Event-Triggered Impulsive Quasisynchronization of Coupled Dynamical Networks With Proportional Delay

The quasisynchronization of nonidentically coupled dynamical networks (NCDNs) with proportional delay is achieved by the event-triggered mechanism (ETM). Heterogeneity and proportional delay greatly increase the difficulty on synchronization of NCDNs. As an unbounded delay in the coupling term, proportional delay is dealt with by the comparison principle, constructing parameter equations, and contradiction method. Moreover, different impulsive effects based on ETM are taken into account to reduce the burden of communication, and the quasisynchronization criteria for NCDNs are derived by the impulsive comparison principle and extended variable parameter formula. The synchronization errors and the exponential convergence rates under different impulsive effects are obtained. It is proven that the proposed ETM can avoid Zeno behavior. Finally, examples show the effectiveness of the proposed control scheme.

Periodic Event-Triggered Control for Networked Control Systems With External Disturbance and Input and Output Delays

This article investigates the problem of periodic event-triggered output-feedback control for networked control systems in the presence of external disturbance and input and output delays. With the aid of the prediction technique, we first develop the predictor-based-extended state observer to reconstruct the system information, including the unknown state and disturbance. The periodic event-triggered output-feedback control law is then designed via the disturbance/uncertainty estimation and attenuation (DUEA) method, such that the communication times can be remarkably reduced and, at the same time, the disturbance rejection ability can be effectively enhanced. Under the predictor-based event-triggered control method, the influence of the time delays is effectively attenuated, and the effect of external disturbance is considerably attenuated due to the prediction technique and the DUEA method. By using the small-gain arguments, this article gives some sufficient stability conditions for the overall control system, and the explicit computations of sampling/updating period and time delays are presented as well. Finally, we employ a practical example and show some comparative simulation results to demonstrate the advantages of the predictor-based event-triggered control method proposed in this article.

STING-Dependent Sensing of Self-DNA Driving Pyroptosis Contributes to Radiation-Induced Lung Injury

PURPOSE/OBJECTIVE(S)

Radiation therapy (RT) is indispensable for managing thoracic carcinomas. However, its application is limited by radiation-induced lung injury (RILI), one of the most common and fatal complications of thoracic RT. Nonetheless, the exact molecular mechanisms of RILI remain poorly understood.

MATERIALS/METHODS

To elucidate the underlying mechanisms, various knockout (KO) mouse strains were subjected to 16 Gy whole-thoracic RT. RILI was assessed by qRT-PCR, ELISA, histology, western blot, immunohistochemistry, and CT examination. To perform further mechanistic studies on the signaling cascade during the RILI process, pulldown, CHIP, and rescue assays were conducted.

RESULTS

We found that the cGAS-STING pathway was significantly upregulated after irradiation exposure in both the mouse models and clinical lung tissues. Knocking down either cGAS or STING led to attenuated inflammation and fibrosis in mouse lung tissues. NLRP3 is hardwired to the upstream DNA-sensing cGAS-STING pathway to trigger of the inflammasome and amplification of the inflammatory response. STING deficiency suppressed the expressions of the NLRP3 inflammasome and pyroptosis-pertinent components containing IL-1β, IL-18, and cleaved caspase-1. Mechanistically, interferon regulatory factor 3, the essential transcription factor downstream of cGAS-STING, promoted the pyroptosis by transcriptionally activating NLRP3. Moreover, we found that RT triggered the release of self-dsDNA in the bronchoalveolar space, which is essential for the activation of cGAS-STING and the downstream NLRP3-mediated pyroptosis. Of note, Pulmozyme, an old drug for the management of cystic fibrosis, was revealed to have the potential to mitigate RILI by degrading extracellular dsDNA and then inhibiting the cGAS-STING-NLRP3 signaling pathway.

CONCLUSION

These results delineated the crucial function of cGAS-STING as a key mediator of RILI, and described a mechanism of pyroptosis linking cGAS-STING activation with the amplification of initial RILI. These findings indicate that the dsDNA-cGAS-STING-NLRP3 axis might be potentially amenable to therapeutic targeting for RILI.

