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Recently, a dinosaur assemblage with three-dimensional preservation of the Early Cretaceous Jehol Biota has been discovered in the Ningcheng Basin, Inner Mongolia, China. Of the vertebrate fossils, a new spalacotherioid “symmetro-dont” mammal is described here as Ningchengodon foxi gen. et sp. nov. The specimen is an incomplete right dentary characterized by a lower dental formula of i3/c1/p3/m5, with molar cusps in acute-triangulation. Cusps e and f embraced cusp d of the preceding molar forming a molar interlocking, reported here for the first time for acute-angled spalaco-therioids. Phylogenetically, the new taxon is placed in the clade of Spalacotherioidea, and clustered at the base of the paraphyletic zhangheotheriids although its relationship is not further resolved with other zhangheotheriids. Additionally, the ultimate premolar replacement is simultaneous with the m5 eruption as in Origolestes lii and Zhangheotherium quinquecuspidens. This discovery enriches the biodiversity of spalacotherioids, and further highlights the complexity of the Zhangheotheriidae in evolution.

Atomic-level manufacturing is fundamentally concerned with the precise removal, addition, and migration of material at the atomic and close-to-atomic scale (ACS). Tip-based electrochemical deposition, a quintessential ACS electrochemical additive manufacturing technique, offers promising prospects for achieving bottom-up fabrication of metallic micro/nano structures. However, the complex physicochemical reactions involved in electrodes lead to a limited understanding of the mechanisms underlying atomic electrodeposition and structural evolution. For the first time, this study proposes electric double-layer controlled electrochemical kinetics and investigates the effect of direct current (DC) and pulse current (PC) on nickel atomic electrodeposition using molecular dynamics (MD) simulations. The findings reveal that compared to DC electrodeposition, PC electrodeposition results in more orderly deposition morphology, improved surface smoothness, reduced dislocation density, and lower crystal distortion, with these effects being particularly pronounced under low pulse duty ratio conditions. In addition, the pulse frequency significantly influences the morphology and structure of the deposit. The high pulse frequency yields smoother surfaces with local protrusions, while the low frequency favors the formation of orderly and dense structures excepting slightly increased roughness. This study provides critical insights into understanding the microscopic mechanisms of atomic-scale electrodeposition processes and achieving atomically controlled tip-based electrochemical additive manufacturing of micro/nanodevices.

Waste rubber modified bitumen has gained significant attention as a sustainable and innovative material in the field of pavement engineering. This study aims to evaluate the performance of rubber modified bitumen mastic by considering its rheological properties, specifically focusing on preparation parameters, i.e., rubber content, mesh number, and filler to bitumen ratio. From the experimental results, the rheological properties of rubber modified bitumen mastic were significantly influenced by preparation parameters. Increasing the rubber powder content in bitumen mastic results in higher viscosity. Increasing the rubber content improves high-temperature rutting resistance to a certain extent, however, excessive rubber powder content would result in weakened high-temperature performance improvement. The rutting factor decreases gradually with an increase in the rubber mesh number. A ratio of filler to bitumen of 0.95 exhibits the best resistance to rutting at high temperatures. Higher rubber content and larger mesh number correspond to stronger low-temperature crack resistance in bitumen mastic. As the ratio of filler to bitumen increases, the low-temperature deformation capacity gradually decreases, resulting in weaker low-temperature crack resistance. Based on the grey relation analysis, the ratio of filler to bitumen has the greatest impact on the high and low-temperature rheological properties of bitumen mastic, followed by the rubber content. The rubber mesh number has a relatively lower impact. It is crucial to control the ratio of filler to bitumen to avoid excessive values. When possible, a higher rubber powder content should be used while meeting process requirements. These findings provide valuable insights into the design and optimization of rubber modified bitumen mastic, which can contribute to the development of sustainable and high-performance bitumen mixtures, promoting the use of recycled rubber in pavement engineering.

There are many rich water open-pit mines in China and the water inflow is large, if the mine groundwater or slope is not properly treated, it will face the dilemma of “water disaster, difficult strengthening of slope, and difficult mining of mines”. Open pit mines in China have developed a variety of groundwater control technologies such as vertical hydrophobic holes, horizontal hydrophobic holes, large diameter hydrophobic holes under the pit and well group hydrophobic and other pumping and draining methods, drainage alleys at the bottom of the pit, pit precipitation and radiation well draining, as well as water interception curtains and water curtains such as borehole slurry curtains, concrete diaphragm wall curtains, pile-row curtains and impermeable membrane curtains to solve the flooding and slope instability encountered in open pit mines. The effects of groundwater control techniques on groundwater level, changes in geotechnical properties of end-slope, changes in slope stress and slope stability coefficients in open pit mines were studied, and the effects of groundwater level lifting on slope stability under the action of water curtain were analyzed. The research results show that draining or de-watering the groundwater in the open pit can effectively reduce the depth of the groundwater, reduce the water content of the slope geotechnical body, improve the cohesion and internal friction angle of the slope geotechnical body, reduce the hydrostatic pressure and horizontal thrust of the slope body, and improve the overall stability coefficient of the slope; however, de-watering a large amount of groundwater can cause waste of water resources and pollution. The water cut-off curtain can solve the problems of too large a drop in groundwater level, huge volume of water drainage, waste of water resources and pollution caused by water drainage and precipitation. Under the action of water interception curtain, the water level on the outside of water interception curtain in the open pit mine keeps rising, which causes the water content of geotechnical body on the outside of water interception curtain to increase, the cohesion and internal friction angle to decrease, and the hydrostatic pressure and horizontal thrust of slope body to increase; while the water content of geotechnical body on the inside of water interception curtain decreases, the cohesion and internal friction angle to increase, and the hydrostatic pressure and horizontal thrust of slope body to decrease, which causes the stability coefficient of slope in open pit coal mine to slightly decreased. The slope stability coefficient is still within the safe range, and it also protects the groundwater resources of the open pit mine.

With the development of the brain-computer interface, it is possible to assess the image quality based on an electroencephalogram (EEG) signal, which is essential to construct a quality assessment system that accords with the characteristics of the human visual system. However, as the complexity of image degradation levels rises, the analysis of brain features becomes progressively more challenging, leading the neural mechanisms still unclear. In this paper, an image quality assessment experiment with three levels of degradation to explore the neural mechanisms of subjects when confronted with images degraded at multiple levels. Subsequently, we proposed a dynamic model combined with Topological Data Analysis (TDA) and Grey Theory to extract the feature of brain response to different distortion-level images, which is called grey-topological data analysis (Grey-TDA) in this paper. The results indicate that the proposed method is effective in identifying brain responses under stimuli of varying image qualities, which contributes to the research of image quality assessment from the perspective of brain cognition.

