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Pear anthracnose, caused by Colletotrichum bacteria, is a severe infectious disease that significantly impacts the growth, development, and fruit yield of pear trees. Early detection of pear anthracnose before symptoms manifest is of great importance in preventing its spread and minimizing economic losses. This study utilized hyperspectral imaging (HSI) technology to investigate early detection of pear anthracnose through spectral features, vegetation indices (VIs), and texture features (TFs). Healthy and diseased pear leaves aged 1 to 5 days were selected as subjects for capturing hyperspectral images at various stages of health and disease. Characteristic wavelengths (OWs1 and OWs2) were extracted using the Successive Projection Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) algorithm. Significant VIs were identified using the Random Forest (RF) algorithm, while effective TFs were derived from the Gray Level Co-occurrence Matrix (GLCM). A classification model for pear leaf early anthracnose disease was constructed by integrating different features using three machine learning algorithms: Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Back Propagation Neural Network (BPNN). The results showed that: the classification identification model constructed based on the feature fusion performed better than that of single feature, with the OWs2-VIs-TFs-BPNN model achieving a highest accuracy of 98.61% in detection and identification of pear leaf early anthracnose disease. Additionally, to intuitively and effectively monitor the progression and severity of anthracnose in pear leaves, the visualization of anthracnose lesions was achieved using Successive Maximum Angle Convex Cone (SMACC) and Spectral Information Divergence (SID) techniques. According to our research results, the fusion of multi-source features based on hyperspectral imaging can be a reliable method to detect early asymptomatic infection of pear leaf anthracnose, and provide scientific theoretical support for early warning and prevention of pear leaf diseases.

Abstract Sleep is a modifiable behavior that can be targeted in interventions aimed at promoting healthy aging. This study aims to (i) identify the sleep duration trend in US adults; (ii) investigate the relationship between sleep duration and phenotypic age; and (iii) explore the role of exercise in this relationship. Phenotypic age as a novel index was calculated according to biomarkers collected from US adults based on the National Health and Nutrition Examination Survey (NHANES). Sleep information was self-reported by participants and discerned through individual interviews. The principal analytical method employed was weighted multivariable linear regression modeling, which accommodated for the complex multi-stage sampling design. The potential non-linear relationship was explored using a restricted cubic spline (RCS) model. Furthermore, subgroup analyses evaluated the potential effects of sociodemographic and lifestyle factors on the primary study outcomes. A total of 13,569 participants were finally included in, thereby resulting in a weighted population of 78,880,615. An examination of the temporal trends in sleep duration revealed a declining proportion of individuals with insufficient and markedly deficient sleep time since the 2015–2016 cycle. Taken normal sleep group as a reference, participants with extreme short sleep [β (95% CI) 0.582 (0.018, 1.146), p = 0.044] and long sleep [β (95% CI) 0.694 (0.186, 1.203), p = 0.010] were both positively associated with phenotypic age using the fully adjusted model. According to the dose–response relationship between sleep duration and phenotypic age, long sleep duration can benefit from regular exercise activity, whereas short sleep duration with more exercise tended to have higher phenotypic age. There is an inverted U-shaped relationship between short and long sleep durations and phenotypic age. This study represents an important step forward in our understanding of the complex relationship between sleep and healthy aging. By shedding light on this topic and providing practical exercise recommendations for promoting healthy sleep habits, researchers can help individuals live longer, healthier, and more fulfilling lives.

The Panjin Wetland is a complex ecosystem comprising coastal and inland wetland. It has an important function in wetland conservation and ecological. For this research, we quantified the landscape type changes in Panjin Wetland from 1992–2022, and analyzed the interaction between the combined PLUS and InVEST models to predict the future evolution of spatial and temporal patterns of habitat quality (HQ) and landscape patterns in Panjin Wetland. The results showed the following: (1) The change in natural wetland area from 1992 to 2022 generally showed a decreasing trend, the landscape patterns showed a trend of fragmentation. In 2032, the ecological protection scenarios showed an increase of 79.51 km2 of natural wetland, while the other scenarios showed a decrease. (2) In 2022, the average habitat quality score is 0.441, and in 2032, the average habitat quality scores in the natural development scenarios, farmland protection scenarios, ecological protection scenarios, and economic development scenarios are 0.427, 0.448, 0.438, and 0.416, respectively. (3) The outcomes of this study offer insights into the sustainable management and rational development of coastal wetland, thereby contributing to the existing body of knowledge in the field of coastal wetland research, particularly in the areas of land planning and forecasting.

