Relaxed Conditions Research Articles

Fully Distributed Algorithm for Resource Allocation Over Unbalanced Directed Networks Without Global Lipschitz Condition

This note investigates the distributed optimal resource allocation problem of multi-agent systems over unbalanced directed networks under the relaxed condition that the gradients of local cost functions are locally Lipschitz. The objective is to cooperatively drive the decision variables of the agents to the optimal solution, which minimizes the sum of the local cost functions, while ensuring that the network resource constraints and local feasibility constraints are satisfied. A novel distributed algorithm is developed over unbalanced directed network topologies based on the topology balancing technique and adaptive control approach. The developed algorithm is fully distributed in the sense that it depends on neither the global Lipschitz continuity of the gradients nor prior global information about the network connectivity. By regarding the proposed algorithm as a perturbed system, its input-to-state stability with a vanishing perturbation is first established, and asymptotic convergence of the decision variables toward the optimal solution is then proved. The effectiveness of the proposed fully distributed algorithm is illustrated with two examples.

Receding Horizon Actor–Critic Learning Control for Nonlinear Time-Delay Systems With Unknown Dynamics

With the development of modern mechatronics and networked systems, the controller design of time-delay systems has received notable attention. Time delays can greatly influence the stability and performance of the systems, especially for optimal control design. In this article, we propose a receding horizon actor–critic learning control approach for near-optimal control of nonlinear time-delay systems (RACL-TD) with unknown dynamics. In the proposed approach, a data-driven predictor for nonlinear time-delay systems is first learned based on the Koopman theory using precollected samples. Then, a receding horizon actor–critic architecture is designed to learn a near-optimal control policy. In RACL-TD, the terminal cost is determined by using the Lyapunov–Krasovskii approach so that the influences of the delayed states and control inputs can be well addressed. Furthermore, a relaxed terminal condition is present to reduce the computational cost. The convergence and optimality of RACL-TD in each prediction interval as well as the closed-loop property of the system are discussed and analyzed. Simulation results on a two-stage time-delayed chemical reactor illustrate that RACL-TD can achieve better control performance than nonlinear model predictive control (MPC) and infinite-horizon adaptive dynamic programming. Moreover, RACL-TD can have less computational cost than nonlinear MPC.

Modeling, Control and Implementation of Adaptive Reconfigurable ROtary Wings (ARROWs)

Inspired by the self-rotating motion of the descending maple seeds, we introduce a novel modular aerial robotic platform—ARROWs. With customized wing and control modules, ARROWs can be easily reassembled into different configurations. Unlike conventional multirotor aerial vehicles which rely on the direct thrust from propellers, ARROWs can generate more lift by their revolving wings. However, the complex dynamics causes difficulties in flight controller development. In this work, we first analyze the flight dynamics by considering a combination of effects from the propeller, aerodynamic force on the wing, as well as self-rotating motion. As a result, a cascaded flight controller with a unified framework is designed based on reduced flight dynamics and relaxed hovering conditions to achieve stable flights in all proposed configurations. In addition, a set of inertial measurement units is employed for each flight module to estimate the flight configuration to overcome the dynamic uncertainties caused by manual re-construction. Finally, our proposed platform and flight control strategy are validated using several flight experiments in 12 different configurations (include both centrosymmetric and centrally asymmetric cases). The results show an average position error of 8.9 cm with a deviation of 2.4 cm among all configurations in hovering tests.

A statistical analysis of the second ‘pop-in’ behaviour of the spherical-tip nanoindentation of Zr-based bulk metallic glasses

