullerene triad dissolved in tetrahydrofuran. Results indicate that the simple linear-mapping fully connected neural network (FCN) outperforms the more complicated nonlinear-mapping networks including the gated recurrent unit (GRU) and the convolutional neural network/long short-term memory (CNN-LSTM) in systems with short memory times, such as spin-boson and MSH models. Conversely, the nonlinear CNN-LSTM and GRU models yield higher accuracy in the triad MSH systems characterized by long memory times. These findings offer valuable insights into the role of effective memory time in non-Markovian quantum dynamics, providing practical guidance for the application of time-series machine learning models to complex chemical systems.

Hybrid Oversampling and Undersampling Method (HOUM) via Safe-Level SMOTE and Support Vector Machine

The improvements in collecting and processing data using machine learning algorithms have increased the interest in data mining. This trend has led to the development of real-life decision support systems (DSSs) in diverse areas such as biomedical informatics, fraud detection, natural language processing, face recognition, autonomous vehicles, image processing, and each part of the real production environment. The imbalanced datasets in some of these studies, which result in low performance measures, have highlighted the need for additional efforts to address this issue. The proposed method (HOUM) is used to address the issue of imbalanced datasets for classification problems in this study. The aim of the model is to prevent the overfitting problem caused by oversampling and valuable data loss caused by undersampling in imbalanced data and obtain successful classification results. The HOUM is a hybrid approach that tackles imbalanced class distribution challenges, refines datasets, and improves model robustness. In the first step, majority-class data points that are distant from the decision boundary obtained via SVM are reduced. If the data are not balanced, SLS is employed to augment the minority-class data. This loop continues until the dataset becomes balanced. The main contribution of the proposed method is reproducing informative minority data using SLS and diminishing non-informative majority data using the SVM before applying classification techniques. Firstly, the efficiency of the proposed method, the HOUM, is verified by comparison with the SMOTE, SMOTEENN, and SMOTETomek techniques using eight datasets. Then, the results of the W-SIMO and RusAda algorithms, which were developed for imbalanced datasets, are compared with those of the HOUM. The strength of the HOUM is revealed through this comparison. The proposed HOUM algorithm utilizes a real dataset obtained from a project endorsed by The Scientific and Technical Research Council of Turkey. The collected data include quality control and processing parameters of yarn data. The aim of this project is to prevent yarn breakage errors during the weaving process on looms. This study introduces a decision support system (DSS) designed to prevent yarn breakage during fabric weaving. The high performance of the algorithm may encourage producers to manage yarn flow and enhance the HOUM’s efficiency as a DSS.

Stable Millivolt Range Resistive Switching in Percolating Molybdenum Nanoparticle Networks.

To overcome the limitations of the conventional Von Neumann architecture, inspiration from the mammalian brain has led to the development of nanoscale neuromorphic networks. In the present research, molybdenum nanoparticles (NPs), which were produced by means of gas phase condensation based on magnetron sputtering, are shown to be the constituents of electrically percolating networks that exhibit stable, complex, neuron-like spiking behavior at low potentials in the millivolt range, satisfying well the requirement of low energy consumption. Characterization of the NPs using both scanning electron microscopy and scanning transmission electron microscopy revealed not only pristine shape, size, and density control of Mo NPs but also a preliminary proof of the working mechanism behind the spiking behavior due to filament formations. Furthermore, electrostatics COMSOL Multiphysics simulations of the morphology of the NPs provided evidence that the stable switching is due to the as-deposited stellate Mo NPs creating high electric field strengths, while keeping them separated, not seen before in other percolating networks based on spherical NPs. Hence, our results show the working mechanism behind switching in percolating Mo NP networks and show that they are very promising for realistic neuromorphic systems.

