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Recently, memristors have garnered widespread attention as neuromorphic devices that can simulate synaptic behavior, holding promise for future commercial applications in neuromorphic computing. In this paper, we present a memristor with an Au/Bi3.2La0.8Ti3O12 (BLTO)/ITO structure, demonstrating a switching ratio of nearly 103 over a duration of 104 s. It successfully simulates a range of synaptic behaviors, including long-term potentiation and depression, paired-pulse facilitation, spike-timing-dependent plasticity, spike-rate-dependent plasticity etc. Interestingly, we also employ it to simulate pain threshold, sensitization, and desensitization behaviors of pain-perceptual nociceptor (PPN). Lastly, by introducing memristor differential pairs (1T1R-1T1R), we train a neural network, effectively simplifying the learning process, reducing training time, and achieving a handwriting digit recognition accuracy of up to 97.19 %. Overall, the proposed device holds immense potential in the field of neuromorphic computing, offering possibilities for the next generation of high-performance neuromorphic computing chips.

Abstract Purpose This study aimed to compare the effects of conventional surgery and three-dimension (3D) printing technology-assisted surgery in the treatment of posterolateral tibial plateau fractures (PTPF). Methods A cohort of 61 patients afflicted with PTPF, spanning from June 2015 to October 2021, was enrolled. They were divided randomly into two groups: 31 cases of 3D printing group, 30 cases of conventional group. The personalized 3D-printed models were used to simulate the surgical procedures in 3D printing group. The demographic characteristics and clinical data were recorded, encompassing operation duration, intraoperative blood loss, intraoperative fluoroscopy shoots and fracture union time. The radiographic outcomes were gauged, encompassing tibiofemoral angle (FTA), tibial plateau angle (TPA), posterolateral slope angle (PSA) and Rasmussen’s anatomical score. The functional outcomes were assessed at the 12-month postoperative juncture, encompassing range of motion, Hospital for Special Surgery (HSS) score and Rasmussen’s functional score. Furthermore, fracture complications were evaluated,, encompassing infections, traumatic osteoarthritis, and delayed union. Results The 3D printing group exhibited the operation time of 95.8 ± 30.2 min, intraoperative blood loss of 101.1 ± 55.3 ml, and intraoperative fluoroscopy shoots of 6.3 ± 2.3 times, while the conventional group recorded respective values of 115.5 ± 34.0 min, 137.0 ± 49.2 ml and 9.13 ± 2.5 times. Noteworthy disparities were evident between the conventional and 3D printing groups (p

[INLINE:1] Spinal cord injury is a challenge in orthopedics because it causes irreversible damage to the central nervous system. Therefore, early treatment to prevent lesion expansion is crucial for the management of patients with spinal cord injury. Bexarotene, a type of retinoid, exerts therapeutic effects on patients with cutaneous T-cell lymphoma and Parkinson’s disease. Bexarotene has been proven to promote autophagy, but it has not been used in the treatment of spinal cord injury. To investigate the effects of bexarotene on spinal cord injury, we established a mouse model of T11–T12 spinal cord contusion and performed daily intraperitoneal injection of bexarotene for 5 consecutive days. We found that bexarotene effectively reduced the deposition of collagen and the number of pathological neurons in the injured spinal cord, increased the number of synapses of nerve cells, reduced oxidative stress, inhibited pyroptosis, promoted the recovery of motor function, and reduced death. Inhibition of autophagy with 3-methyladenine reversed the effects of bexarotene on spinal cord injury. Bexarotene enhanced the nuclear translocation of transcription factor E3, which further activated AMP-activated protein kinase-S-phase kinase-associated protein 2-coactivator-associated arginine methyltransferase 1 and AMP-activated protein kinase-mammalian target of rapamycin signaling pathways. Intravenous injection of transcription factor E3 shRNA or intraperitoneal injection of compound C, an AMP-activated protein kinase blocker, inhibited the effects of bexarotene. These findings suggest that bexarotene regulates nuclear translocation of transcription factor E3 through the AMP-activated protein kinase-S-phase kinase-associated protein 2-coactivator-associated arginine methyltransferase 1 and AMP-activated protein kinase-mammalian target of rapamycin signal pathways, promotes autophagy, decreases reactive oxygen species level, inhibits pyroptosis, and improves motor function after spinal cord injury.

