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This paper focuses on the spreading phenomena within modified Leslie–Gower reaction–diffusion predator–prey systems. Our main objective is to investigate the existence of two distinct types of traveling waves. Specifically, with the aid of the upper and lower solution methods, we establish the existence of traveling waves connecting the prey-present state and the coexistence state or the prey-present state and the prey-free state by constructing different and appropriate Lyapunov functions. Moreover, for traveling wave connecting the prey-present state and the prey-free state, more information about the monotonicity of the wave profile can be obtained by analyzing its asymptotic behavior at negative infinity. Finally, our results are applied to modified Leslie–Gower system with Holling-II type functional response or Lotka–Volterra type functional response, and a novel Lyapunov function is constructed for the latter, which further enhances our results. Meanwhile, some numerical simulations are carried out to support our results. [ABSTRACT FROM AUTHOR]

As a promising technology, refractive index (RI) sensing has been developed for biosensing and chemosensing applications as well as for multiple industrial applications. Nevertheless, conventional RI sensors are confined by their operating principle or detection devices in terms of high sensitivity or a wide detection range. This study experimentally demonstrates metasurface-based spectrometer-free RI sensing through a spatial weighted variance (SWV)-based analysis of metasurface-generated vortex beams. The proposed liquid-immersed metasurface device creates a focused vortex beam at a single wavelength of 1550 nm, whose focusing effect varies according to the surrounding medium. The images, captured by a position-fixed near-infrared camera, exhibit divergent beam profiles determined by certain RI values. The SWV is used to assess the degree of divergence and is calculated based on the recorded beam images to quantify the variation of the RI. Thus, the proposed RI sensing scheme concurrently achieves high sensitivity (113 598/RIU), wide detection range (1.3164–1.3825), and high resolution (8.8 × 10−6 RIU). The proposed image-based spectrometer-less RI sensing methods overcome the constraints of instruments that are unavoidable in conventional detection techniques. Compared to the existing methods, the proposed strategy is simpler, less costly, more user-friendly, and usable in a broader range of sensing applications. [ABSTRACT FROM AUTHOR]

Overactive bladder (OAB) is a common, long-term symptom complex with a high prevalence in women worldwide. OAB has caused a social burden, and effective treatments are urgently needed. However, the pathogenesis of OAB has yet to be elucidated. Model rats underwent bladder outlet obstruction surgery. In the 2nd, 3rd, and 4th weeks after surgery, metabolic cages were used to detect the 12 h urine volume of rats in the sham and model groups. The urodynamic parameters bladder leak point pressure (BPLL), maximum voiding pressure (MVP), residual volume (RV), maximum bladder capacity (MBC), bladder compliance (BC), voided efficiency (VE), and non-voiding contractions (NVCs) were also detected. Moreover, the contractile responses of isolated detrusor muscles to electrical and carbachol stimulation were examined at the abovementioned time points. At the 4th week after surgery, the bladders of both groups were obtained for hematoxylin–eosin (H&E) and Masson's trichrome staining. Real-time qPCR and Western blot were performed to quantify the expression of choline acetyltransferase (ChAT) and solute carrier family 17 member 9 (SLC17A9). At week 4, compared with the sham group, the 12 h urine volume of PBOO group increased significantly. The BLPP, MVP, VE, MBC, and NVCs increased significantly, and the VE was significantly reduced in 4-week PBOO group. The contractile responses of isolated detrusor muscles to electrical and carbachol stimulation significantly increased in 4-week PBOO group. In the 4-week PBOO group, the bladder wall and the ratio of bladder muscle to collagen within the bladder smooth muscle layer wall were significantly higher than those in the sham group. ChAT and SLC17A9 mRNA and protein expression in the OAB model rats significantly increased. At 4 weeks after PBOO, the OAB model was successfully established. The gene and protein expression levels of ChAT and SLC17A9 increased in the bladder of the OAB model, suggesting that OAB may be related to increased excitatory purinergic and cholinergic expression. [ABSTRACT FROM AUTHOR]

In the present paper the variable exponent anisotropic weighted Hardy-Lorentz spaces is introduced. We prove a characterization of a modular inequality of the classical Hardy operator on the decreasing cone of the variable exponent Lebesgue spaces which leads to a criterion of the boundedness for the Hardy–Littlewood operator on the variable exponent weighted Lorentz spaces. Furthermore, we get some characterizations of the variable exponent anisotropic weighted Hardy-Lorentz spaces by maximal operators. Also the completeness of these spaces are investigated. Specifying the weights and exponents we recover the existing results as well as we obtain new results in the new and old settings. [ABSTRACT FROM AUTHOR]

The existence of traveling waves to general predator–prey systems is an interesting and ongoing problem, and predator–prey systems that the predator spreads faster than the prey are the major focus of the previous research. In this paper, we are concerned with the existence of traveling waves to predator–prey system with the density death function of the predator when the predator is stationary. First of all, by the unstable manifold theorem, we show that pursuit and evasion waves do not exist for any wave speeds c, as well as invasion waves do not exist for any wave speeds c ⩽ 0 . Then, with the aid of the shooting argument, Wazewski theorem, LaSalle’s invariance principle and sophisticated analyses, we establish the existence of invasion waves for wave speed c > 0 , which partially answers the open problem proposed in Wu (Comput. Math. Appl. 76(5):1139-1160, 2018). Finally, we apply the existence results to Yodzis predator–prey system with Holling type-III functional response and then find a new phenomenon different from previous results. [ABSTRACT FROM AUTHOR]

Spray-based three-dimensional (3D) concrete printing is an innovative method to automatically construct structures on vertical and overhead surfaces, which contributes to less labor investment and construction time. On the other hand, expanded graphite is widely known for its high thermal and electrical conductivity as a lightweight ingredient, and thus its potential in functional cementitious materials has been gradually realized in recent years. In this study, a lightweight cementitious material containing expanded graphite has been developed specifically for spray-based 3D concrete printing with added values. Through the characterization and overall evaluation of densities, mechanical and rheological properties, and Joule-heating performance, the appropriate expanded graphite content was determined as 1% by weight of cement. Afterward, silica fume was introduced into the matrix to tailor the rheological properties of the mixtures. The subsequent spray-based printing showed that the tailored mixture had significant improvements in the building capacity and spray quality. On the other hand, it also possessed superior Joule-heating performance than the control mixture without expanded graphite. The potential engineering applications of the developed material include deicing and structural health monitoring, and the compatibility with spray-based 3D concrete printing provides extra values of higher construction efficiency and adaptivity to the structures of complex contours. [ABSTRACT FROM AUTHOR]

To get a better overview of atmosphere-driven mass changes at Urumqi Glacier No.1, Chinese Tien Shan, the surface energy budget and mass balance is modeled by linking the COupled Snowpack and Ice surface energy and MAss balance model (COSIMA) with in-situ measured meteorological records for the ablation period in 2018. The COSIMA is calibrated by manual optimization and the modeled results agree well with the in-situ surface temperature, snow height and seasonal mass balance. Our results reveal that Urumqi Glacier No.1 experienced a significant mass loss, with an average value of − 0.77 m w.e. over the ablation period 2018. The surface energy budget components can be classified into two categories: radiation (shortwave and longwave) and turbulent fluxes. Surface melt and solid precipitation were dominated components of mass balance. The COSIMA can reproduce the glaciological mass balance compared with other models. Sensitivity analysis showed that the mass balance was more sensitive to the temperature than precipitation, and mass loss caused by temperature increase of 1 K needed to be compensated by at least 40% precipitation increase. Air temperature during the ablation period was more important than annual precipitation in controlling mass balance changes on Urumqi Glacier No. 1. These findings will enhance our understanding of the mechanisms underlying mass balance processes of ablation period and their contribution to the acceleration of glacier retreat in Tien Shan. [ABSTRACT FROM AUTHOR]