Focusing on the comparison of aging resistance between styrene-butadiene-styrene (SBS) modified asphalt (SBSMA) and crumb rubber modified asphalt binders (CRMA), the influences of aging on rheological properties and microstructural characteristics of different modified asphalts were investigated in this work. Dynamic Shear Rheometer (DSR) and Bending Beam Rheometer (BBR) tests were carried out, and the variations in rheological properties for different modified asphalts after the rotating thin film oven test (RTFOT) were analyzed with the rutting factor aging index (RAI) and creep rate aging index (CAI). By using Fluorescence microscopy (FM) and Fourier transform infrared (FTIR) spectroscopy, the evolutions of microstructure and chemical composition for two modified asphalts were analyzed with carbonyl index growth rate (CIR) and sulfoxide index growth rate (SIR). Then, The relationships between CIR/SIR and RAI/CAI were established to show the correlation between the deterioration of macroscopic performance and the evolution of micro-structure. The results indicated that the aging degree of asphalt increases with elevated temperatures, leading to decreasing low-temperature performance while improving high-temperature performance. Nevertheless, SBSMA exhibited strong sensitivity to aging temperature. Under thermo-oxidative aging, the RAI and CAI of SBSMA were lower than those of CRMA, whereas the regularities of CIR and SIR were opposite, indicating that CRMA was superior to SBSMA in terms of anti-aging properties due to the rupture of the cross-linked network structure of SBSMA. However, CRMA experienced aging accompanied by full swelling, and thus, relatively minor performance declined. The CIR and SIR exhibited a better correlation with the RAI and CAI, illustrating that both CIR and SIR could characterize the aging degree of modified asphalts well.

To address the problem that existing semantic communication do not make sufficient use of prior knowledge and have limited decoding capability at the receiver side, a knowledge enhanced semantic communication framework was proposed, in which the receiver could more actively utilize the prior knowledge in the knowledge base for semantic reasoning and decoding, without extra modifications to the neural network structure of the transmitter.Specifically, a transformer-based knowledge extractor was designed to find relevant factual triples for the received noisy signal.Extensive simulation results on the WebNLG dataset demonstrate that the proposed framework has significantly improved performance on the basis of knowledge graph enhanced decoding.

Exostosis (also known as osteochondroma) is the most common benign bony lump of young people, usually arising at the distal femur. Vascular complications associated with exostoses are rare and include true aneurysm or pseudoaneurysm formation, deep vein thrombosis, arteriovenous fistula, and arterial insufficiency of the limbs. Few case reports describe pseudoaneurysms resulting from exostoses in mature adults, and no consensus has been reached regarding the optimal therapy. We report the case of a 51-year-old male patient complaining of persistent right thigh pain with a pulsatile mass and right calf swelling, without a history of trauma or hereditary multiple exostoses. The diagnosis was confirmed by computed tomography angiography, which showed a pseudoaneurysm of the popliteal artery resulting from an exostosis on the lateral aspect of the distal femur. A Doppler ultrasound examination confirmed popliteal vein thrombosis caused by the compression of the pseudoaneurysm. Surgical treatment consisted of removing the exostosis, excision of the pseudoaneurysm, and an end-to-end anastomosis. The deep vein thrombosis was treated with rivaroxaban for 3 months. The patient was discharged after 6 days and followed up for 6 months with satisfactory results.

Efficiently solving the user equilibrium traffic assignment problem with elastic demand (UE-TAPED) for transportation networks is a critical problem for transportation studies. Most existing UE-TAPED algorithms are designed using a sequential computing scheme, which cannot take advantage of advanced parallel computing power. Therefore, this study focuses on model decomposition and parallelization, proposing an origin-based formulation for UE-TAPED and proving an equivalent reformulation of the original problem. Furthermore, the alternative direction method of multipliers (ADMM) is employed to decompose the original problem into independent link-based subproblems, which can solve large-scale problems with small storage space. In addition, to enhance the efficiency of our algorithm, the parallel computing technology with optimal parallel computing schedule is implemented to solve the link-based subproblems. Numerical experiments are performed to validate the computation efficiency of the proposed parallel algorithm.

Abstract Background Postoperative sleep disturbance (PSD) is a prevalent clinical complication that may arise due to various factors. The purpose of this investigation is to identify the risk factors for PSD in spinal surgery and establish a risk prediction nomogram. Methods The clinical records of individuals who underwent spinal surgery from January 2020 to January 2021 were gathered prospectively. The least absolute shrinkage and selection operator (LASSO) regression, along with multivariate logistic regression analysis, was employed to establish independent risk factors. A nomogram prediction model was devised based on these factors. The nomogram’s effectiveness was evaluated and verified via the receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA). Results A total of 640 patients who underwent spinal surgery were analyzed in this investigation, among which 393 patients experienced PSD with an incidence rate of 61.4%. After conducting LASSO regression and logistic regression analyses using R software on the variables in training set, 8 independent risk factors associated to PSD were identified, including female, preoperative sleep disorder, high preoperative anxiety score, high intraoperative bleeding volume, high postoperative pain score, dissatisfaction with ward sleep environment, non-use of dexmedetomidine and non-use of erector spinae plane block (ESPB). The nomogram and online dynamic nomogram were constructed after incorporating these variables. In the training and validation sets, the area under the curve (AUC) in the receiver operating characteristic (ROC) curves were 0.806 (0.768–0.844) and 0.755 (0.667–0.844), respectively. The calibration plots indicated that the mean absolute error (MAE) values in both sets were respectively 1.2% and 1.7%. The decision curve analysis demonstrated the model had a substantial net benefit within the range of threshold probabilities between 20% and 90%. Conclusions The nomogram model proposed in this study included eight frequently observed clinical factors and exhibited favorable accuracy and calibration. Trial registration The study was retrospectively registered with the Chinese Clinical Trial Registry (ChiCTR2200061257, 18/06/2022).

In this study, extruded deformed billet and extruded rod of Mg–6Al–1Zn–1.1Sc (wt. %) alloy were characterized by optical microscopy (OM) and electron backscatter diffraction (EBSD). The room temperature tensile properties and deformation behavior of extruded alloy were investigated using a universal testing machine and the visco-plastic self-consistent (VPSC) model. The results indicated that the coarse deformed grains (DGs) progressively disappeared and the degree of dynamic recrystallization (DRX) of the alloy increased as the sampling position approached the outlet of the extrusion die. In addition, the grain boundary strengthening induced by DRX increased the corresponding Vickers hardness. DRX mechanisms during extrusion included discontinuous DRX, continuous DRX, twin-induced DRX, and particle-stimulated nucleation (PSN). The EBSD-based in-grain misorientation axes (IGMA) analysis revealed that the slip modes within DGs were dominated by prismatic slip in the early stages of extrusion and by multiple slip modes in the later stages. The yield strength, ultimate tensile strength, and elongation of the extruded alloy were 172.4 MPa, 290.1 MPa, and 26.9%, respectively. The combination of grain boundary strengthening, texture strengthening, and secondary phase strengthening resulted in the high yield strength. The VPSC model well simulated the tensile deformation of the extruded alloy with (0001)//ED (extrusion direction) fiber texture. The superior plasticity of the extruded alloy was ascribed to the high Schmid factor (SF) of the non-basal slip and the activation of the non-basal slip facilitated by the reduction in the ratio of the critical resolved shear stress (CRSS) between the non-basal slip and the basal slip.

