Search

Objective Complete and transparent reporting is of critical importance for randomized controlled trials (RCTs). The present study aimed to determine the reporting quality and methodological quality of RCTs for interventions involving artificial intelligence (AI) and their protocols. Methods We searched MEDLINE (via PubMed), Embase, Web of Science, CBMdisc, Wanfang Data, and CNKI from January 1, 2016, to November 11, 2020, to collect RCTs involving AI. We also extracted the protocol of each included RCT if it could be obtained. CONSORT-AI (Consolidated Standards of Reporting Trials–Artificial Intelligence) statement and Cochrane Collaboration's tool for assessing risk of bias (ROB) were used to evaluate the reporting quality and methodological quality, respectively, and SPIRIT-AI (The Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence) statement was used to evaluate the reporting quality of the protocols. The associations of the reporting rate of CONSORT-AI with the publication year, journal's impact factor (IF), number of authors, sample size, and first author's country were analyzed univariately using Pearson's chi-squared test, or Fisher's exact test if the expected values in any of the cells were below 5. The compliance of the retrieved protocols to SPIRIT-AI was presented descriptively. Results Overall, 29 RCTs and three protocols were considered eligible. The CONSORT-AI items “title and abstract” and “interpretation of results” were reported by all RCTs, with the items with the lowest reporting rates being “funding” (0), “implementation” (3.5%), and “harms” (3.5%). The risk of bias was high in 13 (44.8%) RCTs and not clear in 15 (51.7%) RCTs. Only one RCT (3.5%) had a low risk of bias. The compliance was not significantly different in terms of the publication year, journal's IF, number of authors, sample size, or first author's country. Ten of the 35 SPIRIT-AI items (funding, participant timeline, allocation concealment mechanism, implementation, data management, auditing, declaration of interests, access to data, informed consent materials and biological specimens) were not reported by any of the three protocols. Conclusions The reporting and methodological quality of RCTs involving AI need to be improved. Because of the limited availability of protocols, their quality could not be fully judged. Following the CONSORT-AI and SPIRIT-AI statements and with appropriate guidance on the risk of bias when designing and reporting AI-related RCTs can promote standardization and transparency.

Macroscopic fundamental diagram (MFD) has been receiving increasing attention recently due to its potential to describe traffic dynamics and guide the design of traffic control schemes at the network level. Perimeter control and route guidance are two main MFD-based traffic control approaches. However, current MFD-based perimeter control seldom considers travelers’ route choice behavior, while MFD-based route guidance studies usually assume directly that travelers would follow the guidance and neglect the effects of traffic control. This paper aims to integrate the MFD-based perimeter control (i.e., the behavior of a system manager) and the dynamic user equilibrium based route choice behavior (i.e., the behavior of travelers) into one rigorous mathematical framework. Given a traffic network that has been divided into multiple homogeneous regions, we use MFD to describe the dynamics of each region, and use point queue model to capture the dynamics of queues formed at the boundaries. Besides, we model travelers' route choice behavior by the instantaneous dynamic user equilibrium (IDUE) principle, and design an efficient range perimeter control method from the system perspective. We model the interactions between the system manager and the travelers as a non-zero sum, non-cooperative differential game, where the system manager aims to improve the system performance while travelers try to minimize their own travel times. Meanwhile, they share the common constraints (i.e., MFD dynamics and point queue dynamics at boundaries). Mathematically, this leads to a differential complementarity system (DCS). We propose a time-stepping approach to discretize and solve the DCS model, based on which the solution existence and convergence are also established. Numerical results show that the proposed method can limit the vehicle accumulations within the efficient range of each region, which helps improve the network performance. Compared with the condition without perimeter control, the proposed control method can improve network-wide traffic performance up to 14.18%.

Recently, deep learning models have shown promising performances in many research areas, including traffic states prediction, due to their ability to model complex nonlinear relationships. However, deep learning models also have drawbacks that make them less preferable for certain short-term traffic prediction applications. For example, they require a large amount of data for model training, which is also computationally expensive. Moreover, deep learning models lack interpretability of the results. This paper develops a short-term traffic states forecasting algorithm based on partial least square (PLS) to help enhance real-time decision-making and build better insights into traffic data. The proposed model is capable of predicting short-term traffic states accurately and efficiently by capturing dominant spatiotemporal features and day-to-day variations from collinear and correlated traffic data. Three case studies are developed to demonstrate the proposed model in short-term traffic prediction applications.

