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Concrete crack detection is essential for infrastructure safety, and its detection efficiency and accuracy are the key issues. An improved YOLOV5 and three measurement algorithms are proposed in this paper, where the original prediction heads are replaced by Transformer Heads (TH) to expose the prediction potential with one self-attention model. Experiments show that the improved YOLOV5 effectively enhances the detection and classification of concrete cracks, and the Mean Average Precision (MAP) value of all classes increases to 99.5%. The first method is more accurate for small cracks, whilst the average width obtained based on the axial traverse correction method is more exact for large cracks. The crack width obtained from the concrete picture sample is the same as that obtained from the manual detection, with a deviation rate of 0–5.5%. This research demonstrates the recognition and classification of concrete cracks by integrating deep learning and machine vision with high precision and high efficiency. It is helpful for the real-time measurement and analysis of concrete cracks with potential safety hazards in bridges, high-rise buildings, etc.

We find that expected return is related to trading volume positively among underpriced stocks but negatively among overpriced stocks. As such, trading volume amplifies mispricing. Our results are robust to alternative mispricing and trading volume measures, alternative portfolio formation methods, and controlling for variables that are known to have amplification effects on mispricing. By attributing trading volume to investor disagreement, we show that our results are consistent with the recent theoretical model of Atmaz and Basak (2018) in that investor disagreement predicts stock returns conditional on expectation bias.

This paper identifies a trend factor that exploits the short-, intermediate-, and long-run moving averages of settlement prices in commodity futures markets. The trend factor generates statistically and economically large returns during the post-financialization period 2004-2020. It outperforms the well-known momentum factor by more than nine times the Sharpe ratio and has less downside risk. The trend factor cannot be explained by the existing factor models and is priced cross-sectionally. Finally, we find that the trend factor is correlated with funding liquidity measured by the TED spread. Overall, the results indicate that there are significant economic gains from using the information on historical prices in commodity futures markets.

The supersonic mode in hypersonic flows is studied numerically. The radiation characteristics of this mode in a Ma=6 flat-plate boundary layer with cooled walls are focused on. The linear stability...

In this study, the effects of entropy layer on the boundary layer over hypersonic blunt cones for a thermochemical equilibrium gas are investigated. The flow and stability characteristics of the entropy and boundary layers are presented considering the chemical reactions. It is found that the entropy layer has little influence on the inner layer inside the boundary layer. The inner layer thickness increases when chemical equilibrium is considered, which stems from the enhanced viscosity protection near the wall surface. At the leading edge of the blunt cone, due to the effects of the equilibrium gas, the temperature in the boundary and entropy layers decreases and the boundary-layer-edge parameters significantly change. The entropy layer gradually vanishes along the downstream direction, and the effects of chemical reactions are concentrated in the boundary layer. The entropy swallowing point of the equilibrium gas does not significantly differ from that of the perfect gas. For all the analyzed cases, chemical reactions stabilize the entropy layer instability modes. The instability region and the frequency range of the equilibrium gas decrease, and the growth rate of the most unstable perturbation of the equilibrium gas is considerably smaller than that of the perfect gas. Moreover, the results show that the equilibrium gas has a minor influence on the process of the blunt cone boundary layer solution tending to the sharp cone solution.

Many anomalies are based on low frequency attributes, such as annual characteristics, that ignore higher frequency information. In this paper, we provide a simple strategy to incorporate the higher frequency information. We find that there is significant economic value-added. For eight major anomalies, we find that the enhanced anomalies can double the average returns while having similar or lower risks. The results are robust to a number of controls.

Alzheimer’s disease (AD) is a chronic neurodegenerative disease that is characterized by the extracellular accumulation of β-amyloid (Aβ). Many studies have shown a close relationship between autophagy and the formation of Aβ. As AD develops and progresses, mitophagy diminishes insoluble Aβ, and mitochondrial dysfunction seems to be a determining factor in the pathogenesis of AD. In our previous study, we showed that β-asarone pharmacological effects in APP/PS1 transgenic mice, reducing Aβ expression. However, the specific mechanism of this effect remains unclear. In this study, AD model rats induced by intracerebroventricular injection of Aβ1-42 were randomly divided into nine groups, and medical intervention was applied to the animals for 30 days. Subsequently, spatial learning and memory were evaluated by the water maze test. Bcl-2 levels in the hippocampus were determined by western blotting (WB). The protein expression of Aβ1-42, Beclin-1, p62, PINK1, and Parkin was assessed by WB and immunohistochemistry (IHC). The data showed that after β-asarone treatment, the learning and memory of the AD rats were clearly improved compared with those of the model group. Moreover, β-asarone decreased Aβ1-42, Bcl-2, and p62 levels but increased Beclin-1 levels compared with those in the model group. In addition, we treated a group of rats with CsA to inhibit mitophagy. β-Asarone increased PINK1 and Parkin expression compared with that in the model group. The results showed that β-asarone can improve the learning and memory of rats with Aβ1-42-induced AD by effectively promoting PINK1-Parkin-mediated mitophagy. Taken together, these results suggest that β-asarone may have the capacity to become a pharmaceutical agent for the treatment of AD in the future.