In this article, a practical fixed-time adaptive neural network (NN) trajectory tracking control scheme for underactuated autonomous underwater vehicles (AUVs) subject to uncertain dynamics, unknown time-varying disturbances, an unknown dead zone, actuator faults and input quantization is developed for the first time. Here, a hysteresis quantizer is introduced to decrease the oscillation in the signal quantization process. Then, the radial basis function NN is employed to compensate the uncertainty term in the AUVs trajectory tracking control system. By incorporating the bounded estimate, smoothing functions and parameter adaptive technique, the problem of unknown dead zone, actuator fault and input quantization are addressed. The restrictive conditions of boundedness for the disturbance-like item in conventional sector bounded quantizer is resolved. Subsequently, a practical fixed-time adaptive NN trajectory tracking control law is designed does not require any parameter information of the quantizer under the backstepping design framework. The theoretical analysis further confirms that all signals in the AUV trajectory tracking closed-loop control system remain bounded, and the developed control scheme is shown to be effective through simulation results.

Deep neural networks (DNNs) have been incredibly successful at correctly classifying various input types, including images, speech, and data, according to consumer preferences. However, the efficacy of DNNs in Gestalt classification tasks, an important case in manifesting human perceptual capability, remains challenging, as DNNs are generally unable to perceive illusory closure from Gestalt images unless DNNs are carefully calibrated using a significant amount of priori information. However, altering the input, as minute changes typically imperceptible to humans, can confound carefully calibrated DNNs. In this study, EEG signal-empowered deep clustering based on topological data analysis (TDA) is proposed. The adoption of TDA manifests the separability of EEG signals responsive to Gestalt and non-Gestalt images. It also yields new families of features and descriptors for Gestalt illusory contours by extracting topological and geometric information. Furthermore, the combination of analyzed EEG signals and digital images further benefits the recognition of Gestalt illusory contours. Extensive experiments have shown convincing improvements over the state-of-the-art DNNs (e.g., DeepCluster and multi-view clustering [MVC]). In particular, DeepCluster with TDA can perceive illusory contours to some degree, given its 66.5% classification accuracy. Nevertheless, on top of extracted topological features from EEG signals, it produces higher classification accuracy (i.e., 71.9%), indicating the features extracted from the EEG signal contribute to perceive Gestalt illusory contours. On the other hand, the applications of topological features to the latest MVC also bring 4.5% improvement and demonstrate the effectiveness.

Highlights The flexible composite film presents ultrahigh thermal conductivity and good thermal management performance in electronic devices. An original “self-modified nanointerface” strategy is used to reduce the interfacial thermal resistance between boron nitride and the polymer matrix. The ideal phonon spectrum matching between boron nitride nanocrystals and fillers as well as the strong interaction between self-modified fillers and the polymer matrix are the two major contributors to decrease the interfacial thermal resistance.

The Lujiatun Unit of the Yixian Formation yields the only three-dimensionally preserved fossils from the Early Cretaceous Jehol Biota and crops out only in western Liaoning. Here, we report a new fossil site for the Jehol Biota with three-dimensionally preserved fossils from Ningcheng, Inner Mongolia. The fossils that have been discovered at this new site are predominantly dinosaurs and include a Sinovenator-like troodontid skeleton, three isolated sauropod teeth, some disarticulated skeletons of neornithischians and ceratopsians, and fragmentary lower jaws from a lizard and a mammal. The faunal composition, as well as the lithological features of the fossil beds, are comparable with those of the Lujiatun Unit of the Yixian Formation at Beipiao, western Liaoning, China. This discovery expands the geographical range of the Lujiatun-like dinosaurian assemblage of the Jehol Biota, and increases the biodiversity of the Jehol Biota in the Ningcheng Basin, China.

Buton rock asphalt (BRA)-modified asphalt has been extensively applied in the pavement because of its excellent aging resistance and high-temperature stability. The research on the overall behavior of BRA-modified asphalt has been relatively comprehensive. However, the main components in rock asphalt contain not only pure asphalt but also a large amount of ash. Their respective roles in asphalt modification still need to be clarified. This study analyzed rock asphalt's morphological changes and dissolution characteristics after heating. Then, the road performance of BRA ash-modified asphalt and BRA-modified asphalt was revealed. Finally, the contribution of pure asphalt and BRA ash to neat asphalt for elevated temperature properties was analyzed quantitatively. The microstructure of BRA and BRA ash was observed. Furthermore, the Fourier transform infrared spectroscopy (FTIR) results of pure asphalt, BRA and BRA-modified asphalt was analyzed. The test consequences demonstrate that the pure asphalt in BRA is not directly compatible with the neat asphalt. The appearance of BRA is soft honeycomb, and pure asphalt is tightly wrapped in mineral particles. BRA is mainly modified by physical miscibility, thus significantly improving the elevated temperature property of neat asphalt. But it will weaken the cryogenic property of neat asphalt. With the increase in BRA content, this effect is more pronounced. Both ash and pure asphalt play a vital role in modifying neat asphalt. With the temperature rise, the weight of ash in conversion gradually increases, and the weight of pure asphalt gradually declines. The weight of ash enhanced with the rise of BRA content, and the weight of pure aspahlt also increased gradually. The average contribution rate of ash to improve the rutting index is 45.57%, and the average contribution rate of neat asphalt to the rutting index advancement is 54.43%.

Distributed scheduling has become a common manufacturing mode, and the distributed job scheduling problem (DJSP) has attracted more manufacturers and researchers in the field of operation research. For the distributed scheduling problem, it emphasizes the flexibility of factory assignment and determines the sequence of operation related to each machine in related factories. In this paper, a mixed-integer linear programming model for the DJSP is formulated to be optimized by an SMA. Also in this paper, a self-adaptive memetic algorithm (SMA) is proposed to obtain a near-optimal solution in a limited time for the DJSP. To strengthen the effectiveness of the SMA, an independent encoding is designed with jobs assigned to factories and the sequence of operation. In the proposed algorithm, various local search strategies related to the critical path in the critical factory are designed to enhance the quality of the solution. Moreover, the self-adaptive scheme for solution improvement is formulated to reduce the search time and avoid prematurity effectively. To demonstrate the performance of the proposed algorithm, numerical experiments are carried out on 120 different instances extended from the well-known job shop scheduling benchmarks. The proposed SMA has updated 30 instance records in 120 instances and it has obtained the 91 best records in 120 instances. According to the comparison, an SMA is a more effective algorithm that could update several records of benchmarks.