As the key for biological sequence structure and function prediction, disease diagnosis and treatment, biological sequence similarity analysis has attracted more and more attentions. However, the exiting computational methods failed to accurately analyse the biological sequence similarities because of the various data types (DNA, RNA, protein, disease, etc) and their low sequence similarities (remote homology). Therefore, new concepts and techniques are desired to solve this challenging problem. Biological sequences (DNA, RNA and protein sequences) can be considered as the sentences of "the book of life", and their similarities can be considered as the biological language semantics (BLS). In this study, we are seeking the semantics analysis techniques derived from the natural language processing (NLP) to comprehensively and accurately analyse the biological sequence similarities. 27 semantics analysis methods derived from NLP were introduced to analyse biological sequence similarities, bringing new concepts and techniques to biological sequence similarity analysis. Experimental results show that these semantics analysis methods are able to facilitate the development of protein remote homology detection, circRNA-disease associations identification and protein function annotation, achieving better performance than the other state-of-the-art predictors in the related fields. Based on these semantics analysis methods, a platform called BioSeq-Diabolo has been constructed, which is named after a popular traditional sport in China. The users only need to input the embeddings of the biological sequence data. BioSeq-Diabolo will intelligently identify the task, and then accurately analyse the biological sequence similarities based on biological language semantics. BioSeq-Diabolo will integrate different biological sequence similarities in a supervised manner by using Learning to Rank (LTR), and the performance of the constructed methods will be evaluated and analysed so as to recommend the best methods for the users. The web server and stand-alone package of BioSeq-Diabolo can be accessed at http://bliulab.net/BioSeq-Diabolo/server/. Author summary: Inspired by the similarities between the biological sequences and human languages, we apply the semantics analysis techniques derived from the natural language processing (NLP) to comprehensively and accurately analyse the biological sequence similarities, and propose a platform called BioSeq-Diabolo for intelligently and automatically analysing biological sequence similarities. BioSeq-Diabolo is an important updated version of BioSeq-BLM focusing on the homogeneous and heterogeneous biological sequence similarity analysing, which is beyond the reach of any exiting software tool or platform. BioSeq-Diabolo is named after a popular traditional sport in China whose components reflect its analysing procedures. When playing diabolo, the diabolo stably spins reflecting that the BioSeq-Diabolo is able to automatically construct the optimized predictors, and analyse the corresponding performance for a specific biological sequence similarity analysis task. [ABSTRACT FROM AUTHOR]

In this paper, the problem of attack detection for actuator attacks and power balance on cyber physical power systems (CPPSs) is investigated. An economic optimization strategy based on observer data is proposed to enable optimal power balance of the attacked system. System states and cyber attacks are estimated by a sliding mode observer satisfying the H∞$H_{\infty }$ performance index. Then, the system power is redistributed according to the optimal economic dispatch. Finally, several simulation cases are presented to illustrate the effectiveness of attack detection and economic optimization strategy. [ABSTRACT FROM AUTHOR]

Pasteurized yogurt is a healthy yogurt that can be stored in ambient temperature conditions. Dynamic headspace sampling (DHS) combined with gas chromatography-olfactory mass spectrometry (GC-O-MS), sensory evaluation, electronic nose (E-nose), and partial least squares discriminant analysis (PLS-DA) were used to analyze the flavor changes of pasteurized yogurt with different process combinations before and after aging. The results of odor profiles showed that the sensory descriptors of fermented, sweet, and sour were greatly affected by different process combinations. The results of odor-active compounds and relative odor activity value (r-OAV) showed that the combination of the production process affected the overall odor profile of pasteurized yogurt, which was consistent with the sensory evaluation results. A total of 15 odor-active compounds of 38 volatile compounds were detected in pasteurized yogurt samples. r-OAV results revealed that hexanal, (E)-2-octenal, 2-heptanone, and butanoic acid may be important odor-active compounds responsible for off-odor in aged, pasteurized yogurt samples. PLS-DA and variable importance of projection (VIP) results showed that butanoic acid, hexanal, acetoin, decanoic acid, 1-pentanol, 1-nonanal, and hexanoic acid were differential compounds that distinguish pasteurized yogurt before and after aging. [ABSTRACT FROM AUTHOR]

Panoptic segmentation has recently received increasing attention since it generates coherent scene segmentation by unifying semantic and instance segmentation. The most popular methods for panoptic segmentation are currently based on an instance segmentation framework with a semantic segmentation branch in parallel. However, these methods are too bloated for real-world applications. In this paper, we propose a simple yet effective fully convolutional network for fast panoptic segmentation. Instead of directly generating the mask for each instance, we leverage a simple graph convolutional layer to construct a pixel relationship head to predict the relationship between two adjacent pixels and determine whether they belong to the same instance. Besides, we leverage boundary information to enhance supervision information and help our method distinguish adjacent objects. Combining predicted category labels for each pixel from the semantic segmentation branch, we can generate a unified panoptic segmentation mask in a parameter-free step. We demonstrate our method's effectiveness on MS COCO dataset and Cityscapes dataset, which obtain competitive results. [ABSTRACT FROM AUTHOR]

In recent years, the economy of enterprises has developed rapidly. Most enterprises focus on economic interests and ignore the occupational health of employees, resulting in an increase in the occupational morbidity rate of employees. Occupational insecurity of corporate employees will reduce the work efficiency of employees. In turn, it will affect the economic development of the enterprise. Paying attention to the occupational health and safety development of corporate employees is a necessary measure for corporate economic development. The main reasons that endanger the occupational health of employees are the unreasonable discharge of pollutants and the substandard treatment of pollutants. The sustainable environmental strategy of enterprise development can well control the risk factors that endanger the occupation of enterprise employees, and it is conducive to the health and safety development of enterprise employees and enterprises. This paper studied the impact of sustainable environmental factors on the occupational health and safety development of enterprise employees through the fuzzy analytic hierarchy process and analyzed the impact on the occupation of enterprise employees according to the larger influencing factors. The experimental results showed that heavy metal wastewater, dust, hydrogen sulfide gas, and high-temperature radiation are the four most weighted impact indicators. Different concentrations of these four indicators are tested; when the concentration value of these impact indicators is low, it has little impact on the occupational safety and development of enterprise employees. However, when the concentration increases, the occupational safety and development of enterprise employees are seriously reduced. When the safety is the lowest, it is only 20%, and the development is only 23%. Carrying out sustainable environmental development can reduce the discharge of industrial waste; hence, while protecting the environment, the occupational health and safety and development potential of employees can be improved. [ABSTRACT FROM AUTHOR]

It is known that changes in the mechanical properties of tissues are associated with the onset and progression of certain diseases. Ultrasound elastography is a technique to characterize tissue stiffness using ultrasound imaging either by measuring tissue strain using quasi‐static elastography or natural organ pulsation elastography, or by tracing a propagated shear wave induced by a source or a natural vibration using dynamic elastography. In recent years, deep learning has begun to emerge in ultrasound elastography research. In this review, several common deep learning frameworks in the computer vision community, such as multilayered perceptron, convolutional neural network, and recurrent neural network, are described. Then, recent advances in ultrasound elastography using such deep learning techniques are revisited in terms of algorithm development and clinical diagnosis. Finally, the current challenges and future developments of deep learning in ultrasound elastography are prospected. [ABSTRACT FROM AUTHOR]

