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In order to obtain conductive concrete with good electrical conductivity and good mechanical properties, nanographite and magnetite sand excited by different activators and their combinations are added to ordinary concrete to obtain high quality and efficient conductive concrete. The optimal mixture ratio of alkali-excited conductive concrete and the effects of different activators and their combinations on the mechanics and electrical conductivity of concrete were studied. The microstructure of alkali-excited conductive concrete was analyzed by scanning electron microscope (SEM) to study its conductive mechanism. Results show that the conductive concrete obtained by compounding sodium hydroxide, sodium sulfate and calcium hydroxide has optimal mechanical and electrical properties when the graphite is 6% cement, and magnetite sand is 40% fine aggregate. The conductive concrete sample prepared by this method has a flexural strength of 6.84 MPa, a compressive strength of 47.79 MPa and a resistivity of 4805 Ω·cm (28 days). Compared with ordinary concrete (no nanographite and no magnetite sand), the compressive strength of conductive concrete is increased by 122.3%, the bending strength is increased by 116.5%, and the resistivity is reduced by 99.1%. SEM shows that the distribution of conductive materials in concrete is more uniform due to alkali excitation and calcium silicate hydrate (CSH) gel can be formed, which leads to better performance. The research in this paper is only a preliminary exploration of the characteristics of green conductive concrete, and the conductive heating characteristics and electromagnetic wave absorption properties of concrete, along with strength characteristics after adding conductive fillers, need to be further studied. It is suggested that further research should be carried out on the deicing characteristics of conductive concrete and the electromagnetic wave absorption properties used in stealth military engineering.

The Lie-Trotter formula has been a fundamental tool in quantum mechanics, quantum computing, and quantum simulations. The error estimations for the Lie-Trotter product formula play a crucial role in achieving scalability and computational efficiency. In this note, we present two error estimates of Lie-Trotter product formulas, utilizing Jordan product within Banach algebras. Additionally, we introduce two generalized Lie-Trotter formula and provide two explicit estimation formulas. Consequently, the renowned Suzuki symmetrized approximation for the exponentiated sums \cite[Formula 3, Equation (1.15)]{Suzuki1985} follows directly from Theorem 2.3., 8 pages

Lie-Trotter-Suzuki product formulas are ubiquitous in quantum mechanics, computing, and simulations. Approximating exponentiated sums with Jordan product formulas are investigated in the setting of JB-algebras. We show that the Suzuki type approximation for exponentiated sums hold in JB-algebras, we give explicit estimation formulas, and we deduce three generalizations of Lie-Trotter formulas for arbitrary number elements in such algebras. We also extended the Lie-Trotter formulas in a Jordan Banach algebra from three elements to an arbitrary number of elements., 12

By simulating the aroma changes during in vitro mastication, we can better understand the aroma changes during rice eating, which is helpful in studying people’s sensory preferences. To investigate the rice aroma released during the in vitro mastication, the present study analyzed rice bolus’s odor fingerprints in vitro mastication using electronic nose and gas chromatography-ion mobility spectrometry (GC-IMS). The electronic nose analysis results showed significant differences in the flavor of japonica rice in vitro mastication. In addition, GC-IMS determined 30 volatile organic compounds (VOCs) during rice in vitro mastication. Among these compounds, the most important content was aldehydes, followed by ketones and alcohols. Although the concentration of various chemicals was relatively high in cooked rice, most compounds decreased after mastication. The concentration of propan-2-ol, ethanol, and methanol increased after mastication. Multivariate data analysis showed that isoamyl sovalerate, pentanal, hexanal, acetone, hexanal, and limonene were the main VOCs of japonica rice during in vitro mastication. GC-IMS and e-nose analyses are complementary and recommended for using the two techniques to achieve the VOCs’ rapid and comprehensive detection during in vitro mastication. Results from this study allowed us to understand rice flavor during oral processing.

提要 在社会互动中，不管是口语还是书面语，语篇总是有起始、发展和终结。语篇分析或语类研究对语篇的起始、发展与终结的研究成果丰硕。语法学研究陈述句时主要关注语法结构及其意义，很少将陈述句置于语篇中讨论它们扮演的起始、发展和终结的语篇角色。本文在陈述句语法功能的基础上，在系统功能语言学框架下，将陈述句置于大语篇中进行研究，从语篇语义视角讨论陈述句的语篇角色，探索它们的生成机制以及对语篇组织策略的贡献，以此为不同句类的语篇角色研究带来启示。

IntroductionBioactive peptides based on foodstuffs are of particular interest as carriers for calcium delivery due to their safety and high activity. The phosphorylated peptide has been shown to enhance calcium absorption and bone formation.MethodA novel complex of peptide phosphorylation modification derived from soybean protein was introduced, and the mechanism, stability, and osteogenic differentiation bioactivity of the peptide with or without calcium were studied.ResultThe calcium-binding capacity of phosphorylated soy peptide (SPP) reached 50.24 ± 0.20 mg/g. The result of computer stimulation and vibration spectrum showed that SPP could chelate with calcium by the phosphoric acid group, carboxyl oxygen of C-terminal Glu, Asp, and Arg, and phosphoric acid group of Ser on the SPP at a stoichiometric ratio of 1:1, resulting in the formation of the complex of ligand and peptide. Thermal stability showed that chelation enhanced peptide stability compared with SPP alone. Additionally, in vitro results showed that SPP-Ca could facilitate osteogenic proliferation and differentiation ability.DiscussionSPP may function as a promising alternative to current therapeutic agents for bone loss.

