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Software specifications are of great importance to improve the quality of software. To automatically mine specifications from software systems, some specification mining approaches based on finite-state automatons have been proposed. However, these approaches are inaccurate when dealing with large-scale systems. In order to improve the accuracy of mined specifications, we propose a specification mining approach based on the ordering points to identify the clustering structure clustering algorithm and model checking. In the approach, the neural network model is first used to produce the feature values of states in the traces of the program. Then, according to the feature values, finite-state automatons are generated based on the ordering points to identify the clustering structure clustering algorithm. Further, the finite-state automaton with the highest F-measure is selected. To improve the quality of the finite-state automatons, we refine it based on model checking. The proposed approach was implemented in a tool named MCLSM and experiments, including 13 target classes, were conducted to evaluate its effectiveness. The experimental results show that the average F-measure of finite-state automatons generated by our method reaches 92.19%, which is higher than most related tools. [ABSTRACT FROM AUTHOR]

Remote sensing image scene classification is a fundamental task in intelligent interpretation of remote sensing images. Although Transformers possess a powerful attention mechanism, they require lengthy training procedures to achieve good performance levels. To address this issue, this paper proposes a novel deep learning network model by combining CNN and Swin Transformer named SwinHCST. Firstly, the model uses Weighted Normalized CNN to quickly extract low-level features of the image. Secondly, the Receptive Field Block module facilitates multi-scale information fusion, Thirdly, the Information Fusion Transformer further excavates the deep-level features of the image. Furthermore, this paper has designed a plug-and-play Cross Spatial Information Fusion Block, which is used to encodes dimensional information and extracts global information to enhance information exchange. The scene classification experiments show that the proposed model outperforms other methods on the three selected datasets and can achieve excellent performance without requiring large amounts of data and training. Specifically, the classification accuracy of the proposed method on the three datasets is 93.76%, 93.60%, and 98.10%, which is 1.7% to 3.71% higher than ResNet50 and 3.7% to 5.7% higher than Swin Transformer. [ABSTRACT FROM AUTHOR]

We consider the problem of modeling heterogeneous materials where microscale dynamics and interactions affect global behavior. In the presence of heterogeneities in material microstructure it is often impractical, if not impossible, to provide quantitative characterization of material response. The goal of this work is to develop a Bayesian framework for uncertainty quantification (UQ) in material response prediction when using nonlocal models. Our approach combines the nonlocal operator regression (NOR) technique and Bayesian inference. Specifically, additive independent identically distributed Gaussian noise is employed to model the discrepancy between the nonlocal model and the data. Then, we use a Markov chain Monte Carlo (MCMC) method to sample the posterior probability distribution on parameters involved in the nonlocal constitutive law and associated modeling discrepancies relative to higher-fidelity computations. As an application, we consider the propagation of stress waves through a one-dimensional heterogeneous bar with randomly generated microstructure. Several numerical tests illustrate the construction, enabling UQ in nonlocal model predictions. Although nonlocal models have become popular means for homogenization, their statistical calibration with respect to high-fidelity models has not been presented before. This work is a first step in this direction, focused on Bayesian parameter calibration. [ABSTRACT FROM AUTHOR]

Early rehabilitation with the right intensity contributes to the physical recovery of stroke survivors. In clinical practice, physicians determine whether the training intensity is suitable for rehabilitation based on patients' narratives, training scores, and evaluation scales, which puts tremendous pressure on medical resources. In this study, a lightweight facial expression recognition algorithm is proposed to diagnose stroke patients' training motivations automatically. First, the properties of convolution are introduced into the Vision Transformer's structure, allowing the model to extract both local and global features of facial expressions. Second, the pyramid-shaped feature output mode in Convolutional Neural Networks is also introduced to reduce the model's parameters and calculation costs significantly. Moreover, a classifier that can better classify facial expressions of stroke patients is designed to improve performance further. We verified the proposed algorithm on the Real-world Affective Faces Database (RAF-DB), the Face Expression Recognition Plus Dataset (FER+), and a private dataset for stroke patients. Experiments show that the backbone network of the proposed algorithm achieves better performance than Pyramid Vision Transformer (PvT) and Convolutional Vision Transformer (CvT) with fewer parameters and Floating-point Operations Per Second (FLOPs). In addition, the algorithm reaches an 89.44% accuracy on the RAF-DB dataset, which is higher than other recent studies. In particular, it obtains an accuracy of 99.81% on the private dataset, with only 4.10M parameters. [ABSTRACT FROM AUTHOR]