Background and objectiveThe rapid diagnosis of rice nitrogen nutrition is of great significance to rice field management and precision fertilization. The nitrogen nutrition index (NNI) based on the standard nitrogen concentration curve is a common parameter for the quantitative diagnosis of rice nitrogen nutrition. However, the current NNI estimation methods based on hyperspectral techniques mainly focus on finding a better estimation model while ignoring the relationship between the critical nitrogen concentration curve and rice hyperspectral reflectance.MethodsThis study obtained canopy spectral data using unmanned aerial vehicle (UAV) hyperspectral remote sensing and determined the rice critical nitrogen concentration curve and NNI. Taking the spectrum at critical nitrogen concentration as the standard spectrum, the original spectral reflectance and logarithmic spectral reflectance data were transformed by the difference method, and the features of the spectral data were extracted by a Autoencoder. Finally, the NNI inversion models of rice based on Extreme Learning Machine (ELM) and Bald Eagle Search-Extreme Learning Machine (BES-ELM) were constructed by taking the feature bands of four spectral extractions as input variables.Results1) from the feature extraction results of the self-encoder, simple logarithmic or difference transformation had little effect on NNI estimation, and logarithmic difference transformation effectively improved the NNI estimation results; 2) the estimation model based on the logarithmic difference spectrum and BES-ELM had the highest estimation accuracy, and the coefficient of determination (R2) values of the training set and verification set were 0.839 and 0.837, and the root mean square error (RMSE) values were 0.075 and 0.073, respectively; 3) according to the NNI, the samples were divided into a nitrogen-rich group (NNI ≥ 1) and nitrogen-deficient group (NNI < 1).ConclusionThe logarithmic difference transformation of the spectrum can effectively improve the estimation accuracy of the NNI estimation model, providing a new approach for improving NNI estimation methods based on hyperspectral technology.

Exosomes are biological small spherical lipid bilayer vesicles secreted by most cells in the body. Their contents include nucleic acids, proteins, and lipids. Exosomes can transfer material molecules between cells and consequently have a variety of biological functions, participating in disease development while exhibiting potential value as biomarkers and therapeutics. Growing evidence suggests that exosomes are vital mediators of vascular remodeling. Endothelial cells (ECs), vascular smooth muscle cells (VSMCs), inflammatory cells, and adventitial fibroblasts (AFs) can communicate through exosomes; such communication is associated with inflammatory responses, cell migration and proliferation, and cell metabolism, leading to changes in vascular function and structure. Essential hypertension (EH), atherosclerosis (AS), and pulmonary arterial hypertension (PAH) are the most common vascular diseases and are associated with significant vascular remodeling. This paper reviews the latest research progress on the involvement of exosomes in vascular remodeling through intercellular information exchange and provides new ideas for understanding related diseases.

A crowd intelligent (CI) system often acquires, calculates, and transmits a large amount of redundant information during the performing of exploration tasks, which inevitably results in inefficient use of the limited resources.Therefore, it emerges a strong incentive to design a task-driven mechanism for efficient utilization of computing and communication resources.A crowd intelligent cooperative obstacle avoidance method inspired by brain attention mechanism was proposed.Inspired by brain attention mechanism, the CI system introduced an intelligent selection module based on the deep Q network, by efficiently tuning the working state of sensors exploring the unknown environment and realized the acquisition and calculation of key necessary information with as little sensor overhead as possible.Meanwhile, based on the optimal reciprocal collision avoidance algorithm, a single agent fuses a small amount of limited information from neighbor agents to drive the intelligent selection module, so as to greatly reduce the redundancy of sensor acquisition and information calculation required for the obstacle avoidance task.The effectiveness of this proposed method was verified through extensive simulation analyses and practical realization empowered with Kehepera IV robots.The results show that the proposed method can significantly reduce the redundancy of sensor information in the CI system.More importantly, as the number of agents and the amount of information interaction increase, there also emerged a clear trend in the increase of performance gains.

Intelligent transportation systems need to realize accurate traffic congestion prediction. The spatio-temporal features of traffic flow are essential to analyze and predict congestion. Our study proposes a data-driven model to predict the traffic congested flow. Firstly, the traffic zone/grid method is used to store the local area roads’ average speed of the vehicles. Second, the discrete snapshot set is proposed to characterize traffic flow’s spatial and temporal features over a continuous period. Third, the evolution of traffic congested flow in various time dimensions (weekly days, weekend days, and one week) is examined by transforming the global urban transportation network into traffic zones. Finally, the data-driven model is constructed to predict urban road traffic congestion by using the extracted spatio-temporal characteristics of traffic zones’ traffic flow, the snapshot set of which serves as inputs for this model. The model adopts the convolutional LSTM network to learn the temporal and local spatial features of traffic flow, while utilizing a convolutional neural network to effectively capture the global spatial features inherent in traffic flow. The numerical experiments are conducted on two cities’ transportation networks, and the results demonstrate that the performance of the proposed model outperforms traditional traffic flow prediction models.

Semantic communication has witnessed a great progress with the development of natural language processing (NLP) and deep learning (DL). Although existing semantic communication technologies can effectively reduce errors in semantic interpretation, most of these solutions adopt a fixed bit length structure, along with a rigid transmission scheme, which is inefficient and lacks scalability faced with different meanings and signal-to-noise ratio (SNR) conditions. In this paper, we explore the impact of adaptive bit lengths on semantic coding (SC) under various channel conditions. First, we propose progressive semantic hybrid automatic repeat request (HARQ) schemes that utilize incremental knowledge (IK) to simultaneously reduce the communication cost and semantic error. On top of this, we design a novel semantic encoding solution with multi-bit length selection. In this fashion, the transmitter employs a policy network to decide the appropriate coding rate, so as to secure the correct information delivery at the cost of minimal bits. Moreover, a specific denoiser is further introduced to reduce the semantic errors encountered in the transmission process according to the semantic characteristics of context. Extensive simulation results have been conducted to verify the effectiveness of the proposed solution.

Mounting evidence has confirmed that essential hypertension (EH) is closely related to low-grade inflammation, but there is still a lack of in-depth understanding of the state of immune cells in the circulating blood of patients with EH. We analyzed whether hypertensive peripheral blood immune cell balance was destroyed. The peripheral blood mononuclear cells (PBMCs) of all subjects were analyzed using time-of-flight cytometry (CyTOF) based on 42 kinds of metal-binding antibodies. CD45+ cells were categorized into 32 kinds of subsets. Compared with the health control (HC) group, the percentage of total dendritic cells, two kinds of myeloid dendritic cell subsets, one intermediate/nonclassical monocyte subset and one CD4+ central memory T cell subset in the EH group, was significantly higher; the percentage of low-density neutrophils, four kinds of classical monocyte subsets, one CD14lowCD16- monocyte subset, one naive CD4+ and one naive CD8+ T cell subsets, one CD4+ effector and one CD4+ central memory T cell subsets, one CD8+ effector memory T cell subset, and one terminally differentiated γδ T cell subset, decreased significantly in EH. What is more, the expression of many important antigens was enhanced in CD45+ immune cells, granulocytes, and B cells in patients with EH. In conclusion, the altered number and antigen expression of immune cells reflect the imbalanced immune state of the peripheral blood in patients with EH.