This paper considers an infinite-horizon Markov decision process (MDP) that allows for general non-exponential discount functions, in both discrete and continuous time. Due to the inherent time inconsistency, we look for a randomized equilibrium policy (i.e., relaxed equilibrium) in an intra-personal game between an agent's current and future selves. When we modify the MDP by entropy regularization, a relaxed equilibrium is shown to exist by a nontrivial entropy estimate. As the degree of regularization diminishes, the entropy-regularized MDPs approximate the original MDP, which gives the general existence of a relaxed equilibrium in the limit by weak convergence arguments. As opposed to prior studies that consider only deterministic policies, our existence of an equilibrium does not require any convexity (or concavity) of the controlled transition probabilities and reward function. Interestingly, this benefit of considering randomized policies is unique to the time-inconsistent case.

A comprehensive understanding of interested human-to-human interactions in video streams, such as queuing, handshaking, fighting and chasing, is of immense importance to the surveillance of public security in regions like campuses, squares and parks. Different from conventional human interaction recognition, which uses choreographed videos as inputs, neglects concurrent interactive groups, and performs detection and recognition in separate stages, we introduce a new task named human-to-human interaction detection (HID). HID devotes to detecting subjects, recognizing person-wise actions, and grouping people according to their interactive relations, in one model. First, based on the popular AVA dataset created for action detection, we establish a new HID benchmark, termed AVA-Interaction (AVA-I), by adding annotations on interactive relations in a frame-by-frame manner. AVA-I consists of 85,254 frames and 86,338 interactive groups, and each image includes up to 4 concurrent interactive groups. Second, we present a novel baseline approach SaMFormer for HID, containing a visual feature extractor, a split stage which leverages a Transformer-based model to decode action instances and interactive groups, and a merging stage which reconstructs the relationship between instances and groups. All SaMFormer components are jointly trained in an end-to-end manner. Extensive experiments on AVA-I validate the superiority of SaMFormer over representative methods. The dataset and code will be made public to encourage more follow-up studies.

Monocular depth estimation has been actively studied in fields such as robot vision, autonomous driving, and 3D scene understanding. Given a sequence of color images, unsupervised learning methods based on the framework of Structure-From-Motion (SfM) simultaneously predict depth and camera relative pose. However, dynamically moving objects in the scene violate the static world assumption, resulting in inaccurate depths of dynamic objects. In this work, we propose a new method to address such dynamic object movements through monocular 3D object detection. Specifically, we first detect 3D objects in the images and build the per-pixel correspondence of the dynamic pixels with the detected object pose while leaving the static pixels corresponding to the rigid background to be modeled with camera motion. In this way, the depth of every pixel can be learned via a meaningful geometry model. Besides, objects are detected as cuboids with absolute scale, which is used to eliminate the scale ambiguity problem inherent in monocular vision. Experiments on the KITTI depth dataset show that our method achieves State-of-The-Art performance for depth estimation. Furthermore, joint training of depth, camera motion and object pose also improves monocular 3D object detection performance. To the best of our knowledge, this is the first work that allows a monocular 3D object detection network to be fine-tuned in a self-supervised manner.

Selective Laser Melting (SLM) is an increasingly concerned trend in Ti-6Al-4V blade manufacturing, while the SLMed Ti-6Al-4V blade cannot be used directly because of poor surface integrity and high residual stresses. Precise machining after SLM is a feasible solution but also a challenge. The low rigidity of the blade will lead to deformation when machining. The deformation can lead to surface error and may make defect parts. Two-steps machining processes to address the problems were proposed in this paper. First, a non-uniform allowance distribution was allocated and optimized in semi-finishing based on Ritz solution to elastic deformation. The blade was simplified as a cantilever thin plate with various thickness, and the thickness of finishing allowance was designed and optimized on the premise of ensuring the thin-wall stiffness of the blade, so as to realize the design of Ritz non-uniform allowance. Then, finishing machining was conducted to achieve precise parts. A blade deformation model was established to evaluate surface error with cutting force moving and changing. Finite element analysis and experimental validation in ball-end milling of a blade were conducted. FEA results and experimental results showed dimensional errors have been reduced up to 50%. Further surface tests demonstrated that the mean surface roughness reduced from 7.88 μm to 0.815 μm. And the residual surface stresses of the SLM samples changed after semi-finishing machining due to the residual stress relaxation and redistribution. The results demonstrated that the proposed method enhanced the surface quality of blade fabricated by SLM.

Hazard and operability analysis (HAZOP) is the paradigm of industrial safety that can reveal the hazards of process from its node deviations, consequences, causes, measures and suggestions, and such hazards can be considered as hazard events (HaE). The classification research on HaE has much irreplaceable pragmatic values. In this paper, we present a novel deep learning model termed DLF through multifractal to explore HaE classification where the motivation is that HaE can be naturally regarded as a kind of time series. Specifically, first HaE is vectorized to get HaE time series by employing BERT. Then, a new multifractal analysis method termed HmF-DFA is proposed to win HaE fractal series by analyzing HaE time series. Finally, a new hierarchical gating neural network (HGNN) is designed to process HaE fractal series to accomplish the classification of HaE from three aspects: severity, possibility and risk. We take HAZOP reports of 18 processes as cases, and launch the experiments on this basis. Results demonstrate that compared with other classifiers, DLF classifier performs better under metrics of precision, recall and F1-score, especially for the severity aspect. Also, HmF-DFA and HGNN effectively promote HaE classification. Our HaE classification system can serve application incentives to experts, engineers, employees, and other enterprises. We hope our research can contribute added support to the daily practice in industrial safety., accepted by Computers In Industry