Finite/fixed-time synchronization of inertial memristive neural networks by interval matrix method for secure communication

This paper investigates the finite/fixed-time synchronization problem of delayed inertial memristive neural networks (DIMNNs) using interval matrix-based methods within a unified control framework. By employing set-valued mapping and differential inclusion theory, two distinct methods are applied to handle the switching behavior of memristor parameters: the maximum absolute value method and the interval matrix method. Based on these different approaches, two control strategies are proposed to select appropriate control parameters, enabling the system to achieve finite and fixed-time synchronization, respectively. Additionally, the resulting theoretical criteria differ based on the chosen control strategy, with one expressed in algebraic form and the other in the form of linear matrix inequalities (LMIs). Numerical simulations demonstrate that the interval matrix method outperforms the maximum absolute value method in terms of handling memristor parameter switching, achieving faster finite/fixed-time synchronization. Furthermore, the theoretical results are extended to the field of image encryption, where the response system is utilized for decryption and expanding the keyspace.

Serum albumin and prognosis in elderly patients with nonischemic dilated cardiomyopathy

AIMS

Hypoalbuminemia was extensively used to diagnose malnutrition in older adults. Malnutrition was associated with mortality in elderly patients with cardiovascular diseases. The relationship between hypoalbuminemia and clinical outcomes in elderly patients with nonischemic dilated cardiomyopathy (NIDCM) remains unknown.

METHODS

A total of 1058 consecutive patients with NIDCM (age ≥60 years) were retrospectively enrolled from January 2010 to December 2019. Univariate and multivariate analyses were performed to assess the association of hypoalbuminemia with clinical outcomes.

RESULTS

Patients with hypoalbuminemia were older (69.29 ± 6.67 vs. 67.61 ± 5.90 years, P  < 0.001) and had higher prevalence of in-hospital and long-term death than those without (6.9 vs. 1.7%, 50.7 vs. 35.2%, P  < 0.001). Logistic regression analysis showed that hypoalbuminemia was significantly related to in-hospital death [odds ratio (OR): 4.334, 95% confidence interval (CI): 2.185-8.597, P  < 0.001]. Kaplan-Meier survival analysis showed that patients with hypoalbuminemia had worse prognosis than those with nonhypoalbuminemia (log-rank χ2 28.96, P  < 0.001). After adjusting for age, serum creatinine, HDL-C, AST/ALT hypoalbuminemia, LVEF and diabetes, hypoalbuminemia remained an independent predictor for long-term death (hazard ratio 1.322, 95% CI 0.046-1.670, P  = 0.019).

CONCLUSION

Hypoalbuminemia was associated with increased risk of in-hospital and long-term mortality in elderly patients with NIDCM.

Optimal Cisplatin Cycles in Locally Advanced Cervical Carcinoma Patients Treated with Concurrent Chemoradiotherapy

PURPOSE/OBJECTIVE(S)

To analyze the effect of cisplatin cycles on the clinical outcomes of patients with locally advanced cervical cancer (LACC) treated with concurrent chemoradiotherapy (CCRT).

MATERIALS/METHODS

This study included 749 patients with LACC treated with CCRT between January 2011 and December 2015. A receiver operating characteristic (ROC) curve was used to analyze the optimal cut-off of cisplatin cycles in predicting clinical outcomes. Clinicopathological features of the patients were compared using the Chi-square test. Prognosis was assessed using log-rank tests and Cox proportional hazard models. Toxicities were compared among different cisplatin cycle groups.

RESULTS

Based on the ROC curve, the optimal cut-off of the cisplatin cycles was 4.5 (sensitivity, 64.3%; specificity, 54.3%). The 3-year overall, disease-free, loco-regional relapse-free, and distant metastasis-free survival for patients with low-cycles (cisplatin cycles < 5) and high-cycles (≥ 5) were 81.5% and 89.0% (P < 0.001), 73.4% and 80.1% (P = 0.024), 83.0% and 90.8% (P = 0.005), and 84.9% and 86.8% (P = 0.271), respectively. In multivariate analysis, cisplatin cycles were an independent prognostic factor for overall survival. In the subgroup analysis of high-cycle patients, patients who received over five cisplatin cycles had similar overall, disease-free, loco-regional relapse-free, and distant metastasis-free survival to patients treated with five cycles. Acute and late toxicities were not different between the two groups.

CONCLUSION

Cisplatin cycles were associated with overall, disease-free, and loco-regional relapse-free survival in LACC patients who received CCRT. Five cycles appeared to be the optimal number of cisplatin cycles during CCRT.