MET exon 14 (METex14) skipping is the most reported MET mutation in non-small cell lung cancer (NSCLC) and has been confirmed to respond to MET tyrosine kinase inhibitors (TKI) in clinical trials. While MET TKI tepotinib was recently approved for METex14 skipping NSCLC in China, real-world evidence is limited. We report our experience treating NSCLC patients referred from oncology sites across China with tepotinib in the Boao Lecheng Pilot Zone. Four patients have been prescribed the drug with a median age of 67 years (range, 61–71 years). One patient has concomitant BRAF V600E mutation, and another patient had savolitinib as first line of therapy but discontinued due to hepatotoxicity. Till the end of follow-up, four patients were all on tepotinib therapy, with a median duration of therapy of 19 months. One patient achieved partial response and three achieved stable disease. Three patients had peripheral edema, but all were mild. Our experience showed in real clinical setting, tepotinib had robust and durable clinical activity and a favorable toxicity profile in Chinese patients with METex14 skipping NSCLC. It is the first report on the effectiveness of tepotinib in a patient with both METex14 skipping and BRAF V600E mutations and successful MET inhibitor switch after MET inhibitor-induced liver injury.

Image denoising is a fundamental tool in the fields of image processing and computer vision. With the rapid development of multimedia and cloud computing, it has become popular for resource-constrained users to outsource the storage and denoising of massive images. However, it may cause privacy concerns and response delays. In this scenario, we propose an ef F icient priv A cy-prese R ving I mage de N oising sch E me (FARINE) for outsourcing digital images. By introducing a key conversion mechanism, FARINE allows removing noise from a given noisy image using a non-local mean way without leaking any information about the plaintext content. Due to its low computational latency/communication cost, edge computing is considered to improve the user experience. To achieve a dynamic user set efficiently, we design a fine-grained access control mechanism to support user authorization and revocation in multi-user scenarios. Extensive experiments over several benchmark data sets show that FARINE obtains comparable performance to plaintext image denoising.

Abstract Background To achieve different central preset force levels requires various fine-tuning efforts and may elicit different uptight responses. The mandibular lever system has a distinct regularity in the fine-tuning function of the upper limbs. The purpose of the present study was to detect whether the uptight responses elicited from motivating clenching differ from those induced by motivating forearm raising at different force levels. Methods Twenty-five healthy females were enrolled in this study. The target was low, medium, and maximum force levels with or without visual feedback and/or maintenance effort. Surface electromyographic (SEMG) activity was recorded from the bilateral anterior temporalis and masseter or left biceps brachii muscle (BicL), and the T-Scan III System synchronously recorded the sensitive force values. The uptight responses and task difficulties were recorded for occlusal and left forearm lifting tasks using a unique visual analogue scale. Results The highest uptight response value was achieved at a low clenching force level with visual feedback requiring no maintenance effort but at a maximum forearm-raising force level with visual feedback and maintenance effort. The SEMG activities of both jaw-closing muscles and BicL were associated with the central preset force level (P

Option is an important financial derivative. Accurate option pricing is essential to the development of financial markets. For option pricing, existing time series models and neural networks are difficult to extract multi-scale temporal features from option data, which greatly limits their performance. To solve this problem, we propose a novel deep learning model named as MRC-LSTM-CI. It contains three modules, including Multi-scale Residual CNN module (MRC), Long Short-Term Memory neural network module (LSTM) and confidence interval output module (CI). The proposed model can effectively extract multi-scale features from real market option data, and make interval prediction to provide more information to the decision maker. In addition, the proposed model is further improved using the residual prediction strategy, where the output value is chosen as the residual value between BS theory price and actual market price. Experimental results show that our model has better prediction accuracy than other deep learning models and achieves the state-of-the-art performance.

To solve the problem of privacy leakage and response latency in outsourced image watermark embedding in cloud computing, an efficient and privacy-preserving watermark embedding method for outsourced digital images was proposed by introducing edge computing technology. We had proposed a perturbing encryption method with homomorphism to ensure the information security and the correctness of discrete wavelet transformation in the encrypted domain. In addition, the framework was designed to guarantee the safety of singular value decomposition that edge server could not recover the original image matrix. The experimental results show that the proposed method is superior to similar secure watermarking schemes in terms of encryption/decryption time and ciphertext expansion. The proposed method enables the watermarking operation to be performed in an unsafe outsourced environment while achieving a watermarking effect similar to the plaintext equivalent.