Hazards can be exposed by HAZOP as text information, and studying their classification is of great significance to the development of industrial informatics, which is conducive to safety early warning, decision support, policy evaluation, etc. However, there is no research on this important field at present. In this paper, we propose a novel model termed DLGM via deep learning for hazard classification. Specifically, first, we leverage BERT to vectorize the hazard and treat it as a type of time series (HTS). Secondly, we build a grey model FSGM(1, 1) to model it, and get the grey guidance in the sense of the structural parameters. Finally, we design a hierarchical-feature fusion neural network (HFFNN) to investigate the HTS with grey guidance (HTSGG) from three themes, where, HFFNN is a hierarchical structure with four types of modules: two feature encoders, a gating mechanism, and a deepening mechanism. We take 18 industrial processes as application cases and launch a series of experiments. The experimental results prove that DLGM has promising aptitudes for hazard classification and that FSGM(1, 1) and HFFNN are effective. We hope our research can contribute added value and support to the daily practice in industrial safety., information science

We demonstrate an electrically driven compact hybrid lithium niobate microring laser by butt coupling a commercial 980-nm pump laser diode chip with a high quality Er3+-doped lithium niobate microring chip. Single mode lasing emission at 1531 nm wavelength from the Er3+-doped lithium niobate microring can be observed with the integrated 980-nm laser pumping. The compact hybrid lithium niobate microring laser occupies the chip size of 3 mmx4mmx0.5 mm. The threshold pumping laser power is 6 mW and the threshold current is 0.5 A (operating voltage 1.64 V) in the atmospheric temperature. The spectrum featuring single mode lasing with small linewidth of 0.05 nm is observed. This work explores a robust hybrid lithium niobate microring laser source which has potential applications in coherent optical communication and precision metrology., 4 pages, 4 figures

Colonization by beneficial microbes can enhance plant tolerance to abiotic stresses. However, there are still many unknown fields regarding the beneficial plant-microbe interactions. In this study, we have assessed the amount or impact of horizontal gene transfer (HGT)-derived genes in plants that have potentials to confer abiotic stress resistance. We have identified a total of 235 gene entries in fourteen high-quality plant genomes belonging to phyla Chlorophyta and Streptophyta that confer resistance against a wide range of abiotic pressures acquired from microbes through independent HGTs. These genes encode proteins contributed to toxic metal resistance (e.g., ChrA, CopA, CorA), osmotic and drought stress resistance (e.g., Na+/proline symporter, potassium/proton antiporter), acid resistance (e.g., PcxA, ArcA, YhdG), heat and cold stress resistance (e.g., DnaJ, Hsp20, CspA), oxidative stress resistance (e.g., GST, PoxA, glutaredoxin), DNA damage resistance (e.g., Rad25, Rad51, UvrD), and organic pollutant resistance (e.g., CytP450, laccase, CbbY). Phylogenetic analyses have supported the HGT inferences as the plant lineages are all clustering closely with distant microbial lineages. Deep-learning-based protein structure prediction and analyses, in combination with expression assessment based on codon adaption index (CAI) further corroborated the functionality and expressivity of the HGT genes in plant genomes. A case-study applying fold comparison and molecular dynamics (MD) of the HGT-driven CytP450 gave a more detailed illustration on the resemblance and evolutionary linkage between the plant recipient and microbial donor sequences. Together, the microbe-originated HGT genes identified in plant genomes and their participation in abiotic pressures resistance indicate a more profound impact of HGT on the adaptive evolution of plants.

We investigate the stability of the equilibrium-induced optimal value in one-dimensional diffusion setting for a time-inconsistent stopping problem under non-exponential discounting. We show that the optimal value is semi-continuous with respect to the drift, volatility, and reward function. An example is provided showing that the exact continuity may fail. With equilibria extended to $\varepsilon$-equilibria, we establish the relaxed continuity of the optimal value.

Maize (Zea mays L.) is the most important food crop in the world, with significant acreage and production across the globe. However, it is affected by low temperatures throughout its growth process, especially during germination. Therefore, it is important to identify more QTLs or genes associated with germination under low-temperature conditions. For the QTL analysis of traits related to low-temperature germination, we used a high-res genetic map of 213 lines of the intermated B73 × Mo17 (IBM) Syn10 doubled haploid (DH) population, which had 6618 bin markers. We detected 28 QTLs of eight phenotypic characteristics associated with low-temperature germination, while they explained the phenotypic contribution rate of 5.4 ~ 13.34%. Additionally, 14 overlapping QTLs produced six QTL clusters on every chromosome, except for 8 and 10. RNA-Seq found six genes related to low-temperature tolerance in these QTLs, while qRT-PCR analysis demonstrated that the expression trends of the Zm00001d045568 gene in the LT_BvsLT_M group and the CK_BvsCK_M group were highly significantly different at all four-time points (P

Objective To examine the research status, hot spots, and development trend of virtual vascular intervention simulation training in order to organize the knowledge and support its practice. Methods Publications on virtual vascular intervention and respective teaching evaluation (2001–2021) were retrieved from Web of Science. Citespace 5.2.R2 and Carrot2 software was used to perform the bibliometric and visual analysis on virtual vascular intervention research networks. Results Of the 1543 articles retrieved, 1520 were relevant to virtual vascular intervention research. Studies on vascular intervention emerged from 2002 with an upward trend being observed from 2010. The trend peaked between 2019 and 2020 with ∼140 published articles (9.21%) during the period. Spain published most studies ( n = 146) followed by USA ( n = 99) and England ( n = 49). COMPUT EDUC ( n = 177; 14.47%) journal publishing the highest number of articles. Among 705 authors, GARRISON D R, MAYER RE, DEDE C, COOK DA had highest publications. The frequent keywords used were “Virtual reality” ( n = 105) and “education” ( n = 105). Low cooperation between countries, authors’ networks, and relatively stable academic team was noted making clinical application of virtual vascular intervention weak. Conclusion Considerable growth was observed in vascular intervention therapy using VR technology in the last 20 years. With consistent efforts like strengthening cross-institutional and multidisciplinary collaboration networks, VR based simulation training capability can be increased that in turn can improve the outcomes of complex vascular interventions.