The Indian Ocean Dipole (IOD) is the predominant interannual climate mode and is critical in regulating the biogeochemical cycles of the equatorial Indian Ocean (EIO). However, the dynamics of nutrient supply and the magnitude of biological responses are less understood. Here, by comparing the biophysical in situ observations across the eastern EIO during the decaying period of a positive IOD in 2019 and neutral condition in 2017, we identify that the shoaling thermocline, initiated by reinforced easterly wind, lifts the nutricline into the euphotic layer under positive IOD condition. Coincidently, the strong turbulent mixing induced by the shear instability between the opposite‐flowing surface current and subsurface Equatorial Undercurrent increases the upward turbulent nutrient flux into the euphotic zone. Their combined effect triggers a vigorous subsurface biological response over the eastern EIO, with approximately twofold higher integrated chlorophyll‐a contents in the water column during a positive IOD than under neutral condition. Plain Language Summary: The availability of nutrients in the sunlit layer is the main factor limiting microalgae growth, which is the primary producer of marine ecosystems. Particularly in the tropical oceans, strong stratification due to warm and less salty surface water inhibits nutrient replenishment from the deep layer, leading to weak seasonality in primary production. With the occurrence of climate variability, for example, the Indian Ocean Dipole (IOD), an ocean‐atmosphere coupled climatic phenomenon, can significantly impact the biogeochemistry and ecosystem in the equatorial Indian Ocean (EIO). However, high‐resolution in situ observations, which can reveal the mechanism of nutrient supply and biological response under IOD conditions, are still lacking. We show that the combined effect of thermocline shoaling and subsurface turbulent mixing triggered an apparently elevated nutrient supply and a subsequent robust subsurface biological response in the eastern EIO during the decaying period of an extreme positive IOD in 2019. This study may aid in understanding the evolution of marine ecosystems during the greenhouse warming era. Key Points: A biological subsurface eastern equatorial Indian Ocean was observed during decaying period in a positive Indian Ocean Dipole (pIOD)The shoaling thermocline driven by reinforced easterly wind lift the nutricline into the euphotic layer in the pIOD phaseIncreased turbulent nutrient flux is caused by vertical shear due to the opposite‐flowing surface current and Equatorial Undercurrent [ABSTRACT FROM AUTHOR]

Underwater wet welding processes with minimal energy consumption and high efficiency is the future goal for researchers in the field. Al/CuO exothermic mixtures in consumables for land welding process can improve welding process stability and decrease the electrical dependence of the welding process. To confirm the beneficial effects of Al/CuO thermite and gain a better understanding of metallurgical properties of the thermite welds in underwater environment, this study investigated underwater wet self-shielded flux-cored arc welding (FCAW-S) of high-strength low-alloy steel with highly exothermic Al/CuO mixture. The effects of Al/CuO thermite on arc stability, weld metallurgical behavior and melting characteristics of flux-cored wire were clarified. The ratio of chemical heat in the flux to the heat required to melt welding wire increased from 4.7 % to 38.4 % with the addition of Al/CuO, potentially accelerating the melting of flux-cored wires. The arc stability with thermite addition was superior to that without the addition of thermite. The presence of copper as a result of metallurgical reactions had a substantial impact on metallurgical behavior of underwater wet welds. The ferrite weld was a non-equilibrium Cu supersaturated solid solution, which increased the microhardness and tensile strength of wet welds. The measured cooling time proved that the incorporation of Al/CuO had the capacity to reduce cooling rate. The incorporation of 50 wt% thermite yielded a 7.8 % increase in welding process efficiency compared to the base formulation. Current work provided an important direction for the development of a chemically self-heating flux-cored wire. [Display omitted] • High-strength low-alloy steel was welded using flux-cored wire with Al/CuO mixture. • The Al/CuO mixture changed metallurgical behavior of underwater wet welds. • The incorporation of Al/CuO had the capacity to reduce the cooling rate. • The Al/CuO addition to the flux-cored wire increased arc process efficiency. [ABSTRACT FROM AUTHOR]

Video caption aims to generate descriptive sentences about the video, and the most critical problem is how to achieve accurate word prediction with standardized and coherent syntax structure, which requires the model to thoroughly understand video content and precisely map them into corresponding sentence components. Many existing methods usually fuse different video features into a single visual feature for generating sentences. However, they ignore the word dataset prior information in the annotations (such as Part-Of-Speech) and they also ignore the association between sentence components and types of visual features. To solve these problems, we propose a POS-trends dynamic-aware model (PDA) to fully exploit the word dataset prior information in the captions to predict POS tag, so as to assist generating captions. We propose a POS feature extraction (PFE) module to use different filters to extract different POS-trends features, predict POS tags and fuse visual features. Furthermore, we propose a visual-dynamic-aware (VDA) module to dynamically adjust the mapping way of words and supplement the visual information into the local features. The fusion features provide directional visual information to generate correct words, and the predicted POS tags to guide the decoding process to generate a more standardized and coherent syntax structure. A large number of experiments based on MSVD, MSR-VTT and VATEX demonstrated that our method outperforms the state-of-the-art methods in BLEU-4, ROUGE-L, METEOR, CIDEr. Code can be available at: https://github.com/WangLanxiao/PDA-for-video-caption. [ABSTRACT FROM AUTHOR]

Oxidative stress and abnormal proliferation and migration of vascular smooth muscle cells (VSMCs) influence atherosclerosis formation and development. Oxidative stress significantly influences the abnormal proliferation and migration of VSMCs, and nuclear factor erythroid 2-related factor 2 (Nrf2) is a major antioxidant factor. However, the precise function of Nrf2 in the regulation of abnormal proliferation and migration of VSMCs and atherosclerosis is unclear. We investigated the proliferation and migration of VSMCs in atherosclerosis in male Apoe −/− and Apoe −/− Nrf2 −/− mice fed a high-fat diet for 12 weeks. In cultured mouse VSMCs, we studied the effect of Nrf2 on ox-LDL-stimulated proliferation and migration by using siRNA treatment to silence Nrf2. We then performed dual luciferase reporter and immunoprecipitation assays to study the interaction between Nrf2 and the promoter sequence of lectin-like oxidized low-density lipoprotein receptor-1 (LOX-1). Our results demonstrate that Nrf2 expression levels were increased in the aorta and VSMCs of mice in the atherosclerosis model group compared with the control group. We also provide evidence that Nrf2 deficiency attenuated atherosclerotic plaque burden, diminished proliferation, and migration of VSMCs but enhanced VSMC-specific marker gene expression in vitro and in vivo. This is related to Nrf2 binding to the promoter sequence of LOX-1. Furthermore, Nrf2 downregulation contributes to restrain both transcriptional and translational activities of LOX-1. Together, our data indicate that Nrf2 insufficiency is linked to attenuation of atherosclerosis, and could diminish the pathological process by blunting LOX-1-mediated proliferation and migration of VSMCs. [Display omitted] • Nrf2 drives atherosclerotic plaque formation. • Ox-LDL augments the expression of Nrf2 in mouse vascular smooth muscle cells (VSMCs). • Nrf2 induces proliferation and migration of VSMCs in Apoe −/− mice and in vitro. • Nrf2 deficiency diminishes LOX-1 activity and the oxidative stress level of VSMCs in vitro and in vivo. • Nrf2 binds LOX-1 promoter and increases its expression. [ABSTRACT FROM AUTHOR]

Peroxisome proliferator-activated receptors (PPARs) have been suggested to play crucial roles in the pathology of NAFLD with a vague understanding of the underlying mechanism. Here, we integrated large-scale literature data and clinical data to explore the potential role of the PPAR-APOA1 signaling pathway in the pathology of NAFLD. First, the signaling pathway connecting PPARs, APOA1, and NAFLD was constructed. Then, we employed clinical data to explore the association between APOA1 levels and NAFLD. In addition, we built the APOA1-driven pathway analysis to explore the potential mechanism of the APOA1-NAFLD association. Pathway analysis showed that APOA1 serves as a hubprotein connecting PPARs and NAFLD through a beneficial modulation of 16 out of 21 NAFLD upstream regulators. Each relationship within the composed pathway was supported by results from multiple previous studies. Clinical data analysis showed that an increase of APOA1 level was associated with a significantly decreased NAFLD prevalence ( χ 2 = 292.109 ; P < 0.001). When other confounding factors were adjusted, serum APOA1 level was shown as an independent risk factor for the prevalence of NAFLD (P value<.0001; OR = 0.562). Our results suggested that the three PPARs (PPARA, PPARD, and PPARG) might promote the expression and molecular transportation of APOA1 to form a PPAR-APOA1 signaling pathway that demonstrated a beneficial role in the pathogenesis of NAFLD. [ABSTRACT FROM AUTHOR]

