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BackgroundThyroid-associated ophthalmopathy (TAO) is the most prevalent autoimmune orbital condition, significantly impacting patients’ appearance and quality of life. Early and accurate identification of active TAO along with timely treatment can enhance prognosis and reduce the occurrence of severe cases. Although the Clinical Activity Score (CAS) serves as an effective assessment system for TAO, it is susceptible to assessor experience bias. This study aimed to develop an ensemble deep learning system that combines anterior segment slit-lamp photographs of patients with facial images to simulate expert assessment of TAO.MethodThe study included 156 patients with TAO who underwent detailed diagnosis and treatment at Shanxi Eye Hospital Affiliated to Shanxi Medical University from May 2020 to September 2023. Anterior segment slit-lamp photographs and facial images were used as different modalities and analyzed from multiple perspectives. Two ophthalmologists with more than 10 years of clinical experience independently determined the reference CAS for each image. An ensemble deep learning model based on the residual network was constructed under supervised learning to predict five key inflammatory signs (redness of the eyelids and conjunctiva, and swelling of the eyelids, conjunctiva, and caruncle or plica) associated with TAO, and to integrate these objective signs with two subjective symptoms (spontaneous retrobulbar pain and pain on attempted upward or downward gaze) in order to assess TAO activity.ResultsThe proposed model achieved 0.906 accuracy, 0.833 specificity, 0.906 precision, 0.906 recall, and 0.906 F1-score in active TAO diagnosis, demonstrating advanced performance in predicting CAS and TAO activity signs compared to conventional single-view unimodal approaches. The integration of multiple views and modalities, encompassing both anterior segment slit-lamp photographs and facial images, significantly improved the prediction accuracy of the model for TAO activity and CAS.ConclusionThe ensemble multi-view multimodal deep learning system developed in this study can more accurately assess the clinical activity of TAO than traditional methods that solely rely on facial images. This innovative approach is intended to enhance the efficiency of TAO activity assessment, providing a novel means for its comprehensive, early, and precise evaluation.

Abstract Xinhui chenpi (XHCP), the sun‐dried peel of the mandarin orange, Citrus reticulata “Chachi,” is the most famous crude drug, as well as a traditional seasoning in Chinese cooking. The main cultivation methods of XHCP are cutting and grafting, but it is generally considered that the quality of XHCP after cutting is superior to that obtained from plants propagated by graftings, which had a negative impact on the marketing of the finished product. In our study, a total of 25 samples of XHCP obtained from plants cultivated by either traditional methods (i.e., from cuttings) or by grafting were collected to compare the contents of four types of metabolites (essential oils, flavonoids, synephrine, and total polysaccharides) as well as antioxidant activity. The results revealed that the quality of XHCP did not decline after cutting, and marked individual differences between XHCP samples, even when prepared from plants grown in the same way. In general, grafting had no significant effect on the most essential oils components, total polysaccharides, synephrine, total flavonoids, total polymethoxylated flavones, hesperidin, nobiletin, tangeretin content, and antioxidant activity. Nevertheless, five volatile compounds can be used as potential chemical markers (p

Abstract User and entity behavior analytics (UEBA) is an anomaly detection technique that identifies potential threat events in the enterprise's internal threat analysis and external intrusion detection. One limitation of existing methods in UEBA is that many algorithms use deterministic algorithms only for one category labeling and only compare with other samples within this category. In order to improve the efficiency of potential threat identification, we propose a model to detect multi-homed abnormal behavior based on fuzzy particle swarm clustering. Using the behavior frequency-inverse entities frequency (BF-IEF) technology, the method of measuring the similarity of entity and user behavior is optimized. To improve the iterative speed of the fuzzy clustering algorithm, the particle swarm is introduced into the search process of the category centroid. The entity's nearest neighbor relative anomaly factor (NNRAF) in multiple fuzzy categories is calculated according to the category membership matrix, and it is combined with boxplot to detect outliers. Our model solves the problem that the sample in UEBA is evaluated only in one certain class, and the characteristics of the particle swarm optimization algorithm can avoid clustering results falling into local optimal. The results show that compared with the traditional UEBA approach, the abnormal behavior detection ability of the new method is significantly improved, which can improve the ability of information systems to resist unknown threats in practical applications. In the experiment, the accuracy rate, accuracy rate, recall rate, and F1 score of the new method reach 0.92, 0.96, 0.90, and 0.93 respectively, which is significantly better than the traditional abnormal detections.