This study aimed to explore the protective roles of malvidin in life-threatened sepsis-associated encephalopathy (SAE) and illustrate the underlying mechanism. SAE mice models were developed and treated with malvidin for subsequently protective effects evaluation. Malvidin restored neurobehavioral retardation, declined serum S100β and NSE levels, sustained cerebrum morphological structure, improved blood-brain barrier integrity with elevated tight junction proteins, and decreased evans blue leakage, and finally protect SAE mice from brain injury. Mechanistically, malvidin prevented cerebrum from mitochondrial dysfunction with enhanced JC-1 aggregates and ATP levels, and ROS accumulation with decreased lipid peroxidation and increased antioxidant enzymes. UCP2 protein levels were found to be decreased after LPS stimulation in the cerebrum and BV-2 cells, and malvidin recovered its levels in a ROS dependent manner. In vivo inhibition of UCP2 with genipin or in vitro interference with siRNA UCP2 both disrupted the mitochondrial membrane potential, decreased ATP levels and intensified DCF signals, being a key target for malvidin. Moreover, dorsomorphin block assays verified that malvidin upregulated UCP2 expression through phosphorylating AMPK in SAE models. Also, malvidin alleviated SAE progression through inhibition of ROS-dependent NLRP3 inflammasome activation mediated serum pro-inflammatory cytokines secretion and mitochondrial pathway mediated apoptosis with weakened apoptosis body formation and tunel positive signals, and decreased Bax, cytochrome C, caspase-3 and increased Bcl-2 protein levels. Overall, this study illustrated that malvidin targeted AMPK-α/UCP2 axis to restore LPS-induced mitochondrial dysfunction and alleviate ROS accumulation, which further inhibits NLRP3 inflammasome activation and mitochondrial apoptosis in a ROS dependent way, and ultimately protected SAE mice, providing a reference for the targeted development of SAE prophylactic approach.

Rice is the world’s most important food crop and is of great importance to ensure world food security. In the rice cultivation process, weeds are a key factor that affects rice production. Weeds in the field compete with rice for sunlight, water, nutrients, and other resources, thus affecting the quality and yield of rice. The chemical treatment of weeds in rice fields using herbicides suffers from the problem of sloppy herbicide application methods. In most cases, farmers do not consider the distribution of weeds in paddy fields, but use uniform doses for uniform spraying of the whole field. Excessive use of herbicides not only pollutes the environment and causes soil and water pollution, but also leaves residues of herbicides on the crop, affecting the quality of rice. In this study, we created a weed identification index based on UAV multispectral images and constructed the WDVINIR vegetation index from the reflectance of three bands, RE, G, and NIR. WDVINIR was compared with five traditional vegetation indices, NDVI, LCI, NDRE, and OSAVI, and the results showed that WDVINIR was the most effective for weed identification and could clearly distinguish weeds from rice, water cotton, and soil. The weed identification method based on WDVINIR was constructed, and the weed index identification results were subjected to small patch removal and clustering processing operations to produce weed identification vector results. The results of the weed identification vector were verified using the confusion matrix accuracy verification method and the results showed that the weed identification accuracy could reach 93.47%, and the Kappa coefficient was 0.859. This study provides a new method for weed identification in rice fields.

The integrated development of information communication technology and neuroscience heralds the possibility and great potential of brain-to-brain wireless communication(B2BC).The physiologically meaningful features of brain responses were extracted to different contour and shape in images in Gestalt cognitive tests by combining persistent homology analysis with electroencephalogram (EEG).The experimental results show that more brain regions in the frontal lobe were involved when the subject perceives the random and disordered combination of images compared to the ordered Gestalt images.Meanwhile, the persistence entropy of EEG data evoked by random sequence diagram (RSD) was observed to be significantly different from that by the ordered Gestalt images(GST) in several frequency bands, which indicated that human cognition of shapes and contours, such as a preliminary advanced cognition process, could be separated to some extent through topological analysis.This method can digitize the neural signals while preserving the whole and local features of the original signals.In general, the cognitively related neural correlates by persistent homology features of EEG signal are revaluated and quantified, which provides an approach to realize the digitization of neural signals in brain-to-brain wireless communication.

As a key technology of 5G, massive MIMO system can significantly improve spectrum efficiency and energy efficiency by equipping a large number of antennas in base stations.However, in massive MIMO system, accurate channel estimation faces severe challenges.In order to estimate the channel accurately when the pilot sequence length is smaller than the number of transmitting antennas and the channel noise is strong, the estimation method N2N-GAN was proposed.N2N-GAN firstly denoised the pilot channel at the receiving end, and then used the conditional generative adversarial network to estimate the channel matrix according to the denoised pilot signal.Simulation experiments show that N2N-GAN achieves better robustness against noise compared with traditional channel estimation algorithms and deep learning-based methods.Meanwhile, it can adapt to scenarios with fewer pilot symbols and more antennas.

Abstract Nowadays, deep neural networks (DNNs) have been rapidly deployed to realize a number of functionalities like sensing, imaging, classification, recognition, etc. However, the computational-intensive requirement of DNNs makes it difficult to be applicable for resource-limited Internet of Things (IoT) devices. In this paper, we propose a novel pruning-based paradigm that aims to reduce the computational cost of DNNs, by uncovering a more compact structure and learning the effective weights therein, on the basis of not compromising the expressive capability of DNNs. In particular, our algorithm can achieve efficient end-to-end training that transfers a redundant neural network to a compact one with a specifically targeted compression rate directly. We comprehensively evaluate our approach on various representative benchmark datasets and compared with typical advanced convolutional neural network (CNN) architectures. The experimental results verify the superior performance and robust effectiveness of our scheme. For example, when pruning VGG on CIFAR-10, our proposed scheme is able to significantly reduce its FLOPs (floating-point operations) and number of parameters with a proportion of 76.2% and 94.1%, respectively, while still maintaining a satisfactory accuracy. To sum up, our scheme could facilitate the integration of DNNs into the common machine-learning-based IoT framework and establish distributed training of neural networks in both cloud and edge.

Underwater unmanned vehicle cluster cooperative area target searching is an inexpensive and efficient effort for salvaging and vigilance at sea. In order to realize the autonomous area searching of the attacking underwater unmanned vehicle (AUUV) cluster, a multiple AUUV cooperative area target searching method is put forward and simulated in this paper. The proposed method combines the AUUV with an underwater environment to create an environmental model, an AUUV motion model, a target probability and update model, and an object optimization function. Moreover, the global-best difference-mutation brain storm optimization algorithm is adopted to test the multiple AUUV cooperative area target searching path planning. The results of the simulation experiment verify the effectiveness of the proposed method and models. Hence, a new scheme is advanced to realize multiple AUUV cooperative area target searching and expand multiple AUUV applications.