Instance segmentation has made great progress in recent years. However, current mainstream detection-based methods ignore the process of distinguishing different instances in the same detected region, making it hard to segment the correct instance when a detected region contains multiple instances. To address this problem, we propose an Instance-level Context Attention Network (ICANet) to generate more discriminative features for different instances based on the context from their respective instance scopes, called instance-level context. Specifically, given a detected region, we first propose an instance attention module to obtain the specific attention maps that focus on the relationships between pixel pairs from the same instance by learning an embedding space. With this type of attention map, the features from the same instance achieve mutual gains, while the features from different instances become more discriminative. Then, we propose a spatial attention module to incorporate spatial information and enhance feature representations based on both feature and spatial relations. Moreover, to obtain clearer attention maps, we further propose a weight clipping strategy to filter out the noise by cutting off the lower weight. We perform extensive experiments to verify the effectiveness of the proposed method. As reported in the results, our method steadily outperforms the baseline by over 1.5% on the COCO dataset using different backbones and 3.7% on the Cityscapes dataset, which demonstrates the effectiveness of our method. [ABSTRACT FROM AUTHOR]

This study deploys RTK-GNSS in 2012, TLS in 2015 and UAV in 2018 to monitor the changes of Urumqi Glacier No. 1 (UG1), eastern Tien Shan, and analyzes the feasibility of three technologies in monitoring the mountain glaciers. DEM differencing shows that UG1 has experienced a pronounced thinning and mass loss for the period of 2012–18. The glacier surface elevation change of −0.83 ± 0.57 m w.e. a−1 has been recorded for 2012–15, whereas the changes of glacier tongue surface elevation in 2015–18 and 2012–18 were −2.03 ± 0.95 and −1.34 ± 0.88 m w.e. a−1, respectively. The glacier area shrunk by 0.07 ± 0.07 × 10−3 km2 and the terminus retreat rate was 6.28 ± 0.83 m a−1 during 2012–18. The good agreement between the glaciological and geodetic specific mass-balances is promising, showing the combination of the three technologies is suitable to monitor glacier mass change. We recommend application of the three technologies to assess each other in different locations of the glacier, e.g. RTK-GNSS base stations, ground control points, glacier tongue and terminus, in order to avoid the inherent limitations of each technology and to provide reliable data for the future studies of mountain glacier changes in western China. [ABSTRACT FROM AUTHOR]

Instance segmentation needs to locate all instances in an image correctly and segment each instance precisely. Currently, the most dominant methods for instance segmentation take object detection as a pre-task. However, they rely on the accuracy of object detection incredibly. If the pre-task cannot predict an accurate bounding box, the performance of instance segmentation will degenerate. In this paper, we present a novel method for instance segmentation to solve this problem, which is called Segmenting Beyond the Bounding Box (S3B-Net). Our S3B-Net designs a sub-network to help instance segmentation methods based on object detection to segment the part of an instance beyond the bounding box. Specifically, the sub-network first predicts a two-dimensional pixel embedding for each pixel. Then, the Gaussian function is employed to calculate a pixel’s probability belongs to a corresponding instance according to the two-dimensional pixel embedding. Finally, the output of the sub-network combines with the output of instance segmentation based on object detection to generate a more precise instance mask. Our sub-network can easily extend on the existing instance segmentation method based on object detection to segment instance beyond the bounding box. We do our experiments on dominant instance segmentation datasets, such as the COCO dataset and Cityscapes dataset. The results show that our method can achieve 6.8 points gain compared with the baseline Mask R-CNN with ResNet-50-FPN in Cityscapes datasets, and 1.7 points gain with ResNet-101-FPN-DCN in COCO datasets. Our S3B-Net outperforms the previous state-of-the-art instance segmentation method, which proves our method is competitive. The source code of our method will be made available. [ABSTRACT FROM AUTHOR]

Post–heating operation is crucial to produce ambient yogurt, which is also a direct cause of sensory defects, especially graininess. Consumers' perception of graininess varies based on individual sensitivities. Accurate evaluation of graininess is a prerequisite for improving the sensory quality of ambient yogurts. The purpose of this study was to propose a method to discriminate consumers' sensitivity to grainy perception of post–heating (55 °C–85 °C, 25 s) fermented milk, and to further determine the volume proportion threshold required to produce different graininess intensities by blending fermented milk with different particle sizes. The results suggested that increasing the post–heating temperature induced various graininess by promoting different microgel aggregation. Fermented milks with microgel aggregate sizes of ∼14–∼20 μm, ∼27 μm, and ∼46–∼65 μm were classified as "weak", "moderate", and "strong" graininess, respectively. This result was obtained by approximately 81% of the panelists with moderate and high sensitivity on the perception of graininess. Furthermore, the graininess can be modulated by regulating the fermented milks with larger microgels and with smaller microgels; however, the blending volume of the latter must exceed 50% to reduce the graininess of the former by 1–2 sensory intensity ranks. • The graininess of fermented milk was adjusted by regulating particle size distributions. • Smaller particles with a volume ratio exceeding 50% masked the perception of larger particles. • Consumers's sensitivity was identified by a power-law fitting between particle size and graininess. [ABSTRACT FROM AUTHOR]

We propose an asymptotic-preserving finite element method for a fourth order singular perturbation problem, which completely preserves the asymptotic transition of the underlying partial differential equation. As a representative, we analyze the NZT element and a linear convergence rate is proved for the solution with sharp boundary layer. Numerical examples in two and three dimensions are consistent with the theoretical prediction. [ABSTRACT FROM AUTHOR]

• Changes of the cryosphere elements from 2000 to 2021 were revealed based on new remote sensing data and in-situ measurements. • Compared to 2000-2010, glacier mass loss including area and thickness has slowed during 2010-2021. Snow-cover water equivalent has increased while permafrost has been degrading during 2000-2021. • The impact of cryosphere change on the river runoff was discussed which is of vital importance for regional water resources management and the sustainable development of ecological environment and social economy. The cryosphere has an important impact on regional water resources and ecosystems in the Chinese Altai Mountains and its piedmont zone. Using the latest remote-sensing datasets of cryosphere changes and combining with in-situ observation data from glacier monitoring stations and snow cover surveys, the main cryosphere elements including glaciers, snow cover, and permafrost are investigated with emphasis on their changes since 2000 and the current situation. Their water resource effects are also discussed. The results indicate that although the glaciers in the region have experienced continuous and intensive melting, mass loss has slowed because both glacier area shrinkage and thickness reduction were larger during 2000–2010 than during 2010–2021. Snow-cover water equivalent (w.e.) has increased due to obvious increases in snow depth, although snow-cover area has decreased slightly. Permafrost has been degrading. Overall, cryosphere contributions to the regional water resource are approximately 40.9% since 2000, among which snow-cover melting is the largest, contributing 37.1% to water resources in the Irtysh River Basin and significantly more in the mountainous sub-basins with increased snowfall. Glacier melting contributes 2.9%∼3.4%, lower than earlier estimations of 3.4%∼3.6% for the late 20th century. Permafrost thaw caused by active layer thickening contributes approximately 0.59%. Meteorological data shows a warming and wetting trend, but summer temperature has a much lower increase rate and a slowing increase trend after 2013. Moreover, snowfall frequency has increased. In the future, glacier water resource contribution will continue to decrease, but the water resource effects of snow-cover melting and permafrost degradation would increase. [ABSTRACT FROM AUTHOR]