Background: The number of cluster Randomized Controlled Trials (cRCTs) have been increasing and application in pediatrics is also growing. However, we did not know the quality of cRCTs in pediatrics and no previous study has comprehensively analyzed cRCTs in pediatrics. To investigate the status of cRCTs in pediatrics and analyzed influencing factors of the methodological quality.Methods: Through a search of 10 highest-impact-factor journals from PEDIATRICS and MEDICINE, GENERAL & INTERNAL, we identified cRCTs published from 1 January 2015 to 7 June 2020. The associations between study factors and grade of ROB (Risk of Bias) quality were analyzed by Pearson’s chi-squared test.Results: A total of 73 cRCTs were included. For the number of cRCTs, it has fluctuated in the past five years. For the International Classification of Diseases Eleventh Revision (ICD-11), 34.2% cRCTs could not be classified, of which more focused on vaccines; 15.1% cRCTs were classified into "X Extension Codes", of which more focused on vaccines and school bullying. For research settings, studies were concentrated in schools, communities, hospitals, clinics, and kindergartens, of which schools (36.5%) were the most. The number of clusters ranged from 1 to 1533 (M=28) and the participants ranged from 135 to 190,238 (M=1903). For the results of ROB assessment, 31 cRCTs were in high-quality, 28 were in low-quality, and 14 were unclear. The majority of cRCTs provided flow diagrams (87.8%), registered (98.6%), and made a distinction between primary and secondary outcomes (86.5%).Conclusions: The further improvement of the quality of cRCTs in pediatrics is needed, particularly in the terms of randomization, allocation concealment, and blinding. Writing and publishing the protocol can enhance transparency. Comply with of CONSORT statement for future studies is needed.

Head and neck cancer (HNC) comprises a heterogeneous group of cancers. In view of the distinct biological characteristics and treatment strategies, clinical physicians require high-quality clinical practice guidelines (CPGs) which could provide reliable recommendations on medical practices. We aimed to evaluate the reporting quality of CPGs in the field of HNC.We developed rigorous search strategies before searching the domestic and international literature databases (n=568) including Medline (via PubMed), Chinese National Knowledge Infrastructure (CNKI) and Wanfang as well as websites of guideline organizations (n=8) published between January 1, 2018 to July 1, 2021 for appropriate guidelines on HNC. We included all evidence-based guidelines about HNC in English or Chinese. We excluded translations, summaries and interpretations of guidelines, as well as older versions of guidelines if an updated edition was available. Data were extracted and the reporting quality was evaluated by two investigators independently guided by the Reporting Items for Practice Guidelines in Healthcare (RIGHT) checklist.A total of 21 guidelines complied with the inclusion criteria. Items show distinctions with reporting proportions among seven RIGHT domains. The proportions of reported items in each RIGHT domain were 75.4% for basic information, 63.1% for background, 42.9% for evidence, 55.1% for recommendations, 42.9% for review and quality assurance, 26.2% for funding and declaration and management of interests, and 50.8% for other information.The average reporting quality of the recently published guidelines for HNC was moderate. Our research would help optimize the development processes of guidelines, resulting in high-quality guidelines for healthcare professionals.

Background: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the type of coronavirus that causes the Coronavirus Disease 2019 (COVID-19), is mainly spread by respiratory droplets and aerosols. This study aims to investigate the risk of COVID-19 transmission on aircraft. This in turn provides the scientific basis for the return of air travel from pandemic to normal conditions.Methods: We obtained data on all international flights to Lanzhou, China, from June 1 to August 1, 2020, through the Gansu Province National Health Information Platform and the official website of the Gansu Provincial Center for Disease Control and Prevention. We calculated the period prevalence rate of COVID-19 among the passengers of all flights during the 14-day period following the flight, and stratified the prevalence by the seat positions (aisle, middle, window), and the relationship to other confirmed cases (1-2 rows ahead, same row, 1-2 rows behind). Results: Three international flights arrived in Lanzhou, China, during the study period, from Riyadh (MU7792), Jeddah (MU7790), and Moscow (CA608) each. The flights had a total of 700 passengers, of whom 405 (57.9%) were male and 80 (11.4%) were children below age fourteen. Twenty-seven (3.9%) passengers were confirmed to have COVID-19. There were no fatalities and all patients were cured. We identified three family clusters of cases. Confirmed patients were primarily male (n=17, 65.4%) with a median age of 27.0 years. The majority of confirmed cases were seated in the middle rows of the economy class, or near public facility areas such as restrooms and galleys. The prevalence of COVID-19 did not differ between passengers sitting on window, aisle or middle seats. Compared with passengers sitting on the same row up to two rows behind a confirmed case, passengers seated in the two rows ahead a confirmed case were at a slightly higher risk of being infected. Conclusions: COVID-19 may possibly be transmitted during a passenger flight, although there is still no direct evidence.

