Different from other 3D printing techniques such as selective laser sintering (SLS), stereolithography (SLA), three-dimensional printing (3DP), and laminated object manufacturing (LOM), the fused ...deposition modeling (FDM) technology is widely used in aerospace, automobile making, bio-medicals, smart home, stationery and training aids, and creative gifts for its easy use, simple operation, and low cost. The polylactic acid (PLA) is a material most extensively applied in FDM technology for its low melting point, non-poison, non-irritation, and sound biocompatibility. The FDM 3D-printed PLA parts are a research hotspot in the 3D printing field. This paper is intended to sum up the latest research results and achievements made in recent years in the interface bonding property, mechanical properties, and shape precision promotion of FDM 3D-printed PLA parts as well as the functional expansion of the PLA parts based on vast domestic and overseas literature. The literature research collection focuses on the following two aspects: one is the macroscopic technical research on the optimal settings of key technological parameters; the other one is the PLA modification research on improvement of cross-linking state and crystallinity of PLA molecular chains, carbon reinforced phase modification of PLA, and PLA functional compound modification. The researches in the two aspects are of importance in improving whole properties, enhancing functional applications, and expanding and enriching the applications of FDM 3D-printed PLA parts. This paper is expected to give some helps and references to the researchers who are specializing in the 3D printing field.
Boosted by mobile communication technologies, Human Activity Recognition (HAR) based on smartphones has attracted more and more attentions of researchers. One of the main challenges is the ...classification time and accuracy in processing long-time dependent sequence samples with noisy or missed data. In this paper, a 1-D Convolution Neural Network (CNN)-based bi-directional Long Short-Term Memory (LSTM) parallel model with attention mechanism (ConvBLSTM-PMwA) is proposed. The original features of sensors are segmented into sub-segments by well-designed equal time step sliding window, and fed into 1-D CNN-based bi-directional LSTM parallel layer to accelerate feature extraction with noisy and missed data. The weights of extracted features are redistributed by attention mechanism and integrated into complete features. At last, the final classification results are obtained with the full connection layer. The performance is evaluated on public UCI and WISDM HAR datasets. The results show that the ConvBLSTM-PMwA model performs better than the existing CNN and RNN models in both classification accuracy (96.71%) and computational time complexity (1.1 times faster at least), even if facing HAR data with noise.
Low-rate distributed denial of service attacks, as known as LDDoS attacks, pose the notorious security risks in cloud computing network. They overload the cloud servers and degrade network service ...quality with the stealthy strategy. Furthermore, this kind of small ratio and pulse-like abnormal traffic leads to a serious data scale problem. As a result, the existing models for detecting minority and adversary LDDoS attacks are insufficient in both detection accuracy and time consumption. This paper proposes a novel multi-scale Convolutional Neural Networks (CNN) and bidirectional Long-short Term Memory (bi-LSTM) arbitration dense network model (called MSCBL-ADN) for learning and detecting LDDoS attack behaviors under the condition of limited dataset and time consumption. The MSCBL-ADN incorporates CNN for preliminary spatial feature extraction and embedding-based bi-LSTM for time relationship extraction. And then, it employs arbitration network to re-weigh feature importance for higher accuracy. At last, it uses 2-block dense connection network to perform final classification. The experimental results conducted on popular ISCX-2016-SlowDos dataset have demonstrated that the proposed MSCBL-ADN model has a significant improvement with high detection accuracy and superior time performance over the state-of-the-art models.
