Lignocellulose is a kind of renewable bioresource containing abundant polysaccharides, which can be used for biochemicals and biofuels production. However, the complex structure hinders the final ...efficiency of lignocellulosic biorefinery. This review comprehensively summarizes the hydrolases and typical microorganisms for lignocellulosic degradation. Moreover, the commonly used bioprocesses for lignocellulosic biorefinery are also discussed, including separated hydrolysis and fermentation, simultaneous saccharification and fermentation and consolidated bioprocessing. Among these methods, construction of microbial co-culturing systems via consolidated bioprocessing is regarded as a potential strategy to efficiently produce biochemicals and biofuels, providing theoretical direction for constructing efficient and stable biorefinery process system in the future.
A challenge to many real-world data streams is imbalance with concept drift, which is one of the most critical tasks in anomaly detection. Learning nonstationary data streams for anomaly detection ...has been well studied in recent years. However, most of the researches assume that the class of data streams is relatively balanced. Only a few approaches tackle the joint issue of imbalance and concept drift. To overcome this joint issue, we propose an ensemble learning method with generative adversarial network-based sampling and consistency check (EGSCC) in this paper. First, we design a comprehensive anomaly detection framework that includes an oversampling module by generative adversarial network, an ensemble classifier, and a consistency check module. Next, we introduce double encoders into GAN to better capture the distribution characteristics of imbalanced data for oversampling. Then, we apply the stacking ensemble learning to deal with concept drift. Four base classifiers of SVM, KNN, DT and RF are used in the first layer, and LR is used as meta classifier in second layer. Last but not least, we take consistency check of the incremental instance and check set to determine whether it is anormal by statistical learning, instead of threshold-based method. And the validation set is dynamic updated according to the consistency check result. Finally, three artificial data sets obtained from Massive Online Analysis platform and two real data sets are used to verify the performance of the proposed method from four aspects: detection performance, parameter sensitivity, algorithm cost and anti-noise ability. Experimental results show that the proposed method has significant advantages in anomaly detection of imbalanced data streams with concept drift.
Herein, alumina green bodies are fabricated by three dimensional (3D) printing technology, then, the influence of debinding holding time under vacuum and argon on mechanical properties is ...systematically investigated by comparing the changes in microstructure, bulk density, open porosity, grain connection situation and flexural strength of ceramics. The flexural strength of alumina ceramics acquired the maximum values of 26.4 ± 0.7 MPa and 25.1 ± 0.5 MPa after debinding under vacuum and argon for 120 min and 180 min, respectively. However, the alumina ceramics rendered the flexural strength of 19.4 ± 0.6 MPa and 9.5 ± 0.4 MPa under vacuum and argon without extended holding time, respectively. The relatively low mechanical properties can be mainly attributed to the weak interlayer binding force, which is caused by layer-by-layer forming mode during 3D printing process and anisotropic shrinkage during the sintering process. Moreover, the alumina ceramics exhibited moderate bulk density and open porosity of 2.4 g/cm3 and 42% after the sintering process, respectively, which are mainly influenced by the microstructural evolution of alumina ceramics during thermal treatment. Also, the diffusion of gases is achieved by curing of photosensitive resin and influenced by different holding times during debinding, affecting the mechanical properties of sintered ceramics. The mechanical properties of as-sintered ceramics are suitable for the utilization of ceramic cores in the manufacturing of hollow blades.
Lyocell fabrics are widely applied in textiles, however, its high flammability increases the risk of fire. Therefore, to resolve the issue, a novel biomass-based flame retardant with phosphorus and ...nitrogen elements was designed and synthesized by the reaction of arginine with phosphoric acid and urea. It was then grafted onto the lyocell fabric by a dip-dry-cure technique to prepare durable flame-retardant lyocell fabric (FR-lyocell). X-ray photoelectron spectroscopy (XPS) and Fourier-transform infrared spectroscopy (FTIR) analysis demonstrated that the flame retardant was successfully introduced into the lyocell sample. Thermogravimetric (TG) and Raman analyses confirmed that the modified lyocell fabric featured excellent thermal stability and significantly increased char residue. Vertical combustion results indicated that FR-lyocell before and after washing formed a complete and dense char layer. Thermogravimetric Fourier-transform infrared (TG-FTIR) analysis suggested that incombustible substances (such as H2O and CO2) were produced and played a significant fire retarding role in the gas phase. The cone calorimeter test corroborated that the peak of heat release rate (PHRR) and total heat release (THR) declined by 89.4% and 56.4%, respectively. These results indicated that the flame retardancy of the lyocell fabric was observably ameliorated.
Anomaly detection is a highly important task in the field of data analysis. Traditional anomaly detection approaches often strongly depend on data size, structure and features, while introducing the ...idea of ensemble into anomaly detection can greatly improve the generalization ability. Ensemble-based anomaly detection methods still face some challenges, however, such as data imbalance, time and space demand and the selection of base detectors. To this end, we propose a selective ensemble method for anomaly detection based on parallel learning (SEAD-PL). First, a differentiated stratified sampling method is designed to alleviate the problem of data imbalance. Then, a distributed parallel training frame is built to address the problem of excessive time and space consumption for base detector training. Finally, a clustering-based ensemble selection strategy is introduced to balance the accuracy and diversity of base detectors. Experiments are performed on six datasets, which demonstrate that the proposed method has obvious advantages over four selected methods.