The Black-Scholes option pricing model (B-S model) generally requires the assumption that the volatility of the underlying asset be a piecewise constant. However, empirical analysis shows that there are discrepancies between the option prices obtained from the B-S model and the market prices. Most current modifications to the B-S model rely on modelling the implied volatility or interest rate. In contrast to the existing modifications to the Black-Scholes model, this paper proposes the concept of including a modification term to the B-S model itself. Using the actual discrepancies of the results of the Black-Scholes model and the market prices, the modification term related to the implied volatility is derived. Experimental results show that the modified model produces a better option pricing results when compare to market data.

Wheel-legged robots have fast and stable motion characteristics on flat roads, but there are the problems of poor balance ability and low movement level in special terrains such as rough roads. In this paper, a new type of wheel-legged robot with parallel four-bar mechanism is proposed, and the linear quadratic regulator (LQR) controller and fuzzy proportion differentiation (PD) jumping controller are designed and developed to achieve stable motion so that the robot has the ability to jump over obstacles and adapt to rough terrain. The amount of energy released by the parallel four-bar linkage mechanism changes with the change of the link angle, and the height of the jump trajectory changes accordingly, which improves the robot’s ability to overcome obstacles facing vertical obstacles. Simulations and real scene tests are performed in different terrain environments to verify obstacle crossing capabilities. The simulation results show that, in the pothole terrain, the maximum height error of the two hip joint motors is 2 mm for the obstacle surmounting method of the adaptive retractable wheel-legs; in the process of single leg obstacle surmounting, the maximum height error of the hip joint motors is only 6.6 mm. The comparison of simulation data and real scene experimental results shows that the robot has better robustness in moving under complex terrains.

Abstract The use of traditional finite element method (FEM) in occlusal stress analysis is limited due to the complexity of musculature simulation. The present purpose was to develop a displacement boundary condition (DBC)-FEM, which evaded the muscle factor, to predict the dynamic occlusal stress. The geometry of the DBC-FEM was developed based on the scanned plastic casts obtained from a volunteer. The electrognathographic and video recorded jaw positional messages were adopted to analyze the dynamic occlusal stress. The volunteer exhibited asymmetrical lateral movements, so that the occlusal stress was further analyzed by using the parameters obtained from the right-side eccentric movement, which was 6.9 mm long, in the stress task of the left-side eccentric movement, which was 4.1 mm long. Further, virtual occlusion modification was performed by using the carving tool software aiming to improve the occlusal morphology at the loading sites. T-Scan Occlusal System was used as a control of the in vivo detection for the location and strength of the occlusal contacts. Data obtained from the calculation using the present developed DBC-FEM indicated that the stress distribution on the dental surface changed dynamically with the occlusal contacts. Consistent with the T-Scan recordings, the right-side molars always showed contacts and higher levels of stress. Replacing the left-side eccentric movement trace by the right-side one enhanced the simulated stress on the right-side molars while modification of the right-side molars reduced the simulated stress. The present DBC-FEM offers a creative approach for pragmatic occlusion stress prediction.

Abstract In vertebrates, the type 1 parathyroid hormone receptor (PTH1R) is a critical regulator of skeletal development and homeostasis; however, how it is modulated is incompletely understood. Here we report that deleting Kindlin-2 in osteoblastic cells using the mouse 10-kb Dmp1-Cre largely neutralizes the intermittent PTH-stimulated increasing of bone volume fraction and bone mineral density by impairing both osteoblast and osteoclast formation in murine adult bone. Single-cell profiling reveals that Kindlin-2 loss increases the proportion of osteoblasts, but not mesenchymal stem cells, chondrocytes and fibroblasts, in non-hematopoietic bone marrow cells, with concomitant depletion of osteoblasts on the bone surfaces, especially those stimulated by PTH. Furthermore, haploinsufficiency of Kindlin-2 and Pth1r genes, but not that of either gene, in mice significantly decreases basal and, to a larger extent, PTH-stimulated bone mass, supporting the notion that both factors function in the same genetic pathway. Mechanistically, Kindlin-2 interacts with the C-terminal cytoplasmic domain of PTH1R via aa 474–475 and Gsα. Kindlin-2 loss suppresses PTH induction of cAMP production and CREB phosphorylation in cultured osteoblasts and in bone. Interestingly, PTH promotes Kindlin-2 expression in vitro and in vivo, thus creating a positive feedback regulatory loop. Finally, estrogen deficiency induced by ovariectomy drastically decreases expression of Kindlin-2 protein in osteocytes embedded in the bone matrix and Kindlin-2 loss essentially abolishes the PTH anabolic activity in bone in ovariectomized mice. Thus, we demonstrate that Kindlin-2 functions as an intrinsic component of the PTH1R signaling pathway in osteoblastic cells to regulate bone mass accrual and homeostasis.