The escalating costs of aggregates and stringent sustainable development policies have emphasized the significance of effective high-content recycled asphalt pavement (RAP) regeneration technology. This study aims to present a novel laboratory separation and treatment approach for RAP materials, ensuring optimal performance of high-content RAP recycled asphalt mixtures. The proposed method is compared against factory-separated RAP materials in terms of agglomeration evaluation and mixture performance comparison. High- and low-temperature performance of the recycled asphalt mixture were evaluated through wheel tracking and trabecular bending tests. Furthermore, the moisture sensitivity of the recycled mixture was studied through immersion Marshall and freeze-thaw splitting tests. The uniformity and fusion between aggregates and asphalt in the recycled asphalt mixtures were meticulously analyzed using advanced techniques such as Scanning Electron Microscope and industrial Computed Tomography scans. The laboratory separation method encompasses flexible separation for 5–10 mm RAP materials and rigid separation for 10–20 mm RAP materials. Notably, the laboratory separation methods proved more effective in removing aged asphalt from the surface of 10–20 mm RAP materials compared to factory separation, thereby enhancing the adhesion between aggregates and asphalt and ensuring a more uniform recycled mixture. Although the dynamic stability of recycled asphalt mixture was reduced using laboratory separation, it still meets the requirements of relevant specifications. Moreover, the low-temperature maximum flexural tensile strain of the recycled asphalt mixture increased by 16%, with the residual stability and tensile strength ratio rising by 1.33% and 2.69%, respectively. Compared with the factory separation method, the laboratory separation method reduces the porosity percentage of the recycled asphalt mixture cross-section by 3.47%. Overall, the laboratory separation method for RAP materials provides valuable insights for enhancing the factory processing of RAP materials, contributing to the successful application of high RAP content regeneration technology in the pursuit of sustainable and cost-effective road construction practices. • Proposed a laboratory method and identified suitable parameters for RAP material separation. • Compared the efficacy of the laboratory separation method with the factory separation method. • Established that the recycled asphalt mixture exhibits superior road performance and stability. [ABSTRACT FROM AUTHOR]

A two-dimensional multiphase cellular automaton (CA) model is proposed for the prediction of growth kinetics and microstructural evolution during peritectic transformation of Fe-C alloys. The proposed model is validated by comparing the simulation results with the experimental measurements and analytical predictions for the growth kinetics of the γ -phase and the concentration distributions. The simulated time evolution of the γ -phase thickness and the concentration distribution in the γ -phase agree well with the experimental data, demonstrating the quantitative capabilities of the proposed model. The influences of the holding temperature and γ -phase thickness on the γ -phase growth behavior are analyzed based on the simulation results. The γ -phase growth velocity is found to decrease with increasing the γ -phase thickness and holding temperature. Simulations are also performed for the microstructural evolution during isothermal peritectic transformation of Fe-C alloys with the primary δ -phase being an equiaxed dendrite under different holding temperatures. It is found that the driving force for γ -phase growth increases with decreasing temperature. [ABSTRACT FROM AUTHOR]

This study presents a multiscale analysis for describing the fluid diffusion in polymeric sandwich composites, comprising of glass‐epoxy fiber reinforced polymer (FRP) skins and polyurethane foam core. The multiscale framework includes the unit‐cell micromechanics model for determining the overall diffusion behavior in the FRP skins by incorporating different diffusivities of the fiber and matrix and the diffusion model for foam core. In addition, multiscale diffusion tests have been conducted for the sandwich composites, FRP skins, foam core, and epoxy resin. The specimens were immersed in deionized water at 50°C. The capability in predicting the overall diffusion behavior of sandwich composites while recognizing the different diffusion processes of the constituents is not only beneficial for designing polymeric sandwich composites with desired performance but also enhances understanding on the durability of polymeric sandwich composites. POLYM. COMPOS., 40:3520–3532, 2019. © 2019 Society of Plastics Engineers [ABSTRACT FROM AUTHOR]

This study examines the effects of temperature and fiber and matrix diffusivities on the diffusion of fluid in glass fiber-reinforced polymer composites. Glass fiber-reinforced polymer thin plates were immersed in deionized water at two temperatures: room temperature and 50℃. During the diffusion process, the overall mass changes and dimension changes were recorded, which relate to the volumetric change and the through-the-thickness strain. Different constitutive models are considered in order to understand the diffusion of fluid through the glass fiber-reinforced polymer plates. The macroscopic models of this work, Fickian and Gurtin coupled deformation–diffusion, are first considered in order to describe the macroscopic diffusion behaviors. Two microscopic models that include fiber volume contents and diffusivities of the constituents (fiber and matrix) are then considered in order to gain fundamental insight into the effects of microstructural morphologies and constituents' diffusivities on the diffusion process in the glass fiber-reinforced polymer specimens. [ABSTRACT FROM AUTHOR]