With the continuous development of computer networks, the security of the network has become increasingly prominent. A major threat to network security is the intrusion of information systems through ...the network. Intrusion detection of the traditional intrusion detection and alarm technology is not sufficient. Based on neural network technology, this paper studies the intrusion detection and alarm correlation technology. Based on the research on the working principle and workflow of the existing intrusion detection system, a new neural network-based intrusion detection and alarm method is proposed. A neural network-based intrusion detection and alarm system is designed and implemented. Through the experiment of the system prototype, the results show that the intrusion detection and alarm system based on the neural network has a higher detection rate and a lower false alarm rate for intrusion behaviors such as denial of service attack and has higher detection ability for unknown attack behaviors.
The development of artificial intelligence and the Internet of things has motivated extensive research on self-powered flexible sensors. The conventional sensor must be powered by a battery device, ...while innovative self-powered sensors can provide power for the sensing device. Self-powered flexible sensors can have higher mobility, wider distribution, and even wireless operation, while solving the problem of the limited life of the battery so that it can be continuously operated and widely utilized. In recent years, the studies on piezoelectric nanogenerators (PENGs) and triboelectric nanogenerators (TENGs) have mainly concentrated on self-powered flexible sensors. Self-powered flexible sensors based on PENGs and TENGs have been reported as sensing devices in many application fields, such as human health monitoring, environmental monitoring, wearable devices, electronic skin, human-machine interfaces, robots, and intelligent transportation and cities. This review summarizes the development process of the sensor in terms of material design and structural optimization, as well as introduces its frontier applications in related fields. We also look forward to the development prospects and future of self-powered flexible sensors.
Our purpose was to describe the outcomes of transvaginal in-bag tissue extraction tissue through an incision in the posterior vaginal wall the middle part incision of posterior vagina in laparoscopic ...myomectomy.
This was a retrospective study of patients who received laparoscopic myomectomy and in-bag tissue extraction through an incision in the posterior vaginal wall between January 2016 and December 2022. Patient characteristics, intra- and post-operative complications, and outcomes were collected and analyzed.
A total of 511women were included in the analysis. The mean largest myoma diameter was 8.44 ± 3.56 cm; mean specimen weight was 789.23 ± 276.97 g; mean operative time was 129.01 ± 53.13minutes; and mean blood loss was 175.99 ± 210.96 mL. Within 30-days of surgery, no fever, infection, or vaginal bleeding was noted in any patient, and the vaginal incisions of all patients had healed well. There were no incisional hernias, pelvic infections, and vaginal adhesions noted at follow-up 3 months after the operation. There were 37 cases of vaginal delivery of the patients after surgery, and there were no lacerations of the posterior wall vaginal incision.
Transvaginal in-bag extraction though an incision in the posterior vaginal wall is feasible and safe for removing tissue after laparoscopic myomectomy.
Landslide susceptibility evaluation is critical for landslide prevention and risk management. Based on the slope unit, this study uses the information value method- random forest (IV-RF) model to ...evaluate the landslide susceptibility in the deep valley area. First, based on the historical landslide data, a landslide inventory was developed by using remote sensing technology (InSAR and optical remote sensing) and field investigation methods. Twelve factors were then selected as the input data for a landslide susceptibility model. Second, slope units with different scales were obtained by the r.slopeunits method and the information value method- random forest (IV-RF) model is used to evaluate the landslide susceptibility. Finally, the spatial distribution characteristics of landslide susceptibility grade under the optimal scale are analyzed. The results showed that under the slope unit obtained when c = 0.1 and a = 3 × 105 m2, the internal homogeneity/external heterogeneity of 8425 slope units extracted by the r.slopeunits method is the best, with an AUC of 0.905 and an F1 of 0.908. In this case, the accuracy of landslide susceptibility evaluation is the highest as well; it is shown that the finer slope units would not always lead to the higher accuracy of landslide susceptibility evaluation results; it is necessary to comprehensively consider the internal homogeneity and external heterogeneity of the slope units. Under the optimal slope unit scale, the number of landslides in the highly and extremely highly susceptible areas in the landslide susceptibility map accounted for 82.60% of the total number of landslides, which was consistent with the actual distribution of landslides; this study shows that the method, combining the slope unit and the information value method- random forest (IV-RF) model, for landslide susceptibility evaluation can obtain high accuracy.
Ceramic casting mould is a kind of complex part which is used in the field of investment precision casting to mold the inner and outer structure of casting. With the increase of the complexity of ...casting, more delicate and complex casting mould is needed to meet the casting demand. However, the traditional ceramic casting method like injection molding has many disadvantages such as high cost, long research and development cycle, and it is difficult to meet the requirements of increasingly complex fine structure molding. As a rapid prototyping method, 3D printing technology can accurately form complex structures. Its application in casting production can not only solve the problem of forming complex structures, but also reduce the production cost and shorten the production cycle. The 3D printing technology in the application of the ceramic mould production was mainly expounded in this article. The research and application of the ceramic mould was introduced from the types and the features of 3D printing cerami
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