Electric vehicles (EVs) are one of the most promising applications that are reshaping the future urban mobility market and benefitting the urban environment. Analyzing the adoption of EVs helps both vehicle sales market management and urban transportation-related environmental cost estimation. Previous studies have shown that EV adoption is mostly affected by the economic environment and users’ psychological factors; however, both factors vary among specific urban transportation networks. This paper thus proposes network-related vehicle operating cost functions and a logit-based choice model, which considers both the economic environment and users’ psychological factors at a network level. The model can thus estimate the vehicle adoption for specific networks. Numerical experiments and sensitivity analyses were conducted to illustrate the proposed method and provide practical insights in estimating EV adoption, respectively. The results suggest that EV adoption greatly varies among different cities.

Collective intelligence is manifested when multiple agents coherently work in observation, interaction, decision-making and action. In this paper, we define and quantify the intelligence level of heterogeneous agents group organized in a flat structure without explicit leadership by the improved Anytime Universal Intelligence Test (AUIT), an extension of the existing evaluation of homogeneous agents group. The relationship of intelligence level with agents' composition, group size, spatial complexity and testing time is analyzed. The intelligence level of heterogeneous agents groups is compared with that for the homogeneous groups as well, so as to demonstrate the effects of heterogeneity on collective intelligence. Our work will contribute to understand the essence of collective intelligence more deeply and reveal the effect of various key factors on group intelligence level.

Standard methods of subspace clustering are based on self-expressiveness in the original data space, which states that a data point in a subspace can be expressed as a linear combination of other points. However, the real data in raw form are usually not well aligned with the linear subspace model. Therefore, it is crucial to obtain a proper feature space for performing high quality subspace clustering. Inspired by the success of Convolutional Neural Networks (CNN) for extraction powerful features from visual data and the block diagonal prior for learning a good affinity matrix from self-expression coefficients, in this paper, we propose a jointly trainable feature extraction and affinity learning framework with the block diagonal prior, termed as Convolutional Subspace Clustering Network with Block Diagonal prior (ConvSCN-BD), in which we solve the joint optimization problem in ConvSCN-BD via an alternating minimization algorithm, which updates the parameters in the convolutional modules and the self-expression coefficients with stochastic gradients descent and updates other variables with close-form solutions alternatingly. In addition, we derive the connection between the block diagonal prior and the subspace structured norm, and reveal that using the block diagonal prior on the affinity matrix is essentially incorporating the feedback information from spectral clustering. Experiments on three benchmark datasets demonstrated the effectiveness of our proposal.

Main-lobe interrupted-sampling repeater jamming is a coherent type of interference. As this interference is injected through the main lobe of the antenna into a radar system, the interference heavily degrades the performance of the radar system. In this paper, we propose an anti-jamming method based on minimum variance distortionless response (MVDR) that estimates a covariance matrix by obtaining small-sample pure jamming signals through jamming recognition. Time-frequency analysis is performed in the one-dimensional range profile of the pulse-compressed radar echo, and the snapshot sampling of pure jamming is achieved by searching the range gates for the target and the jamming in a two-dimensional range-frequency graph considering the time-frequency differences between the target and jamming signals. Assuming that the available data of snapshot sampling are insufficient, a covariance matrix for pure jamming is reconstructed through an iterative adaptive approach (IAA) and used as the training sample for the MVDR beamformer to suppress main-lobe interrupted-sampling repeater jamming. Finally, the method is validated by comparing the simulation results with the measured results.

As a core technique to quantitatively assess the stenosis severity of coronary arteries, quantitative coronary analysis (QCA) is urgently supposed to become more automated and intelligent, especially for regions lacking expertise and technology. The existing QCA methods highly depend on manual operation, which is time-consuming and subject to personal experience. This study innovatively proposes a fully automatic QCA workflow based on artificial intelligence (AI-QCA), which can quickly and accurately make a quantitative assessment of stenosis severity. The whole AI-QCA workflow mainly consists of three parts: the boundary-aware segmentation on the coronary angiogram (CAG) images, the AI-enabled coronary artery tree construction, and the diameter fitting and stenosis detection. Experiments show that the precision, recall, and F1 score of the segmentation, evaluated on 1322 CAGs, are 0.866, 0.897, and 0.879, respectively. Furthermore, the RMSE between diameter stenosis assessed by AI-QCA and manual QCA served by senior experts, evaluated on 249 CAGs, is 0.064, and the Pearson coefficient is 0.765. Meanwhile, the operation time can be reduced from tens of minutes to several seconds by AI-QCA. As a conclusion, the proposed AI-QCA is able to quickly quantify stenosis parameters as accurately as senior experts, which is significant for the intelligent diagnosis and treatment of coronary artery disease.

The present study subjects the extruded Mg-6Al-1Zn-1.1Sc (wt%) alloy to reveal the deformation during in-situ tensile testing at room temperature by scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD). The results demonstrate that the parallel slip traces are increasingly observed on the surfaces of grains with increasing strains owing to the movement of dislocations inside the grains toward the sample surface, while microcracks are frequently observed at grain boundaries. The slip trace analysis indicate that many basal slips are activated during the deformation. The twinning behaviors of the extruded alloy during tensile testing are dominated by extension twinning. The orientation of grains is demonstrated to have a profound effect on their deformation, where grains with orientations deviating greatest from 〈0001〉//TD exhibit the highest dislocation density after deformation. This can be attributed to the fact that these oriented grains are prone to activate basal slip due to the large Schmid factor (SF). Similarly, the deformation of grains is also found to be highly dependent on their size, where the dislocation density of coarser grains increases more significantly than that of finer grains during deformation because coarser grains have greater space available for accommodating dislocations than finer grains.

Discarding Non-Maximum Suppression (NMS) post-processing and realizing fully end-to-end object detection is a recent research focus. Previous works have proved that the one-to-one label assignment strategy provides the chance to eliminate NMS during inference. However, this strategy might also result in multiple predictions with high scores due to the inconsistency of label assignment during training. Thus, how to adaptively identify only one positive sample as a final prediction for each Ground-Truth instance remains important. In this paper, we propose an Enhanced Positive Sample Filter (EPSF) to filter out the single positive sample for each Ground-Truth instance and lower the confidence of other negative samples. This is mainly achieved with two components: a Dual-stream Feature Enhancement module (DsFE) and a Disentangled Max Pooling Filter (DeMF). DsFE makes full use of representations trained with different targets so as to provide rich information clues for positive sample selection, while DeMF enhances the feature discriminability in potential foreground regions with disentangled pooling. With the proposed methods, our end-to-end detector achieves a better performances against existing NMS-free object detectors on COCO, PASCAL VOC, CrowdHuman and Caltech datasets.

