Search

Abstract Objective This study aimed to systematically analyze the efficacy and toxicity of lurbinectedin as a second-line or subsequent treatment for extensive-stage small cell lung cancer (ES-SCLC). Methods Candidate studies were identified in PubMed, Embase, Cochrane Library, ClinicalTrials.gov, CNKI, and Wanfang databases up to 1 May 2024. Objective remission rate (ORR), disease control rate (DCR), duration of response (DOR), progression-free survival (PFS), overall survival (OS), and adverse events (AEs) were extracted, respectively. The efficacy and toxicity of lurbinectedin in ES-SCLC were analyzed by meta-analysis. Results Six eligible prospective studies were included in this meta-analysis, including 536 patients with ES-SCLC who received second-line or subsequent treatment. In pooled analysis, the ORR of lurbinectedin was 35% (95% confidence interval [CI] 29–41), DCR was 67% (95%CI 58–76), DOR was 5.33 months (95%CI 4.51-6.16), PFS was 3.38 months (95%CI 2.59-4.17), and OS was 7.49 months (95%CI 5.11–9.87). The incidence of AEs and severe adverse events (SAEs) was 92% (95%CI 78–100) and 37% (95%CI 19–57), respectively. The most common AEs were leukopenia, neutropenia, anemia, and thrombocytopenia, with incidences of 81% (68–91), 74% (57–88), 73% (35–98) and 57% (46–68), respectively. Conclusion As a promising alternative for second-line treatment for ES-SCLC, lurbinectedin has a certain level of efficacy and a favorable safety profile. The integration of lurbinectedin with other therapeutic modalities presents an emerging area warranting further investigation.

Abstract Electrochemical hydrogen peroxide (H2O2) production (EHPP) via a two-electron oxygen reduction reaction (2e- ORR) provides a promising alternative to replace the energy-intensive anthraquinone process. M-N-C electrocatalysts, which consist of atomically dispersed transition metals and nitrogen-doped carbon, have demonstrated considerable EHPP efficiency. However, their full potential, particularly regarding the correlation between structural configurations and performances in neutral media, remains underexplored. Herein, a series of ultralow metal-loading M-N-C electrocatalysts are synthesized and investigated for the EHPP process in the neutral electrolyte. CoNCB material with the asymmetric Co-C/N/O configuration exhibits the highest EHPP activity and selectivity among various as-prepared M-N-C electrocatalyst, with an outstanding mass activity (6.1 × 105 A gCo −1 at 0.5 V vs. RHE), and a high practical H2O2 production rate (4.72 mol gcatalyst −1 h−1 cm−2). Compared with the popularly recognized square-planar symmetric Co-N4 configuration, the superiority of asymmetric Co-C/N/O configurations is elucidated by X-ray absorption fine structure spectroscopy analysis and computational studies.

In order to establish a suitable restoring force correction model for enveloped steel jacket (ESJ)-confined seismic-damaged rectangular recycled aggregate concrete-filled steel tubular (RRACFST) columns, based on experimental research, a study of the seismic performance and parameters of ESJ-confined seismic-damaged RRACFST columns was carried out. The restoring force theory, model test, and OpenSees simulation of ESJ-confined seismic-damaged RRACFST columns were conducted. Firstly, a trilinear model of the skeleton curve and a suitable restoring force model for ESJ-confined seismic-damaged RRACFST columns were established. The results were compared with the model test results, and it was found that the two results had good consistency. Secondly, the initial damage of the RRACFST column was simulated by the reducing material properties method, and a correct numerical model for ESJ-confined seismic-damaged RRACFST columns was proposed. The influence mechanism of seismic parameters of the RRACFST column was clarified. Finally, the seismic parameter combination with the best seismic performance for ESJ-confined seismic-damaged RRACFST columns was established; namely, the replacement rate of recycled coarse aggregate is 50%, the concrete strength is C40, the axial compression ratio is 0.3, the strength of the rectangular steel tube is Q345, the wall thickness of the steel tube is 4 mm, and the slenderness ratio is 7.5.