Privacy leakage of Internet of Things (IoT) has become a great challenge with the popularity of IoT services through mobile networks, such as smart homes, wearables, and healthcare. While previous work summarized general structures to analyze IoT privacy and provide case studies of specific devices or scenarios, it is still challenging to conduct a comprehensive and systematic quantification study of large-scale IoT privacy leakage in real world. To combine systematic analyses with real-world measurements, we provide a method to quantify IoT privacy leakage on a large-scale mobile network traffic data set containing 47651 IoT devices. We generate privacy fingerprints and attribute them to a privacy quantification framework. The framework is constructed based on the semantics of multiple privacy sensitive markers selected from the traffic along with the involved network entity types in IoT (i.e., user, device, and platform), and the fingerprints are generated from sensitive information extracted in the traffic via their markers. Our quantification shows that IoT users, devices, and platforms have considerable risks, respectively. Moreover, IoT devices have a larger scale of privacy leakage than users and platforms, and they perform different daily patterns on privacy leakage following their working conditions. In addition, we present three case studies on the leakage of location information, application calling, and voice service, which illustrate that a third party can profile a network entity in both cyberspace and physical space.

Most instance segmentation models are not end-to-end trainable due to either the incorporation of proposal estimation (RPN) as a pre-processing or non-maximum suppression (NMS) as a post-processing. Here we propose a novel end-to-end instance segmentation method termed ISDA. It reshapes the task into predicting a set of object masks, which are generated via traditional convolution operation with learned position-aware kernels and features of objects. Such kernels and features are learned by leveraging a deformable attention network with multi-scale representation. Thanks to the introduced set-prediction mechanism, the proposed method is NMS-free. Empirically, ISDA outperforms Mask R-CNN (the strong baseline) by 2.6 points on MS-COCO, and achieves leading performance compared with recent models. Code will be available soon., Comment: Accepted to ICASSP 2022

LiDAR semantic segmentation essential for advanced autonomous driving is required to be accurate, fast, and easy-deployed on mobile platforms. Previous point-based or sparse voxel-based methods are far away from real-time applications since time-consuming neighbor searching or sparse 3D convolution are employed. Recent 2D projection-based methods, including range view and multi-view fusion, can run in real time, but suffer from lower accuracy due to information loss during the 2D projection. Besides, to improve the performance, previous methods usually adopt test time augmentation (TTA), which further slows down the inference process. To achieve a better speed-accuracy trade-off, we propose Cascade Point-Grid Fusion Network (CPGNet), which ensures both effectiveness and efficiency mainly by the following two techniques: 1) the novel Point-Grid (PG) fusion block extracts semantic features mainly on the 2D projected grid for efficiency, while summarizes both 2D and 3D features on 3D point for minimal information loss; 2) the proposed transformation consistency loss narrows the gap between the single-time model inference and TTA. The experiments on the SemanticKITTI and nuScenes benchmarks demonstrate that the CPGNet without ensemble models or TTA is comparable with the state-of-the-art RPVNet, while it runs 4.7 times faster., Accepted in the 2022 IEEE International Conference on Robotics and Automation (ICRA 2022)

This study was undertaken in order to explore the practical effectiveness of the environmental DNA (eDNA) metabarcoding approach in evaluating fish composition and diversity in a high heterogeneous rocky reef habitat. We assessed the fish composition and diversity characteristics of the rocky reef habitat at Dachen Islands, Taizhou and the Zhejiang Province in China in November 2020 by comparing two methods: multi-mesh gillnets and eDNA. A comparative analysis was carried out on the fish composition and diversity characteristics gained under the two methods by using taxonomy, ecotypes and diversity indices. The results showed that there were 28 species of fish collected through gillnets, distributed under 24 genera, 19 families, 6 orders and one class. Among them, 4, 18, and 6 species of near-surface, near groundfish and groundfish were found, respectively, with Thryssa mystax, Johnius belangerii, and Sebastiscus marmoratus being the dominant species in each water layer. A total of 81 species of fish detected by eDNA metabarcoding belonging to 67 genera, 46 families, 15 orders and 2 classes. The near-surface, near groundfish and groundfish species were 17, 42, and 22, with Thryssa vitrirostris, Benthosema pterotum, Harpadon nehereus, and Dasyatis akajei being the dominant species in each water layer. Twenty species (71.4%) and 41 species (50.6%) of reef fish were counted by gillnets and eDNA, respectively. The results showed that multi-mesh gillnets can accurately obtain information on fish composition in rocky reef habitats, but with some selectivity. The eDNA technology can detect species not collected by gillnets, but the number of species detected in areas with fast water velocity is significantly less than other eDNA stations where the water velocity is slow. In summary, the combination of traditional nets and eDNA will provide more information on taxonomic diversity and population biomass, transforming natural resource management and ecological studies of fish communities on a larger spatial and temporal scale.