Statistical analyses of the shear stresses, τy, at which the first ‘pop-ins’ occur during the spherical tip nanoindentation of a wide variety of materials is often conducted to understand the micromechanisms of incipient plasticity. In an earlier paper, we reported such a study on data generated on several different Zr-based bulk metallic glasses (BMGs) using a wide range of experimental variables, such as the tip radius, Ri, loading rate, P˙, and structural state of the glass. In the present work, we analyse the second pop-in stress, τ2, data employing the expectation maximization algorithm in conjunction with the Akaike Information Criterion to examine which of the single and mixed (bimodal) versions of the Gaussian, Lognormal and Weibull (both 2 and 3 parameter) statistical models best describes the stochasticity of τ2. Results show that the 3-parameter Weibull distribution also captures the stochasticity of τ2, just as it does for τy. For datasets of τ2 that are generated with larger Ri and P˙, the bimodal 3-parameter Weibull distribution is a better descriptor of the dispersion. While Weibull exponent, m, of τ2 datasets is marginally higher than that of τy, their kernel density estimates (KDEs) are similar. However, there is a relative shift of the KDEs of τ2 to smaller values compared to that of τy. From mechanistic arguments, τ2 is determined as the stress to nucleate a second shear band over a previously formed shear band and its stochasticity is attributed solely to the mechanical heterogeneity of the material within it. On this basis, the average shear strength of the shear band is estimated to be ∼8–11% and 15–17% lower than the strength of the undeformed BMG in the as cast condition and structurally relaxed conditions, respectively. This study provides an understanding of how plasticity develops in BMGs during nanoindentation.

Detection of Anomalies in Additively Manufactured Metal Parts Using CNN and LSTM Networks

The process of metal additive manufacturing (AM) involves creating strong, complex components by using fine metal powders. Extensive use of AM methods is expected in near future for the production of small and medium-sized batches of end-use products and tools. The ability to detect loads and defects would enable AM components to be used in critical applications and improve their value. In this study, the Surface Response to Excitation (SuRE) method was used to investigate wave propagation characteristics and load detection on AM metallic specimens. With completely solid infills and the same geometry, three stainless steel test bars are produced: one conventionally and two additively. To investigate the effect of infills, four bars with the same geometries are 3D printed with triangular and gyroid infills with either 0.5 mm or 1 mm skin thickness. Two piezoelectric disks are attached to each end of the test specimens to excite the parts with guided waves from one end and monitor the dynamic response to excitation at the other end. The response to excitation was recorded when bars were in a relaxed condition and when compressive loads were applied at five levels in the middle of them. For converting time-domain signals into 2D time-frequency images, the Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) were implemented. To distinguish the data based on fabrication characteristics and level of loading, two deep learning models (Long Short-term Memory algorithm (LSTM) and Convolutional Neural Networks (2D CNN)) were utilized. Time-frequency images were used to train 2D CNN, while raw signal data was used to train LSTM. It was found that both LSTM and 2D CNN could estimate solid parts' loading level with an accuracy of more than 90%. In parts with infills, CNN outperformed LSTM for the classification of over five classes (internal geometry and loading level simultaneously).

Manipulating Peierls Distortion in van der Waals NbOX2 Maximizes Second-Harmonic Generation.

Two-dimensional (2D) van der Waals (vdW) materials, featuring relaxed phase-matching conditions and highly tunable optical nonlinearity, endow them with potential applications in nanoscale nonlinear optical (NLO) devices. Despite significant progress, fundamental questions in 2D NLO materials remain, such as how structural distortion affects second-order NLO properties, which call for advanced regulation and in situ diagnostic tools. Here, by applying pressure to continuously tune the displacement of Nb atoms in 2D vdW NbOI2, we effectively modulate the polarization and achieve a 3-fold boost of the second-harmonic generation (SHG) at 2.5 GPa. By introducing a Peierls distortion parameter, λ, we establish a quantitative relationship between λ and SHG intensity. Importantly, we further demonstrate that the SHG enhancement can be achieved under ambient conditions by anionic substitution to tune the distortion in NbO(I1-xBrx)2 (x = 0-1) compounds, where the chemical tailoring simulates the pressure effects on the structural optimization. Consequently, NbO(I0.60Br0.40)2 with λ = 0.17 exhibits a giant SHG of over 2 orders of magnitude higher than that in monolayer WSe2, reaching the record-high value among reported 2D vdW NLO materials. This work unambiguously demonstrates the correlation between Peierls distortion and SHG property and, more broadly, opens new paths for the development of advanced NLO materials by manipulating the structure distortions.