Hand gestures are a well-known and straightforward method of human-computer interaction. The majority of the study focused on hand gesture recognition. However, little work has been done to develop a complete set of gesture recognition applications. With the improvement of model feature extraction ability and the increase in the number of model parameters, it is becoming more challenging to achieve a small memory footprint on mobile devices based on an ARM architecture or CPU devices based on x86 architecture. However, these existing methods are heavy, requiring more memory and inference time. The execution of memory-efficient CNNs without compromising accuracy has been a challenge, especially when the inference has to be performed on an edge computing device in real time. Therefore, we propose a lightweight network for hand gesture recognition (LHGR-Net) and deploy it on a Raspberry Pi. LHGR-Net consists of three main parts: the base network structure, the multiscale structure (MSS), and the lightweight attention structure (LAS). We present pre-trained weights that are learned from other data to initialize the network structure. In addition, the LHGR-Net model was made to be deployed on a Raspberry Pi, and a deployed model can be used to control home appliances. Extensive experiments show that our method achieves almost as good as state-of-the-art performance in hand gesture recognition and running time.

To achieve stabilisation control of an underactuated bridge crane system, a new robust control strategy for the sliding mode is proposed in this paper. It can realise finite-time-convergent stabilisation control under the conditions of model uncertainty, parameter perturbation and external interference. In contrast to the existing methods, our method does not need prior information of the dynamic characteristics of the bridge crane system, and can make the system converge to the equilibrium state at the preset time. Specifically, the nonlinear model of the bridge crane system is linearised with partial feedback, and adaptive signals are introduced. Then, according to the form of the transformed system, a fast terminal sliding mode surface is constructed, and an adaptive terminal sliding mode controller is designed. According to strict analysis, the proposed control law ensures that the system converges to the equilibrium point in finite time and provides the convergence time. Finally, the effectiveness and robustness of the proposed control method are verified by comparing the simulation and experimental results with existing methods.

Abstract The paper presents a novel algorithm to classify children's epileptic syndromes based on the fused features of electroencephalogram (EEG) and electrocardiogram (ECG). The purpose is to assess whether multimodal physiological signals could improve the classification performance of epileptic syndromes over a single physiological signal. The study is carried out on the epileptic syndromes database recorded by the Children's Hospital, Zhejiang University School of Medicine (CHZU), that includes the synchronised EEGs and ECGs of 16 children suffered from the infantile spasms (known as the WEST syndrome, named) and the childhood absence epilepsy (CAE), respectively. Experiments are conducted and compared using the EEGs and ECGs in the ictal and interictal periods. The data imbalanced issue between the ictal and interictal periods is also considered by applying a synthetic minority sample generating approach. The experimental results show that using the fused feature of EEG + ECG can achieve an average of 98.15% overall classification accuracy, which is better than using the single physiological signal.

Objectives: Eye blink artifact detection in scalp electroencephalogram (EEG) of epilepsy patients is challenging due to its similar waveforms to epileptiform discharges. Developing an accurate detection method is urgent and critical. Methods: In this paper, we proposed a novel multi-dimensional feature optimization based eye blink artifact detection algorithm for EEGs containing rich epileptiform discharges. An unsupervised clustering algorithm based on smoothed nonlinear energy operator (SNEO) and variational mode extraction (VME) is proposed to detect epileptiform discharges in the frontal leads. Then, multi-dimensional time/frequency EEG features extracted from forehead electrodes (FP1 and FP2 channels) combining with the improved VME (IVME) threshold are derived for EEG representation. A variance filtering method is further applied for discriminative feature selection and a machine learning model is finally learned to perform detection. Results: Experiments on EEGs of 16 subjects from the Children’s Hospital of Zhejiang University School of Medicine (CHZU) show that our method achieves the highest average sensitivity, specificity and accuracy of 95.04, 89.52, and 93.01, respectively. That outperforms 5 recent and state-of-the-art (SOTA) eye blink detection algorithms. Significance: The proposed method is robust in eye blink artifact detection for EEGs containing high-frequency epileptiform discharges. It is also effective in dealing with individual differences in EEGs, which is usually ignored in conventional methods.

In this study, an improved African vulture optimization algorithm (IAVOA) that combines the African vulture optimization algorithm (AVOA) with both quasi-oppositional learning and differential evolution is proposed to address specific drawbacks of the AVOA, including low population diversity, bad development capability, and unbalanced exploration and development capabilities. The improved algorithm has three parts. First, quasi-oppositional learning is introduced in the population initialization and exploration stages to improve population diversity. Second, a differential evolution operator is introduced in the local search position update of each population to improve exploration capability. Third, adaptive parameters are introduced to the differential evolution operator, thus balancing the algorithm exploration and development. A numerical simulation experiment based on 36 different types of benchmark functions showed that while the IAVOA can enhance the convergence speed and solution accuracy of the basic AVOA and two variants of AVOA, IAVOA outperforms the other 7 swarm intelligence algorithms in the mean and best values of 33 benchmark functions.