For mode selection in a quantum cascade laser (QCL), we demonstrate an anti-symmetric sampled grating (ASG). The wavelength of the –1-th mode of this laser has been blue-shifted more than 75 nm (∼ 10 cm−1) compared with that of an ordinary sampled grating laser with an emission wavelength of approximately 8.6 μm, when the periodicities within both the base grating and the sample grating are kept constant. Under this condition, an improvement in the continuous tuning capability of the QCL array is ensured. The ASG structure is fabricated in holographic exposure and optical photolithography, thereby enhancing its flexibility, repeatability, and cost-effectiveness. The wavelength modulation capability of the two channels of the grating is insensitive to the variations in channel size, assuming that the overall waveguide width remains constant. The output wavelength can be tailored freely within a certain range by adjusting the width of the ridge and the material of the cladding layer.

Severe Coronavirus Disease 2019 (COVID-19) is characterized by numerous complications, complex disease, and high mortality, making its treatment a top priority in the treatment of COVID-19. Integrated traditional Chinese medicine (TCM) and western medicine played an important role in the prevention, treatment, and rehabilitation of COVID-19 during the epidemic. However, currently there are no evidence-based guidelines for the integrated treatment of severe COVID-19 with TCM and western medicine. Therefore, it is important to develop an evidence-based guideline on the treatment of severe COVID-19 with integrated TCM and western medicine, in order to provide clinical guidance and decision basis for healthcare professionals, public health personnel, and scientific researchers involved in the diagnosis, treatment, and care of COVID-19 patients. We developed and completed the guideline by referring to the standardization process of the “WHO handbook for guideline development”, the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system, and the Reporting Items for Practice Guidelines in Healthcare (RIGHT)., Graphical Abstract ga1

Inefficient traffic control is pervasive in modern urban areas, which would exaggerate traffic congestion as well as deteriorate mobility, fuel economy and safety. In this paper, we systematically review the potential solutions that take advantage of connected and automated vehicles (CAVs) to improve the control performances of urban signalized intersections. We review the methods and models to estimate traffic flow states and optimize traffic signal timing plans based on CAVs. We summarize six types of CAV-based traffic control methods and propose a conceptual mathematical framework that can be specified to each of six three types of methods by selecting different state variables, control inputs, and environment inputs. The benefits and drawbacks of various CAV-based control methods are explained, and future research directions are discussed. We hope that this review could provide readers with a helpful roadmap for future research on CAV-based urban traffic control and draw their attention to the most challenging problems in this important and promising field.

Micro-resonator-based lasers are well suited for high-density optoelectronic integration because of their small volumes and low thresholds. However, microcavity quantum cascade lasers for on-chip sensing have high thermal loads that make continuous-wave operation challenging. In this work, we designed an selective thermal dissipation scheme for the selective electrical isolation process to improve the thermal conductivity of the devices. The lasers operated at 50 °C, with 4.7-µm emission. They were fabricated as a notched elliptical resonator, resulting in a highly unidirectional far-field profile with an in-plane beam divergence of 1.9°. Overall, these directional-emission quantum cascade lasers pave the way for portable and highly integrated sensing applications.