Fixed-time periodic stabilization of discontinuous reaction–diffusion Cohen–Grossberg neural networks

This paper aims to study the fixed-time stabilization of a class of delayed discontinuous reaction–diffusion Cohen–Grossberg neural networks. Firstly, by providing some relaxed conditions containing indefinite functions and based on inequality techniques, a new fixed-time stability lemma is given, which can improve the traditional ones. Secondly, based on state-dependent switching laws, the periodic wave solution of the formulated networks is transformed into the periodic solution of ordinary differential system. By utilizing differential inclusions theory and coincidence theorem, the existence of periodic solutions is obtained. Thirdly, based on the new fixed-time stability lemma, the periodic solutions are stabilized at zero in a fixed-time, which is a new topic on reaction–diffusion networks. Moreover, the established criteria are all delay-dependent, which are less conservative than the previous delay-independent ones for ensuring the stabilization of delayed reaction–diffusion networks. Finally, two examples give numerical explanations of the proposed results and highlight the influence of delays.

Endosymbiosis allows Sitophilus oryzae to persist in dry conditions.

Insects frequently associate with intracellular microbial symbionts (endosymbionts) that enhance their ability to cope with challenging environmental conditions. Endosymbioses with cuticle-enhancing microbes have been reported in several beetle families. However, the ecological relevance of these associations has seldom been demonstrated, particularly in the context of dry environments where high cuticle quality can reduce water loss. Thus, we investigated how cuticle-enhancing symbionts of the rice-weevil, Sitophilus oryzae contribute to desiccation resistance. We exposed symbiotic and symbiont-free (aposymbiotic) beetles to long-term stressful (47% RH) or relaxed (60% RH) humidity conditions and measured population growth. We found that symbiont presence benefits host fitness especially under dry conditions, enabling symbiotic beetles to increase their population size by over 33-fold within 3 months, while aposymbiotic beetles fail to increase in numbers beyond the starting population in the same conditions. To understand the mechanisms underlying this drastic effect, we compared beetle size and body water content and found that endosymbionts confer bigger body size and higher body water content. While chemical analyses revealed no significant differences in composition and quantity of cuticular hydrocarbons after long-term exposure to desiccation stress, symbiotic beetles lost water at a proportionally slower rate than did their aposymbiotic counterparts. We posit that the desiccation resistance and higher fitness observed in symbiotic beetles under dry conditions is due to their symbiont-enhanced thicker cuticle, which provides protection against cuticular transpiration. Thus, we demonstrate that the cuticle enhancing symbiosis of Sitophilus oryzae confers a fitness benefit under drought stress, an ecologically relevant condition for grain pest beetles. This benefit likely extends to many other systems where symbiont-mediated cuticle synthesis has been identified, including taxa spanning beetles and ants that occupy different ecological niches.

Effectiveness of 30o Head Elevation Intervention on Head Pain Levels in Patients with Space Occupying Lesion (SOL) Intracranial Tumor Post-Op Craniotomy : Case Study

ABSTRACT Intracranial tumors are abnormal mass growths in the brain that originate from tissue that grows and divides uncontrollably. Space Occupying Lesion (SOL) is a space pressure caused by an increase in volume within the brain due to the accumulation of blood, cerebro-spinal fluid or brain tissue. A common treatment for intracranial tumors is craniotomy. According to research, about 60% of patients who undergo craniotomy experience moderate to high levels of headache pain. Non-pharmacological pain management that can be done includes deep breathing interventions with guided imagery techniques, monitoring patient hemodynamics and 30o head elevation. The purpose of this study was to present the effect of 30o head elevation nursing intervention on pain levels in patients with SOL intracranial tumors post craniotomy. The method in this study used a case study design conducted in November 2022 at Sumedang Regional General Hospital. The results showed that head elevation of 30o can reduce head pain levels because it can maximize the entry of oxygen into the intracranial and increase the return flow of blood from the brain to the heart, help activate the parasympathetic nervous system which functions to control various activities of the human body at rest and maximize relaxed conditions in patients, resulting in a stable pulse frequency, respiratory frequency, blood pressure. The intervention of elevating the head position or head elevation of 30o is one of the many nonpharmacological therapies that are useful for improving intracranial pressure so that the headache felt by the patient improves and cerebral perfusion can be resolved. Keywords: SOL, Post-op Craniotomy, Head Elevation