International audience; This paper studies interval estimation for discrete-time linear systems with unknown but bounded disturbances. Inspired by the parity space approach, we propose a point estimator with fixed-time convergence property. The estimator is combined with the zonotope-based interval analysis to achieve fast interval estimation. The parameter matrix in the estimator is optimized by minimizing the length of the edges of the outer box of the error zonotope. It is formulated as L1 optimization problem and can be efficiently solved by linear programming. Comparison studies illustrate the superiority of the proposed method over existing techniques.

To combat the problem of residual film pollution and ensure the sustainable development of agriculture in oasis areas, a field experiment was carried out in 2019 at the Wuyi Farm Corps Irrigation Center Test Station in Urumqi, Northwest China. Four types of biodegradable mulches, traditional plastic mulchs and a control group (bare land; referred to as CK) were compared, including a total of six different treatments. Effects of mulching on soil water and heat conditions as well as the yield and quality of processing tomatoes under drip irrigation were examined. In addition, a comparative analysis of economic benefits of biodegradable mulches was performed. Principal component analysis and gray correlation analysis were used to evaluate suitable mulching varieties for planting processing tomatoes under drip irrigation. Our results show that, compared with CK, biodegradable mulches and traditional plastic mulch have a similar effect on retaining soil moisture at the seedling stage but significantly increase soil moisture by 0.5%–1.5% and 1.5%–3.0% in the middle and late growth periods (P

We consider three equilibrium concepts proposed in the literature for time-inconsistent stopping problems, including mild equilibria, weak equilibria and strong equilibria. The discount function is assumed to be log sub-additive and the underlying process is one-dimensional diffusion. We first provide necessary and sufficient conditions for the characterization of weak equilibria. The smooth-fit condition is obtained as a by-product. Next, based on the characterization of weak equilibria, we show that an optimal mild equilibrium is also weak. Then we provide conditions under which a weak equilibrium is strong. We further show that an optimal mild equilibrium is also strong under a certain condition. Finally, we provide several examples including one shows a weak equilibrium may not be strong, and another one shows a strong equilibrium may not be optimal mild.

We overcome the difficulty in realizing a monolithic waveguide-coupled microring laser integrated on erbium-doped thin film lithium niobate (Er: TFLN) using photolithography assisted chemo-mechanical etching (PLACE) technique. We demonstrate an integrated single-frequency microring laser operating around 1531 nm wavelength. The PLACE technique, enabling integrated Er: TFLN photonics with low propagation loss, can thus be used to realize low cost mass production of monolithic on-chip microlasers with applications ranging from optical communication and photonic integrated circuit (PIC) to precision metrology and large-scale sensing., Comment: 4 pages, 5 figures

Entity extraction is critical to the intelligent development of various domains and the construction of knowledge agents. Yet, there is category imbalance problem in documents in some specific domains that some categories of entities are common, while some are rare and scattered. This paper proposes to use Zipf's law to tackle this problem and to promote the performance of entity extraction from documents. Using two forms of Zipf's law, words in the documents are classified into common and rare ones, and then sentences are classified into common and rare ones, and are further processed by text generation models respectively. Rare entities in the generated sentences are labeled with human-designed rules, and serve as a supplement to the raw dataset so as to alleviate the category imbalance problem. A case of extracting entities from technical documents on industrial safety is given and the experiments results on two datasets show the effectiveness of the proposed method., Comment: Journal of Informetrics

For a general entropy-regularized stochastic control problem on an infinite horizon, we prove that a policy improvement algorithm (PIA) converges to an optimal relaxed control. Contrary to the standard stochastic control literature, classical H\"{o}lder estimates of value functions do not ensure the convergence of the PIA, due to the added entropy-regularizing term. To circumvent this, we carry out a delicate estimation by moving back and forth between appropriate H\"{o}lder and Sobolev spaces. This requires new Sobolev estimates designed specifically for the purpose of policy improvement and a nontrivial technique to contain the entropy growth. Ultimately, we obtain a uniform H\"{o}lder bound for the sequence of value functions generated by the PIA, thereby achieving the desired convergence result. Characterization of the optimal value function as the unique solution to an exploratory Hamilton-Jacobi-Bellman equation comes as a by-product.

Geometric dissimilarity between the thickness profile of the entry strip and the deformed roll gap is the main cause of flatness defects. However, the combination of the crown ratio and the lateral metal flow complicates the flatness defect mode, making it difficult to predict. A variety of rolled flatness defects may result, generally occurring as center waviness, edge waviness, M-mode waviness, and W-mode waviness. The effect of lateral metal flow on the flatness has rarely been considered or quantified in the traditional flatness model. To address this deficiency, an original mathematical model is proposed for computing the rolled flatness defects while accounting for lateral metal flow during the rolling process. The proposed analytical model was employed to compute the flatness deviations with and without lateral metal flow, and the flatness calculated results were compared with the numerical solutions provided by a three-dimensional finite element model. The influence of the lateral metal flow on the strip flatness distribution was clearly identified and explained. Finally, the effects of the reduction rate, entry thickness, and exit crown ratio on the flatness deviation and lateral metal flow are described and discussed.

The corrugated rolling is a new rolling technology to obtain the composite plate. Compared with the flat rolling, the corrugated rolling can promote the plastic deformation of hard metal effectively and gain a composite plate with superior mechanical properties. In this paper, the finite element analysis of the copper/aluminum composite plate in corrugated rolling process was carried out. The correctness of the model was verified by experiment. The distributions of rolling normal stress, shear stress, equivalent strain and cross shear zone in corrugated rolling were obtained, and the differences between these from flat rolling were contrasted. Cross shear zone and shear stress in corrugated rolling are larger than flat rolling, which is beneficial to metal composite.