Biological carbon pump (BCP) inefficiency was first observed in the South China Sea (SCS) throughout the 1997–1999 El Niño Southern Oscillation (ENSO) event, but the BCP usually recovers its efficiency when the climate conditions change from El Niño to La Niña conditions in the Pacific Ocean. Enhanced stratification and Kuroshio intrusion led to weak mixing and oligotrophic conditions in the sunlit layer of the SCS during the 1997/1998 El Niño phase, but the particulate organic carbon (POC) flux was comparable to the climatological mean due to the ballast effect of increased lithogenic material and CaCO3 flux. The deepened thermocline rendered the recovered mixing less effective in replenishing subsurface nutrients and subsequently lowered POC flux during the 1998/1999 La Niña phase. Both scenarios were characterized by decreased siliceous plankton but a stimulated calcareous plankton contribution, which reduced BCP efficiency, resulting in a unique asymmetric response to the ENSO in the SCS. Plain Language Summary: The El Niño‐Southern Oscillation (ENSO) in the Pacific Ocean is one of the most important ocean‐atmosphere coupled climatic phenomena on Earth, and the ENSO in turn influences marine biogeochemical cycles and the biological carbon pump (BCP). Usually, as climate conditions transition from the El Niño to La Niña phases, the weakened BCP rebounds to a highly productive level in the central and eastern Pacific. However, the BCP exhibits an asymmetric response to the ENSO in the South China Sea (SCS), the largest tropical marginal sea of the Pacific Ocean. We show that strengthened stratification of the upper water column and enhanced oligotrophic Kuroshio intrusion led to impoverished in bioavailable nutrients near the surface, caused fewer siliceous plankton compared to calcareous plankton, and thus resulted in an inefficient BCP in the 1997/1998 El Niño phase. In contrast, the deepened thermocline prevented the replenishment of nutrients from the subsurface, subsequently inhibiting the rebound of primary production and deep biogenic flux during the 1998/1999 La Niña phase. This study may aid in understanding the response of the BCP to ENSO events in marginal seas and improve predictions of global ocean carbon storage. Key Points: The South China Sea biological carbon pump does not rebound from the 1997/1998 El Niño to La Niña phase as expectedThe enhanced stratification and Kuroshio intrusion are responsible for the low opal:CaCO3 ratio of sinking particles in the El Niño phaseThe deepened thermocline prevents the diapycnal nutrient supply and the recovery of the biological carbon pump in the La Niña phase [ABSTRACT FROM AUTHOR]

Facilitated by the ability to reflect radiation along its incident direction, retroreflectors have been perceived as a pivotal component for establishing reliable free‐space optical links. However, conventional retroreflectors suffer from limited integration because of their bulky size, heavy weight, and nonplanar shape. Metasurface‐based devices consisting of subwavelength nanostructures combine semiconductor manufacturing methods with nanophotonics, regarded as a new platform that outperforms geometrical optics. In this paper, a free‐space optical link exploiting a flat retroreflector (FRR) based on metasurface doublet is proposed and realized at a telecommunications wavelength of 1550 nm. The top‐ and bottom‐layer metasurfaces, comprising hydrogenated amorphous silicon nanopillars based on a meticulously tailored dielectric spacer of silica, achieve the functions of a transmissive Fourier lens and a concave mirror, respectively. The top transmissive metasurface performs a spatial Fourier transform and its inverse, while the bottom reflective metasurface imposes a spatially varying momentum for reflecting beams along their incident direction. As a proof of concept, the designed FRR, precisely created via lithographical nanofabrication, has been readily applied to forge a substantially reliable free‐space optical link, featuring an enhanced angular tolerance of ±25°. This work will initiate a positive prospect for the cooperation between metasurface‐based devices and wireless optical communications. [ABSTRACT FROM AUTHOR]

A fast numerical modeling approach based on network characteristic mode analysis (CMA) is presented and investigated for analyzing electrical layouts in miniature RF filters, such as surface acoustic wave filters. In this approach, a generalized eigenvalue decomposition is performed on the Z‐parameters of an electrical layout at one or two sampling frequencies that can be computed and extracted with any numerical full‐wave method. The obtained eigenvalues are used to extract modal resistance, inductance, and capacitance matrices for each eigenmode. The frequency dependence of the modal resistance matrix can be assumed a priori or determined automatically, and the modal inductance and capacitance matrices are assumed frequency independent. These modal matrices are then used to approximate the Z‐parameters at any other frequencies to provide the response of the electrical layout, which can then be combined with the frequency responses of other components, such as resonators, to yield the electrical response of an entire RF filter. Compared with the previously developed analytic extension of eigenvalues, this fast CMA‐based method is less affected by the frequency variation of eigenmodes since the frequency dependences of the eigenmodes are implicitly canceled out in its formulation. The accuracy of this approach is evaluated by comparing with results from full‐wave analyses. For RF circuits whose electrical sizes are small and whose frequency range of interest is relatively small, the proposed CMA‐based fast frequency sweep approach is found to be sufficiently accurate and highly practical for engineering applications. [ABSTRACT FROM AUTHOR]

The paper described the experimental findings of underwater wet welding of E40 steel using self-shielded flux-cored wire with a TiO2-FeO-MnO slag system. The arc stability, weld quality and corrosion resistance with different heat inputs were studied. The results showed that the wet welding process of the designed wire displayed good operability in the range of investigated parameters. The microstructure and mechanical properties of the weld metal depended on the heat input. Due to the high fraction of acicular ferrite in the weld metal, the mechanical properties of the weld metal under low heat input had better tensile strength and impact toughness. Fracture morphologies at low heat input had uniform and small dimples, which exhibited a ductile characteristic. The diffusible hydrogen content in the deposited metal obtained at a heat input of 26 kJ/cm significantly reduced to 14.6 ml/100g due to the combined effects of Fe2O3 addition and the slow solidification rate of molten metal. The microstructure also had a significant effect on the corrosion resistance of the weld metal. The weld metal with high proportions of acicular ferrite at low heat input exhibited the lowest corrosion rate, while the base metal possessed a reduced corrosion resistance. These results were helpful to promote the application of low alloy high strength steel in the marine fields. [ABSTRACT FROM AUTHOR]

Active deicing technologies, such as conductive asphalt concrete and heating cable, are not applicable to the existing roads, and passive deicing based on mechanical methods damage the roads. A novel scraping deicing system for thin ice using end-of-life wire ropes was proposed, and the scraping deicing mechanism was given to optimizing the inclination angle for minimal external deicing force. The prototype was developed and fabricated, and a laboratory deicing experiment was designed to evaluate the deicing performance for a prefabricate frozen asphalt concrete block (ACB). The results showed that the deicing brush could naturally clean with the thickness of the ice layer of 10 mm. There was good stability when the deicing brush's rotation speed reached 500 r/min, and the removal rate can get 92 %. No apparent scratches occurred on the upper body of the ACB, which will not cause severe damage to the pavement surface. [ABSTRACT FROM AUTHOR]