A model reconstruction approach for control synthesis of Takagi-Sugeno fuzzy systems

This paper investigates the control synthesis for Takagi-Sugeno (TS) fuzzy systems via a model reconstruction approach. Firstly, based on a nonparallel distributed compensation (NPDC) control scheme and a fuzzy Lyapunov function candidate (FLF), LMI-based synthesis conditions are derived to guarantee the asymptotic stability of the closed-loop TS fuzzy system. Then the operation domain of the premise variables is divided into several subregions to provide more flexibility in control design. And a new local model reconstruction method is proposed to transform the time-derivative of the FLF into new fuzzy forms with transformed membership functions in each subregion. This enables better exploitation of information on membership functions, resulting in relaxed conditions for both stabilization and robust H∞ control. Two simulation examples are provided to verify the advantages of the proposed TS fuzzy control approach.

Fuzzy Secure Event-Triggered Control for Networked Nonlinear Systems Under DoS and Deception Attacks

This article investigates the resilient dynamic event-triggered control issue of interval type-2 T-S fuzzy systems (IT2 T-S FSs) under denial-of-service (DoS) and deception attacks. A novel resilient dynamic event-triggering mechanism (DETM) and the corresponding fuzzy dynamic event-triggered controller are proposed to solve the stochastically exponential stability (SES) problem of concerned IT2 T-S FSs in the presence of DoS and deception attacks. Meanwhile, an aperiodic-sampling-based piecewise loop Lyapunov functional is constructed. It is shown that the resulting closed-loop IT2 T-S FSs can achieve SES, and under the piecewise loop Lyapunov functional, some relaxed conditions are derived. In addition, the derived results can apply to IT2 T-S FSs subject to DoS attacks, static event-triggered control, or dynamic periodic-sampling-based event-triggered control. Finally, the effectiveness of the achieved results is verified by a numerical simulation on one-link manipulator.

Relaxed Stability Criteria for Delayed Generalized Neural Networks via a Novel Reciprocally Convex Combination

Dear Editor This letter examines the stability issue of generalized neural networks (GNNs) with time-varying delay based on a novel reciprocally convex combination (RCC). By considering a new matrix polynomial, the proposed novel reciprocally convex method leads to a tight bound for integral inequality combination and encompasses several existing approaches as special cases. The relaxed stability conditions with less conservatism are developed by employing the proposed reciprocally convex combination and the Lyapunov-Krasovskii (L-K) functional. Finally, several numerical examples are conducted to show the superiorities of the stability conditions.

Policy Optimization Adaptive Dynamic Programming for Optimal Control of Input-Affine Discrete-Time Nonlinear Systems

In this article, a policy optimization adaptive dynamic programming (POADP) method is developed for optimal control of discrete-time unknown nonlinear systems, where the iterative control policy is parameterized to optimize the iterative <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula> -function directly. The relaxed condition for the learning rate is given to guarantee the convergence of the present algorithm. Furthermore, the Polyak–Łojasiewicz inequality is introduced to analyze the optimality, i.e., the iterative <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula> -function converges to the optimum within a given computational threshold under a finite iteration, and the rate of convergence (i.e., the required minimum number of iterations) for the developed POADP method is also illustrated. To ease real implementations, the iterative <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula> -function and the iterative control policy are approximated by employing an actor–critic structure. Then, an experiment-based method is developed to obtain the initial weights of actor–critic structure. Finally, numerical simulation results of two examples are provided to validate the effectiveness of the POADP algorithm.

High-Throughput Exploration of Triple-Cation Perovskites via All-in-One Compositionally-Graded Films.