Fluid diffusion in fiber reinforced composites is typically anisotropic. Diffusivity in the fiber direction is faster than in the transverse direction. The reason for this behavior is not yet fully understood. In this work, dealing with glass fiber epoxy composite immersed in distilled water, an experimental procedure for determination of anisotropic diffusion constants from a laminate is presented. The method has the advantage that it does not require sealing of the samples edges because 3-D anisotropic diffusion theory is implemented for obtaining the diffusion constants. A microscale model is presented, where matrix and fiber bundles are modeled separately. The matrix properties have been obtained experimentally and the fiber bundle properties have been deduced by the composite homogenized diffusivity model. The analysis indicates that the anisotropic diffusion of the composite is due to inherent anisotropic properties of the fiber bundles. [ABSTRACT FROM AUTHOR]

In this paper we present an asymptotically compatible meshfree method for solving nonlocal equations with random coefficients, describing diffusion in heterogeneous media. In particular, the random diffusivity coefficient is described by a finite-dimensional random variable or a truncated combination of random variables with the Karhunen-Loève decomposition, then a probabilistic collocation method (PCM) with sparse grids is employed to sample the stochastic process. On each sample, the deterministic nonlocal diffusion problem is discretized with an optimization-based meshfree quadrature rule. In terms of analysis, we first show the analytic regularity of solutions with respect to parameters in the diffusion coefficients and then present the rigorous convergence analysis for the proposed numerical scheme, i.e., the scheme is asymptotic compatible spatially and achieves an algebraic or sub-exponential convergence rate in the random coefficients space as the number of collocation points grows. These results are further confirmed by a number of benchmark problems. Finally, to validate the applicability of this approach we consider a randomly heterogeneous nonlocal problem with a given spatial correlation structure, demonstrating that the proposed PCM approach achieves substantial speed-up compared to conventional Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

In this work we aim to develop a unified mathematical framework and a reliable computational approach to model the brittle fracture in heterogeneous materials with variability in material microstructures, and to provide statistical metrics for quantities of interest, such as the fracture toughness. To describe the material responses such as the nucleation and growth of fractures in a uniform setting, we consider the peridynamics model. In particular, a stochastic state-based peridynamic model is developed, where the micromechanical parameters are modeled by a finite-dimensional random field, e.g., a combination of truncated random variables determined by the Karhunen–Loève decomposition or the principal component analysis (PCA). To solve this stochastic peridynamic problem, probabilistic collocation method (PCM) is employed to sample the random field that represents the micromechanical parameters. For each sample, the corresponding peridynamic problem is solved by an optimization-based meshfree quadrature rule. We present rigorous analysis for the proposed scheme and verify the convergence rate with a number of benchmark problems. The proposed scheme not only possesses the asymptotic compatibility spatially but also achieves an algebraic or sub-exponential convergence rate in the parametric random space when the number of collocation points grows. Finally, to validate the applicability of this approach on real-world fracture problems, we consider the problem of crystallization toughening in glass-ceramic materials, in which the material at the microstructural scale contains both amorphous glass and crystalline phases. The proposed stochastic peridynamic solver is employed to capture the crack initiation and its growth of the glass-ceramics with different crystal volume fractions, and the averaged fracture toughness are also calculated accordingly. The numerical estimates of fracture toughness show good consistency with data from experimental measurements. • A complete workflow is presented to handle quadrature, heterogeneity and fracture. • A stochastic model is proposed, with 2-point functions to describe the heterogeneity. • We provide analysis for the well-posedness and consistency of this formulation. • We validate the fracture toughness estimates against an experiment of glass-ceramics. [ABSTRACT FROM AUTHOR]

Flash evaporation spray is an efficient heat transfer method. Changes in the microstructure and wettability of the cooling surface have important effects on its heat transfer. In this paper, four kinds of cooling surfaces, including a smooth copper surface, a square-column microstructured surface, a superhydrophilic smooth surface, and a superhydrophilic square-column surface, were prepared by chemical corrosion and laser etching. Using liquid nitrogen as the cooling medium, we conducted transient flash evaporation spray experiments on these four types of cooling surfaces. The experimental results show that surface superhydrophilic treatment can substantially enhance the flash evaporation spray of liquid nitrogen. It can more than double the cooling rate for both smooth copper surfaces and square-column microstructured surfaces. In addition, the combination of a microcolumn structure and superhydrophilicity on the cooling surface can provide double reinforcement for the liquid nitrogen flash evaporation spray. Its average cooling rate can reach 131,046 °C/min, and the maximum heat flux is 173 W/cm2. This study sheds some lights on heat transfer mechanism of superhydrophilic microstructure on the flash evaporation spray and contributes to the development of a new method for cell vitrification. [ABSTRACT FROM AUTHOR]