We present a scheme to improve the three-photon blockade in a two-atom cavity QED system in a strong coupling regime. By using electromagnetically induced transparency technique, we show that the frequency range to realize the three-photon blockade can be broadened in the presence of the control field in the case of both in-phase and out-of-phase radiations. We also show that the three-order field correlation function g(3)(0) can be decreased considerably in the presence of the control field especially in the case of out-of-phase radiations. The scheme presented in this paper provides a good candidate to realize and improve the three-photon blockade, as well as generating anti-bunched photon pairs.

Triboelectric nanogenerator (TENG) has been proven effective in converting biomechanical energy into electrical energy, which is expected to be a new energy supply device for wearable electronics and can be utilized as a self-powered sensor. In this work, we have developed a flexible, eco-friendly, and multifunctional fish gelatin based triboelectric nanogenerator (FG-TENG) composed of fish gelatin (FG) film and poly(tetrafluoroethylene)/poly(dimethylsiloxane) (PTFE/PDMS) composite film. The open-circuit voltage (Voc), short-circuit current (Isc), and output power density of this FG-TENG could reach up to 130 V, 0.35 μA, and 45.8 μW cm-2, respectively, which were significantly higher than those of TENGs based on other commonly used positive friction materials such as aluminum foil, poly(ethylene terephthalate) (PET), and print paper. The superior performance of the FG-TENG is attributed to the strong electron-donating ability of the FG during the triboelectric process. The generated electric energy was high enough to light up 50 commercial light-emitting diodes (LEDs) directly. Importantly, owing to the high stability and excellent sensitivity of the FG-TENG, it has been used as a self-powered sensor for real-time monitoring of the human physiological signals such as finger touch, joint movement, and respiration. Furthermore, to expand the usages in real-life applications, a foldable FG-TENG was fabricated by adopting the Miura folding to monitor human movements in real time. This work provides an economical, simple, and environmental-friendly approach to fabricate a biomechanical energy harvester, which has a great potential in powering next-generation wearable electronics and monitoring human physiological signals.

Morphine is the most representative characteristic alkaloid existed in the extract solution of poppy shell. The analysis of morphine can be used to judge whether poppy shell is illegally added to the hot pot soup base. A sensitive, environmental-friendly carbon quantum dots (C-Dots)–based fluorescence immunoassay (C-Dots-FLISA) was developed to detect morphine (MOP). Novel water-soluble C-Dots were synthesized using a one-step hydrothermal reaction, with citric acid serving as the carbon source and ethylene diamine acting as the nitrogen source. Moreover, the C-Dots displayed blue fluorescence with an emission peak at 440 nm (350 nm excitation), which also showed good stability. C-Dots which contained amine were used to conjugate with anti-morphine antibody by glutaraldehyde as coupling reagents. The anti-morphine antibody–labeled C-Dots (Abs-labeled C-Dots) were characterized by fluorescence spectrum, transmission electron microscopy (TEM), and gel electrophoresis. C-Dots-FLISA was developed and applied to quantitative detection of morphine. Under the optimal conditions, the linear range spanned from 3.2 × 10−4 to 10 mg/L (R2 = 0.992), and the detection limit was 3 × 10−4 mg/L. These results demonstrated that the developed C-Dots-FLISA could be applied as a sensitive and convenient tool for rapid detection of morphine.

A scalable process for the novel monobactam antibiotic LYS228 has been developed. This paper covers a novel scalable process for producing β-lactam 10 in the multiple kilogram scale. An alternative...

This paper examines the impact of idiosyncratic volatility (IV) shocks on cross-sectional stock returns. The IV shock is defined by comparing a stock’s idiosyncratic volatility in month t against its own idiosyncratic volatilities estimated in the previous months. The results indicate that stocks with positive (negative) IV shocks subsequently underperform (outperform). Unlike the conventional cross-sectional sorts on the level of IV, the intertemporal IV shocks identified in this paper are better served to capture the very moment when firm specific information arrives or is fully absorbed. Therefore, the performance divergence between stocks with positive and negative IV shocks reflects how investors interpret the new information. We document that cognitive bias, such as the disposition effect and gambler’s fallacy, may provide a reasonable explanation for the IV shock effect.

Based on the second-order harmonic potential theory, the characteristics of the second-order harmonic field generated at the solid-liquid interface induced by P wave incidence are analyzed. A planar model of the solid-liquid interface is established to study the variation of the second-order displacement field versus the incident angles. The homogeneous solution coefficient matrix, refraction and reflection coefficient matrix are introduced. According to the boundary conditions and Lagrange's various parameters method, the second-order displacement field is obtained, and its dependence on the solid-liquid interface is investigated. The different effects of boundary on the tangential displacement and normal displacement are demonstrated. Numerical simulation shows that the complete solution varies slightly at the incident angle, and the tangential displacement and the normal displacement change sharply at a mutation angle θ