Many devices heavily rely on combinatorial material optimization. However, new material alloys are classically developed by studying only a fraction of giant chemical space, while many intermediate compositions remain unmade in light of the lack of methods to synthesize gapless material libraries. Here report a high-throughput all-in-one material platform to obtain and study compositionally-tunable alloys from solution is reported. This strategy is applied to make all Csx MAy FAz PbI3 perovskite alloys (MA and FA stand for methylammonium and formamidinium, respectively), in less than 10min, on a single film, on which 520 unique alloys are then studied. Through stability mapping of all these alloys in air supersaturated with moisture, a range of targeted perovskites are found, which are then chosen to make efficient and stable solar cells in relaxed fabrication conditions, in ambient air. This all-in-one platform provides access to an unprecedented library of compositional space with no unmade alloys, and hence aids in a comprehensive accelerated discovery of efficient energy materials.

All‐Scalable CH3NH3PbI3 Perovskite Solar Cells Fabricated in Ambient Air

Perovskite solar cells (PSCs) are an attractive emerging photovoltaic technology due to their high‐performance while being made by low‐cost fabrication processes. The most efficient PSCs are small area and made by nonscalable coating method in an inert atmosphere, but these sizes and fabrication conditions are commercially irrelevant. Herein, fabrication of PSCs is reported using only scalable methods, that is, slot‐die coating and blade coating methods, all in ambient air. The tolerance to relaxed fabrication conditions is enabled by the use of hydrated nonhalogenated lead source. Resurfacing strategy is then introduced to suppress charge carrier nonradiative recombination and obtained an efficiency of 19.91% for rigid and 17.4% for flexible PSCs by all‐scalable fabrication. To the best of our knowledge, these are the highest efficiencies for n–i–p structured MAPbI3‐based PSCs in ambient air using all‐scalable method to date. The devices showed excellent tolerance to oxygen and moisture (ISOS‐D‐1) as well as stable maximum power point operation following burn in a dry air glove box (relative humidity ≈ 20%) without encapsulation.

Full Temporal Characterization of Ultrabroadband Few-Cycle Laser Pulses Using Atomically Thin WS2

Nowadays, the accurate and full temporal characterization of ultrabroadband few-cycle laser pulses with pulse durations below 7 fs is of great importance in fields of science that investigate ultrafast dynamic processes. There are several indirect methods that use nonlinear optical signals to retrieve the complex electric field of femtosecond lasers. However, the precise characterization of few-cycle femtosecond laser sources with an ultrabroadband spectrum presents additional difficulties, such as reabsorption of nonlinear signals, partial phase matching, and spatiotemporal mismatches. In this work, we combine the dispersion scan (d-scan) method with atomically thin WS2 flakes to overcome these difficulties and fully characterize ultrabroadband laser pulses with a pulse duration of 6.9 fs and a spectrum that ranges from 650 to 1050 nm. Two-dimensional WS2 acts as a remarkably efficient nonlinear medium that offers a broad transparency range and allows for achieving relaxed phase-matching conditions due to its atomic thickness. Using mono- and trilayers of WS2, we acquire d-scan traces by measuring the second-harmonic generation (SHG) signal, originated via laser–WS2 interaction, as a function of optical dispersion (i.e., glass thickness) and wavelength. Our retrieval algorithm extracts a pulse duration at full-width half-maximum of 6.9 fs and the same spectral phase function irrespective of the number of layers. We benchmark and validate our results obtained using WS2 by comparing them with those obtained using a 10-μm-thick BBO crystal. Our findings show that atomically thin media can be an interesting alternative to micrometer-thick bulk crystals to characterize ultrabroadband femtosecond laser pulses using SHG-d-scan with an error below 100 as (attoseconds).