Background . Hundreds of coronavirus disease 2019 (COVID-19) clinical practice guidelines (CPGs) and expert consensus statements have been developed and published since the outbreak of the epidemic. However, these CPGs are of widely variable quality. So, this review is aimed at systematically evaluating the methodological and reporting qualities of COVID-19 CPGs, exploring factors that may influence their quality, and analyzing the change of recommendations in CPGs with evidence published. Methods . We searched five electronic databases and five websites from 1 January to 31 December 2020 to retrieve all COVID-19 CPGs. The assessment of the methodological and reporting qualities of CPGs was performed using the AGREE II instrument and RIGHT checklist. Recommendations and evidence used to make recommendations in the CPGs regarding some treatments for COVID-19 (remdesivir, glucocorticoids, hydroxychloroquine/chloroquine, interferon, and lopinavir-ritonavir) were also systematically assessed. And the statistical inference was performed to identify factors associated with the quality of CPGs. Results . We included a total of 92 COVID-19 CPGs developed by 19 countries. Overall, the RIGHT checklist reporting rate of COVID-19 CPGs was 33.0%, and the AGREE II domain score was 30.4%. The overall methodological and reporting qualities of COVID-19 CPGs gradually improved during the year 2020. Factors associated with high methodological and reporting qualities included the evidence-based development process, management of conflicts of interest, and use of established rating systems to assess the quality of evidence and strength of recommendations. The recommendations of only seven (7.6%) CPGs were informed by a systematic review of evidence, and these seven CPGs have relatively high methodological and reporting qualities, in which six of them fully meet the Institute of Medicine (IOM) criteria of guidelines. Besides, a rapid advice CPG developed by the World Health Organization (WHO) of the seven CPGs got the highest overall scores in methodological (72.8%) and reporting qualities (83.8%). Many CPGs covered the same clinical questions (it refers to the clinical questions on the effectiveness of treatments of remdesivir, glucocorticoids, hydroxychloroquine/chloroquine, interferon, and lopinavir-ritonavir in COVID-19 patients) and were published by different countries or organizations. Although randomized controlled trials and systematic reviews on the effectiveness of treatments of remdesivir, glucocorticoids, hydroxychloroquine/chloroquine, interferon, and lopinavir-ritonavir for patients with COVID-19 have been published, the recommendations on those treatments still varied greatly across COVID-19 CPGs published in different countries or regions, which may suggest that the CPGs do not make sufficient use of the latest evidence. Conclusions . Both the methodological and reporting qualities of COVID-19 CPGs increased over time, but there is still room for further improvement. The lack of effective use of available evidence and management of conflicts of interest were the main reasons for the low quality of the CPGs. The use of formal rating systems for the quality of evidence and strength of recommendations may help to improve the quality of CPGs in the context of the COVID-19 pandemic. During the pandemic, we suggest developing a living guideline of which recommendations are supported by a systematic review for it can facilitate the timely translation of the latest research findings to clinical practice. We also suggest that CPG developers should register the guidelines in a registration platform at the beginning for it can reduce duplication development of guidelines on the same clinical question, increase the transparency of the development process, and promote cooperation among guideline developers all over the world. Since the International Practice Guideline Registry Platform has been created, developers could register guidelines prospectively and internationally on this platform.

Aspect-Based (also known as aspect-level) Sentiment Classification (ABSC) aims at determining the sentimental tendency of a particular target in a sentence. With the successful application of the attention network in multiple fields, attention-based ABSC has aroused great interest. However, most of the previous methods are difficult to parallelize, insufficiently obtain, and fuse the interactive information. In this paper, we proposed a Multiple Interactive Attention Network (MIN). First, we used the Bidirectional Encoder Representations from Transformers (BERT) model to pre-process the data. Then, we used the partial transformer to obtain a hidden state in parallel. Finally, we took the target word and the context word as the core to obtain and fuse the interactive information. Experimental results on the different datasets showed that our model was much more effective.

Knowledge base question answering (KBQA) aims to analyze the semantics of natural language questions and return accurate answers from the knowledge base (KB). More and more studies have applied knowledge bases to question answering systems, and when using a KB to answer a natural language question, there are some words that imply the tense (e.g., original and previous) and play a limiting role in questions. However, most existing methods for KBQA cannot model a question with implicit temporal constraints. In this work, we propose a model based on a bidirectional attentive memory network, which obtains the temporal information in the question through attention mechanisms and external knowledge. Specifically, we encode the external knowledge as vectors, and use additive attention between the question and external knowledge to obtain the temporal information, then further enhance the question vector to increase the accuracy. On the WebQuestions benchmark, our method not only performs better with the overall data, but also has excellent performance regarding questions with implicit temporal constraints, which are separate from the overall data. As we use attention mechanisms, our method also offers better interpretability.

Improving the system performance of a traffic network by dynamically controlling the routes of connected and automated vehicles (CAVs) is an appealing profit that CAVs can bring to our society. Considering that there may be a long way to achieve 100% CAV penetration, we discuss in this paper the mixed traffic flow of human driven vehicles (HDVs) and CAVs on a transportation network. We first propose a double queue (DQ) based mixed traffic flow model to describe the link dynamics as well as the flow transitions at junctions. Based on this mixed flow model, we develop a dynamic bi-level framework to capture the behavior and interaction of HDVs and CAVs. This results in an optimal control problem with equilibrium constraints (OCPEC), where HDVs’ route choice behavior is modeled at the lower level by the instantaneous dynamic user equilibrium (IDUE) principle and the CAVs’ route choice is modelled by the dynamic system optimal (DSO) principle at the upper level. We show how to discretize the OCPEC to a mathematical programming with equilibrium constraints (MPEC) and discuss its properties and solution techniques. The non-convex and non-smooth properties of the MPEC make it hard to be efficiently solved. To overcome this disadvantage, we develop a decomposition based heuristic model predictive control (HMPC) method by decomposing the original MPEC problem into two separate problems: one IDUE problem for HDVs and one DSO problem for CAVs. The experiment results show that, compared with the scenario that all vehicles are HDVs, the proposed methods can significantly improve the network performance under the mixed traffic flow of HDVs and CAVs.