Instabilities of high-enthalpy flat-plate boundary layers in thermochemical equilibrium conditions are studied using a linear stability theory (LST) and parabolized stability equations. A supersonic radiation mode is found to be unstable at the downstream location of the second mode. This mode synchronizes with the second mode as its phase velocity approaches [Formula: see text]. The amplification rate is expected to be comparable to the second mode when [Formula: see text] and [Formula: see text]. The results of disturbance evolution indicate that the radiation mode is generated by the second mode through a synchronization process. The coefficient for the amplitude response of the radiation mode to the forcing second mode is around 1. After the radiation mode is excited, the second mode itself still exists in the boundary layer, leading to a co-existence of dual unstable modes. Owing to the competition between the two modes, the disturbance amplitudes exhibit significant oscillations. A two-mode amplitude prediction model based on the LST is proposed by properly superposing the two modes' amplitudes. The accurate prediction of the N-factor demonstrates that the proposed model can be used to predict a high-enthalpy boundary layer transition involving these two modes.

This paper presents a novel method based on AC impedance for monitoring the curing process of epoxy resin. Effects of DMP-30 and temperature on the reaction rate and curing degree were investigated here. Experimental results showed that DMP-30 has significant influence on reaction rate of the curing system, and the resin system, containing 1.5 wt% DMP-30 can be cured completely in the shortest time at 80 °C. Impedance analysis showed that the curing degree increased obviously with increasing temperature, while the curing time got shortened sharply. DSC technique was also employed here to verify the effectivity of impedance analysis of curing degree, and a good agreement between AC impedance and DSC was observed, especially at higher temperature, demonstrating the great potential application of AC impedance in field of cure monitoring.

High performance protective coatings applied under heavy duty condition will lengthen the structure’s service life, decrease maintenance times, economic and human life loss. Here, high protective performance coatings were prepared by dispersing polyaniline (PANI) nanowires into epoxy modified furfural-acetone (EMFA) resin matrix and then cured with phenalkamine. The protective performance of coatings was investigated by immersion experiments in 12% NaCl solution at 90 °C and Electrochemical Impedance Spectroscopy (EIS) measurements. The coating containing 1.0% PANI nanowires showed high impedance (>1010 ohm·cm2) after 80 days immersion, exhibiting great protective performance. X-ray photoelectron spectroscopy (XPS) surveys verified the formation of passive layer (Fe2O3/Fe3O4) on steel surface beneath the coating with 1.0% PANI. The bending test suggested that both the pure EMFA coating and the EMFA/PANI coating showed excellent flexibility of 2 mm. Here, measurement of water absorption, adhesion strength, pencil hardness and impact resistance were conducted to evaluate coatings performance.

The effects of air dissociation on flat-plate hypersonic boundary-layer flow instability and transition prediction are studied. The air dissociation reactions are assumed to be in the chemical equilibrium. Based on the flat-plate boundary layer, the flow stability is analyzed for the Mach numbers from 8 to 15. The results reveal that the consideration of air dissociation leads to a decrease in the unstable region of the first-mode wave and an increase in the maximum growth rate of the second mode. High frequencies appear earlier in the third mode than in the perfect gas model, and the unstable region moves to a lower frequency region. When the Mach number increases, the second-mode wave dominates the transition process, and the third-mode wave has little effect on the transition. Moreover, when the Mach number increases from 8 to 12, the N-factor envelope becomes higher, and the transition is promoted. However, when the Mach number exceeds 12, the N-factor envelope becomes lower, and the transition is delayed. The N-factor envelope decreases gradually with the increase in the altitude or Mach number.

Norfloxacin (NOR) in milk may influence mammalian cell replication and bring about a decrease in the efficiency for treating infection in humans. However, current techniques for detecting NOR usually require expensive instruments and trained personnel. In this work, we have developed a low-cost and simple method via paper-based fluorescent immunoassay for highly sensitive and selective detection of NOR in milk at picogram level. The NOR monoclonal antibody labeled with quantum dots is used as a detection probe to recognize the corresponding NOR, which can quantitatively detect NOR on paper-based devices. The detection limits in aqueous solution and milk are 1 pg/mL and 10 pg/mL, respectively. The developed paper-based method provides a cheap, sensitive, eco-friendly, and rapid approach for quantitative detection of trace NOR in milk, which may find wide applications in food safety inspection. Noteworthy, the method is especially suitable for applications at resource-limited and on-site settings.

This communication describes the rational design of a transparent paper-based chemosensing platform for multi-target detection by wavelength-dependent absorbance/transmittance. The platform was successfully applied in the examination of bovine serum albumin (BSA) and cholesterol in serum with a low detection limit of 0.1 μM and 0.1 mM, respectively. With low cost and high sensitivity, the paper-based platform shows great promise for multiplexed bioassays.

Technical analysis is the study for forecasting future asset prices with past data. In this survey, we review and extend studies on not only the time-series predictive power of technical indicators on the aggregated stock market and various portfolios, but also the cross-sectional predictability with various firm characteristics. While we focus on reviewing major academic research on using traditional technical indicators, but also discuss briefly recent studies that apply machine learning approaches, such as Lasso, neural network and genetic programming, to forecast returns both in the time-series and on the cross-section.