Abstract The rise of emerging technologies such as Big Data, the Internet of Things, and artificial intelligence, which requires efficient power schemes, is driving brainstorming in data computing and storage technologies. In this study, merely relying on the fundamental structure of two memristors and a resistor, arbitrary Boolean logic can be reconfigured and calculated in two steps, while no additional voltage sources are needed beyond “±VP” and 0, and all state reversals are based on memristor set switching. Utilizing the proposed logic scheme in an elegant form of unity structure and minimum cost, the implementation of a 1‐bit adder is demonstrated economically, and a promising circuit scheme for the N‐bit adder is exhibited. Some critical issues including the crosstalk problem, energy consumption, and peripheral circuits are further simulated and discussed. Compared with existing works on memristive logic, such methods support building a memristor‐based digital in‐memory calculation system with high functional reconfigurability, simple voltage sources, and low power and area consumption.

Image stitching can be employed to stitch images taken from different times, perspectives, or devices into a panorama with a wider view. However, the imaging specification of images to be stitched is strict. If the imaging specification is not satisfied, artefacts caused by inaccurate alignment and unnatural distortion will occur. Semantic segmentation can solve the classification problem at the pixel level; however, image stitching significantly depends on the accuracy of feature points. Therefore, this paper proposes an image stitching algorithm based on semantic segmentation to guide feature point classification and seam fusion. First, the images are recognized by a cascade semantic segmentation network, and the image feature points are classified. Thereafter, the corresponding homography transformations are calculated using different class feature points, and the best homography mapping for the entire target image is selected. Finally, a seam-cutting algorithm based on semantic segmentation is used to compute the seam, and a feathering Poisson fusion with distance transformation is used to eliminate artefacts and light differences. Experiments show that the algorithm can generate transitional natural and perceptual stitching results even under the influence of perspective and light differences.

Temporomandibular joints (TMJs) have a biomechanical relationship with dental occlusion. Aberrant occlusion initiates degenerative remodeling responses in TMJ condyles. Aging is a promoting factor of osteoarthritis (OA) development. The aim of this study was to assess the effect of aging on degenerative remodeling in TMJ condyles in response to occlusal biomechanical stimulation caused by the installation of aberrant prostheses and observe rehabilitation after their removal. The experiments involved 84 female C57BL/6J mice (42 at 6 weeks old and 42 at 28 weeks old). A bilateral anterior crossbite (BAC) model was developed, and the TMJs were sampled at 3, 7, and 11 weeks. BAC was removed at 7 weeks in a subset of mice, which accepted BAC treatment at 6 week of age, and maintained for another 4 weeks after BAC removal. TMJ changes were assessed with micro-CT, histomorphology, immunohistochemistry (IHC), and immunofluorescence staining assays. The results showed that BAC induced typical OA-like TMJ lesions that were more severe in the elder groups as evaluated by the acellular zones, clustered chondrocytes, fissures between cartilage and subchondral bone, reductions in matrix amount and the cartilage thickness as revealed by histomorphological measurements, and subchondral bone loss as detected on micro-CT images. IHC indicated significant increases in cleaved caspase-3-expressing cells and decreases in ki67-positive cells in the BAC groups. There were obvious age-dependent changes in the numbers of superficial zone cells and CD90-expressing cells. Supportively, cleaved caspase-3-expressing cells obviously increased, while ki67-expressing cells significantly decreased with aging. In the elder BAC groups, the superficial zone cells such as CD90-expressing cells were greatly reduced. At 11 weeks, the superficial zone cells were almost non-existent, and there were clear serrated injuries on the cartilage surface. BAC removal attenuated the degenerative changes in the condylar cartilage and subchondral bone. Notably, the rescue effect was more pronounced in the younger animals. Our findings demonstrate the impacts of aging on both TMJ degenerative changes in response to BAC and regenerative changes following BAC removal. The reduced number of chondro-progenitor cells in aged TMJ cartilage provides an explanation for this age-related decline in TMJ rehabilitative behaviors.

In image registration or image matching, the feature extracted by using the traditional methods does not include the depth information which may lead to a mismatch of keypoints. In this paper, we prove that when the camera moves, the ratio of the depth difference of a keypoint and its neighbor pixel before and after the camera movement approximates a constant. That means the depth difference of a keypoint and its neighbor pixel after normalization is invariant to the camera movement. Based on this property, all the depth differences of a keypoint and its neighbor pixels constitute a local depth-based feature, which can be used as a supplement of the traditional feature. We combine the local depth-based feature with the SIFT feature descriptor to form a new feature descriptor, and the experimental results show the feasibility and effectiveness of the new feature descriptor.