Abstract The extreme learning machine‐based autoencoder (ELM‐AE) has attracted a lot of attention due to its fast learning speed and promising representation capability. However, the existing ELM‐AE algorithms only reconstruct the original input and generally ignore the probability distribution of the data. The minimum error entropy (MEE), as an optimal criterion considering the distribution statistics of the data, is robust in handling non‐linear systems and non‐Gaussian noises. The MEE is equivalent to the minimisation of the Kullback–Leibaler divergence. Inspired by these advantages, a novel randomised AE is proposed by adopting the MEE criterion as the loss function in the ELM‐AE (in short, the MEE‐RAE) in this study. Instead of solving the output weight by the Moore–Penrose generalised inverse, the optimal output weight is obtained by the fixed‐point iteration method. Further, a quantised MEE (QMEE) is applied to reduce the computational complexity of. Simulations have shown that the QMEE‐RAE not only achieves superior generalisation performance but is also more robust to non‐Gaussian noises than the ELM‐AE.

Human intelligence has the advantage for making high-level decisions in the remote control of underwater vehicles, while autonomous control is superior for accurate and fast close-range pose adjustment. Combining the advantages of both remote and autonomous control, this paper proposes a visual-aided shared-control method for a semi-autonomous underwater vehicle (sAUV) to conduct flexible, efficient and stable underwater grasping. The proposed method utilizes an arbitration mechanism to assign the authority weights of the human command and the automatic controller according to the attraction field (AF) generated by the target objects. The AF intensity is adjusted by understanding the human intention, and the remote-operation command is fused with a visual servo controller. The shared controller is designed based on the kinematic and dynamic models, and model parameter uncertainties are also addressed. Efficient and stable control performance is validated by both simulation and experiment. Faster and accurate dynamic positioning in front of the target object is achieved using the shared-control method. Compared to the pure remote operation mode, the shared-control mode significantly reduces the average time consumption on grasping tasks for both skilled and unskilled operators.

Planar type docking enables a convenient underwater energy supply for irregularly shaped autonomous underwater vehicles (AUVs), but the corresponding control method is still challenging. Conventional control methods for torpedo-shaped AUVs are not suitable for planar type docking due to the significant differences in system structures and motion characteristics. This paper proposes a two-step adaptive control method to solve the planar type docking problem. The method makes a seamless combination of horizontal dynamic positioning and visual servo docking considering ocean current disturbance. The current disturbance is estimated and canceled in the pre-docking step using a current observer, and the positioning error is further compensated for by the vertical visual servo technique in the docking step. Reduced order dynamic models are distinctively established for different docking steps according to the motion characteristics, based on which the dynamic controllers are designed considering the model parameter uncertainties. Simulation is conducted with an initial distance of 10 m in the horizontal direction and 3 m in depth. Stable and accurate dynamic positioning under up to 0.4 m/s of current disturbances with different directions is validated. A 0.5 m lateral positioning error is successfully compensated for by the visual servo docking step. The proposed control method provides a valuable reference for similar types of docking application.

Ion–conducting ceramic membranes, such as mixed oxygen ionic and electronic conducting (MIEC) membranes and mixed proton–electron conducting (MPEC) membranes, have the potential for absolute selectivity for specific gases at high temperatures. By utilizing these membranes in membrane reactors, it is possible to combine reaction and separation processes into one unit, leading to a reduction in by–product formation and enabling the use of thermal effects to achieve efficient and sustainable chemical production. As a result, membrane reactors show great promise in the production of various chemicals and fuels. This paper provides an overview of recent developments in dense ceramic catalytic membrane reactors and their potential for chemical production. This review covers different types of membrane reactors and their principles, advantages, disadvantages, and key issues. The paper also discusses the configuration and design of catalytic membrane reactors. Finally, the paper offers insights into the challenges of scaling up membrane reactors from experimental stages to practical applications.