In hidden Markov chain (HMC) models, widely used for target tracking, the process noise and measurement noise are in general assumed to be independent and Gaussian for mathematical simplicity. However, the independence and Gaussian assumptions do not always hold in practice. For instance, in a typical radar tracking application, the measurement noise is correlated over time as the sampling frequency of a radar is generally much higher than the bandwidth of the measurement noise. In addition, target maneuvers and measurement outliers imply that the process noise and measurement noise are non-Gaussian. To solve this problem, we resort to triplet Markov chain (TMC) models to describe stochastic systems with correlated noise and derive a new filter under the maximum correntropy criterion to deal with non-Gaussian noise. By stacking the state vector, measurement vector, and auxiliary vector into a triplet state vector, the TMC model can capture the complete dynamics of stochastic systems, which may be subjected to potential parameter uncertainty, non-stationarity, or error sources. Correntropy is used to measure the similarity of two random variables; unlike the commonly used minimum mean square error criterion, which uses only second-order statistics, correntropy uses second-order and higher-order information, and is more suitable for systems in the presence of non-Gaussian noise, particularly some heavy-tailed noise disturbances. Furthermore, to reduce the influence of round-off errors, a square-root implementation of the new filter is provided using QR decomposition. Instead of the full covariance matrices, corresponding Cholesky factors are recursively calculated in the square-root filtering algorithm. This is more numerically stable for ill-conditioned problems compared to the conventional filter. Finally, the effectiveness of the proposed algorithms is illustrated via three numerical examples.

Aiming at the problems of complex behavior of power entities and concealed attack means in the digital active power grid,a multi-category attribution anomaly detection algorithm based on fuzzy clustering was proposed.Firstly,the similarity measurement method of power entity behavior was optimized,a fuzzy clustering algorithm was constructed based on the measurement value,and the membership matrix of entity behavior corresponding to various classes was obtained through several iterations.Secondly,the nearest neighbor distance,nearest neighbor density,and nearest neighbor relative anomaly factor of entities in each category were calculated according to the category softening membership matrix.Finally,the relative abnormal situation of the entity in various clusters was analyzed to judge whether the behavior of the power entity belongs to the abnormal behavior category.The results show that compared with LocalOutlier Factor(LOF),K-means,and RandomForest algorithms,the new method has detected more abnormal behaviors and achieved better anomaly detection evaluation indexes.The problem of a single evaluation angle of samples in traditional anomaly detection algorithms was solved and the ability of the digital active power grid to resist unknown threats was improved.

IntroductionBetween the ages of 6 and 12 months is a crucial stage for children to develop appetitive self-regulation. Evidence suggests that a combination of parental responsive feeding and infant temperament (surgency, effortful control, negative affect) shapes infant appetitive traits (food approach, food avoidance). There is a need for research to explore these relationships, in order to provide guidance for the design of an effective intervention to improve appetitive traits. The objective of the current study was to explore the moderating role of infant temperament in the relationship between parental responsive feeding and infant appetitive traits.MethodsA total of 616 questionnaires, measuring parental responsive feeding, infant appetitive traits, and infant temperament, were collected from parents with infants aged 6–12 months.ResultsResults revealed that responsive feeding was associated with both food approach and food avoidance. Furthermore, only lower levels of surgency significantly moderated the relationship between responsive feeding and food approach, while responsive feeding was associated with food avoidance irrespective of infant temperament.DiscussionThese findings suggest that a strategy embedding responsive feeding interventions should be adopted to reduce infant food avoidance and low-surgent infant food approach, and interventions that are tailored toward food approach for infants with effortful control, negative affect, or higher levels of surgency should be further sought.

Data loss is ubiquitous in practical engineering applications due to communication delay or congestion. Data loss rate is a key metric to evaluate the reliability of state estimation. To jointly estimate system state and data loss rate, we propose a class of Gaussian-Beta filters for linear and moderate nonlinear Gaussian state-space models with unknown probability of measurement loss. In the filters, the arrival of the measurement at each time is formulated as a binary random variable, which is determined by the classical threshold technology. In addition, the hidden state and the unknown probability of measurement loss are modeled as a product of Gaussian and Beta distributions, and the form remains unchanged through recursive operations. Simulation results verify the effectiveness of the proposed Gaussian-Beta filters compared with the existing filtering algorithms.