In practical decision-making problems, decision makers are often affected by uncertain parameters because the exact distributions of uncertain parameters are usually difficult to determine. In order to deal with this issue, the major contribution in this paper is to propose a new type of type-2 fuzzy variable called level interval type-2 fuzzy variable from the perspective of level-sets, which is a useful tool in modeling distribution uncertainty. With our level interval type-2 fuzzy variable, we give a method for constructing a parametric level interval (PLI) type-2 fuzzy variable from a nominal possibility distribution by introducing the horizontal perturbation parameters. The proposed horizontal perturbation around the nominal distribution is different from the vertical perturbation discussed in the literature. In order to facilitate the modeling in practical decision-making problems, for a level interval type-2 fuzzy variable, we define its selection variable whose distribution can be determined via its level-sets. The numerical characteristics like expected value and second order moments are important indices in practical optimization and decision-making problems. With this consideration, we establish the analytical expressions about the expected values and second order moments of the selection variables of PLI type-2 trapezoidal, normal and log-normal fuzzy variables. Furthermore, in order to derive the analytical expressions about the numerical characteristics of the selection variable for the sums of the common PLI type-2 fuzzy variables, we discuss the arithmetic about the sums of common PLI type-2 fuzzy variables. Finally, we apply the proposed optimization method to a pricing decision problem to demonstrate the efficiency of our new method. The computational results show that even a small perturbation of the nominal possibility distribution can affect the quality of solutions. [ABSTRACT FROM AUTHOR]

New Findings: What is the central question of this study?Is there a causal relationship between gonadotrophin‐releasing hormone (GnRH) receptor‐activating autoantibodies and polycystic ovary syndrome (PCOS)?What is the main finding and its importance?Induction of GnRH receptor‐activating autoantibodies in rats resulted in increased luteinizing hormone pulsatility and testosterone concentrations, disrupted oestrous cycles, increased atretic follicles, and activation of insulin signalling in the pituitary and ovary. These changes replicate those seen in humans with PCOS, suggesting that GnRH receptor‐activating autoantibodies might be involved in the pathogenesis of PCOS. Gonadotrophin‐releasing hormone receptor‐activating autoantibodies (GnRHR‐AAb) are associated with polycystic ovary syndrome (PCOS). In the present study, we examined the impact of GnRHR‐AAb on reproductive function in GnRHR‐immunized female rats. All immunized rats produced high titres of GnRHR‐AAb targeting a dominant epitope located in the central region of the second extracellular loop of the GnRHR. Increased pulsatile luteinizing hormone secretion and testosterone concentrations were found in immunized rats. These rats exhibited disrupted oestrous cycles, increased ovarian follicular atresia, and activation of insulin signalling in the pituitary and ovary, as indicated by increased mRNA expressions of insulin receptor substrate, phosphatidylinositol 3‐kinase and glucose transporter 1. No significant changes in inflammatory cytokines were detected in the ovarian tissue. These features mimic those observed in humans with PCOS. Our findings support the concept that chronic stimulation of the GnRHR by GnRHR‐AAb, with an associated increase in pituitary luteinizing hormone secretion and ovarian androgen overproduction, might represent a new aetiological mechanism for PCOS. [ABSTRACT FROM AUTHOR]

Suppose that (X, d, µ) is a metric measure space of homogeneous type in the sense of Coifman and Weiss with a reverse doubling condition. By establishing the Littlewood-Paley characterization of Lipschitz spaces, a density argument in the weak sense and almost orthogonality estimate we prove that a Caldeŕon-Zygmund operator T is bounded on Lipschitz space if and only if T 1 = 0. [ABSTRACT FROM AUTHOR]

A second-order analytic extension of eigenvalue (AEE) method is presented and investigated for efficiently computing the Z- parameters of passive RF circuits over a wide frequency band with full-wave accuracy. The Z - parameters of an RF circuit are first extracted based on a full-wave simulation on sampling frequencies and then decomposed into eigenmodes, whose eigenvalues are analytically extended to all other frequencies within the frequency band of interest based on functional equations constructed from second-order series and parallel RLC circuits. An eigenvector-eigenvalue identity is adopted to compute the frequency-dependent eigenvectors from the eigenvalues of the submatrices, which are used in the expansion of the Z - parameters. A comparison with full-wave solutions is given for the second-order AEE where four frequencies are employed to accurately approximate the Z - parameters and predict the frequency response over the entire band of interest that goes to much higher frequencies than the first-order AEE. With this, the previously developed first-order AEE for a quasi-static analysis is successfully extended to much higher frequencies. Numerical examples are provided to validate the accuracy and demonstrate the capability of the second-order AEE. It is found that the second-order AEE is very accurate for modeling RF circuits with electrical sizes up to one wavelength that possibly contains resonances, as compared to the first-order AEE, which is applicable only to RF circuits with electrical sizes smaller than one-tenth to one-fifth of a wavelength that contains no resonance. [ABSTRACT FROM AUTHOR]

Object proposals are used in two-stage detectors, such as R-CNN, to generate detection results, including category predictions and refined bounding-boxes. As a result, classification scores are assigned to refined bounding-boxes rather than object proposals. However, this procedure ignores the discrepancy of data distribution between object proposals and refined bounding-boxes. We consider this discrepancy could limit the detection accuracy. Specifically, the foreground/background imbalance on object proposals and inaccurate information from low-IoU proposals could hinder the category prediction. In this paper, we propose a detector called the Multi-Path Detection Calibration Network (PDC-Net) to address this problem. The key idea behind PDC-Net is calibrating detection results from R-CNN by considering the statistical discrepancy between object proposals and refined bounding-boxes. PDC-Net is built on Faster R-CNN. The core component in PDC-Net is the multi-path detection head, in which the base detector (from Faster R-CNN) generates detection results from object proposals and multiple calibration detectors fix incorrect outputs from the base detector using refined bounding-boxes. Experiments reveal that PDC-Net can boost detection results. Our method could reach 83.1% and 43.3% mAP respectively on PASCAL VOC and MSCOCO benchmarks, which is comparable to several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Distributed deep learning frameworks facilitate large deep learning workloads. These frameworks support sharing one GPU device among multiple jobs to improve resource utilization. Modern deep learning training jobs consume a large amount of GPU memory. Despite that, sharing GPU memory among jobs is still possible because a training job has iterative steps that its memory usage fluctuates over time. However, resource sharing also introduces the risk of job performance degradation. Co-located jobs sharing a GPU device may suffer from different levels of interference, mainly caused by memory oversharing. How to improve resource utilization while maintaining good job performance is a novel challenge for job placement strategies. This paper studies the job placement problem. We propose an opportunistic memory sharing model to describe the time-varying job memory requirements. Based on this model, we introduce an Opportunistic Job Placement Problem (OJPP) for shared GPU clusters that seek job placement configurations using a minimum number of GPU devices and guarantee user-defined performance requirements at the same time. We propose a greedy algorithm and a heuristic algorithm with computational complexities of O (n log ⁡ n) and O (n 2 log ⁡ n) , respectively, to solve the problem. We also propose an online adjustment algorithm with the computational complexity of O (n log ⁡ n) to perform updates to job placement configurations in runtime. A machine-learning-based interference prediction method is used to prepare accurate interference estimations. Extensive experiments are conducted on a GPU cluster to verify the correctness and effectiveness of our algorithms. Compared with standalone training jobs on dedicated clusters, the proposed approach reduces resource consumption by 46% in a shared cluster, while guaranteeing over 92.97% of the job performance, in terms of average job completion time. • Introduce a stochastic model to describe memory oversharing of deep learning jobs on a shared GPU device. • Introduce a novel interference-aware Opportunistic Job Placement Problem for shared distributed deep learning clusters. • Propose algorithms to handle both offline job placement and online adjustment problems. • Illustrate extensive experimental results to verify the correctness and effectiveness of the approaches. [ABSTRACT FROM AUTHOR]