A Logical Approach to Type Soundness

Type soundness, which asserts that “well-typed programs cannot go wrong,” is widely viewed as the canonical theorem one must prove to establish that a type system is doing its job. It is commonly proved using the so-called syntactic approach (also known as progress and preservation ), which has had a huge impact on the study and teaching of programming language foundations. Unfortunately, syntactic type soundness is a rather weak theorem. It only applies to programs that are well typed in their entirety and thus tells us nothing about the many programs written in “safe” languages that make use of “unsafe” language features. Even worse, it tells us nothing about whether type systems achieve one of their main goals: enforcement of data abstraction. One can easily define a language that enjoys syntactic type soundness and yet fails to support even the most basic modular reasoning principles for abstraction mechanisms like closures, objects, and abstract data types. Given these concerns, we argue that programming languages researchers should no longer be satisfied with proving syntactic type soundness and should instead start proving semantic type soundness , a more useful theorem that captures more accurately what type systems are actually good for. Semantic type soundness is an old idea—Milner’s original account of type soundness from 1978 was semantic—but it fell out of favor in the 1990s due to limitations and complexities of denotational models. In the succeeding decades, thanks to a series of technical advances—notably, step-indexed Kripke logical relations constructed over operational semantics and higher-order concurrent separation logic as consolidated in the Iris framework in Coq—we can now build (machine-checked) semantic soundness proofs at a much higher level of abstraction than was previously possible. The resulting “logical” approach to semantic type soundness has already been employed to great effect in a number of recent papers, but those papers typically (a) concern advanced problem scenarios that complicate the presentation, (b) assume significant prior knowledge of the reader, and (c) suppress many details of the proofs. Here, we aim to provide a gentler, more pedagogically motivated introduction to logical type soundness, targeted at a broader audience that may or may not be familiar with logical relations and Iris. As a bonus, we also show how logical type soundness proofs can easily be generalized to establish an even stronger relational property— representation independence —for realistic type systems.

Implementation of magnetic compressional effects at arbitrary wavelength in the global version of GENE

The global tokamak code GENE has been extended including the effect of magnetic compression caused by B1,|| turbulent fluctuations of the magnetic field parallel to the equilibrium one. This paper outlines the basic structure of the algorithm, valid at arbitrary wavelengths of the gyrokinetic fluctuations, with emphasis on the numerical construction of the so-called “gyrodisk-integral” operators necessary for the model. The numerical implementation is successfully verified against radially local simulations, recovering excellent agreement. Global tokamak simulations are presented as well. The upgrade enables studying a large variety of new physical scenarios at high plasma-β, such as kinetic ballooning modes, MHD-like modes or the interaction of B1,|| with fast particle modes, reducing the gap between gyrokinetic models and physically realistic systems.

Quantum decoherence effects: A complete treatment

Physical systems in real life are inextricably linked to their surroundings and never completely separated from them. Truly closed systems do not exist. The phenomenon of decoherence, which is brought about by the interaction with the environment, removes the relative phase of quantum states in superposition and makes them incoherent. In neutrino physics, decoherence, although extensively studied has only been analyzed thus far exclusively in terms of its dissipative characteristics. While it is true that dissipation, or the exponential suppression, eventually is the main observable effect, the exchange of energy between the medium and the system, is an important factor that has been overlooked up until now. In this work, we introduce this term and analyze its consequences. Published by the American Physical Society 2024

Anisotropic quantum transport in a programmable photonic topological insulator

Quantum transport in materials describes the behavior of particles at the quantum level. Topological materials exhibit nontrivial transport properties with topological invariants, leading to the emergence of protected states that are immune against disorders at the material boundaries. In many real-world materials, especially those with anisotropic crystal structures, the transport properties can vary significantly along different directions within the material bulk. Here, we experimentally observe counterintuitive quantum transport phenomena in anisotropic topological insulators with controllable anisotropy and disorder, implemented on a programmable topological photonic chip. We examine phase transition from the topological phase to the Anderson phase, between which a new quasi-diffusive phase emerges. Anisotropic topological transport demonstrates unconventional superior robustness in the bulk mode compared to the edge mode, in the presence of disorder and loss in realistic systems. Peculiar topological transport with sophisticated gradient anisotropy, emulating stretched topological materials, occurs at the gradient domain wall that can be reconfigured. Our findings provide fresh insights into the intricate interplay between anisotropy within the bulk and robustness at the boundary of topological materials, which could lead to advancements in the field of topological material science and the development of topological devices with tailored functionalities.