PurposeThis study aimed to develop a deep learning model to generate a postoperative corneal axial curvature map of femtosecond laser arcuate keratotomy (FLAK) based on corneal tomography using a pix2pix conditional generative adversarial network (pix2pix cGAN) for surgical planning.MethodsA total of 451 eyes of 318 nonconsecutive patients were subjected to FLAK for corneal astigmatism correction during cataract surgery. Paired or single anterior penetrating FLAKs were performed at an 8.0-mm optical zone with a depth of 90% using a femtosecond laser (LenSx laser, Alcon Laboratories, Inc.). Corneal tomography images were acquired from Oculus Pentacam HR (Optikgeräte GmbH, Wetzlar, Germany) before and 3 months after the surgery. The raw data required for analysis consisted of the anterior corneal curvature for a range of ± 3.5 mm around the corneal apex in 0.1-mm steps, which the pseudo-color corneal curvature map synthesized was based on. The deep learning model used was a pix2pix conditional generative adversarial network. The prediction accuracy of synthetic postoperative corneal astigmatism in zones of different diameters centered on the corneal apex was assessed using vector analysis. The synthetic postoperative corneal axial curvature maps were compared with the real postoperative corneal axial curvature maps using the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR).ResultsA total of 386 pairs of preoperative and postoperative corneal tomography data were included in the training set, whereas 65 preoperative data were retrospectively included in the test set. The correlation coefficient between synthetic and real postoperative astigmatism (difference vector) in the 3-mm zone was 0.89, and that between surgically induced astigmatism (SIA) was 0.93. The mean absolute errors of SIA for real and synthetic postoperative corneal axial curvature maps in the 1-, 3-, and 5-mm zone were 0.20 ± 0.25, 0.12 ± 0.17, and 0.09 ± 0.13 diopters, respectively. The average SSIM and PSNR of the 3-mm zone were 0.86 ± 0.04 and 18.24 ± 5.78, respectively.ConclusionOur results showed that the application of pix2pix cGAN can synthesize plausible postoperative corneal tomography for FLAK, showing the possibility of using GAN to predict corneal tomography, with the potential of applying artificial intelligence to construct surgical planning models.

The classical Kalman filter is a very important state estimation approach, which has been widely used in many engineering applications. The Kalman filter is optimal for linear dynamic systems with independent Gaussian noises. However, the independence and Gaussian assumptions may not be satisfied in practice. On the one hand, modeling physical systems usually results in discrete-time state-space models with correlated process and measurement noises. On the other hand, the noise is non-Gaussian when the system is disturbed by heavy-tailed noise. In this case, the performance of the Kalman filter will deteriorate, or even diverge. This paper is devoted to addressing the state estimation problem of linear dynamic systems with high-order autoregressive moving average (ARMA) non-Gaussian noise. First, a triplet Markov model is introduced to model the system with high-order ARMA noise, since this model relaxes the independence assumption of the hidden Markov model. Then, a new filter is derived based on correntropy, instead of the commonly used minimum mean square error (MMSE), to deal with non-Gaussian noise. Unlike the MMSE, which uses only second-order statistics of error, correntropy can capture second-order and higher-order statistics. Finally, simulation results verify the effectiveness of the proposed algorithm.

In order to take into account the availability of shared location data and privacy protection requirements,aiming at the shared location information collected by the third party,a location privacy protection scheme of LBS users was proposed based on differential privacy.Firstly,the shared location data set was preprocessed,the dictionary query mode was used to build the location transaction database,and the trie tree structure was adopted to store the location data and frequency,so as to improve the query efficiency and reduce the number of noise.Secondly,frequent location selection was carried out in the Trie tree,and Laplacian mechanism under differential privacy was used to disturb the location frequency.Finally,the perturbed data was optimized based on the two techniques of upward post-processing and consistency constrained post-processing,and theoretically prove that the proposed scheme satisfies ε[KG-*4]-differential privacy.The experimental results show that,compared with the existing methods,this scheme improves the efficiency of data processing,generalizes the sensitive locations,and has higher accuracy and lower rejection rate.This scheme has a certain reference value in user location privacy protection and shared location data availability.

Diabetic retinopathy (DR) is one of the most threatening complications in diabetic patients, leading to permanent blindness without timely treatment. However, DR screening is not only a time-consuming task that requires experienced ophthalmologists but also easy to produce misdiagnosis. In recent years, deep learning techniques based on convolutional neural networks have attracted increasing research attention in medical image analysis, especially for DR diagnosis. However, dataset labeling is expensive work and it is necessary for existing deep-learning-based DR detection models. For this study, a novel domain adaptation method (multi-model domain adaptation) is developed for unsupervised DR classification in unlabeled retinal images. At the same time, it only exploits discriminative information from multiple source models without access to any data. In detail, we integrate a weight mechanism into the multi-model-based domain adaptation by measuring the importance of each source domain in a novel way, and a weighted pseudo-labeling strategy is attached to the source feature extractors for training the target DR classification model. Extensive experiments are performed on four source datasets (DDR, IDRiD, Messidor, and Messidor-2) to a target domain APTOS 2019, showing that MMDA produces competitive performance for present state-of-the-art methods for DR classification. As a novel DR detection approach, this article presents a new domain adaptation solution for medical image analysis when the source data is unavailable.