Seventeen undescribed guaianolide sesquiterpene lactones, millefoliumines A−Q, and seven known analogues were isolated from the whole plant of Achillea millefolium L. growing in Xinjiang, China. Their structures were elucidated based on the HRESIMS and NMR data analyses. The absolute configurations of millefoliumines A−Q were determined by single-crystal X-ray crystallography, ECD data analysis along with quantum-chemical ECD calculations. The anti-inflammatory effects of these compounds on the LPS-induced RAW264.7 cell model were evaluated. As a result, millefoliumine G exhibited potential inhibitory effects on the release of NO, the level of pro-inflammatory cytokines including TNF- α and IL-6. Above results indicated a potential of the guaianolides from A. millefolium in the anti-inflammatory drug development. 24 guaianolide sesquiterpene lactones were isolated from Achillea millefolium and their anti-inflammatory were evaluated. [Display omitted] • Seventeen undescribed guaianolides were isolated from Achillea millefolium. • Compound 7 exhibited potential anti-inflammatory activity. • Anti-inflammatory mechanism of compound 7 was investigated. [ABSTRACT FROM AUTHOR]

In this study, an overlap configuration for Ti6Al4V alloy (TC4) and SiC particle reinforced aluminum matrix composites (AMC) was achieved by laser welding process. The influence of laser power on the microstructural evolution, intermetallic compounds formation and mechanical properties of TC4/AMC joints were investigated. The presence of reinforced phase SiC complicated the metallurgical reaction in the molten pool, resulting in the formation of intermetallic compounds, such as Al 4 C 3 , TiC, TiAl, and TiAl 3. The influence of laser power was more pronounced on the penetration depth compared to its impact on the weld width. The thickness of Ti Al intermetallic compounds at the TC4/AMC interface increased as the laser power was raised. Abundant dislocations existed surrounding the TiAl 3 phase because of the different thermal-mechanical properties between Al matrix and TiAl 3 phase. The tensile shear force of TC4/AMC joints can be considerably improved by reducing the thickness of the TiAl 3 phase. The maximum tensile shear force of 1675.6 N for the TC4/AMC joint was obtained at a laser power of 1500 W when the thickness of the TiAl 3 layer reached the lowest value of 7.1 μm. The research results have the potential to improve laser welding techniques for TC4/AMC hybrid structures and provide valuable guidance for the design of TC4/AMC welded joints in the future. • A dissimilar joining of TC4 and SiCp/6092Al matrix composite was successfully achieved. • Effect of laser power on the microstructural and properties in the joints was studied. • The formation mechanism of intermetallic compounds in the joints was elucidated. • Reducing the content of TiAl 3 phases in TC4/AMC joint improved tensile shear force. [ABSTRACT FROM AUTHOR]

• A dissimilar joining of TC4 and SiCp/6092Al matrix composite was successfully achieved. • The formation mechanism of intermetallic compounds (IMCs) in the joint was elucidated. • The tensile shear force of the lap joint relied mainly on the number of pores and IMCs. This investigation examined the feasibility of laser welding of TC4 titanium alloy (TC4) and SiC p /6092Al matrix composites (AMC) with different lap structures. The welding formation, microstructure, mechanical properties, and intermetallic compounds (IMCs) for the two different laser lap joints were analyzed. The results showed that a Y-shape welded joint was obtained for TC4/AMC lap structure and an X-shape joint was formed for AMC/TC4 lap structure at the same welding parameters. The intermetallic compounds generated in lap joints between TC4 and AMC were mainly composed of TiAl 3 , Al 4 C 3 , TiAl, and TiC. The amount of porosity and cracks in the TC4/AMC joints was less than that in the AMC/TC4 welded joints. The maximum tensile shear strength of 210.3 N/mm was achieved for the TC4/AMC lap joints, which was 46.2% higher than that of the AMC/TC4 joint. [ABSTRACT FROM AUTHOR]

In this paper, the attack detection problem of smart grid under dynamic load altering attack (DLAA) is investigated. An attack detection scheme based on deviation signal is proposed. The system model is described by generator swing equation and power flow DC equation, and the PI controller model is considered in the model input. For state estimation, H ∞ performance index is used to enhance the robustness of sliding mode observer against attacks. Then, an attack detection logic is designed to detect attacks by comparing the deviation signal and threshold. Finally, the proposed attack detection scheme is verified by numerical simulations, and the feasibility of the proposed algorithm in reality is verified by StaeSim hardware-in-the-loop simulation platform. • Describe CPPS as a state space descriptor model, and consider closed-loop DLAA in the model. • A sliding mode observer is designed to estimate the system state and generate a deviation signal. • An attack detection logic is introduced to detect attacks by comparing the threshold value and deviation signal. [ABSTRACT FROM AUTHOR]

Polycystic ovary syndrome (PCOS), a metabolic and reproductive disease, is frequently associated with type 2 diabetes. We have demonstrated activating autoantibodies (AAb) directed toward the second extracellular loop (ECL2) of the gonadotropin‐releasing hormone receptor (GnRHR) are present in a significant subgroup of PCOS patients. It is unclear whether GnRHR‐AAb can induce peripheral tissue insulin resistance (IR) in animal models. Sixteen rats were divided equally into a GnRHR ECL2 peptide‐immunized group (IMM group) and a control group (CON group). Sera GnRHR‐AAb titer, luteinizing hormone (LH), and testosterone (T) were higher in IMM rats compared with CON rats. No significant difference in fasting blood glucose was observed between the two groups. However, the plasma glucose level at other time points of the IMM group was higher than that of the CON group during an intraperitoneal glucose tolerance test (IPGTT) and an insulin tolerance test (ITT) (p < 0.01). These data support the likelihood of the GnRHR‐AAb induction of glucose intolerance and IR. Compared with the CON group, the IMM group showed a significant increase in insulin‐stimulated phosphorylation of IRS‐1 (p‐IRS‐1 S636/639) and a decrease in insulin‐stimulated phosphorylation of Akt (p‐AKT S473). Expression of the glucose transport genes including GLUT‐2 in liver and GLUT‐4 in white adipose tissue and skeletal muscle was significantly decreased in IMM rats compared with the CON rats. Serum levels of proinflammatory cytokines (TNF‐α, IL‐1α, and IL‐18) were increased, while anti‐inflammatory cytokines (IL‐4 and IL‐10) were decreased in the IMM group. Taken together, elevated GnRHR‐AAb enhanced LH, hyperandrogenism, and inflammation. These changes are likely related to the observed peripheral tissue IR through the downregulation of the insulin‐stimulated IRS/PI3K/Akt/Glut signaling pathway. [ABSTRACT FROM AUTHOR]

Biochar is beneficial for improving soil quality and crop productivity. However, the long‐term effects of biochar addition on temporal dynamics of plant shoot and root growth, and the changes in soil properties and nitrogen (N) leaching are still obscure. Here, based on a long‐term (7 years) biochar field experiment with rice in northwest China, we investigated the effects of two biochar rates (0 and 9 t ha−1 year−1) and two N fertilizer rates (0 and 300 kg N ha−1 year−1) on shoot and root growth, root morphology, N leaching, and soil physicochemical properties. The results showed that both biochar and N fertilizer significantly promoted rice growth, with their interaction significant only in some cases. Both fertilizers enhanced rice shoot biomass and N accumulation in various growth stages as well as increased grain yield. Nitrogen fertilizer significantly promoted root growth regardless of biochar application. However, biochar application without N fertilizer increased root biomass and length during the whole growth period, except in the booting stage; biochar with N application promoted root growth at tillering, reduced root biomass but maintained root length with low root diameter and high specific root length during the jointing and booting stages, and then delayed root senescence in the grain filling stage. Long‐term applications of biochar and N fertilizer reduced 10%–12% bulk density of topsoil compared to the control treatment with no N fertilizer and no biochar. Long‐term biochar application also improved soil total organic carbon and concentrations of available N, phosphorus, and potassium. In addition, biochar and N fertilizer applied together significantly reduced nitrate and ammonium concentration in leachate at different soil depths. In conclusion, biochar could regulate root growth, root morphology, soil properties, and N leaching to increase rice N fertilizer‐use efficiency. [ABSTRACT FROM AUTHOR]