Due to their convenience, water measuring structures have become an important means of measuring water in irrigation canal systems However, relevant research on upstream and downstream water-depth monitoring point locations is scarce. Our study aims to determine the functional relationship between the locations of the water-depth monitoring points and the opening width of the sluice. We established 14 trunk-channel and branch-channel hydrodynamic models. The locations of the water-depth monitoring points for the upstream and downstream reaches and their hydraulic characteristics were assessed using a numerical simulation and hydraulic test. The results showed that the locations of the upstream and downstream water-depth monitoring points were, respectively, 16.26 and 15.51 times the width of the sluice. The average error between the calculated flow rate and the simulated value was 14.37%, the average error between the flow rates calculated by the modified and the simulated values was 3.36%. To further verify the accuracy of the modified discharge calculation formula, by comparing the measured values, we reduced the average error of the modified formula by 19.29% compared with the standard formula. This research provides new insights into optimizing water measurements in irrigation canal systems. The results provide an engineering basis for the site selection of water-depth monitoring points that is suitable to be widely applied in the field.

International audience; This paper considers set-membership estimation for discrete-time linear parameter-varying descriptor systems. Ellipsoid bundle, a new set representation tool combining certain characteristics of ellipsoids and zonotopes, is used to design a setmembership estimation method for the considered systems. We use the L∞ technique to design the estimator parameters in order to optimize estimation accuracy. This paper also studies the stability problem of the proposed method and establishes an offline sufficient stability condition. Finally, simulation results are presented to illustrate the effectiveness of the proposed method.

The physical properties of maize seeds are closely related to food processing and production. To study and evaluate the characteristics of maize seeds, typical maize seeds in a cold region of North China were used as test varieties. A variety of agricultural material test benches were built to measure the maize seeds’ physical parameters, such as thousand-grain weight, moisture content, triaxial arithmetic mean particle size, coefficient of static friction, coefficient of rolling friction, angle of natural repose, coefficient of restitution, and stiffness coefficient. Principal component and cluster comprehensive analyses were used to simplify the characteristic parameter index used to judge the comprehensive score of maize seeds. The results showed that there were significant differences in the main physical characteristics parameters of the typical maize varieties in this cold area, and there were different degrees of correlation among the physical characteristics. Principal component analysis was used to extract the first three principal component factors, whose cumulative contribution rate was over 80%, representing most of the information of the original eight physical characteristic parameters, and had good representativeness and objectivity. According to the test results, the classification standard of the evaluation of the physical characteristics of 15 kinds of maize seeds were determined, and appropriate evaluations were conducted. The 15 kinds of maize seeds were clustered into four groups by cluster analysis, and the physical characteristics of each groups were different. This study provides a new idea for the evaluation and analysis of the physical properties of agricultural materials, and provides a new method for the screening and classification of food processing raw materials.

Macular edema is considered as a major cause of visual loss and blindness in patients with ocular fundus diseases. Optical coherence tomography (OCT) is a non-invasive imaging technique, which has been widely applied for diagnosing macular edema due to its non-invasive and high resolution properties. However, the practical applications remain challenges due to the distorted retinal morphology and blurred boundaries near macular edema. Herein, we developed a novel deep learning model for the segmentation of macular edema in OCT images based on DeepLab framework (OCT-DeepLab). In this model, we used atrous spatial pyramid pooling (ASPP) to detect macular edema at multiple features and used the fully connected conditional random field (CRF) to refine the boundary of macular edema. OCT-DeepLab model was compared against the traditional hand-crafted methods (C-V and SBG) and the end-to-end methods (FCN, PSPnet, and U-net) to estimate the segmentation performance. OCT-DeepLab showed great advantage over the hand-crafted methods (C-V and SBG) and end-to-end methods (FCN, PSPnet, and U-net) as shown by higher precision, sensitivity, specificity, and F1-score. The segmentation performance of OCT-DeepLab was comparable to that of manual label, with an average area under the curve (AUC) of 0.963, which was superior to other end-to-end methods (FCN, PSPnet, and U-net). Collectively, OCT-DeepLab model is suitable for the segmentation of macular edema and assist ophthalmologists in the management of ocular disease.

The lowering of salt content in the field, especially in arid areas, after consecutive application of mulched drip irrigation (MDI) is of vital importance for sustainable cotton plantation. To elucidate the effects of long-term MDI on soil properties and cotton growth, this paper systematically monitored the soil salinity, ion concentrations and the yield of cotton in the field using MDI consecutively for six years in a typical oasis in Xinjiang, China. The results showed that MDI could significantly change salt distribution in the cotton field. During the six years tested, the soil salt content using MDI declined fast at first, and then the decline rate gradually decreased. In the 1st and 2nd year, the average salt content within 0–100 cm soil layer was larger than 20 g kg−1, which belonging to the saline soil. Then the salt content decreased to 10–20 g kg−1 in the 3rd and 4th year, and the cotton field declined to heavily saline soil. After 5 years of MDI, the soil turned to non-salinized. The Cl− and SO42− equivalence ratio (CSER) also decreased with the increase of application years of MDI. Saline-alkaline land developed from chloride-sulphate solonchak (0.2 &lt, CSER &lt, 1) into sulphate solonchak (CSER &lt, 0.2) after 6 years of MDI. The survival rate of the cotton increased from 1.48% (1 year of MDI) to 76.3% (6 years of MDI), and the yield increased from 72.43 kg ha−1 to 4515.48 kg ha−1. When the average CSER, SAR and the soil salinity in 0–140 cm soil layer decreased to 0.60, 0.98 (mol kg−1)0.5 and 6.25 g kg−1, farmers can achieve a balance between income and expenditure. Moreover, when CSER, SAR, and the soil salinity continuously decreased to 0.44, 0.69 (mol kg−1)0.5 and 0.77 g kg−1, the cotton yield will exceed the average production level of cotton in Xinjiang. Under the current irrigation schedule in the oasis irrigation area, the soil salinity and groundwater level after applying MDI could be conducive to cotton growth. However, this situation had also caused a waste of nearly 200 mm of water resources. Therefore, authors suggested that further research on water-saving irrigation systems suitable for different soil conditions should be carried out, and also the differential quota management in production practice should be adopted.