• We propose a novel dynamic network embedding model with structural attention. • Our model can capture the evolving characteristic of dynamic networks. • The proposed method models the process of developing an open triad into a closed triad. • The learned representations can preserve both the first-order and second-order proximities. • The experimental results demonstrate the efficiency of our method. Network embedding aims to learn low-dimensional vector representations for each node in a network, which facilitates various learning tasks such as node classification, link prediction and so on. The majority of existing embedding methods mainly focus on static networks. However, many real-world networks are dynamic and change over time. Although a small number of very recent literatures have been developed for dynamic network embedding, they either need to be retrained without closed-form expression, or suffer high-time complexity. Additionally, a large number of real-world networks may be both large and noisy, presenting great challenges to effective network representation learning. In this paper, we propose a novel method named Dynamic Network Embedding via Structural Attention (DNESA). Specifically, we incorporate the attention mechanism into network embedding, which facilitates our method mainly concentrating on task-related parts of the given graph while avoiding or ignoring noisy parts of the network. Furthermore, we can capture the evolving characteristic of dynamic networks and learn embedding vectors of each node at different time steps by modeling the process of developing an open triad into a closed triad under the attention mechanism. Meanwhile, we carefully design an optimization function for preserving both the first-order and second-order proximities. Empirical experiments conducted on six real-world networks illustrate the efficiency of the proposed method, which outperforms state-of-the-art network embedding methods in applications including link prediction and node classification. [ABSTRACT FROM AUTHOR]

We propose a scheme of optical frequency comb (OFC) generation based on cascaded Mach–Zehnder modulator (MZM) and electroabsorption modulator (EAM) with Gaussian-shaped pulse signal. In the proposed scheme, the MZM and the nonzero-chirp EAM are driven by a periodic Gaussian-shaped pulse signal that is controlled with binary sequences, and then an OFC is output from EAM. The theoretical model of the OFC generation is developed by improving the theory of electroabsorption modulation. The performance of OFC is investigated. The results show that the frequency spacing of the OFC can be tuned in the range of 1 to 10 GHz, the number of effective comb-lines is 35, and the power variation of comb-lines is 2.14 dB. [ABSTRACT FROM AUTHOR]

This study examines the diffusion of water in fiber-reinforced polymer (FRP) laminated composites, comprising epoxy resin and unidirectional glass fibers, at different material and structural scales. Experiments on the diffusion of neat resin, FRP laminas in the axial and transverse fiber directions, FRP laminated composites with different stacking sequences, and FRP plates, rods, and pipes are conducted to obtain the diffusion processes at different length scales. Simultaneously, a diffusion model for a laminated composite is formulated to mathematically describe the multi-axial diffusion processes in FRP composites at various length scales, i.e., single lamina, laminated stacking sequence, and component. The study shows that the diffusivity constants calibrated from neat epoxy resin and unidirectional laminas are adequate to predict the diffusion responses of laminated composite systems and structural components of larger sizes. This capability is useful in predicting the long-term diffusion responses of FRP composites of complex geometries and large sizes where conducting diffusion tests on such systems is not feasible. [ABSTRACT FROM AUTHOR]

We propose a scheme of a digitally controlled microwave frequency comb (MFC) based on a Mach–Zehnder modulator (MZM) and pulse signals. In the proposed scheme, an MZM is driven by a periodic pulse signal that is controlled with binary sequences and then outputs an optical frequency comb (OFC). After the beat frequency occurs among modes of the OFC, an MFC with a wide spectrum coverage and small power variation is obtained using a photodetector. The theoretical model of the MFC generation based on pulse signals is developed. The performance of the MFC generation scheme is investigated. The results show that the minimum power variation and maximum spectrum bandwidth of the generated MFC are 0.31 dB and 300 GHz, respectively. By programming the binary sequence, the frequency spacing of MFC and the number of microwave signals can be tuned in the range of 1.25 to 10 GHz and 31 to 255, respectively. [ABSTRACT FROM AUTHOR]

Moisture content (MC) serves as a pivotal metric for assessing the wood quality, impacting crucial properties such as density, stiffness, and strength. Accurate determination of MC is imperative for ensuring quality and meeting specifications. This study introduces a novel dual-parameters microwave measurement methodology designed for precise MC quantification in spruce. By analyzing the sensitivity and linearity of the S21 with MC across both the S and X bands, we identify the most effective measurement parameters. Experimental work further investigates the correlation between the amplitude and phase of S21 and the MC in spruce, laying the groundwork for a comprehensive understanding of moisture influence on wood properties through microwave techniques. Finally, considering the impact of MC on both the amplitude and phase of S21, we introduce a dual-parameters microwave approach for accurately determining spruce MC. This method has been validated through case studies in both X and S bands. Our discussion extends to the effects of wood material characteristics, moisture distribution within samples, wood texture directions, and specimen dimensions on S21. Experimental results confirm that this approach significantly improves the precision of MC measurements in spruce, achieving an error rate of 0.15% in X band and 0.49% in S band measurements. [ABSTRACT FROM AUTHOR]