A fast frequency sweep approach based on analytic extension of eigenvalues (AEE) is presented and investigated for an efficient analysis of miniature passive RF circuits. In this approach, an eigenvalue decomposition is performed to the Z-parameters of the circuit components at one or a few frequencies, and analytic extension is applied to obtain the eigenvalues at all other frequencies within the band of interest. For electrically small circuit components, the frequency-independent characteristic inductances and capacitances, as well as the frequency-dependent resistances, are extracted from the eigenvalues at the sampling frequencies. The extracted characteristic parameters are then used to approximate the Z- and Y-parameters and predict the responses of the circuit over the entire frequency band. The accuracy of this approach is evaluated by comparing it with the results from a full-wave analysis. It is found that the proposed AEE is very accurate with a relative error of less than 1% for miniature RF circuits whose electrical sizes are smaller than one tenth of the wavelength and is more general and powerful than the one based on lumped equivalent circuits. [ABSTRACT FROM AUTHOR]

A quasi‐static approach based on lumped equivalent circuits is presented and investigated for an efficient numerical analysis of miniature RF circuits. In this approach, the static capacitances and inductances including mutual capacitances and inductances of individual circuit components are first evaluated using the finite element method. An equivalent lumped circuit based on the calculated capacitances and inductances is then constructed for the entire circuit, whose analysis predicts the frequency responses of the circuit. The accuracy of this quasi‐static approach is investigated by comparing its solution with that of the full‐wave analysis based on Maxwell's equations. It is found that the quasi‐static analysis is very accurate for a miniature RF circuit whose electrical size is smaller than a fraction of the effective wavelength. [ABSTRACT FROM AUTHOR]

Visual content analysis and understanding attract tremendous attention because of its potentially wide range of applications including human activity analysis, automated photo face tagging, multicamera tracking, crowded counting, and biometric security. With recent progress in end-to-end differentiable learning, the accuracy of algorithms has been significantly improved and even outperforms humans in some tasks. In addition, multimodality methods, targeting on making full use of various visual data sources, are further investigated. These developments contribute to the innovations of two core modules for a typical intelligent vision system, i.e., image and video description and recognition, which are critical for the success of the visual content analysis and understanding in more complex and challenging open world. [ABSTRACT FROM AUTHOR]

Ultrasound vascular strain imaging has shown its potential to interrogate the motion of the vessel wall induced by the cardiac pulsation for predicting plaque instability. In this study, a sparse model strain estimator (SMSE) is proposed to reconstruct a dense strain field at a high resolution, with no spatial derivatives, and a high computation efficiency. This sparse model utilizes the highly-compacted property of discrete cosine transform (DCT) coefficients, thereby allowing to parameterize displacement and strain fields with truncated DCT coefficients. The derivation of affine strain components (axial and lateral strains and shears) was reformulated into solving truncated DCT coefficients and then reconstructed with them. Moreover, an analytical solution was derived to reduce estimation time. With simulations, the SMSE reduced estimation errors by up to 50% compared with the state-of-the-art window-based Lagrangian speckle model estimator (LSME). The SMSE was also proven to be more robust than the LSME against global and local noise. For in vitro and in vivo tests, residual strains assessing cumulated errors with the SMSE were 2 to 3 times lower than with the LSME. Regarding computation efficiency, the processing time of the SMSE was reduced by 4 to 25 times compared with the LSME, according to simulations, in vitro and in vivo results. Finally, phantom studies demonstrated the enhanced spatial resolution of the proposed SMSE algorithm against LSME. [ABSTRACT FROM AUTHOR]

Let (X , ρ , μ) be Ahlfors-1 regular metric spaces. By developing the Littlewood–Paley characterization of Lipschitz spaces over (X , ρ , μ) and establishing a density argument in the weak sense, the authors give a necessary and sufficient condition for the boundedness of Calderón–Zygmund operators on the Lipschitz spaces. [ABSTRACT FROM AUTHOR]

Multi-loss-joint-optimization has been proven to be valid in computer vision literature. However, the learned deep sub-features usually fit their disjoint constraints, which yield confrontation and spatial inconsistency among the sub-features with nonshared FC layers. In this paper, we propose a Hybrid-loss supervision (HLS) framework in order to obtain smoother and more spatially consistent features with shared FC layers. First, we analyze the shortcomings of the monitoring with single-loss in the existing framework theoretically. Then, we selected two notable loss functions (e.g., Center loss and Weighted loss) to instantiate the HLS framework by linear combination. By instantiating the framework with two standard loss functions, the network has learned more compact intra-class deep features and uniform inter-class deep features. The HLS framework can significantly boost the efficiency of existing convolution networks for both image classification task and object detection task without increasing network parameters and computational complexity. Extensive experimental results on different vision tasks demonstrate consistent improvement can be achieved across a variety of datasets (e.g., CIFAR-10/100, ImageNet-2012, PASCAL VOC and MS-COCO) and different convolutional neural network architectures. [ABSTRACT FROM AUTHOR]

In this paper, we obtain characterizations of the boundedness of multivariate weighted Hardy-type operators on monotone functions. We also study properties of the weight functions u on ℝ + n satisfying the condition B p ϕ → (ℝ + n) , which is an extension of the well-known conditions Bp and B p (ℝ + n) . Finally, we give simpler characterizations of the boundedness of Hardy-type operators when the weight u admits the separation of variables. [ABSTRACT FROM AUTHOR]

Rectified activation units make an important contribution to the success of deep neural networks in many computer vision tasks. In this paper, we propose a Parametric Deformable Exponential Linear Unit (PDELU) and theoretically verify its effectiveness for improving the convergence speed of learning procedure. By means of flexible map shape, the proposed PDELU could push the mean value of activation responses closer to zero, which ensures the steepest descent in training a deep neural network. We verify the effectiveness of the proposed method in the image classification task. Extensive experiments on three classical databases (i.e., CIFAR-10, CIFAR-100, and ImageNet-2015) indicate that the proposed method leads to higher convergence speed and better accuracy when it is embedded into different CNN architectures (i.e., NIN, ResNet, WRN, and DenseNet). Meanwhile, the proposed PDELU outperforms many existing shape-specific activation functions (i.e., Maxout, ReLU, LeakyReLU, ELU, SELU, SoftPlus, Swish) and the shape-adaptive activation functions (i.e., APL, PReLU, MPELU, FReLU). [ABSTRACT FROM AUTHOR]

Thanks to its storage and computation efficiency, hashing as a kind of nearest neighbor search method can facilitate massive data processing in recent vision and learning studies. Particularly, deep supervised hashing methods have significantly improved the retrieval performance compared with non-deep supervised hashing methods. However, most existing deep supervised hashing methods approximate the similarity between two images with the hamming distance between the outputs of the same hash function, i.e., the symmetric strategy. Consequently, it is typically time-consuming to train these symmetric hashing methods, and these methods can hardly take full advantage of the supervised information in the large-scale database. In this paper, we propose a novel discriminative deep m etric learning approach for a symmetric d iscrete h ashing (ADMH) approach for supervised hashing learning. ADMH integrates an asymmetric strategy with a deep metric learning method to learn the hash function for the query images and the discrete hash codes for database images directly. More specifically, we train a deep neural network to extract the features of the query images. Subsequently, we introduce a metric learning scheme for learning the discrete discriminative hash codes of the database images directly. Finally, the feature learning procedure for generating the hash codes of the query images and the discrete coding procedure for generating the hash codes of the database images are integrated into an end-to-end learning framework. Extensive experiments on various benchmark datasets show that the proposed asymmetric deep hashing method outperforms the existing hashing methods. [ABSTRACT FROM AUTHOR]