Objective To investigate macroprolactinemia caused by antipsychotics and its clinical significance. Methods A total of 133 patients with schizophrenia were selected, all of whom were treated with either risperidone or amisulpride alone. The levels of total prolactin (T-PRL) and macroprolactin (MPRL) were measured before treatment as well as the second, fourth, and sixth weeks of treatment. Results After 2 weeks of treatment, 75.09% (100/133) of the patients met the diagnostic criteria for hyperprolactinemia, the incidence of macroprolactinemia was 43% (43/100), and MPRL levels were positively correlated T-PRL levels. Conclusion Risperidone and amisulpride caused hyperprolactinemia and macroprolactinemia; thus, detection of MPRL in the clinical setting should be performed as this phenomenon appears early in treatment (the second week) and continues, that can avoid unnecessary examination and treatment for asymptomatic patients with macroprolactinemia.

THICK TASSEL DWARF1 (TD1) is a critical gene that negatively modulates meristem size during maize inflorescence development and may also regulate ear-related traits. In the present study, the whole genomic DNA sequences and the promoter regions of TD1 were analyzed in 165 diverse maize inbred lines. Polymorphism analysis identified 39 SNPs and five InDels in the genic region of TD1 and allowed 23 haplotypes to be classified. Among these sites, eight SNPs and one InDel were significantly associated with kernel number (KN) (p < 0.05), seven SNPs and one InDel were significantly associated with kernel number per row (KNPR) (p < 0.05), and three SNPs were significantly associated with kernel row number (KRN) (p < 0.05). In addition, 21 SNPs and 14 InDels were identified in the promoter regions of TD1, and two SNPs and seven InDels of these sites were found to be significantly associated with KRN (p < 0.05). The results denote that Hap_7 was the favorable haplotype for both KN and KNPR, and Hap_2 was the elite haplotype for KRN. These favorable haplotypes could be utilized in molecular marker-assisted selection (MAS) to improve KN, KNPR, or KRN, and thereby further increase grain yield in maize breeding programs.

In this work, a trans-scale precision positioning stage without guideway based on the inertial stick-slip driving is proposed, which can provide nano-scale resolution ratio and output long-range motion. The stage consists of an encapsulated piezoelectric actuator, a connection block, a base, a pair of ceramic bars, and magnets, and the volume is 25 mm × 17 mm × 13.5 mm. The structure and the principle are introduced in detail. To investigate the locomotion characteristics, a prototype is manufactured, and a series of experiments are carried out. The velocity can reach 13 mm/s when the driving frequency is 2.5 kHz and the resolution is 2 nm. The experimental results confirm that the prototype based on the inertial stick-slip driving is valid through magnets. The good linear relationships exist between the displacement curves and time under various driving voltages and frequencies.

Strip shape prediction is one of the most important technical to improve the quality of products in hot strip rolling process. In this paper, three hybrid models, including GA-MLP, MEA-MLP and PCA-MEA-MLP, are proposed for profile and flatness predictions by combining genetic algorithm (GA), mind evolutionary algorithm (MEA), principal component analysis (PCA) and multi-layer perceptron (MLP) neural networks. Mean absolute error (MAE), mean absolute percentage error, root mean squared error are adapted to evaluate the performance of the models. The results show that the data-driven model based on intelligent algorithm optimization neural networks can achieve good prediction of profile and flatness. Comparing with the hybrid GA-MLP model, the training speed of the hybrid MEA-MLP model is faster and the training time is greatly reduced. The model establishing with the input data after dimensionality reduction by PCA can reduce training time and become simple. The innovation of this paper is to propose a data-driven fast response model based on intelligent algorithm optimization neural network to replace the traditional mechanism model based on mathematical formula analysis to study complex, non-linear strip shape control in hot rolling process.

Mill chatter in tandem cold rolling mill is a major rejection to the quality and production of the strips. In most mill vibration models, either the roll mass is usually limited to vibrate in vertical direction and vertical-horizontal directions, or the multiple rolls system is simplified to a single mass system. However, the torsional chatter is also a typical type of mill chatter, and the presence of intermediate roll and backup roll will affect the overall vibration of the mill structure system. In this paper, a newly cold rolling mill vibration model coupled with the dynamic rolling processing model and nonlinear vibration model is proposed with the consideration of dynamic coupling and nonlinear characteristics of the rolling process, multiroll equilibrium, and roll movement in both vertical-horizontal-torsional directions. By using Hopf bifurcation theorem and Routh–Hurwitz determinant, the existence of the Hopf bifurcation point of the mill vibration system and bifurcation characteristics are analyzed. At last, the influence of different rolling conditions on the stability of the coupled mill system is investigated, and these results can also be used to design an optimum rolling schedule and determine the appearance of mill chatter under certain rolling conditions.