Low-rate Distributed DoS (LDDoS) attack is a complex large-scale attack behavior with strong time-domain characteristics in blockchain-based wireless network. Blockchain with Machine learning-based ...models, as promising ways, are taken to detect them and secure wireless network. However, researchers focused on how to improve models' detection performance and work out new blockchain-based protection technologies during the past decades. Due to lack of evolving data, these models and technologies may have poor stability in the face of confrontational samples. To cope with the problem, this paper proposes a novel LSTM-CGAN method to generate high-quality LDDoS adversarial samples for blockchain-based wireless network detection models. In this method, we give a brief feature analysis about LDDoS attack in blockchain-based wireless network and work out its corresponding time series model firstly. And then, we take use of Long Short-Term Memory Networks (LSTM) to learn relationships among sequenced network packages in the same flow. At last, we establish a Condition Generative Adversarial Networks (CGAN) model to use above relationships as specific conditions for generating mimicking behaviors of LDDoS attacks in blockchain-based wireless network. The experimental results show that these generated adversarial samples based on both public and private datasets can cheat the machine learning detection models, and have the similar attack characteristics with the real samples. Consequently, they can be used as blockchain-based wireless network dataset of machine learning classifiers for training to enhance models' stability.
Existing intrusion detection and defense models for CPSS (Cyber-Physical-Social Systems) are based on analyzing the static intrusion characteristics, which cannot effectively detect large-scale ...Low-Rate Denial-of-Service (LR-DDoS) attacks, especially in the edge environment. In this paper, we firstly explore and enhance Mirai botnet to a sophisticated multi-targets low-rate TCP attack network, which makes edge LR-DDoS more powerful and obfuscates their activity. And then, we develop a novel intrusion detection and defense hybrid method for above CPSS LR-DDoS scenario in edge environment, which takes advantage of locality sensitive features extraction and Deep Convolution Neural Network (DCNN) to auto learn the optimal features of the original data distribution and employs deep reinforcement learning Q-network as the powerful decision maker to defend attacks. The experimental results in detection phase prove the proposed method can distinguish abnormal network attack flows with higher detection accuracy and faster response time than kinds of Support Vector Machines (SVM), K-means and Surface Learning Neural Network etc. Even more, it has a certain detection rate for unknown new attacks, which means the method is effective and suitable for the actual network environment. The experimental results in defense phase prove it can defense LR-DDoS attacks smoothly.
Low-rate Distributed Denial of Service (LDDoS) attacks have been one of the most notorious network security threats, which use periodic slight multi-variate time series pulse flows to degrade network ...quality. Limited by the poor data in a single client, a powerful and satisfactory LDDoS attack detection model is hard to be trained. Federated Learning (FL) is a promising paradigm offering joint learning through multiple clients. We propose an asynchronous federated learning arbitration framework based on bidirectional LSTM (bi-LSTM) and attention mechanism (AsyncFL-bLAM). In the AsyncFL-bLAM, the leader node election algorithm is proposed for constructing the framework of asynchronous federated learning. The proposed bLAM model composed of feature extracter and arbitrator takes on the responsibility of LDDoS detection locally. Furthermore, the novel AsyncFL framework helps to upload and aggregate the bLAM models' parameters asynchronously between leader node and client nodes. Experimental results show that the AsyncFL-bLAM outperforms the state-of-the-art models in accuracy, and reduces the overall communication rounds.
The potential energy recovery of hydraulic excavators is very significant for improving energy efficiency and reducing pollutant emissions. However, the more common solutions for potential energy ...recovery require more energy conversion processes before these potential energies can be reused, which adds to the complexity and high cost of the system. To tackle the above challenges, we proposed a novel energy recovery system for hydraulic hybrid excavators based on the digital pump with an energy recovery function. The new system could operate in three different modes: pump, energy recovery, and direct reuse. Based on the descriptions of the working principle of the digital pump and the whole energy recovery system, the mathematical models of the digital pump, the excavator arm cylinder, and the accumulator were established and the AMESim simulation model (combining mechanics, hydraulics, and electrics) was developed. The dynamic characteristics of the energy recovery system were studied under no-load and full-load conditions. The simulation results showed that this scheme could achieve 86% energy recovery when the boom was lowered and reused the recovered energy directly when raised, which could decrease the system input energy by 78.1%. This paper can provide an optimized solution for construction machinery or off-road vehicles and presents a reference for the research on digital hydraulics.