Adaptive Synchronization of Reaction-Diffusion Neural Networks With Nondifferentiable Delay via State Coupling and Spatial Coupling

In this article, master-slave synchronization of reaction-diffusion neural networks (RDNNs) with nondifferentiable delay is investigated via the adaptive control method. First, centralized and decentralized adaptive controllers with state coupling are designed, respectively, and a new analytical method by discussing the size of adaptive gain is proposed to prove the convergence of the adaptively controlled error system with general delay. Then, spatial coupling with adaptive gains depending on the diffusion information of the state is first proposed to achieve the master-slave synchronization of delayed RDNNs, while this coupling structure was regarded as a negative effect in most of the existing works. Finally, numerical examples are given to show the effectiveness of the proposed adaptive controllers. In comparison with the existing adaptive controllers, the proposed adaptive controllers in this article are still effective even if the network parameters are unknown and the delay is nonsmooth, and thus have a wider range of applications.

[Research advances of sepsis biomarkers]

Sepsis is a life-threatening condition for patients. Biomarkers can be used for the diagnosis, treatment, and prognostic assessment of sepsis. In recent years, new biomarkers for sepsis have been discovered, and more than 250 biomarkers have been identified so far. The complexity of the sepsis process and the increased sensitivity of various detection techniques will lead to the emergence of new biomarkers. However, there is still a lack of specific diagnostic biomarkers and effective therapeutic approaches for sepsis in clinical practice. Therefore, the search for reliable biomarkers and the evaluation of the role of biomarkers in sepsis will undoubtedly aid in clinical decision-making. This article reviews the advances on research of sepsis biomarkers in order to improve understanding of current biomarkers of sepsis, and provide reference for the application of biomarkers in clinical diagnosis, treatment, and prognosis of sepsis.

Early strategy vs. late initiation of renal replacement therapy in adult patients with acute kidney injury: an updated systematic review and meta-analysis of randomized controlled trials

OBJECTIVE

The optimal time to start renal replacement therapy (RRT) for acute kidney injury (AKI) remains controversial. We aim to compare the effects of early vs. delayed RRT initiation on clinical outcomes in adult patients with AKI.

MATERIALS AND METHODS

PubMed, Embase, Cochrane Library, Web of Science, Chinese Biomedical Literature Database, ClinicalTrials.gov, and the International Clinical Trial registry platform were systematically searched from inception to 7 August 2022. The review included randomized clinical trials (RCTs) comparing early and delayed initiation of RRT in AKI patients. The selected primary outcomes were short-term and long-term mortality. Secondary outcomes included RRT dependency, intensive care unit (ICU) length of stay, hospital length of stay, mechanical ventilator-free days, vasoactive agents-free days, RRT-free days, and adverse events.

RESULTS

Overall, 15 RCTs, including 5,625 patients, were analyzed. Early RRT showed no survival benefit when compared to the delayed therapy (28-or 30-day mortality: RR, 1.01, 95% CI: 0.94-1.08, p = 0.87; 60-day mortality: RR, 0.87, 95% CI: 0.71-1.06, p = 0.16; 90-day mortality: RR, 1.00, 95% CI: 0.88-1.13, p = 0.97; in-hospital mortality: RR, 1.05, 95% CI: 0.88-1.24, p = 0.58; ICU mortality: RR, 1.00, 95% CI: 0.91-1.10, p = 0.98). The delayed RRT did not lead to a higher risk of RRT dependency, ICU, or hospital length of stay than the early RRT. Similarly, early initiation of RRT did not lead to longer ventilator-free, vasoactive agent-free, and RRT-free days. However, early RRT initiation was associated with more adverse events.

CONCLUSIONS

Our study suggested that early RRT initiation was not associated with survival benefits or better clinical outcomes and increased the risk of RRT-associated adverse events. Current evidence does not support the use of early RRT for AKI patients without urgent indications.

Muricauda okinawensis sp. Nov. and Muricauda yonaguniensis sp. Nov., Two Marine Bacteria Isolated from the Sediment Core near Hydrothermal Fields of Southern Okinawa Trough