For road transportation, adaptive cruise control (ACC) is a widely used driver assistance technique with partially longitudinal automation. The existing ACC systems usually ignore the road elevation information, which often ends up with large tracking error and poor fuel economy on the roads with up and down slopes. This paper presents a model-based predictive controller (MPC) for fuel-saving ACC to improve the performances on tracking accuracy and fuel consumption by simultaneously considering the road elevation information, nonlinear powertrain dynamics, and spatiotemporal constraint from preceding vehicle. The feasibility of MPC is enhanced by softening nonsafety related hard constraints, and its asymptotical stability is proved by considering the monotonicity property of a properly chosen Lyapunov function. To efficiently compute the optimal control sequence, a pseudospectral discretization technique, instead of evenly spaced discretization, is adopted to transform the continuous-time optimal control problem into a nonlinear programming. Simulation results demonstrate that the proposed method could reduce fuel consumption by 2.65% in a typical driving scenario and significantly improve the tracking accuracy compared to the one without road slope consideration.

Fuel consumption of fossil-based road vehicles is significantly affected by the way vehicles are driven. The same is true for automated vehicles with longitudinal control. This paper presents a periodic servo-loop longitudinal control algorithm for an adaptive cruise control (ACC) system to minimize fuel consumption in car-following scenarios. The fuel-saving mechanism of pulse-and-glide (PnG) operation is first discussed for the powertrain with internal combustion engine and step-gear transmission. The servo-loop controller is then designed based on a periodic switching map for real-time implementation and adjusted with a range-bounded feedback regulator to enhance the robustness to model mismatch. Simulations in both uniform and natural traffic flows demonstrate that this algorithm achieves a significant fuel-saving benefit in automated car-following scenarios up to 8.9% in naturalistic traffic flow (when coasting at neutral gear), compared with a linear quadratic (LQ) controller. Meanwhile, its intervehicle range is preferably bounded so that the negative impact on safety and traffic smoothness is contained.

Eco-Driving has great potential in reducing the fuel consumption of road vehicles, especially under the connected and automated vehicles (CAVs) environment. Traditional model-based Eco-Driving methods usually require sophisticated models and cannot deal with complex driving scenarios. This paper proposes a hybrid reinforcement learning (RL) based Eco-Driving algorithm considering both the longitudinal acceleration/deceleration and the lateral lane-changing operations. A deep deterministic policy gradient (DDPG) algorithm is designed to learn the continuous longitudinal acceleration/deceleration to reduce the fuel consumption as well as to maintain acceptable travel time. Collecting the critic’s value of each single lane from DDPG and integrating the information of adjacent lanes, a deep Q-learning algorithm is developed to make the discrete lane-changing decision. Together, a hybrid deep Q-learning and policy gradient (HDQPG) method is developed for vehicles driving along multi-lane urban signalized corridors. The method can enable the controlled vehicle to learn well-established longitudinal fuel-saving strategies, and to perform appropriate lane-changing operations at proper times to avoid congested lanes. Numerical experiments show that HDQPG can reduce fuel consumption by up to 46% with marginal or no increase of travel times.

Road traffic crashes cause fatalities and injuries of both drivers/passengers in vehicles and pedestrians outside, thus challenge public health especially in big cities in developing countries like China. Previous efforts mainly focus on a specific crash type or causation to examine the crash characteristics in China while lacking the characteristics of various crash types, factors, and the interplay between them. This study investigated the crash characteristics in Shenzhen, one of the biggest four cities in China, based on the police-reported crashes from 2014 to 2016. The descriptive characteristics were reported in detail with respect to each of the crash attributes. Based on the recorded crash locations, the land-use pattern was obtained as one of the attributes for each crash. Then, the relationship between the attributes in motor-vehicle-involved crashes was examined using the Bayesian network analysis. We revealed the distinct crash characteristics observed between the examined levels of each attribute, as well the interplay between the attributes. This study provides an insight into the crash characteristics in Shenzhen, which would help understand the driving behavior of Chinese drivers, identify the traffic safety problems, guide the research focuses on advanced driver assistance systems (ADASs) and traffic management countermeasures in China.