Neurons in the trigeminal mesencephalic nucleus (Vme) have axons that branch peripherally to innervate the orofacial region and project centrally to several motor nuclei in brainstem. The dorsal motor nucleus of vagus nerve (DMV) resides in the brainstem and takes a role in visceral motor function such as pancreatic exocrine secretion. The present study aimed to demonstrate the presence of Vme–DMV circuit, activation of which would elicit a trigeminal neuroendocrine response. A masticatory dysfunctional animal model termed unilateral anterior crossbite (UAC) model created by disturbing the dental occlusion was used. Cholera toxin B subunit (CTb) was injected into the inferior alveolar nerve of rats to help identify the central axon terminals of Vme neurons around the choline acetyltransferase (ChAT) positive motor neurons in the DMV. The level of vesicular glutamate transporter 1 (VGLUT1) expressed in DMV, the level of acetylcholinesterase (AChE) expressed in pancreas, the level of glucagon and insulin expression in islets and serum, and the blood glucose level were detected and compared between UAC and the age matched sham-operation control mice. Data indicated that compared with the controls, there were more CTb/VGLUT1 double labeled axon endings around the ChAT positive neurons in the DMV of UAC groups. Mice in UAC group expressed a higher VGLUT1 protein level in DMV, AChE protein level in pancreas, glucagon and insulin level in islet and serum, and higher postprandial blood glucose level, but lower fasting blood glucose level. All these were reversed at 15-weeks when UAC cessation was performed from 11-weeks (all, P < 0.05). Our findings demonstrated Vme–DMV circuit via which the aberrant occlusion elicited a trigeminal neuroendocrine response such as alteration in the postprandial blood glucose level. Dental occlusion is proposed as a potential therapeutic target for reversing the increased postprandial glucose level.

Recent advances in the corporate processing of images have exhibited its advantages over the individual processing. Image co-segmentation aims to segment shared objects from two or more relevant images, and is becoming more and more interesting for computer vision researchers. Many applications need accurate and efficient segmentation techniques: indoor navigation, autonomous driving, and virtual reality systems to name a few. Although numerous techniques have been proposed, it is still lacking a deep review of image co-segmentation techniques. In this paper, we provide a review on the fundamentals and challenges of image co-segmentation techniques. We organize the recent advances of image co-segmentation into seven major frameworks: graph based framework, clustering based framework, partial differential equation based framework, quadratic programming based framework, low rank matrix recovery based framework, joint optimization based framework, and machine learning based framework. We expect this review to be beneficial to both fresh and senior researchers in this field.

Keratinocytes are organised into a periodic pattern in feather branching, but how this is regulated is unclear. Here, the authors show that there is a coordinated change in cell shape and adherence, mediated by Notch, FGF signalling and Rho GTPases, which in turn regulates feather branching.

Abstract Runx2 may play an important role in development of osteoarthritis (OA). However, the specific role of Runx2 in articular chondrocyte function and in OA development in adult mice has not been fully defined. In this study, we performed the destabilization of the medial meniscus (DMM) surgery at 12-week-old mice to induce OA in adult Runx2 Agc1CreER mice, in which Runx2 was specifically deleted in Aggrecan-expressing chondrocytes by administering tamoxifen at 8-weeks of age. Knee joint samples were collected 8- and 12-weeks post-surgery and analyzed through histology, histomorphometry and micro-computed tomography (μCT). Our results showed that severe OA-like defects were observed after DMM surgery in Cre-negative control mice, including articular cartilage degradation and subchondral sclerosis, while the defects were significantly ameliorated in Runx2 Agc1CreER KO mice. Immunohistochemical (IHC) results showed significantly reduced expression of MMP13 in Runx2 Agc1CreER KO mice compared to that in Cre-negative control mice. Results of quantitative reverse-transcription PCR (qRT-PCR) demonstrated that expression of the genes encoding for matrix degradation enzymes was significantly decreased in Runx2 Agc1CreER KO mice. Thus, our findings suggest that inhibition of Runx2 in chondrocytes could at least partially rescue DMM-induced OA-like defects in adult mice.