This paper aims to explain the design of a novel time-varying sliding mode control of variable parameter (VP-TVSMC), which can effectively solve the anti-swing and positioning problem of distributed-mass double pendulum bridge crane system with its quick responsiveness and strong robustness to external interference. More specifically, this model initiates with the establishment of the dynamic equation of double pendulum crane model based on distributed-mass, then followed by the design of a time-varying parameter to realize the dynamic adjustment of the sliding mode surface and enhance the adjustment ability of the sliding mode surface, which is conducive to the global robustness of the double pendulum crane system under VP-TVSMC. With Lyapunov method and LaSalle’s invariance principle, the asymptotic stability of the system can be sufficiently proved. Finally, the adoption of three kinds of external interference signals and uncertain system parameters successfully verified the preeminent control performance and global robustness against external interference of the proposed controller. The simulation results indicate that compared with the conventional CSMC and PDSMC, the proposed control method can reduce the driving force of the trolley, ensure the rapid and precise positioning of the trolley, as well as restrain the load swing angle within 5° in an effective manner. In addition, compared with the symbolic function sgn( $S$ ), the designed continuous function th( $S$ ) possesses a better anti-chattering effect, thus strengthening of the control performance of VP-TVSMC.

A wide application of sliding mode variable structure control as a nonlinear robust control method, has been witnessed in anti-swing positioning control of bridge crane system. Aiming at the problem that the sliding mode variable structure control system of bridge crane is not robust in approaching process, a new Global-Equivalent Sliding Mode Controller (GESMC) based on bridge crane system is proposed. This controller can realize the anti-sway positioning control of the bridge crane system under the condition of uncertain model parameters and external disturbance. The proposed controller, different from the traditional sliding mode control, excels in improving system robustness through keeping the system states in the sliding surface during the whole response process. Specifically, it initiates with the design of a global sliding surface, which can eliminate the sliding mode approach process of the system and achieve global robustness in the system. Afterwards, a new switching function combined with the equivalent sliding mode control method is incorporated to effectively reduce the chattering generated when the system reaches the sliding mode manifold. Its asymptotic stability is proven without a priori knowledge on the bounds of unknown disturbances by using the Lyapunov stability theory. Lastly, the simulation conducted verifies the effectiveness and robustness of the GESMC proposed in this paper and meanwhile demonstrates a comparatively favorable performance for the GESMC in reducing chattering.

This paper presents WALLACE, a new framework of deep convolutional neural networks, which perform ConvNet's pyramidal feature hierarchy for weakly supervised learning. Most prior works rely on the image pyramid or network ensemble, which is both complicated and usually expensive. Instead, WALLACE is a more simple single-stage network that can predict objects present and location in an image without multiple rescale. Our model is trained efficiently using only global image-level labels, and it could generate meaningful multi-scale semantic feature maps by only one evaluation. Furthermore, a novel constrain-to-highlight loss is proposed to balances region selection among hierarchical feature maps, which additional improve model performance. Extensive experiments on object classification and weakly supervised pointwise object localization show that WALLACE achieves state-of-the-art results on the VOC 2007 and VOC 2012 benchmark without bells and whistles.

Over the past decades, fossil fuel combustion has emitted large quantities of CO2 into the atmosphere, resulting in global climate change. Nowadays, it's considered a feasible strategy to capture CO2 from some significant point sources. Three main strategies have been developed, namely post-combustion, pre-combustion, and oxy-fuel combustion. Recent research indicates that the membrane technology for CO2 capture has become competitive compared with conventional technologies because of the improved separation performance in materials and process designs. This paper mainly reviews the progress and breakthroughs of membrane materials for the three gas separation systems corresponding to the CO2 capture strategies. Besides, the CO2 utilization by the membrane process has also been highlighted.

The target of radar echo extrapolation is to predict the motion and development of radar echo in the future based on historical radar observation data. For such spatiotemporal prediction problems, a deep learning method based on Long Short-Term Memory (LSTM) networks has been widely used in recent years, although such models generally suffer from weak and blurry prediction. This paper proposes two models called Residual Convolution LSTM (rcLSTM) and Generative Adversarial Networks-rcLSTM (GAN-rcLSTM): The former introduces the residual module, and the latter introduces the discriminator. We use the historical data of 2017 and 2018 in the Jiangsu region as training and test sets. Experiments show that in long sequence forecasts, our model can provide more stable and clear images, while achieving higher CSI scores.