Background: Persistent neurocognitive deficits are often associated with poor outcomes of major depressive disorder (MDD). Executive dysfunction is the most common cognitive deficit in MDD. However, it remains unclear which subcomponent of executive dysfunction is state-independent with distinct neural substrates.Methods: A comprehensive neurocognitive test battery was used to assess four subcomponents of executive function (working memory, inhibition, shifting, and verbal fluency) in 95 MDD patients and 111 matched healthy controls (HCs). After 6 months of paroxetine treatment, 56 patients achieved clinical remission (rMDD) and completed the second-time neurocognitive test. Network-based statistics analysis was utilized to explore the changes in functional connectivity (FC).Results: Compared with the HCs, all the four subcomponents of MDD patients were significantly impaired. After treatment, there was a significant improvement in working memory, inhibition, and verbal fluency in the rMDD group. And shifting and verbal fluency of the rMDD group remained impaired compared with the HCs. Fifteen functional connections were interrupted in the MDD group, and 11 connections remained in a disrupted state after treatment. Importantly, verbal fluency was negatively correlated with the disrupted FC between the right dorsal prefrontal cortex and the left inferior parietal lobule in patients with MDD and remitted MDD.Limitations: The correlation analysis of the association between cognitive impairment and connectivity alterations precluded us from making causal inferences.Conclusions: Verbal fluency is the potential state-independent cognitive deficit with distinct neural basis in patients with MDD. [ABSTRACT FROM AUTHOR]

It has been reported that local weather is associated with the symptoms of joint pain in patients with rheumatoid arthritis (RA), and many people believe their pain becomes worse when facing rainy days. However, limited studies explored the effects of weather on RA patients' healthcare-seeking behavior. Our study aimed to investigate the relationship between rainfall and readmission behavior of patients with RA in Hefei, China, based on hospitalization data from the First Affiliated Hospital of Anhui University of Chinese Medicine from May 2012 to June 2016 and weather data from National Meteorological Information Center during the same study period. Using a time-stratified case-crossover study design and conditional logistic regression, we found a negative association between current day rainfall and readmission (unadjusted: OR = 0.82, p < 0.05; adjusted: OR = 0.83, p < 0.1), which is contrary to our common belief. In lagged models, we observed that rainfall was significantly and positively associated with readmissions at lag 6 days (unadjusted: OR = 1.12, p < 0.1; adjusted: OR = 1.17, p < 0.05) and lag 7 days (unadjusted: OR = 1.13, p < 0.05; adjusted: OR = 1.21, p < 0.01). Additionally, stratified analyses showed the unanticipated finding was only statistically significant for younger patients (< 65 years) and females. Our study adds new evidence that the association between the healthcare-seeking behavior of patients with RA and local rainfall may be different, compared with the positive relationship between symptoms of joint pain and rainfall. [ABSTRACT FROM AUTHOR]

Previous studies have shown that ambient air pollution is associated with respiratory morbidity. However, the effects of air pollutants on health have rarely been studied in China. Our study aimed to estimate the short-term effects of particulate air pollutants on hospitalizations for three types of respiratory disease: pneumonia, chronic obstructive pulmonary disease (COPD), and asthma. We collected data on daily admissions for patients with each disease from the New Rural Cooperative Medical System (NRCMS) in Hefei, China. Daily records of air pollutants and meteorological data from January 2014 to March 2016 were also obtained. Distributed lag nonlinear models were employed in the analysis to evaluate the association between daily air pollutants and admissions. The highest effect of each pollutant on COPD hospital admission was observed with PM2.5 at lag 12 (RR = 1.068, 95% CI 1.017 to 1.121) and PM10 at lag 10 (RR = 1.031, 95% CI 1.002 to 1.060), for an increase of 10 μg/m3 in concentrations of the pollutants. The short-term effects of PM10 on asthma hospital admissions peaked at lag 12 (RR = 1.057, 95% CI 1.010 to 1.107). According to our stratified analysis, we found that the effects on COPD admission were more pronounced in the warm season than in the cold season, and the elderly (≥ 65 years) and females were more vulnerable to air pollution. [ABSTRACT FROM AUTHOR]