Abstract A new type of stretchable poly(caprolactone)/graphene oxide (PCL/GO) aerogel with fast chemo‐responsive shape memory effect is fabricated by one‐step method of a sol‐gel procedure. The PCL/GO aerogels show uniformly circular and interconnected pores formed by twisted PCL nanolayers. GO platelets improve the crystallinity of PCL and increase the fracture stress and strain by 150% and 300% respectively, although the GO loading is only 0.5%. The dramatic increment of break strain is attributed to the uniform and circular pores that can afford large deformation and the interaction of GO and PCL. The aerogels can be programmed by external stress at ambient temperature without heating and recover upon ethyl acetate (EA) in 1 s. The fast chemo‐responsive shape recovery is ascribed to the fast wrinkle of the PCL nanolayers that decrease the diffusion time greatly and the interconnected micrometer pores that are in favor of penetrating for EA molecules.

Internal patch prior (e.g. self-similarity) has achieved a great success in image denoising. However, it is a challenging task to utilize clean external natural patches for denoising. Natural image patch comes from very complex distributions which are hard to learn without supervision. In this paper, we use an autoencoder to discover and utilize these underlying distributions to learn a compact representation that is more robust to realistic noises. By exploiting learned external prior and internal self-similarity jointly, we develop an efficient patch sparse coding scheme for real-world image denoising. Numerical experiments demonstrate that the proposed method outperforms many state-of-the-art denoising methods, especially on removing realistic noise.

Heart rate (HR) is a vital bio-signal that is relatively easy to monitor with contact sensors and is related to a living organism’s state of health, stress and well-being. The objective of this study was to develop an algorithm to extract HR (in beats per minute) of an anesthetized and a resting pig from raw video data as a first step towards continuous monitoring of health and welfare of pigs. Data were obtained from two experiments, wherein the pigs were video recorded whilst wearing an electrocardiography (ECG) monitoring system as gold standard (GS). In order to develop the algorithm, this study used a bandpass filter to remove noise. Then, a short-time Fourier transform (STFT) method was tested by evaluating different window sizes and window functions to accurately identify the HR. The resulting algorithm was first tested on videos of an anesthetized pig that maintained a relatively constant HR. The GS HR measurements for the anesthetized pig had a mean value of 71.76 bpm and standard deviation (SD) of 3.57 bpm. The developed algorithm had 2.33 bpm in mean absolute error (MAE), 3.09 bpm in root mean square error (RMSE) and 67% in HR estimation error below 3.5 bpm (PE3.5). The sensitivity of the algorithm was then tested on the video of a non-anaesthetized resting pig, as an animal in this state has more fluctuations in HR than an anaesthetized pig, while motion artefacts are still minimized due to resting. The GS HR measurements for the resting pig had a mean value of 161.43 bpm and SD of 10.11 bpm. The video-extracted HR showed a performance of 4.69 bpm in MAE, 6.43 bpm in RMSE and 57% in PE3.5. The results showed that HR monitoring using only the green channel of the video signal was better than using three color channels, which reduces computing complexity. By comparing different regions of interest (ROI), the region around the abdomen was found physiologically better than the face and front leg parts. In summary, the developed algorithm based on video data has potential to be used for contactless HR measurement and may be applied on resting pigs for real-time monitoring of their health and welfare status, which is of significant interest for veterinarians and farmers.

Modeling the implied volatility has received extensive attention, as the implied volatility is an important parameter in option pricing. Usually the implied volatility can be approximated by fitting a polynomial about the strike and the maturity or by stochastic methods. In this article, a Gaussian semi-parametric model is proposed based on the quadratic polynomial semi-parametric model suggested by Borovkova. In the new model, the Gaussian function is used to construct a smooth term substituting the quadratic term in the polynomial model, and the arbitrage-free constraints are used to calibrate the model. The empirical tests show that the Gaussian semi-parametric model has a better performance in fitting and forecasting.

In the past decade years, much attention has been attached on assembly process reliability in manufacturing system, because the quality and cost of product are highly determined by assembly process. However, existing research on reliability in assembly are mainly focused on study of size deviation propagation. In this paper, the method for risk evaluation in assembly process based on the discrete-time SIRS epidemic model and information entropy was proposed. Firstly, aiming at the issue of assembly process optimization, innovative solutions are proposed from the perspectives of reliability and cost by decomposing the assembly into general path and rework path. Secondly, the propagation mechanism of defects in optimal assembly approach were studied through combining the infectious disease model and information entropy. According to the bifurcation phenomenon in the SIRS model, the entropy increment of assembly process Δ H b a s e when defect emergence occurs is calculated. Thirdly, the information entropy increment of optimal assembly approach Δ H is used to evaluate the assembly risk by comparing with the Δ H b a s e . Finally, a case study of assembly risk evaluation for the oil pump was presented to verify the advantage of this method.