BackgroundIn the context of global warming, the impact of meteorological factors on human health has gradually become a research hotspot at home and abroad.ObjectiveTo describe the distribution of children's bronchopneumonia in Huzhou City, and explore the influence of diurnal temperature range (DTR) on children with bronchopneumonia, so as to provide guidance for identifying vulnerable populations and developing targeted measures.MethodsA distributed lag nonlinear model (DLNM) was used to explore the potential nonlinear lag effect of DTR on admission of children with bronchopneumonia in Huzhou City from 2014 to 2019. Then a generalized additive model (GAM) was used to calculate the hospital admission risk of exposure to DTR in total population and sex-, age-, season-stratified populations.ResultsA total of 17658 hospitalized children with bronchopneumonia were included in the study. When DTR exceeded 7.5 ℃, the relative risk rose abruptly. When DTR reached 17 ℃, the risk of admission to hospital of children with bronchopneumonia was the greatest. In terms of the single-day lag effect, the admission risk of DTR for bronchopneumonia in children began on the same day and persisted until lag day 2; it peaked on the same day, and RR was 1.353 (95%CI: 1.220-1.502). The cumulative lag effect occurred from lag0 to lag0-6, and the highest RR value was at lag0-3, which was 1.938 (95%CI:1.483-2.533). The results of stratified analysis showed that the maximum effect values for boys and girls appeared at lag0-3 (RRboys=2.301, 95%CI: 1.671-3.169) and lag0-2(RRgirls=1.566, 95%CI: 1.152-2.129) respectively, and the effect value and duration of DTR in boys were higher and longer than those in girls. Among different age groups, both children of 0-3 years old and 4-14 years old had the maximum effect value at lag 0-3, the RR values were 1.734 (95%CI: 1.454-2.572) and 1.998 (95%CI: 1.226-2.254) respectively, and the effect value and duration of DTR on the children of 4-14 years old were higher and longer than those of the children of 0-3 years old. As to seasons, no significant increase was found in cumulative effect in summer and autumn (P>0.05); in winter and spring, the maximum effect value appeared at lag0-10, and the RR value was 4.164 (95%CI:1.191-14.561).ConclusionThe impact of DTR on bronchopneumonia in children is acute. Boys and children aged 4-14 years old are more sensitive to DTR changes. Therefore, we should be alert to severe DTR changes and take protective measures in advance

Enhancing the availability and reliability of dielectric ceramic energy storage devices is of great importance. In this work, (1-[Formula: see text])[Formula: see text]–[Formula: see text]Bi([Formula: see text])[Formula: see text] (NBT–[Formula: see text]BMH) lead-free ceramics were created utilizing a solid-state reaction technique. All NBT–[Formula: see text]BMH ceramics have a single perovskite structure. With increasing BMH doping, the grain size shrinks drastically, which greatly enhances the breakdown electric field (310 kV/cm at [Formula: see text] = 0.25). Additionally, the relaxation behaviors of NBT–[Formula: see text]BMH ceramics with high BMH content are more remarkable. Among all designed components, the NBT–0.25BMH ceramic exhibits the best energy storage performance with a high [Formula: see text] of 4.63 J/[Formula: see text] and an [Formula: see text] of 75.1% at 310 kV/cm. The NBT–0.25BMH ceramic has exceptional resistance to fluctuations in both frequency (5–500 Hz) and temperature (30–100[Formula: see text]C). Charge–discharge test shows that the NBT–0.25BMH ceramic has a quick discharge rate ([Formula: see text] 110 ns). With these properties, the NBT–0.25BMH ceramic may have applications in microdevices as well as in ultra-high power electronic systems.