Attack Reconstruction and Attack-Resilient Consensus Control for Fuzzy Markovian Jump Multi-Agent Systems

Driven by the rapid development of modern industrial applications, multi-agent systems (MASs), integrating computational and physical resources, have become increasingly important in recent years. However, the performance of MASs can be easily compromised by malicious false data injection attacks (FDIAs) due to the inherent vulnerability of the cyber layer. This work focuses on an event-triggered framework for secure reconstruction and consensus control in MASs subject to both sensor and actuator attacks. First, we introduce a class of Takagi–Sugeno fuzzy multi-agent systems that relax the traditional Lipschitz condition and incorporate realistic system dynamics by considering parameter variations governed by Markovian jump principles. Second, an adaptive fuzzy estimator is developed for the simultaneous reconstruction of states and attacks in MASs. The derived estimates are utilized to design an attack-resilient consensus control strategy that compensates for the effects of FDIAs on the closed-loop consensus error dynamics. Meanwhile, the sufficient conditions for the convergence of both estimation and consensus errors are presented and rigorously proved. Finally, evaluation results on an experimental platform through multiple truck-trailer systems are provided to demonstrate the effectiveness and performance of the proposed approach.

Adaptive connectivity-preserving formation control with a dynamic event-triggering mechanism

Connectivity preservation for real-life multi-agent systems is key to designing an effective and reliable control algorithm to achieve desired objectives. But the coexistence of system nonlinearities and uncertainties makes it difficult for the algorithm design, and especially in the event-triggered setting, such difficulty becomes severer due to the demand for integration of multiple ingredients. This paper focuses on proposing an adaptive connectivity-preserving formation control strategy that incorporates a dynamic event-triggering mechanism, in the scenario of the unknown control directions and intrinsic nonlinearities coupling with parameter uncertainties. First, a cluster of potential functions serving as control barrier functions are given to maintain the prescribed connectivity of communication graph. Second, two types of dynamic gains (one type is based on a Nussbaum function) are introduced for agents to cope with the system nonlinearities, uncertainties and the adverse effect of the execution error. As such, an adaptive event-triggered formation protocol is constructed such that the desired formation with connectivity preservation is achieved while a positive minimum inter-execution interval is ensured for each agent to avoid Zeno behavior. Particularly, the threshold in the developed event-triggering mechanism is dynamically adjusted online, rather than static, which better improves resource efficiency. A simulation example is given to demonstrate the effectiveness and advantage of the proposed strategy.

Quantum many-body scars in few-body dipole-dipole interactions

We simulate the dynamics of Rydberg atoms resonantly exchanging energy via two-, three-, and four-body dipole-dipole interactions in a one-dimensional array. Using simplified models of a realistic experimental system, we study the initial-state survival probability, mean level spacing, spread of entanglement, and properties of the energy eigenstates. By exploring a range of disorders and interaction strengths, we find regions in parameter space where the three- and four-body dynamics either fail to thermalize or do so slowly. The interplay between the stronger hopping and weaker field-tuned interactions gives rise to quantum many-body scar states, which play a critical role in slowing the dynamics of the three- and four-body interactions. Published by the American Physical Society 2024

Design, optimization, and autonomous construction of Martian infrastructure including slabs using in-situ CO2-based polyethylene

A crucial development in 21st-century space exploration is the establishment of human settlements on Mars. In recent years, Mars exploration rovers have been deployed, and studies investigating human journeys to Mars have been conducted, with the objective of a human-crewed mission to Mars before 2050. This study presents an innovative approach to designing and optimizing Martian habitats by using in-situ CO2-based polyethylene (PE) as a construction material for Martian buildings and greenhouses. It seeks to develop sustainable and resilient habitats on Mars, leveraging local resources to address the environmental challenges specific to the Martian environment. The research targets several key areas: realistic construction techniques, effective utilization of in-situ materials, and building resilience under the harsh Martian conditions. These conditions include radiation, micrometeoroid impacts, low atmospheric pressure, low gravity, wind storms, temperature extremes, Paschen discharge, and marsquakes. Finite element analysis (FEA) of several pressurized spherical structures is used to design and optimize for minimum material usage, inspired by soap bubbles that naturally stick together with separating walls. The proposed habitat design features a central spherical structure surrounded by nine smaller spheres, optimized for material efficiency. It is demonstrated that utilizing two layers of 0.1 m thick PE and a 0.5 m thick CO2 layer in between them offers significant protection against Martian environmental loads such as radiation shielding and thermal insulation. It facilitates light transmission into the building. This study offers a realistic structural system for buildings and greenhouses that can be constructed using in-situ materials and is suitable for an optimal 539-day stay on Mars. The proposed autonomous robotic arm technology can perform all necessary tasks, from producing PE to automatically assembling the Mars Spherical Building (MSB), including in-place slab printing.