Two strains, 81s02^T and 334s03^T, were isolated from the sediment core near the hydrothermal field of southern Okinawa Trough. The cells of both strains were observed to be rod-shaped, non-gliding, Gram-staining negative, yellow-pigmented, facultatively anaerobic, catalase and oxidase positive, and showing optimum growth at 30 °C and pH 7.5. The strains 81s02^T and 334s03^T were able to tolerate up to 10% and 9% (w/v) NaCl concentration, respectively. Based on phylogenomic analysis, the average nucleotide identity (ANI) and the digital DNA-DNA hybridization (dDDH) values between the two strains and the nearest phylogenetic neighbors of the genus Muricauda were in range of 78.0-86.3% and 21.5-33.9%, respectively. The strains 81s02^T and 334s03^T shared 98.1% 16S rRNA gene sequence similarity to each other but were identified as two distinct species based on 81.4-81.5% ANIb, 85.5-85.6% ANIm and 25.4% dDDH values calculated using whole genome sequences. The strains 81s02^T and 334s03^T shared the highest 16S rRNA gene sequence similarity to M. lutimaris SMK-108^T (98.7%) and M. aurea BC31-1-A7^T (98.8%), respectively. The major fatty acid of strains 81s02^T and 334s03^T were identified similarly as iso-C_15:0, iso-C_17:0 3-OH and iso-C_15:1 G, and the major polar lipids of the both strains consisted of phosphatidylethanolamine and two unidentified lipids. The strains contained MK-6 as their predominant menaquinone. The genomic G+C contents of strains 81s02^T and 334s03^T were determined to be 41.6 and 41.9 mol%, respectively. Based on the phylogenetic and phenotypic characteristics, both strains are considered to represent two novel species of the genus Muricauda, and the names Muricauda okinawensis sp. nov. and Muricauda yonaguniensis sp. nov. are proposed for strains 81s02^T (=KCTC 92889^T = MCCC 1K08502^T) and 334s03^T (=KCTC 92890^T = MCCC 1K08503^T).

Finite-Time Synchronization of Neural Networks With Infinite Discrete Time-Varying Delays and Discontinuous Activations

This article investigates finite-time synchronization of neural networks (NNs) with infinite discrete time-varying delays and discontinuous activations (DDNNs). By virtue of theory of differential inclusions, comparison strategies, and inequality techniques, finite-time synchronization of the underlying DDNNs can be developed via a discontinuous state feedback control law, and the synchronous settling time can be estimated. The delayed state feedback controller and finite-time stability theorem are not employed during the analysis. As a special case, finite-time synchronization of NNs with bounded delays and discontinuous activations is given. Finally, two examples are provided to illustrate the validity of the theories.

Adaptive Dynamic Event-Triggered Fault-Tolerant Consensus for Nonlinear Multiagent Systems With Directed/Undirected Networks

The fault-tolerant consensus of nonlinear multiagent systems (MASs) is studied by using the dynamic event-triggered control and the adaptive control techniques. First, a general dynamic adaptive event-triggered mechanism (DAETM) is proposed, which promotes and improves many existing dynamic event-triggered mechanism and static event-triggered mechanism. On this basis, a new distributed dynamic adaptive event-triggered fault-tolerant controller (DDAETFTC) is designed. Then, two simple and clear criteria are derived, respectively, to ensure consensus can be reached asymptotically for nonlinear MASs with directed networks and with undirected networks under the new DDAETFTC. The obtained results also can apply to linear MASs. Furthermore, it is proven that there is no agent to show the Zeno behavior in MASs under the new DAETM. Finally, an example is given to simulate the obtained results.

Semiglobal Robust Consensus of General Linear MASs Subject to Input Saturation and Additive Perturbations

This article considers the robust consensus problem of the general linear multiagent system (MAS) subject to both heterogeneous additive stable disturbances and input saturation. Distributed low gain feedback-based dynamic output feedback control protocols are proposed, which do not need the controller interaction. Algebraic Riccati equation and unified H_∞ controller design method are employed to design the output feedback control protocols. It is established that under the assumption that each agent is asymptotically null controllable with bounded controls, semiglobal robust consensus can always be reached under the proposed controller. Furthermore, the design method specialized for leader-following consensus is addressed, under the assumption that the Laplacian matrix is diagonalizable, one can design the control protocol only with the number of follower agents. Finally, several simulations are provided to show the effectiveness of our results.

Synchronization of Delayed Complex Networks on Time Scales via Aperiodically Intermittent Control Using Matrix-Based Convex Combination Method

This article reconsiders synchronization problem of linear complex networks with time-varying delay on time scales. For different types of time scales, aperiodically intermittent control scheme is established by using a matrix-based convex combination method, which has great potential in reducing control consumption and saving communication bandwidth. By employing a common Lyapunov function, aperiodically intermittent controllers are utilized successfully to achieve synchronization of linear delayed complex networks on special time scales onto an isolated node. Next, by constructing a special Lyapunov function with time-varying coefficients, sufficient criteria that consist of two linear matrix inequalities are demonstrated to make linear delayed complex networks on general time scales synchronized onto an isolated system with an exponential convergence rate given in advance. Due to delayed complex networks in this article defined on time scales, the proposed control schemes are applicable to continuous-time networks, their discrete-time forms, and any combination of them. Four numerical examples are offered to highlight the effectiveness and superiority of the proposed aperiodically intermittent control schemes at last.

EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain and wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks. This paper proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which makes adversarial attacks much easier to implement. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs and calling for urgent attention to address it.

Master-Slave Synchronization of Neural Networks With Unbounded Delays via Adaptive Method

Master-slave synchronization of two delayed neural networks with adaptive controller has been studied in recent years; however, the existing delays in network models are bounded or unbounded with some derivative constraints. For more general delay without these restrictions, how to design proper adaptive controller and prove rigorously the convergence of error system is still a challenging problem. This article gives a positive answer for this problem. By means of the stability result of unbounded delayed system and some analytical techniques, we prove that the traditional centralized adaptive algorithms can achieve global asymptotical synchronization even if the network delays are unbounded without any derivative constraints. To describe the convergence speed of the synchronization error, adaptive designs depending on a flexible ω -type function are also provided to control the synchronization error, which can lead exponential synchronization, polynomial synchronization, and logarithmically synchronization. Numerical examples on delayed neural networks and chaotic Ikeda-like oscillator are presented to verify the adaptive designs, and we find that in the case of unbounded delay, the intervention of ω -type function can promote the realization of synchronization but may destroy the convergence of control gain, and this however will not happen in the case of bounded delay.

Neural Network-Based Fixed-Time Tracking and Containment Control of Second-Order Heterogeneous Nonlinear Multiagent Systems

This study concentrates on the fixed-time tracking consensus and containment control of second-order heterogeneous nonlinear multiagent systems (MASs) with and without measurable velocity under directed topology. By defining a time-varying scaling function and approximating the unknown nonlinear dynamics with radial basis function neural networks (RBFNNs), a novel distributed protocol for solving the fixed-time tracking consensus and containment control problems of second-order heterogeneous nonlinear MASs with full states available is proposed based on a nonsingular sliding-mode control method constructed by designing a prescribed-time convergent sliding surface. For the scenario of immeasurable velocity, a fixed-time convergent states' observer is designed to reveal the velocity information when the unknown linearity is bounded. Subsequently, a distributed fixed-time consensus protocol based on observed velocity information is proposed for the extended results. Ultimately, the acquired results are verified by three simulation examples.

On Pinning Linear and Adaptive Synchronization of Multiple Fractional-Order Neural Networks With Unbounded Time-Varying Delays

In this article, the synchronization of multiple fractional-order neural networks with unbounded time-varying delays (FNNUDs) is investigated. By introducing a pinning linear control, sufficient conditions are provided for achieving the synchronization of multiple FNNUDs via an extended Halanay inequality. Moreover, a new effective adaptive control which applies to the fractional differential equations with unbounded time-varying delays is designed, under which sufficient criteria are presented to ensure the synchronization of multiple FNNUDs. The introduced control in this article is also workable in traditional integer-order neural networks. Finally, the validity of obtained results is demonstrated by a numerical example.

CRNet: A Fast Continual Learning Framework With Random Theory

Artificial neural networks are prone to suffer from catastrophic forgetting. Networks trained on something new tend to rapidly forget what was learned previously, a common phenomenon within connectionist models. In this work, we propose an effective and efficient continual learning framework using random theory, together with Bayes' rule, to equip a single model with the ability to learn streaming data. The core idea of our framework is to preserve the performance of old tasks by guiding output weights to stay in a region of low error while encountering new tasks. In contrast to the existing continual learning approaches, our main contributions concern (1) closed-formed solutions with detailed theoretical analysis; (2) training continual learners by one-pass observation of samples; (3) remarkable advantages in terms of easy implementation, efficient parameters, fast convergence, and strong task-order robustness. Comprehensive experiments under popular image classification benchmarks, FashionMNIST, CIFAR-100, and ImageNet, demonstrate that our methods predominately outperform the extensive state-of-the-art methods on training speed while maintaining superior accuracy and the number of parameters, in the class incremental learning scenario. Code is available at https://github.com/toil2sweet/CRNet.

Global synchronization of complex-valued neural networks with unbounded time-varying delays

This paper investigates global synchronization of complex-valued neural networks (CVNNs) with unbounded time-varying delays. By applying analytical method and inequality techniques, an algebraic criterion is established to ensure global synchronization of the CVNNs via a devised feedback controller, which generalizes some existing outcomes. Finally, two numerical simulations and one application in image encryption are provided to verify the effectiveness of the theoretical results.