Underwater images play a crucial role in various fields, including oceanographic engineering, marine exploitation, and marine environmental protection. However, the quality of underwater images is often severely degraded due to the complexities of the underwater environment and equipment limitations. This degradation hinders advancements in relevant research. Consequently, underwater image restoration has gained significant attention as a research area. With the growing interest in deep-sea exploration, deep-sea image restoration has emerged as a new focus, presenting unique challenges. This paper aims to conduct a systematic review of underwater image restoration technology, bridging the gap between shallow-sea and deep-sea image restoration fields through experimental analysis. This paper first categorizes shallow-sea image restoration methods into three types: physical model-based methods, prior-based methods, and deep learning-based methods that integrate physical models. The core concepts and characteristics of representative methods are analyzed. The research status and primary challenges in deep-sea image restoration are then summarized, including color cast and blur caused by underwater environmental characteristics, as well as insufficient and uneven lighting caused by artificial light sources. Potential solutions are explored, such as applying general shallow-sea restoration methods to address color cast and blur, and leveraging techniques from related fields like exposure image correction and low-light image enhancement to tackle lighting issues. Comprehensive experiments are conducted to examine the feasibility of shallow-sea image restoration methods and related image enhancement techniques for deep-sea image restoration. The experimental results provide valuable insights into existing methods for addressing the challenges of deep-sea image restoration. An in-depth discussion is presented, suggesting several future development directions in deep-sea image restoration. Three main points emerged from the research findings: i) Existing shallow-sea image restoration methods are insufficient to address the degradation issues in deep-sea environments, such as low-light and uneven illumination. ii) Combining imaging physical models with deep learning to restore deep-sea image quality may potentially yield desirable results. iii) The application potential of unsupervised and zero-shot learning methods in deep-sea image restoration warrants further investigation, given their ability to work with limited training data.

Cerussite is a lead oxide mineral resource that is typically enriched through sulfidization flotation. The surface sulfidation degree and the high solubility of cerussite strongly affect the flotation ability of cerussite. In the current work, lead ions were used to pretreat cerussite to intensify its sulfidization flotation. The sulfidization mechanism regulating the lead ions pretreatment on cerussite was investigated by the micro-flotation test, ToF-SIMS, zeta potential measurement, adsorption test, and XPS. The results from the micro-flotation test demonstrated that the floatability of cerussite could be improved by adding an appropriate amount of lead ions. Compared with the treatment involving only Na2S, the maximum recovery increased by 17.57%. Adsorption experiments showed that lead modification improved the stability of xanthate products on the surface of cerussite. According to the measurement of zeta potential and the results of ToF-SIMS, the addition of lead ion Pb pretreatment increased the number of active Pb sites adsorbed by xanthate, thereby improving the formation of hydrophobic Pb-dilute precipitate. Therefore, the interaction between lead ions and the surface of cerussite enhances the strength and stability of the hydrophobic layer, resulting in enhanced hydrophobicity of cerussite.

Herein, two asymmetric chiral disc-bent core amphiphiles containing hexa‑peri-hexabenzocoronene (HBC), R-HBC-BC and S-HBC-BC, were synthesized to study the effects of chiral alkyl chain on the self-assembly of the disc-bent core amphiphiles. Interestingly, instead of forming chiral superstructure, pure R-HBC-BC or S-HBC-BC self-assembled into irregular crystalline texture resulting from the introduction of the chiral alkyl chains. Surprisingly, the formation of helical superstructure can be achieved by co-assembling the chiral disc-bent core amphiphile with achiral HBC-BC, with twisted superstructures having saddle-like curvature observed, which was different from the conventional spiral superstructures. The shape effect on the chirality transfer mechanism from molecule to hierarchical superstructure was thus proposed based on structural analysis of the twisted superstructures. Such interesting morphological transitions among different self-assembled helical morphologies in thin film may provide new insights for the self-assembly of chiral supramolecules.

In the context of the industrial internet,automatic and effective anomaly detection methods are of great significance to the safe and stable production of industrial systems.Traditional anomaly detection methods have the disadvantages of requiring a large number of labeled samples,and not adapting to high-dimensional time series data.Aiming at these limitations,an industrial system anomaly detection method based on LSTM (long short-term memory)auto-encoder was proposed.Firstly,to address the limitation of relying on labeled samples,an encoder used to learn the features and patterns of a large number of normal samples in an unsupervised manner.Then,anomaly detection was performed via reconstructing samples and calculating the reconstruction error.Secondly,to adapt to high-dimensional time series data,a BiLSTM (bidirectional LSTM) was used as an encoder,and then the potential characteristics of multi-dimensional time series data were mined.Experiments based on a real paper industry data set which demonstrate this method has improved the existing unsupervised anomaly detection methods in various indicators,and the overall accuracy of the detection has reached 93.4%.