Experimental study of data-driven model predictive control on transcritical CO2 thermal system in electric vehicles

The transcritical CO2 thermal system has been considered an effective and completable candidate for providing space/battery cooling and heating in electric vehicles. For a realistic system, the optimal performance relies on an effective control strategy. This paper presents a model predictive control approach to optimize the real-time operation of the transcritical CO2 thermal system and conduct a complete experimental investigation. A data-driven control-oriented model is first developed to predict the next steps in system behaviours in a finite time domain. The model predictive controller is designed to provide the optimal inputs based on the control-oriented model and the designed objective function, in which optimal system COP can be achieved provided that the cooling/heating capacity is maintained. Then, a complete test rig is built in a psychrometric test room to experimentally investigate the operating performance using the proposed model predictive control strategy. The experiments are conducted under fixed and variable ambient temperatures for both cooling and heating conditions. The experimental results indicate that the model predictive control strategy can accurately forecast system states and determine optimal control inputs for the transcritical CO2 thermal system to achieve the highest operating COP with the required cooling/heating capacity in electric vehicles.

Parallel Simulations of the Sharp Wave-Ripples of the Hippocampus on Multicore CPUs and GPUs

The simulation of realistic systems plays a crucial role in modern sciences. Complex organs such as the brain can be described by mathematical models to reproduce biological behaviors. In the brain, the hippocampus is a critical region for memory and learning. In the literature, a model to reproduce the memory consolidation mechanism has been proposed. This model exhibits a high degree of biological realism, though it is accompanied by a significant increase in computational complexity. This paper proposes the development of parallel simulation targeting different devices, namely multicore CPUs and GPUs. The experiments highlighted that the biological realism is maintained, together with a significant decrease of the processing times. Finally, the conducted analysis highlights that the GPU is one of the most suitable technologies for this kind of simulation.

Cluster consensus of finite-field networks with stochastic communication topology

Most of the multi-agent systems (MASs) in real life are with limited storage and communication capabilities; studies on such systems, including consensus problem, however have been mainly based on real number field models. In this article, the cluster consensus problem of MASs with stochastic communication topology over finite-field known as the cluster consensus of finite-field networks (FFNs), is considered. Using the semi-tensor product of matrices, the dynamics of FFNs with stochastic communication topology are equivalently converted into a logical algebraic form, which facilitates further studies. Then a permutation system is introduced. The relationship between cluster consensus of FFNs with stochastic communication topology and the set stability with probability one (SSPO) of the permutation system is revealed. In such an approach, some verifiable criteria can be derived for the cluster consensus of FFNs with stochastic communication topology, without requesting the connectivity of the whole network. Finally, an example is presented to validate the main results.

Exact Thermal Eigenstates of Nonintegrable Spin Chains at Infinite Temperature.

The eigenstate thermalization hypothesis (ETH) plays a major role in explaining thermalization of isolated quantum many-body systems. However, there has been no proof of the ETH in realistic systems due to the difficulty in the theoretical treatment of thermal energy eigenstates of nonintegrable systems. Here, we write down analytically thermal eigenstates of nonintegrable spin chains. We consider a class of theoretically tractable volume-law states, which we call entangled antipodal pair (EAP) states. These states are thermal, in the most fundamental sense that they are indistinguishable from the Gibbs state with respect to all local observables, with infinite temperature. We then identify Hamiltonians having the EAP state as an eigenstate and rigorously show that some of these Hamiltonians are nonintegrable. Furthermore, a thermal pure state at an arbitrary temperature is obtained by the imaginary time evolution of an EAP state. Our results offer a potential avenue for providing a provable example of the ETH.