The quality of a machined surface plays a critical role in assembly performance, especially for precise matching parts, and therefore it is necessary to develop a surface quality monitoring system in the machining process. In this paper, an indirect surface quality monitoring approach is proposed with a wireless sensory tool holder. First, experimentation is conducted to collect the machining process signals from the tool holder. Then, the time domain, frequency domain and time–frequency domain features are extracted, and the deep forest algorithm is adopted to identify the surface quality, which is evaluated through the surface average parameter. Finally, the results of the experiment and the comparisons with other approaches demonstrate the effectiveness of the proposed method, which could be applied to ensure the surface quality, improve the machining efficiency and reduce the rejection rate of the machining process.

There is a growing body of literature which recognizes the importance of mechanical equipment reliability during processing, and reliability assessment is important in guaranteeing the precision, function, and use life span of mechanical equipment. For products with a long lifetime and high reliability, it is difficult to assess lifetime and reliability using traditional statistical inference based on a large sample of data from the lifetime test. Therefore, this study contributed to this growing area of research, through a reliability evaluation method based on degradation path distribution related to signal characteristics. In this research, an effective method for reliability assessment was constructed, in which the signal features of the machining process were used to replace traditional time data and fit equipment degradation model. The pseudo failure characteristic (PFC) was obtained according to the failure threshold and the reliability curve was plotted by a PFC distribution model. Experimental investigation on tool reliability assessment was used to verify the effectiveness of this method, in which the trend that tool wear changes with the features was fitted by a Gaussian distribution function and Logarithmic distribution function, to obtain a better tool degradation model. The results illustrated the model could evaluate reliability of mechanical equipment effectively.

According to the Industry 4.0 paradigm, all objects in a factory, including people, are equipped with communication capabilities and integrated into cyber-physical systems (CPS). Human activity recognition (HAR) based on wearable sensors provides a method to connect people to CPS. Deep learning has shown surpassing performance in HAR. Data preprocessing is an important part of deep learning projects and takes up a large part of the whole analytical pipeline. Data segmentation and data transformation are two critical steps of data preprocessing. This study analyzes the impact of segmentation methods on deep learning model performance, and compares four data transformation approaches. An experiment with HAR based on acceleration data from multiple wearable devices was conducted. The multichannel method, which treats the data for the three axes as three overlapped color channels, produced the best performance. The highest overall recognition accuracy achieved was 97.20% for eight daily activities, based on the data from seven wearable sensors, which outperformed most of the other machine learning techniques. Moreover, the multichannel approach was applied to three public datasets and produced satisfying results for multi-source acceleration data. The proposed method can help better analyze workers’ activities and help to integrate people into CPS.

There is a growing body of literature that recognizes the importance of product safety and the quality problems during processing. The working status of cutting tools may lead to project delay and cost overrun if broken down accidentally, and tool wear is crucial to processing precision in mechanical manufacturing, therefore, this study contributes to this growing area of research by monitoring condition and estimating wear. In this research, an effective method for tool wear estimation was constructed, in which, the signal features of machining process were extracted by ensemble empirical mode decomposition (EEMD) and were used to estimate the tool wear. Based on signal analysis, vibration signals that had better linear relationship with tool wearing process were decomposed, then the intrinsic mode functions (IMFs), frequency spectrums of IMFs and the features relating to amplitude changes of frequency spectrum were obtained. The trend that tool wear changes with the features was fitted by Gaussian fitting function to estimate the tool wear. Experimental investigation was used to verify the effectiveness of this method and the results illustrated the correlation between tool wear and the modal features of monitored signals.

The geodesic active contour (GAC) model has been widely used due to the high precision of edge detection and the continuity of boundaries. However, compared to the gradient vector flow (GVF) field, it usually has poor performance in the object boundaries with concave shape. In this paper, the improvements that integrate the good performance of the GAC model and the GVF field are described in detail and the GAC_GVF&B model combining the GAC model with the GVF field and a balloon force is proposed. The experimental results demonstrate that the proposed method is insensitive to the positions of initial curves and can segment complex-shape or multi-objects images correctly with higher convergence.

PDE-based image segmentation based on the active contour model attracts many researchers due to its high precision of edge detection and the continuity of boundaries. Its basic idea is to define an energy functional on a dynamic curve which achieves its minimum when the curve conforms to the boundary of the objects. Thus, the image segmentation problem is in essence an optimization problem. The most widely used optimization method is the gradient descent method in PDE-based image segmentation. However, the convergence of the gradient descent method is very poor. In this paper, a quasi-Newton method is extended to the generalized quasi-Newton method, and then the generalized Newton method and the generalized quasi-Newton method are used to solve a simple region-based model and compared with the gradient decent method. Experimental results show that the generalized quasi-Newton method has accurate segmentation results in the least possible number of iteration. Moreover it is able to segment noisy images correctly.