• Blue-light was better for improving the umami and aroma of dried S. granulatus. • The lowest umami and aroma were in red-light and in green-light respectively. • Umami and aroma in five light treatments were better at 6 and 9 days. • Glu, Asp, alcohols, ketones and pyrazines contributed to flavor differences under different light treatments. Dried edible mushrooms have a unique flavor which is worth exploring to preserve and improve their flavor. Light can improve flavor for dried edible mushrooms, but there are few reports about monochromatic light. In this study, effects of red-light, yellow-light, green-light, blue-light and white-light on the umami taste and aroma of dried Suillus granulatus were investigated. The results showed that contents of umami amino acids and 5-nucleotides, equivalent umami concentration (EUC) and electronic tongue umami scores were higher under blue-light treatment. Principal component analysis (PCA) of volatiles showed that comprehensive scores under blue-light treatment were higher. The flavor was better under five light treatments at 6 and 9 days. Partial least squares-discrimination analysis (PLS-DA) further revealed the detailed differences between various light treatments, which were mainly caused by umami amino acids, alcohols, ketones and pyrazines. Collectively, blue-light treatment can be used as a processing technology to improve the flavor quality of dried edible mushrooms. [ABSTRACT FROM AUTHOR]

Reactive power is the core problem of voltage stability and economical operation in power systems. Aiming at the problem that multi-objective normalization reactive power optimization function is dependent on weight, an integrated synthesis of adaptive multi-objective particle swarm optimization (ISAMOPSO) is proposed to achieve weight adaptive. Through seven test functions and three algorithm comparison experiments, it is proved that the ISAMOPSO algorithm has stronger global search capability and better convergence. Considering the optimal access position and capacity of distributed generation (DG), the ISAMOPSO algorithm is used for three-objective reactive power optimization. Finally, the results indicate that the ISAMOPSO algorithm can not only provide a variety of optimization schemes to meet different needs, but also realize dynamic reactive power optimization, which further proves that the algorithm can provide effective technical support for solving reactive power optimization problems in practical engineering. [ABSTRACT FROM AUTHOR]

Tree height is an essential indicator in forestry research. This indicator is difficult to measure directly, as well as wind disturbance adds to the measurement difficulty. Therefore, tree height measurement has always been an issue that experts and scholars strive to improve. We propose a tree height measurement method based on tree fisheye images to improve the accuracy of tree height measurements. Our aim is to extract tree height extreme points in fisheye images by proposing an improved lightweight target detection network YOLOX-tiny. We added CBAM attention mechanism, transfer learning, and data enhancement methods to improve the recall rate, F1 score, AP, and other indicators of YOLOX-tiny. This study improves the detection performance of YOLOX-tiny. The use of deep learning can improve measurement efficiency while ensuring measurement accuracy and stability. The results showed that the highest relative error of tree measurements was 4.06% and the average relative error was 1.62%. The analysis showed that the method performed better at all stages than in previous studies. [ABSTRACT FROM AUTHOR]

Ocular trauma is a major cause of monocular blindness worldwide. Vitrectomy at correct timing can significantly improve the efficacy and prognosis, but the timing of vitrectomy has remained highly controversial for decades. Trauma cases are different from each other, thus, a flexible timing system based on the details of each individual case is recommended. Unfortunately, no such a timing system is available for clinical application up to now. To establish the vitrectomy timing individualization system for ocular trauma (VTISOT), we first identified 6 independent tPVR risk factors (including Zone 3 Injury, Zone 3 retinal Laceration, Massive Vitreous Hemorrhage, Retinal Disorder, Timing of Vitrectomy and Type of Injury) by retrospective study. Then, the tPVR score was established by binary logistic regression analysis. Most importantly and critically, the vitrectomy timing individualization system for ocular trauma was established based on the identified tPVR risk factors and the tPVR score. The following evaluation of the VTISOT showed that the patients consistent with the VTISOT principles exhibited reduced tPVR incidence and better surgical results. In short, the VTISOT principles were established, which may provide a new approach to individualize the timing of vitrectomy and improve the prognosis after trauma. [ABSTRACT FROM AUTHOR]