Tillering fertilization is an important part of field management in rice production. As the first peak fertilizer requirement period of rice, tillering fertilization directly affects the number of tillers and the growth of rice in the middle and late stages. In order to investigate a method of constructing an accurate fertilizer prescription map in the tillering stage using an unmanned aerial vehicle (UAV) remote sensing nitrogen demand diagnosis and reduce the amount of chemical fertilizer while ensuring the rice yield, this study realized the diagnosis of the rice nitrogen nutrient demand using UAV hyperspectral remote sensing during the tilling stage fertilization window. The results showed that the fertilizer amount was determined using the characteristic waveband and remote sensing. The results showed that five rice hyperspectral variables were extracted in the range of 450–950 nm by the feature band selection and feature extraction for the inversion of rice nitrogen content, and the inversion model of rice nitrogen content constructed by the whale-optimized extreme learning machine (WOA-ELM) was better than that constructed by the whale-optimized extreme learning machine (ELM). The model coefficient of determination was 0.899 and the prescription map variable fertilizer application method based on the nitrogen content inversion results reduced the nitrogen fertilizer by 23.21%. The results of the study can provide data and a model basis for precise variable fertilizer tracking by agricultural drones in the cold rice tillering stage.

Cis-alkenes are ubiquitous in biological molecules, which makes it greatly significant to develop efficient methods toward construction of cis-olefins. Herein, we reported a facile semi-hydrogenation of alkynes to cis-alkenes in an efficient way with cuprous bromide/tributylphosphine as the catalyst and bis(pinacolato)diboron/methanol as the hydrogen donor. The method features convenient and facile reaction conditions, wide substrate scope, high yields, and high stereoselectivity.

Polymeric micro devices are gaining huge market potential in broad areas of medical devices, diagnostic devices, drug delivery, and optical applications. Current research is focusing on developing functional polymeric micro devices on a mass-production scale. Microinjection moulding is a promising technique suitable for fabricating polymeric micro devices. This review aims to summarise the primary achievements that have been achieved in various aspects of microinjection moulding of polymer micro devices, consisting of micro parts and micro surface structures. The relationships of the machine, process, rheology, tooling, micro/nanoscale replication, morphology, properties, and typical applications are reviewed in detail. Finally, a conclusion and challenges are highlighted.

This work was aimed to study skin blood perfusion, vasomotion and vascular responses of the Wistar-Kyoto rats (WKY) and spontaneously hypertensive rats (SHR) in different stages of age using spectral. Laser-Doppler flowmetry (LDF) was used to examine the ears and limbs of WKY (12 and 48 weeks old) and SHR (12 and 48 weeks old). The skin blood flow oscillations (SBFOs) were studied by wavelet spectral analysis of LDF tracings. Then, we observed that old groups showed decreased perfusion and SBFO in the ears of both SHR and WKY. The SHR showed obviously lower postocclusive reactive hypera (PORH) ratio at the same age. A decreased peak-time occurred in the SHR of old age group. After PORH test, a statistically significant increase was observed within all subintervals in the absolute amplitude of 12-week WKY and only within IV and III subintervals in the absolute amplitude of 12-week SHR. But, the absolute amplitude of 48-week WKY and SHR showed no statistically significant increase within all subintervals. Results indicated that local regulating function of peripheral vascular was impaired in rat with hypertension and aging. Abbreviations LDF: Laser-Doppler flowmetry; SBF: Skin blood flow; SBFO: Skin blood flow oscillation; PORH: Postocclusive reactive hyperemia; SHR: Spontaneously hypertensive rats; WKY: Wistar-Kyoto rats; LDF: Laser-Doppler flowmetry; LDI: Laser Doppler Imaging; BP: Blood pressure.

Multiple-input-multiple-output (MIMO) radar has received significant attention because it offers broad design freedom and superior performance. Polyphase code sets with low autocorrelation sidelobe peaks and low cross-correlation peaks, also known as orthogonal polyphase code sets, can be used as transmission signals by MIMO radar. We present an effective algorithm named the greedy code search-based memetic algorithm (MA-GCS) to design such code sets. MA-GCS is a novel variation of the new evolutionary search, which can be used to design only a single binary sequence with low autocorrelation peaks. MA-GCS integrates an evolutionary search for global searches and a GCS for local searches to improve the accuracy of solutions. Moreover, an accelerating algorithm is designed to reduce the computational complexity of the GCS-based local search. The computational complexity of our algorithm is derived and compared with other algorithms. It is demonstrated by the experimental results that our algorithm is truly effective. On one hand, the code sets designed using our proposed algorithm have better autocorrelation and cross-correlation properties than other methods. On the other hand, the time consumed is acceptable. In addition, the influence of the code set parameters on the performance of the design results is also investigated.

Originating from entomology, stigmergy has provided an effective framework for swarm collaboration. Based on new discoveries on astrocytes in regulating synaptic transmission in the brain, this paper has mapped stigmergy mechanism into the interaction mediated by the propagation of calcium waves in astrocytes between synapses and investigated its characteristics and advantages. Particularly, we have divided the short-range interaction between synapses that are not directly connected by neurons into three phases and proposed a stigmergic learning model. In this model, the state change of an agent will expand its influence to affect the states of others. The strength of the interaction is determined by the level of neural activity as well as the distance between the agents. Inspired by these findings, we propose a model to help solve task assignment and coordination problems. The simulation results have verified the effectiveness of the proposed model.

In recent years, retinal vessel segmentation technology has become an important component for disease screening and diagnosing in clinical medicine. However, retinal vessel segmentation is a challenging task due to complex distribution of blood vessels, relatively low contrast between target and background, and potential presence of illumination and pathologies. In this paper, we propose an automatic retinal vessel segmentation network using deep supervision and smoothness regularization, which integrates holistically-nested edge detector (HED) and global smoothness regularization from conditional random fields. It is an end-to-end and pixel-to-pixel deep convolutional network, can perform better results than HED-based methods and the methods where CRF inference is applied as a post-processing method. With co-constraints between pixels, the proposed DSSRN obtains better results. Finally, we show that our proposed method obtains the state-of-the-art vessel segmentation performance on all three benchmarks, DRIVE, STARE, and CHASE_DB1.

Existing deep trackers mainly use deep neural networks pre-trained on the object recognition training sets to generate deep features as target representation. However, pre-trained deep features are not effective in representing arbitrary forms of target objects which are likely to be unseen for the pre-trained deep networks. To narrow the gap of representation capability, we propose to transfer the objectness information within pre-trained deep networks. The transferred objectness information is utilized to generate deep features aware of any arbitrary form of target objects for robust visual tracking. Specifically, we design a novel network branch on top of pre-trained deep models to perform incremental transfer learning. The learned network with the transferred objectness information helps to locate target objects undergoing large appearance changes precisely. Experimental results on standard benchmark datasets demonstrate that the proposed algorithm performs favorably against the start-of-the-art trackers.