A chloride process is especially prone to leakage incidents via vaporizer rupture and pipeline perforation, both of which are usually caused by corrosion. Rupture accidents may not occur frequently but nevertheless can results in explosions, poisoning and other severe consequences. The main factors affecting the corrosion process are the chloride ion concentration and the environmental temperature. In order to study the probability of rupture accidents, this paper first presents experimental research on the corrosion performance of liquid chlorine vaporizers and pipelines made with the steel Q345 and 20G, respectively. Different chloride ion concentrations (1–20 g/L) and environmental temperatures (30–90 °C) were considered in the corrosion experiment. The experiment lasted for 42 days, and the corrosion rate was calculated using weight loss technique. The experimental results show that 20G steel suffers more serious corrosion than Q345 steel and the degree of corrosion is determined by different factors working together. Among these experimental data, the corrosivity data related to 20G steel at the corrosion environment of low temperature with low concentration (30 °C, 1 g/L), as well as at high temperature with high concentration (80 °C, 20 g/L) were selected to establish the corrosion weight loss prediction model, based on which the accumulated wall thickness reduction was further calculated. Finally, a probability prediction model for the corrosive state was established based on Markov chains. Using the dynamic prediction method proposed in this work, it is capable to determine the corrosion situation of the chloride process through predicting the rupture probability before the coming into service of process equipment. Also, these prediction results can provide some guidance for the maintenance and help to reduce the risk of rupture accidents caused by corrosion in a chloride process.

The long−term performance of an anoxic−oxic−hydrocyclone (AOH) process with an in−situ hydrocyclone treatment unit in the mixed liquid return line for sludge reduction and carbon reuse has been observed, in comparison with a conventional anoxic−oxic (AO) process. Three parallel side−stream systems, including one AOH25 system with a 25−mm hydrocyclone, one AOH35 system with a 35−mm hydrocyclone and one AO system, were built and fed with real wastewater for a comparative study in a wastewater treatment plant. The results demonstrate that the hydrocyclone in the AOH process was able to break macro−flocs into smaller flocs. And the desorption of the extracellular polymeric substance from return activated sludge (AS) leaded to an average increase of 62.97% and 36.36% in SCOD in the AOH25 and AOH35 system, respectively. In addition, shear forces, centrifugal forces of revolution and flocs’ rotation in the hydrocyclone were proposed to be the main influence mechanism of hydrocyclone treatment on AS properties. Compared with the AO process, the SCOD concentration in the effluent of the AOH processes presented a decrease of 12.0 mg/L and the TN was reduced by 21.50% owing to the released carbon sources reuse. Moreover, the sludge production was reduced by 36.81% and 35.92% in the AOH25 and AOH35 process, respectively. By contrast, the AOH25 system was better than the AOH35 system.

Optical coherence tomography (OCT) provides the visualization of macular edema which can assist ophthalmologists in the diagnosis of ocular diseases. Macular edema is a major cause of vision loss in patients with retinal vein occlusion (RVO). However, manual delineation of macular edema is a laborious and time-consuming task. This study proposes a joint model for automatic delineation of macular edema in OCT images. This model consists of two steps: image enhancement using a bioinspired algorithm and macular edema segmentation using a Gaussian-filtering regularized level set (SBGFRLS) algorithm. We then evaluated the delineation efficiency using the following parameters: accuracy, precision, sensitivity, specificity, Dice’s similarity coefficient, IOU, and kappa coefficient. Compared with the traditional level set algorithms, including C-V and GAC, the proposed model had higher efficiency in macular edema delineation as shown by reduced processing time and iteration times. Moreover, the accuracy, precision, sensitivity, specificity, Dice’s similarity coefficient, IOU, and kappa coefficient for macular edema delineation could reach 99.7%, 97.8%, 96.0%, 99.0%, 96.9%, 94.0%, and 96.8%, respectively. More importantly, the proposed model had comparable precision but shorter processing time compared with manual delineation. Collectively, this study provides a novel model for the delineation of macular edema in OCT images, which can assist the ophthalmologists for the screening and diagnosis of retinal diseases.

The dual function and role of iron(0) pentacarbonyl [Fe(CO)5] has been identified in gaseous CO-free carbonylative Suzuki-Miyaura cross-couplings, in which Fe(CO)5supplied COin situ, leading to the propagation of catalytically active Pd-Fe nanoparticles. Compared with typical carbonylative reaction conditions, CO gas (at high pressures), specialised exogenous ligands and inert reaction conditions were avoided. Our developed reaction conditions are mild, do not require specialised CO high pressure equipment, and exhibit wide functional group tolerance, giving a library of biaryl ketones in good yields.

According to the orientation of vocational-oriented higher education and the requirements of Shanghai Municipal Education Commission for first-class undergraduate training of practical talents, the main direction of laboratory construction in universities tends to be “integration of production and education”. In particular, the experimental training platform constructed should meet the production process and production equipment status of actual manufacturing enterprises. Therefore, after three-year construction, the laboratory of Shanghai Polytechnic University Intelligent Manufacturing Factory is characterized by digital twin technology and virtual simulation technology of intelligent manufacturing, with the goal of integrating production and education. It is planned to build the laboratory into an a intelligent manufacturing demonstration center with integrated teaching and scientific research, reasonable system, open, sharing and distinctive features.