Dissipativity-based conditions for the feedback stabilization of systems with time-varying input delays

This note is concerned with presenting new dissipativity-based conditions for the stabilizability of discrete-time systems with time-varying delays by linear static output feedback (SOF). We demonstrate that the feasibility of nonlinear matrix inequalities for the design of feedback stabilizing gains derived from the literature is equivalent to the feasibility of a linear matrix inequality establishing dissipativity of the system plus one matrix inequality constraint. An iterative strategy allowing the computation of stabilizing SOF gains is then developed based on a new relaxed sufficient condition. Compared with other popular approaches in the literature, such as the celebrated cone complementarity linearization (CCL) method, the strategy avoids providing initial “guesses” for the matrix variables, which is especially complicated when dealing with a large number of matrices. Due to being a particular case of SOF with an identity output matrix, static state feedback (SSF) gains can also trivially be computed by exploiting the developed conditions.

Sliding-Mode Control for Interval Type-2 Fuzzy Systems: Event-Triggering WTOD Scheme.

This article investigates the sliding-mode control issue for interval type-2 (IT2) T-S fuzzy systems under limited communication resources. An event-triggering weight try-once-discard (ET-WTOD) protocol is formulated via two thresholds to determine the transmission of the state signal. The proposed ET-WTOD protocol can dynamically adjust the transmitted nodes and permits only partial components with larger error to be sent at each triggering instant, which is just the key distinction from the existing protocols. Under the imperfect premise matching framework, the controller's membership functions are reconstructed via the received state and the known upper and lower bounds, and then, a new scheduling signal set is established to design the scheduling-signal-dependent fuzzy sliding-mode controller. With the aid of the membership-function-dependent approach, the mismatching premise variables of the fuzzy model and the controller are effectively handled by introducing some slack matrices, while the relaxed stability conditions are derived to ensure the stability of the closed-loop system and the reachability of the specified sliding surface. Moreover, an optimized sliding domain is further obtained via the genetic algorithm (GA). Finally, the proposed control strategy is verified via the mass-spring-damper system.

COMPUTATIONAL FLUID DYNAMICS ANALYSIS ON OVERWEIGHT SLEEP APNEA PATIENT UNDER VARIOUS BREATHING FLOW PATTERNS

Obstructive Sleep Apnea (OSA) is a breathing disorder that occurs during sleep. This syndrome affects numerous people, especially those with abnormal body fat composition parameters such as body mass index (BMI) of more than 25 kg/m2 (overweight &amp; obesity). OSA ensues when the tongue and soft palate muscles in a relaxed condition move towards gravity when the patient is in a supine position; this causes narrowing and blockage on the upper airway affecting breathing. There are several treatments for OSA, including upper airway surgery. A better understanding of airflow characteristics will assist ENT surgeons in identifying the blockage area. This paper examines airflow characteristics of the upper airway for overweight sleep apnea patients. The narrow and blockage area on the respiratory tract causes turbulence formation that is evaluated using Computational Fluid Dynamic (CFD) based on an actual parameter of the 3D model obtained by CT scan image result. Reynold’s averaged Navier-Stoke (RANS) equation and turbulent model, k-ω shear stress transport (SST), were applied. Airflow fluctuation was characterized by crucial parameters such as velocity, pressure, and turbulent kinetic energy (TKE). The result shows that the narrow cross-sectional area of the airway causes accretion of the velocity and pressure in the pharyngeal airway. The increasing airflow parameter results in high turbulent kinetic energy (TKE) that will determine the severity level of OSA patients. Investigating airflow characteristics in overweight OSA patients will help the medical practitioner validate the narrow and blockage area for the surgery.

Computationally efficient and data-adaptive changepoint inference in high dimension

Abstract High-dimensional changepoint inference that adapts to various change patterns has received much attention recently. We propose a simple, fast yet effective approach for adaptive changepoint testing. The key observation is that two statistics based on aggregating cumulative sum statistics over all dimensions and possible changepoints by taking their maximum and summation, respectively, are asymptotically independent under some mild conditions. Hence, we are able to form a new test by combining the p-values of the maximum- and summation-type statistics according to their asymptotic null distributions. To this end, we develop new tools and techniques to establish the asymptotic distribution of the maximum-type statistic under a more relaxed condition on componentwise correlations among all variables than those in existing literature. The proposed method is simple to use. It is adaptive to different levels of the sparsity of change signals, and is comparable to or even outperforms existing approaches as revealed by our numerical studies.