Purpose. The aim of this study is to describe the epidemiological and visual outcomes and to identify the main prognostic factors of intralenticular foreign body (ILFB) injuries. Methods. We performed a retrospective review of 21 patients (21 eyes) referred to Hebei Eye Hospital in North China from January 2012 to December 2017, who underwent surgical removal of ILFBs and associated ocular trauma repairs. Data regarding the patient demographics, cause of the injury, nature of the ILFB, clinical features, time interval between the injury and the ILFB removal, time interval between the presentation and the surgery, and the initial and final best-corrected visual acuities (BCVAs) were analyzed, and the main prognostic factors were identified. Results. Male adults were most affected by ILFBs (90.5%). The mean age of the patients was 41.5 years (median: 46 years, range: 21 to 60 years). None of the patients were wearing goggles at the time of the injury. The most common ILFB cause was hammering the metal (57.1%), and most of the ILFBs were metallic (71.4%). After medical treatment, the final BCVA was improved significantly (Z = 2.49, P=0.015). There was a significant association between the ILFBs with posterior segment injuries and the final BCVA (χ2 = 10.03, P=0.01). Those factors showing no statistical association with the final BCVA included the age (χ2 = 0.36, P=1.0), gender (χ2 = 0.52, P=1.0), nature of the ILFB (χ2 = 1.11, P=0.54), entrance wound location (χ2 = 2.85, P=0.25), and time interval between the injury and the ILFB removal (χ2 = 1.87, P=0.23). Conclusion. This is the first local study to explore the epidemiology of ILFB injuries and to identify the main prognostic factors. There was a significant association between the ILFBs with posterior segment injuries and the final BCVA. Improved public awareness and strengthened education regarding safety are the key approaches to reduce the incidence of eye injuries. [ABSTRACT FROM AUTHOR]

Lymphangiogenesis in inflammation has received considerable attention in recent years. Administration of modulating lymphangiogenesis provides more possibilities of treating inflammation-associated diseases. However, the main mediators and factors governing inflammation-induced lymphangiogenesis (ILA) are yet to be defined. Here, we explored the role of HMGB1-TLR4 signalling pathway in modulating inflammation-induced lymphangiogenesis and its underlying mechanisms using an ILA mouse model and 2 cell lines. Our results show that HMGB1 promoted VEGF-C-induced HDLECs proliferation in a dose-dependent manner and TLR4 mediates HMGB1-induced LECs proliferation and tube formation in vitro. And in vivo, rHMGB1 treatment significantly promoted ILA, and the promoting effects was inhibited notably when HMGB1-TLR4 was blocked. HMGB1-associated ILA is primarily dependent on TLR4 but not on TLR2. In mechanisms, the recruitment and activation of CD11b+ cells are important cellular mechanisms in HMGB1-TLR4 associated ILA, and multiple key pro-lymphangiogenesis molecules mediates HMGB1-TLR4 associated ILA, including VEGF-C/VEGFR3, inflammatory factors IL-1β and TNF-α, MMP-2 and MMP-9 and NF-κB p65. In conclusion, HMGB1-associated ILA is primarily dependent on TLR4, and CD11b+ cells and multiple molecular mechanisms mediate HMGB1-TLR4 associated ILA. Furthermore, the ILA can be effectively modulated by HMGB1-TLR4 signalling. Consequently, administration of modulating ILA through HMGB1-TLR4 pathway may provide us more possibilities of treating inflammation and lymphangiogenesis associated diseases. [ABSTRACT FROM AUTHOR]

Claudins (Cldn) are the major components of tight junctions (TJs) sealing the paracellular cleft in tissue barriers of various organs. Zebrafish Cldnb, the homolog of mammalian Cldn4, is expressed at epithelial cell-cell contacts and is important for regulating epidermal permeability. The bacterial toxin Clostridium perfringens enterotoxin ( CPE) has been shown to bind to a subset of mammalian Cldns. In this study, we used the Cldn-binding C-terminal domain of CPE (194-319 amino acids, c CPE194-319) to investigate its functional role in modulating zebrafish larval epidermal barriers. In vitro analyses show that c CPE194-319 removed Cldn4 from epithelial cells and disrupted the monolayer tightness, which could be rescued by the removal of c CPE194-319. Incubation of zebrafish larvae with c CPE194-319 removed Cldnb specifically from the epidermal cell membrane. Dye diffusion analysis with 4-kDa fluorescent dextran indicated that the permeability of the epidermal barrier increased due to c CPE194-319 incubation. Electron microscopic investigation revealed reversible loss of TJ integrity by Cldnb removal. Collectively, these results suggest that c CPE194-319 could be used as a Cldnb modulator to transiently open the epidermal barrier in zebrafish. In addition, zebrafish might be used as an in vivo system to investigate the capability of c CPE to enhance drug delivery across tissue barriers. [ABSTRACT FROM AUTHOR]