El Niño and Southern Oscillation (ENSO) is closely related to a series of regional extreme climates, so robust long-term forecasting is of great significance for reducing economic losses caused by natural disasters. Here, we regard ENSO prediction as an unsupervised spatiotemporal prediction problem, and design a deep learning model called Dense Convolution-Long Short-Term Memory (DC-LSTM). For a more sufficient training model, we will also add historical simulation data to the training set. The experimental results show that DC-LSTM is more suitable for the prediction of a large region and a single factor. During the 1994–2010 verification period, the all-season correlation skill of the Nino3.4 index of the DC-LSTM is higher than that of the current dynamic model and regression neural network, and it can provide effective forecasts for lead times of up to 20 months. Therefore, DC-LSTM can be used as a powerful tool for predicting ENSO events.

This work reviews the current progress of tight-binding methods and the recent edge-modified mean-field Hubbard model. Undercoordinated atoms (atoms not fully coordinated) exist at a high rate in nanomaterials with their impact overlooked. A quantum theory was proposed to calculate electronic structure of nanomaterials by incorporating bond order-length-strength (BOLS) correlation to mean-field Hubbard model, i.e., BOLS-HM. Consistency between the BOLS-HM calculation and density functional theory (DFT) calculation on 2D materials verified that (i) bond contractions and potential well depression occur at the edge of graphene, phosphorene, and antimonene nanoribbons; (ii) the physical origin of the band gap opening of graphene, phosphorene, and antimonene nanoribbons lays in the enhancement of edge potentials and hopping integrals due to the shorter and stronger bonds between undercoordinated atoms; (iii) the band gap of 2D material nanoribbons expand as the width decreases due to the increasing under-coordination effects of edges which modulates the conductive behaviors; and (iv) non-bond electrons at the edges and atomic vacancies of 2D material accompanied with the broken bond contribute to the Dirac-Fermi polaron (DFP) with a local magnetic moment.

In this work, we have successfully designed ordered luminescent multilayer films based on La-doped nonmagnetic or magnetic inorganic nanostructure with electronic microenvironment (EM). The inorganic nanosheets with opposite charge can assemble EM between the interlayers. At the same time, their elements on nanosheets of layer double hydroxides (LDHs) are facile to be replaced so that we can introduce transition metal or lanthanide elements. Besides, ferromagnetic effect (FE) can be formed in this microenvironment due to introducing transition metal on LDHs nanosheets. As a result, we confirm that EM, FE, and doping La element in the LDHs can affect the vibration of backbone of chromophores and then prolong the luminescent lifetime, which suggests a new pathway for developing the novel light-emitting thin films.

Supercapacitors have been one of the highest potential candidates for energy storage because of their significant advantages beyond rechargeable batteries in terms of large power density, short recharging time, and long cycle lifespan. In this work, Cu–Co sulfides with uniform flower-like structure have been successfully obtained via a traditional two-step hydrothermal method. The as-fabricated Cu–Co sulfide vulcanized from precursor (P–Cu–Co sulfide) is able to deliver superior specific capacitance of 592 F g−1 at 1 A g−1 and 518 F g−1 at 10 A g−1 which are surprisingly about 1.44 times and 2.39 times higher than those of Cu–Co oxide electrode, respectively. At the same time, excellent cycling stability of P–Cu–Co sulfide is indicated by 90.4% capacitance retention at high current density of 10 A g−1 after 3000 cycles. Because of the introduction of sulfur during the vulcanization process, these new developed sulfides can get more flexible structure and larger reaction surface area, and will own richer redox reaction sites between the interfaces of active material/electrolyte. The uniform flower-like P–Cu–Co sulfide electrode materials will have more potential alternatives for oxides electrode materials in the future.

Layered double hydroxides (LDHs), luminescent π-conjugated anionic polymer and montmorillonite (MMT) were orderly assembled into luminescent multilayer films via layer-by-layer self-assembly method. The electronic microenvironment (EME), the structure of which is like a traditional capacitor, can be constructed by exfoliated LDHs or MMT nanosheets. In addition, the rigid inorganic laminated configuration can offer stable surroundings between the interlayers. As a result, we conclude that EME can extend the luminescent lifespans of multilayer films substantially, due to affecting relaxation times of π-conjugated anionic polymer. Consequently, because of the remarkable impact on better photoemission behaviors of luminescent π-conjugated anionic polymer, EME assembled by LDHs or MMT nanosheets have had high hopes attached to them. They are expected to have the potential for designing, constructing, and investigating novel light-emitting thin films.