To eliminate the adverse effect of the fluctuation and intermittence of wind power on the quality and stability of electrical power system, an energy storage system is introduced into the closed-loop ...hydraulic system of hydraulic wind turbine for the first time. The whole hydraulic system consists of a fixed displacement pump, a variable displacement motor, two proportional control valves and an energy storage system. The energy storage system absorbs or releases oil as the wind fluctuates. When the wind suddenly disappears, the generator can continue to produce electrical energy by means of the discharge of the hydraulic energy stored in the energy storage system. On the basis of modelling all hydraulic components, the simulation model of the hydraulic system is established. A new control method is presented within this article, which keeps the motor speed constant to generate constant frequency electrical power when the rotational speed of the wind wheel changes. Ultimately, simulations under the two conditions of step and sine wind wheel speeds are done. The simulation results demonstrate how the motor, the proportional valves and the energy storage system work together when the wind wheel speed varies and also prove validity of the control method we designed.
A detailed analysis of the electrical response of In
0.3
Ga
0.7
As surface quantum dots (SQDs) coupled to 5-layer buried quantum dots (BQDs) is carried out as a function of ethanol and acetone ...concentration while temperature-dependent photoluminescence (PL) spectra are also analyzed. The coupling structure is grown by solid source molecular beam epitaxy. Carrier transport from BQDs to SQDs is confirmed by the temperature-dependent PL spectra. The importance of the surface states for the sensing application is once more highlighted. The results show that not only the exposure to the target gas but also the illumination affect the electrical response of the coupling sample strongly. In the ethanol atmosphere and under the illumination, the sheet resistance of the coupling structure decays by 50% while it remains nearly constant for the reference structure with only the 5-layer BQDs but not the SQDs. The strong dependence of the electrical response on the gas concentration makes SQDs very suitable for the development of integrated micrometer-sized gas sensor devices.
Concurrent programmed death 1 (PD-1) or programmed death ligand 1 (PD-L1) inhibitors with sequential chemoradiotherapy (SCRT) have been reported in only a limited number of studies involving patients ...with unresectable stage III non-small-cell lung cancer (NSCLC). A retrospective study was conducted to systematically analyze the efficacy and safety of the emerging therapy among Chinese patients.
We included patients with unresectable, stage III NSCLC who received concurrent sintilimab with chemotherapy or chemotherapy alone for 3-6 cycles, followed by radical radiotherapy at the First Hospital of Jilin University from Dec 15, 2019, to Jul 15, 2022. The primary end point was the objective response rate (ORR). The secondary end points included progression-free survival (PFS), overall survival (OS), 12-month and 18-month PFS rates, the duration of response (DoR), and safety.
The retrospective study involved 77 patients, of which 49 receiving concurrent sintilimab with SCRT were assigned to cohort A, and 28 receiving SCRT alone were assigned to cohort B. The ORR was significantly higher in cohort A (79.6%, 95% CI 65.7-89.8) than in cohort B (35.7%, 95% CI 18.6-55.9) (p<0.001). Median PFS was significantly longer in cohort A than in cohort B (NR 95% CI 21.4-NR vs. 16.0 months 13.0-22.5; HR 0.375, 95% CI 0.192-0.735; p=0.003). The PFS rates at 12 and 18 months were 84.8% (95% CI 75.0-95.9) and 71.3% (95% CI 58.7-86.7) in cohort A and 75.0% (95% CI 60.6-92.9) and 38.3% (95% CI 23.7-61.7) in cohort B, respectively. Grade 3 or 4 adverse events (AEs) were reported in 19 patients (38.8%) and seven patients (25.0%) in two cohorts, respectively. Grade 3 or 4 pneumonitis or immune-mediated pneumonitis, radiation pneumonitis, and pneumonia occurred in five (10.2%), four (8.2%), and two (4.1%) cohort A patients, and zero, two (7.1%), and two (7.1%) cohort B patients, respectively. Only cohort A reported AE leading to death in one (2.0%) patient (immune-mediated pneumonitis).
Concurrent sintilimab with SCRT resulted in a significantly better ORR and longer PFS than SCRT alone, with manageable safety profiles in Chinese patients with unresectable stage III NSCLC.
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