To determine if temporomandibular joint chondrocyte apoptosis is induced in rats with dental biomechanical stimulation and what a role TNF takes.Thirty-two rats were divided into 4 groups (n = 8/group) and exposed to incisor mal-occlusion induced by unilateral anterior crossbite biomechanical stimulation. Two groups were sampled at 2 or 4 weeks. The other two groups were treated with local injections of a TNF inhibitor or PBS into the temporomandibular joints area at 2 weeks and then sampled at 4 weeks. Twenty-four rats either served as unilateral anterior crossbite mock operation controls (n = 8/group) with sampling at 2 or 4 weeks or received a local injection of the TNF inhibitor at 2 weeks with sampling at 4 weeks. Chondrocytes were isolated from the temporomandibular joints of 6 additional rats and treated with TNF in vitro. Joint samples were assessed using Hematoxylin&eosin, Safranin O, TUNEL and immunohistochemistry staining, real-time PCR, fluorogenic activity assays and Western blot analyses. The isolated chondrocytes were also analyzed by flow cytometry.Unilateral anterior crossbite stimulation led to temporomandibular joint cartilage degradation, associated with an increase in TUNEL-positive chondrocytes number, caspase-9 expression levels, and the release of cytochrome c from mitochondria at 2 weeks without changes in TNF and caspase-8 levels until after 4 weeks. TNF stimulated apoptosis of the isolated chondrocytes and up-regulated caspase-8 expression, but did not change caspase-9 expression levels. Local injection of TNF inhibitor down-regulated caspase-8 expression and reduced TUNEL-positive cell number, but did not reverse cartilage thickness reduction, caspase-9 up-regulation or cytochrome c release.Unilateral anterior crossbite stimulation induces mitochondrion-mediated apoptosis of articular chondrocytes. TNF accelerated the unilateral anterior crossbite induced chondrocytes apoptosis via death-receptor pathway. However, anti-TNF therapy does not prevent cartilage loss in this model of temporomandibular joint.

Wafer dicing is a basic step in building electronic devices such as CPU or GPU. It is automatically accomplished in the wafer Dicing Machine in which a little misalignment of wafers may lead to damaged chips. The information of misalignment may be obtained by detecting the paddle vat to calculate the misalignment between the blade and the part of the wafer which has been diced in real-time. In this paper, methods for detecting the paddle vat are studied. For most of simple wafer images where the intensities of paddle vats are quite different from that of the background, a binary segmentation approach is used to detect the paddle vat of the wafer image. The threshold for binarization is calculated by the grey-scale histogram of the wafer image which is smoothed by using exponentially smoothed method. For wafer images with complex backgrounds, the improved Canny detection algorithm with anisotropic diffusion is used to detect the edges of paddle vats. Experimental results show the validity of the methods.

Digital image inpainting technique has been widely used in many socioeconomic areas and methods based on partial differential equations (PDE) attract intensive research in the hope of an automatic inpainting methodology. However the methods based on PDE are very time-consuming because they need to be solved by numerical integration along the temporal axis. In this paper, a fast convergent image inpainting method is proposed by combining Richardson extrapolation with an improved BSCB inpainting model, which improves the convergence rate of the numerical iterations. Experimental results show that the new model is effective and efficient.

Fourth-order partial differential equation (PDE) proposed by You and Kaveh (You-Kaveh fourth-order PDE), which replaces the gradient operator in classical second-order nonlinear diffusion methods with a Laplacian operator, is able to avoid blocky effects often caused by second-order nonlinear PDEs. However, the equation brought forward by You and Kaveh tends to leave the processed images with isolated black and white speckles. Although You and Kaveh use median filters to filter these speckles, median filters can blur the processed images to some extent, which weakens the result of You-Kaveh fourth-order PDE. In this paper, the reason why You-Kaveh fourth-order PDE can leave the processed images with isolated black and white speckles is analyzed, and a new fourth-order PDE based on the changes of Laplacian (LC fourth-order PDE) is proposed and tested. The new fourth-order PDE preserves the advantage of You-Kaveh fourth-order PDE and avoids leaving isolated black and white speckles. Moreover, the new fourth-order PDE keeps the boundary from being blurred and preserves the nuance in the processed images, so, the processed images look very natural.