Modeling the charge collection efficiency in the Li-diffused inactive layer of P-type high purity germanium detector

A model of the Li-diffused inactive layer in P-type high purity germanium detectors is built to describe the transportation of charge carriers and calculate the charge collection efficiency therein. The model is applied to calculate charge collection efficiency of a P-type point-contact germanium detector used in rare event physics experiments and validated in another P-type semi-planar germanium detector. The calculated charge collection efficiency curves are well consistent with measurements for both detectors. Effects of the Li doping processes on the charge collection efficiency are discussed based on the model. This model can be easily extended to other P-type germanium detectors, for instance, the P-type broad-energy Ge detector, and the P-type inverted-coaxial point-contact detector.

An Overview of the Stability Analysis of Recurrent Neural Networks With Multiple Equilibria

The stability analysis of recurrent neural networks (RNNs) with multiple equilibria has received extensive interest since it is a prerequisite for successful applications of RNNs. With the increasing theoretical results on this topic, it is desirable to review the results for a systematical understanding of the state of the art. This article provides an overview of the stability results of RNNs with multiple equilibria including complete stability and multistability. First, preliminaries on the complete stability and multistability analysis of RNNs are introduced. Second, the complete stability results of RNNs are summarized. Third, the multistability results of various RNNs are reviewed in detail. Finally, future directions in these interesting topics are suggested.

Multistability of Dynamic Memristor Delayed Cellular Neural Networks With Application to Associative Memories

Recently, dynamic memristor (DM)-cellular neural networks (CNNs) have received widespread attention due to their advantage of low power consumption. The previous works showed that DM-CNNs have at most 318 equilibrium points (EPs) with n=16 cells. Since time delay is unavoidable during the process of information transmission, the goal of this article is to research the multistability of DM-CNNs with time delay, and, meanwhile, to increase the storage capacity of DM-delay (D)CNNs. Depending on the different constitutive relations of memristors, two cases of the multistability for DM-DCNNs are discussed. After determining the constitutive relations, the number of EPs of DM-DCNNs is increased to 3ⁿ with n cells by means of the appropriate state-space decomposition and the Brouwer's fixed point theorem. Furthermore, the enlarged attraction domains of EPs can be obtained, and 2ⁿ of these EPs are locally exponentially stable in two cases. Compared with standard CNNs, the dynamic behavior of DM-DCNNs shows an outstanding merit. That is, the value of voltage and current approach to zero when the system becomes stable, and the memristor provides a nonvolatile memory to store the computation results. Finally, two numerical simulations are presented to illustrate the effectiveness of the theoretical results, and the applications of associative memories are shown at the end of this article.

Effect of surface gallium termination on the formation and emission energy of an InGaAs wetting layer during the growth of InGaAs quantum dots by droplet epitaxy

Self-assembled quantum dots (QDs) based on III-V semiconductors have excellent properties for applications in quantum optics. However, the presence of a 2D wetting layer (WL) which forms during the Stranski-Krastanov growth of QDs can limit their performance. Here, we investigate WL formation during QD growth by the droplet epitaxy technique. We use a combination of photoluminescence excitation spectroscopy, lifetime measurements, and transmission electron microscopy to identify the presence of an InGaAs WL in these droplet epitaxy QDs, even in the absence of distinguishable WL luminescence. We observe that increasing the amount of Ga deposited on a GaAs (100) surface prior to the growth of InGaAs QDs leads to a significant reduction in the emission wavelength of the WL to the point where it can no longer be distinguished from the GaAs acceptor peak emission in photoluminescence measurements. However increasing the amount of Ga deposited does not suppress the formation of a WL under the growth conditions used here.

Chemoenzymatic Synthesis of Sterically Hindered Biaryls by Suzuki Coupling and Vanadium Chloroperoxidase Catalyzed Halogenations

Halogenated biaryls are vital structural skeletons in bioactive products. In this study, an effective chemoenzymatic halogenation by vanadium-dependent chloroperoxidase from Camponotus inaequalis (CiVCPO) enabled the transformation of freely rotating biaryl bonds to sterically hindered axis. The yields were up to 84 % for the tribrominated biaryl products and up to 65 % when isolated. Furthermore, a one-pot, two-step chemoenzymatic strategy by incorporating transition metal catalyzed Suzuki coupling and the chemoenzymatic halogenation in aqueous phase were described. This strategy demonstrates a simplified one-pot reaction sequence with organometallic and biocatalytic procedures under economical and environmentally beneficial conditions that may inspire further research on synthesis of sterically hindered biaryls.