Human mobility prediction is of great importance in a wide range of modern applications in different fields such as personalized recommendation systems, the fifth-generation (5G) mobile communication systems, and so on. Generally, the prediction goal varies from different application scenarios. For the applications of 5G network including resource allocation and mobility management, it is essential to predict the positions of mobile users in the near future from dozens of seconds to a few minutes so as to make preparation in advance, which is actually a trajectory prediction problem. In this paper, with the particular focus on multi-user multi-step trajectory prediction, we first design a basic deep learning-based prediction framework, where the long short-term memory (LSTM) network is directly applied as the most critical component to learn user-specific mobility pattern from the user's historical trajectories and predict his/her movement trends in the future. Motivated by the related findings after testifying and analyzing this basic framework on a model-based dataset, we extend it to a region-oriented prediction scheme and propose a multi-user multi-step trajectory prediction framework by further incorporating the sequence-to-sequence (Seq2Seq) learning. The experimental results on a realistic dataset demonstrate that the proposed framework has significant improvements on generalization ability and reduces error-accumulation effect for multi-step prediction.

The rubber molecular chain in waste vulcanized tire rubber will be crosslinked to form a network structure that would be difficult to degrade in asphalt. Crumb rubber treated by desulfurization activation could form active groups on the surface by interrupting the crosslinking bond to improve the compatibility between crumb rubber powder and asphalt. To explore the influence of activation modes on crumb rubber powder and the corresponding rubber-modified asphalt binder, crumb rubber powder was firstly activated through three commonly used activation methods and asphalt binder samples modified by activated crumb rubber powder were also prepared. The basic properties of activated crumb rubber powder were characterized by infrared spectroscopy, and conventional tests were used to study the conventional physical properties of the asphalt binder. The infrared spectroscopy and elemental analysis showed that the crumb rubber powder was mainly composed of alkanes, alkenes, sulfonic acids, aromatics, and a little silica rubber and antioxidant zinc oxide, which is suitable for asphalt modification. The simple heat activation treatment method is not enough to greatly destroy the cross-linking structure of crumb rubber powder, but the “C=C” bond was destroyed more seriously. Under the action of adjuvants, the polysulfide cross-linking bond could be broken in crumb rubber powder. The heat treatment and chemical treatment could not achieve the purpose of reducing the viscosity and improving the compatibility of rubber asphalt binder through desulfurization activation. The mechanochemical treatment would help to improve the performance of crumb-rubber-powder-modified asphalt binder. The data correlation analysis based on the grey relational degree can provide a reference for the selection of activated crumb rubber powder for different application requirements in the asphalt modification procedure.

It has been confirmed that polyphosphoric acid (PPA) can significantly improve the high-temperature resistance, storage stability, and aging resistance of asphalt. The low price of PPA is conducive to the development of modified asphalt with high performance and excellent economy, suggesting its great application prospects. At present, there is little research on the role of PPA in delaying the aging process of PPA-modified asphalt, and a consensus has not yet been reached. Therefore, PPA-modified asphalt with different blending amounts (0 wt%, 0.4 wt%, 0.8 wt%, and 1.2 wt%) was prepared in this study. On the basis of variable performance, the role of PPA in the short-term antiaging process of asphalt is analyzed through Fourier transform infrared spectroscopy (FTIR) analysis of PPA-modified asphalt before and after aging, combined with the analysis of the change in components. The results showed that after PPA addition, the high-temperature resistance and the thermal stability of asphalt were improved and the temperature sensitivity was weakened. Both curves of ductility and G∗ indicate the potential saturation effect of PPA addition; together with infrared spectroscopy, we proved that there are both chemical reactions and physical mixing in the PPA-modified asphalt. The component fractions show that PPA will cause the asphalt to transform from sol to gel and the main function of PPA in retarding asphalt aging is the dispersion of the agglomerates of asphaltene micelles by PPA.

In this paper, the algebraic topological characteristics of brain networks composed of electroencephalogram(EEG) signals induced by different quality images were studied, and on that basis, a neurophysiological image quality assessment approach was proposed. Our approach acquired quality perception-related neural information via integrating the EEG collection with conventional image assessment procedures, and the physiologically meaningful brain responses to different distortion-level images were obtained by topological data analysis. According to the validation experiment results, statistically significant discrepancies of the algebraic topological characteristics of EEG data evoked by a clear image compared to that of an unclear image are observed in several frequency bands, especially in the beta band. Furthermore, the phase transition difference of brain network caused by JPEG compression is more significant, indicating that humans are more sensitive to JPEG compression other than Gaussian blur. In general, the algebraic topological characteristics of EEG signals evoked by distorted images were investigated in this paper, which contributes to the study of neurophysiological assessment of image quality.

A large number of waste tires are in urgent need of effective treatment, and breaking waste tires into crumb rubber powder for modifying asphalt has been proved as a good idea to solve waste tires. Crumb rubber modified asphalt not only has good high and low temperature performance, durability, and aging resistance but can also reduce pavement noise and diseases, which has wide application prospects. In this study, crumb rubber powder was desulfurized by mechanochemical method to prepare desulfurized crumb rubber modified asphalt. During the desulfurization process of crumb rubber, the effects of desulfurization process variables including desulfurizer type, desulfurizer content, and desulfurization mixing temperature and time were considered, and then the physical properties of modified asphalt were tested. The test results showed that after mixing crumb rubber powder with desulfurizer, the viscosity of crumb rubber powder modified asphalt can be reduced. Moreover, the storage stability of crumb rubber powder modified asphalt could also be improved by mixing crumb rubber with desulfurizer. Based on the physical properties of crumb rubber powder modified asphalt, the desulfurization process of selected organic disulfide (OD) desulfurizer was optimized as follows: the OD desulfurizer content was 3%, the desulfurization mixing temperature was 160 °C, and the mixing time was 30 min. In addition, Fourier infrared spectroscopy analysis was carried out to explore the modification mechanism of desulfurized crumb rubber powder modified asphalt. There is no fracture and formation of chemical bonds, and the modification of asphalt by crumb rubber powder is mainly physical modification.

Objectives This study presented the feasibility of oxycodone as the sole opioid for general anaesthesia in minor/moderate surgery. Methods In this prospective, observational, descriptive study, 62 patients were enrolled and received intravenous oxycodone as the sole opioid for the induction and maintenance of general anaesthesia. We observed all of these patients to determine whether oxycodone alone could provide sufficient intraoperative and postoperative analgesia, as well as to record the extubation times and adverse events. Results A total oxycodone dose of 0.316 ± 0.05 mg/kg was used for induction and maintenance of general anaesthesia. The dose could maintain haemodynamic stability during surgery and good postoperative analgesia. Oxycodone caused deep sedation (nine patients had Ramsey sedation scores ≥4), leading to respiratory depression and long stays in the post-anaesthesia care unit (PACU). The extubation time (16.9 ± 6.4 minutes) increased with increasing oxycodone doses. Conclusion Oxycodone can be used as the sole opioid for general anaesthesia in minor/moderate surgery. However, care should be taken because of its deep sedation effect. Trial registration: This study is registered at the Chinese Clinical Trial Registry ( www.chictr.org.cn ): ChiCTR-opc-16009175