A novel method for preparing high porosity and high strength porous YB 4 ceramics was developed combining the in-situ borothermal reaction synthesis and high temperature partial sintering. Since pores could be produced by gasses such as B 2 O 3 and CO generated in the reaction between Y 2 O 3 and B 4 C, high porosity porous YB 4 ceramics were obtained after synthesis at 1650 °C and sintering at 1780 °C for 2 h in vacuum, respectively. Using this new and simple method, porous YB 4 with controllable porosities of 60–70% and compressive strengths of 7.4–19.9 MPa were obtained depending on the green density. The phase composition, porosity, pore size distribution and compressive behavior of the porous YB 4 ceramics were systematically investigated. The effects of vacuum on synthesis of YB 4 and pore forming mechanism were also discussed in detail.

A cold state device of a composite spray quench chamber was established. In order to obtain the distribution of atomized droplet sizes at radial and axial positions along the flow field, different combinations of multi-spray nozzles were selected. The atomized droplet size along the axial position was measured using a Malvern Spraytec under different conditions. The results showed that Sauter Mean Diameter (SMD) D32 near the nozzles was mainly influenced by liquid flow rate. However, D32 further away from the nozzles was affected by both liquid and gas flow rate. Along the axial direction, with increasing of the relative height (h/H), D32 decreased. D32 around the gas outlet was mainly affected by the gas flow rate, growing from 80 to 160 µm as the gas flow rate increased from 0.29 to 0.49 m · s−1, which was similar to the calculated data. The research can optimize and improve the internal structure of a spray quench chamber, providing guidance to industrial applications.

The flow, heat, and mass transfer of falling-water films in a dip tube in an industrial-scale water-scrubbing cooling chamber was predicted using a 2D numerical simulation. The VOF model was adopted to describe the free surface between the gas and liquid. The Rosseland model was applied to solve the radiative heat-transfer equation. The surface tension between the gas and liquid phases was also considered. The predicted axial-temperature distribution of the bench-scale results were in good agreement with the experimental data, validating the feasibility of the numerical models applied in this Article. The simulation results indicated that the thickness of the inlet water film and the velocity of the inlet falling-water film in the industrial-scale dip tube had a large influence on the mass, heat transfer, and flow patterns of the falling-water film. When the thickness of the inlet water film was increased to 0.004 m, the outlet hot-syngas temperature of the dip tube decreased to about 100 K. Furthermore, ...

In this paper, we consider how to design a kind of nonlinear neural networks for solving the inverse optimal value problem with convex constraints. Firstly, based on optimal theory, the inverse optimal value problem considered is changed to a class of nonlinear bilevel program problems. Secondly, under the given assumptions, the corresponding nonlinear bilevel programming problem can be reduced to the one-level programming problem. Thirdly, based on the gradient theory, a nonlinear neural network model is designed to solve this one-level programming problem. Moreover, by employing Lyapunov function approach, the proposed neural network is analyzed to be globally Lyapunov stable and capable of generating approximal optimal solution to the inverse optimal value problem. Finally, two illustrative examples are provided to verify the feasibility and the efficiency of the proposed method.

The stability for the switched Cohen-Grossberg neural networks with mixed time delays andα-inverse Hölder activation functions is investigated under the switching rule with the average dwell time property. By applying multiple Lyapunov-Krasovskii functional approach and linear matrix inequality (LMI) technique, a delay-dependent sufficient criterion is achieved to ensure such switched neural networks to be globally exponentially stable in terms of LMIs, and the exponential decay estimation is explicitly developed for the states too. Two illustrative examples are given to demonstrate the validity of the theoretical results.

By combing the theories of the switched systems and the interval neural networks, the mathematics model of the switched interval neural networks with discrete and distributed time-varying delays of neural type is presented. A set of the interval parameter uncertainty neural networks with discrete and distributed time-varying delays of neural type are used as the individual subsystem, and an arbitrary switching rule is assumed to coordinate the switching between these networks. By applying the augmented Lyapunov-Krasovskii functional approach and linear matrix inequality (LMI) techniques, a delay-dependent criterion is achieved to ensure to such switched interval neural networks to be globally asymptotically robustly stable in terms of LMIs. The unknown gain matrix is determined by solving this delay-dependent LMIs. Finally, an illustrative example is given to demonstrate the validity of the theoretical results.