Tungsten is a commercially important metal element that usually coexists with a variety of non-ferrous metals, which makes its extraction difficult. Scheelite is a commonly occurring tungsten-containing ore with the formula CaWO4. Improving the surface properties of scheelite to increase its adsorption of the collector for flotation separation is the focus of our current research. In this paper, the effects of manganese ions on scheelite flotation in benzohydroxamic acid (BHA) system were studied by micro-flotation tests, adsorption tests, fourier transform infrared spectroscopy (FTIR), zeta potential, and X-ray photoelectron spectroscopy (XPS) analysis. The addition of Mn2+ was found to improve the recovery of scheelite. The addition of Mn2+ greatly improved the recovery of scheelite. Infrared spectroscopy, adsorption tests, zeta potential measurements and XPS analysis all confirmed that BHA had a higher adsorption capacity and a stronger bond to the surface of scheelite after the addition of manganese ions, increasing the floatability of scheelite particles. Therefore, Mn2+ shows great potential for the improvement of the flotation index of scheelite in a system with BHA.

In a refrigeration unit, the amount of refrigerant has a substantial influence on the entire refrigeration system. To predict the amount of refrigerant in refrigerators with the best performance, this study used refrigerator data collected in real time via the Internet of Things, which were screened to include only the effective parameters related to the compressor and refrigeration properties (based on their practical significance and the research background) and cleaned by applying longitudinal dimensionality reduction and transverse dimensionality reduction. Then, on the basis of an idealized model for refrigerator data, a model of the relationships between refrigerant amount (the dependent variable) and temperature variation, refrigerator compartment temperature, freezer temperature, and other relevant parameters (independent variables) was established. A refrigeration model based on a neural network was then established for predicting the amount of refrigerant and was used to predict five unknown amounts of refrigerant from data sets. BP neural network and RBF neural network models were used to compare the prediction results and analyze the loss functions. From the results, it was concluded that the unknown amount of refrigerant was most likely to be 32.5 g. It is of great practical significance for refrigerator production and maintenance to study the prediction of the amount of refrigerant remaining in a refrigerator.

LiDAR occupies a vital position in self-driving as the advanced detection technology enables autonomous vehicles (AVs) to obtain much environmental information. Ground segmentation for LiDAR point cloud is a crucial procedure to ensure AVs’ driving safety. However, some current algorithms suffer from embarrassments such as unavailability on complex terrains, excessive time and memory usage, and additional pre-training requirements. The Jump-Convolution-Process (JCP) is proposed to solve these issues. JCP converts the segmentation problem of the 3D point cloud into the smoothing problem of the 2D image and takes little time to improve the segmentation effect significantly. First, the point cloud marked by an improved local feature extraction algorithm is projected onto an RGB image. Then, the pixel value is initialized with the points’ label and continuously updated according to image convolution. Finally, a jump operation is introduced in the convolution process to perform calculations only on the low-confidence points filtered by the credibility propagation algorithm, reducing the time cost. Experiments on three datasets show that our approach has a better segmentation accuracy and terrain adaptability than those of the three existing methods. Meanwhile, the average time for the proposed method to deal with one scan data of 64-beam and 128-beam LiDAR is only 8.61 ms and 15.62 ms, which fully meets the AVs’ requirement for real-time performance.

The usage of the word Power quality in recent times acquired intensified interest due to the complex industrial processes. The usage of intelligent tools to improve power quality is increasing day by day, as assumption of present day power system as a linear model is unsatisfactory. This paper deals with analysis of Differential Evolution (DE), Hybrid Differential Evolution (HDE) and Variable Scaling Hybrid Differential Evolution for harmonic reduction in the source current with optimal tuning of PI controller gain values. Shunt Active power Filter is one of the better solution to suppress the source current harmonics which are induced into power system because of nonlinear loads. Current controller called HBCC is considered for gating operation of switches in Voltage Source Inverter. The Intelligent tuned PQ theory is used for reference current generation. The then obtained compensating currents are injected at point of common coupling for current disturbance mitigation. Simulations of MATLAB/SIMULINK environment of the present work shows the efficacy. [ABSTRACT FROM AUTHOR]