Background The toll-like receptors-(TLR)-2 and −4 play a role in the innate immune system and drive the autoimmune T cell cascade in psoriasis. But little is known about the association of the polymorphisms of TLR2/TLR4 and psoriasis vulgaris (PsV). Methods We performed a hospital-based association study involved in355 PsV patients and 213 controls. Genotyping of 11 SNPs rs4696480(−16934A/T), rs121917864 (2029C/T;Arg677Trp), rs5743708 (2408G/A;Arg753Gln), rs3804099 (+597T/C), rs5743699(+1349T/C) of TLR2 on chromosome 4q32; and rs1927914 (−6143A/G), rs10759932(−5724T/C), rs4986790 (896A/G; Asp299Gly), rs4986791 (1196C/T; Thr399lle); rs11536889(+7263G/C), rs11536891(+8469T/C) of TLR4 on chromosome 9q33.1 were determined using SNaPshot Multiplex Kit (Applied Biosystems Co., USA). Results We did not detect the presence of any of the five rare SNPs (rs5743708, rs121917864, rs5743699, rs4986790, rs4986791). The comparison of allele distributions revealed that only one SNP (rs3804099) of 6 common SNPs(rs10759932, rs11536889, rs11536891, rs1927914, rs3804099, rs4696480) was significant associated with the risk of PsV (P < 0.01; FDR p -Value). However, there were no significant differences found in other subphenotypes of family history or PASI between patients positive and those negative for a particular phenotype. Conclusions The SNP rs3804099 of TLR2 may have significant effects on heritability of PsV in our Chinese population. TLR2 and its pathway might take part in the pathogenesis of PsV. [ABSTRACT FROM AUTHOR]

In this work, a multi-phase cellular automaton (CA) model is extended for the quantitative simulation of peritectic phase transition. First, the effects of cooling rate/supersaturation and temperature on the peritectic transformation kinetics in Fe-C alloys are investigated by utilizing the present CA model. The CA simulations show that supersaturations in the parent phases (liquid and δ-ferrite) increase the L/γ interface growth velocity remarkably, but tinily for the δ/γ interface migration velocity. There exists a transition supersaturation for isothermal transformations, at which the growth rates of the two interfaces are equal. The transition supersaturation is found to increase with decreasing temperature. Microstructural evolution at different cooling rates during peritectic transformation is simulated using the experimental conditions. At low cooling rates, the δ/γ interface propagates at a higher velocity than the L/γ interface. At high cooling rates, however, the γ-phase grows more into the L-phase with a cellular morphology. Then, the proposed CA model is applied to simulate the microstructural evolution during peritectic reaction. It is observed that the γ-phase propagates along the L/δ interface and finally encircles the δ-phase. Meanwhile, the intervenient γ-phase grows in thickness through peritectic transformation. The CA simulations are compared reasonably well with the experimental data and analytical calculations. [ABSTRACT FROM AUTHOR]

Cadmium-contaminated wastewater has attracted increasing concerns due to its non-biodegradable properties and high toxicity. To explore eco-friendly and economically feasible strategies, the screened Alcaligenes faecalis K2 were employed for the biomineralization and recovery of Cd2+ from wastewater while producing considerable secretory organo-biominerals (SOBs) as bioadsorbents. At 75 mg/L Cd2+ exposure, 85.5% of Cd2+ was removed by K2, 43.0% of which was fixed in the granular SOBs. SOBs were convenient for separating from the solution. The adsorption capacity of granular sorbent made from SOBs was verified to be greater than 77.1 mg/g. Practically, 89.5% of 75 mg/L of Cd2+ could be stably removed while ereK2 continuously generated SOBs in a moving-bed biofilm reactor (MBBR). To sum up, the production of bioadsorbents can be achieved by K2, while removing Cd with live microorganisms, which was conducive to making full use of materials and improving Cd removal efficiency. [Display omitted] • A strain of Alcaligenes faecalis K2 with good cadmium removal effect was screened. • Bacteria K2 uses biomineralization as the main way to remove cadmium. • Secretory organo-biominerals (SOBs) are easy to collect and can be used as adsorbents. • Realize simultaneous continuous removal of cadmium and production of SOBs in MBBR. [ABSTRACT FROM AUTHOR]

In this research, a new approach has been suggested for providing an optimization bidding strategy in the day-ahead market for case research in China. This research uses a newly developed version of Emperor Penguin Optimizer (CEPO) to govern the fitness function of all individuals based on the Market Clearing Price (MCP) probability function. The resemblance amounts between each day and the next day are used for clustering. The clustering is performed based on the well-known subtractive clustering methodology. A simulation model using the probability function in MCP estimates the fitness function of the generated strategies. The results indicate that this method proposed is a statistically effective bidding design in China's day-ahead market related to two other plans from the literature. • An optimized method for bidding strategy in the day-ahead market in China. • Converged Emperor Penguin Optimizer (CEPO) for problem optimization. • The problem is to minimize the Market Clearing Price (MCP) probability function. • Subtractive clustering for data clustering between each day and the next day. [ABSTRACT FROM AUTHOR]