Maize is an important food crop and its fungal disease has become a limiting factor to improve the yield and quality of maize. In the control of plant pathogens, commercial fungicides have no obvious effect on corn diseases due to the emergence of drug resistance. Therefore, it is of great significance to develop new fungicides with novel structure, high efficiency, and low toxicity to control maize diseases. In this paper, a series of 1,3,4-oxadiazole derivatives were designed and synthesized from benzoyl hydrazine and aromatic aldehydes through condensation and oxidation cyclization reaction. The antifungal activity of oxadiazole derivatives against three maize disease pathogens, such as Rhizoctonia solani (R. solani), Gibberella zeae (G. zeae), and Exserohilum turcicum (E. turcicum), were evaluated by mycelium growth rate method in vitro. The results indicated that most of the synthesized derivatives exhibited positive antifungal activities. Especially against E. turcicum, several compounds demonstrated significant antifungal activities and their EC50 values were lower than positive control carbendazim. The EC50 values of compounds 4k, 5e, and 5k were 50.48, 47.56, 32.25 μg/ml, respectively, and the carbendazim was 102.83 μg/ml. The effects of active compounds on E. turcicum microstructure were observed by scanning electron microscopy (SEM). The results showed that compounds 4k, 5e, and 5k could induce the hyphae of E. turcicum to shrink and collapse obviously. In order to elucidate the preliminary mechanism of oxadiazole derivatives, the target compounds 5e and 5k were docked with the theoretical active site of succinate dehydrogenase (SDH). Compounds 5e and 5k could bind to amino acid residues through hydrophobic contact and hydrogen bonds, which explained the possible mechanism of binding between the inhibitor and target protein. In addition, the compounds with antifungal activities had almost no cytotoxicity to MCF-7. This study showed that 1,3,4-oxadiazole derivatives were worthy for further attention as potential antifungal agents for the control of maize diseases.

Freeze-thaw damage of rocks is one of significant natural causes for geo-hazards such as collapse and rockfall in alpine areas. To explore damage evolution in granite in a freeze-thaw environment, specimens of granite in Nyingchi Prefecture, Tibet, China were collected as research objects. Saturated and dry rock specimens were subjected to cyclic freeze-thaw tests of 0, 36, 72, and 144 cycles and freeze-thaw damage of the rock was analyzed by combining computed tomography (CT) scanning and three-dimensional (3D) visualization. Results show that the peak stress of granite decreases to different extents with the increasing number of freeze-thaw cycles; compared with dry rock specimens, saturated granite deteriorates more significantly and shows obviously different stress–strain curves under loading. The moisture condition exerts significant influences on the degree of freeze-thaw damage to granite: after 144 freeze-thaw cycles, the mass loss rates of dry and saturated rock specimens are 0.06% and 0.44% and their loss rates of uniaxial compressive strength (UCS) reach 4.08% and 26.2%, respectively. Under freeze-thaw cycles, the frost heave of pore water causes initiation and development of micro-defects and new micro-cracks mainly develop along relatively weak areas such as inherent defects (pores and fractures) and boundaries between different mineral crystals, resulting in intergranular and transgranular cracking. For dry rock specimens, the non-uniform contraction and expansion of minerals therein are main causes for deterioration inside the rock. The freeze-thaw damage of rocks is calculated using the improved elastic modulus loss method. The damage development trend conforms to the strength deterioration trend and the pore development in the rock, reflecting the evolution of freeze-thaw damage to granite.

A passive substrate integrated waveguide (SIW) sensor based on the complementary split ring resonator (CSRR) is presented for pressure detection in high-temperature environments. The sensor pressure sensing mechanism is described through circuit analysis and the electromagnetic coupling principle. The pressure sensor is modeled in high frequency structure simulator (HFSS), designed through parameter optimization. According to the optimized parameters, the sensor was customized and fabricated on a high temperature co-fired ceramic (HTCC) substrate using the three-dimensional co-fired technology and screen-printing technology. The pressure sensor was tested in the high-temperature pressure furnace and can work stably in the ambient environment of 25−500 °C and 10−300 kPa. The pressure sensitivity is 139.77 kHz/kPa at 25 °C, and with increasing temperature, the sensitivity increases to 191.97 kHz/kPa at 500 °C. The temperature compensation algorithm is proposed to achieve accurate acquisition of pressure signals in a high-temperature environment.

BackgroundDiabetic retinopathy, as a severe public health problem associated with vision loss, should be diagnosed early using an accurate screening tool. While many previous deep learning models have been proposed for this disease, they need sufficient professional annotation data to train the model, requiring more expensive and time-consuming screening skills.MethodThis study aims to economize manual power and proposes a deep graph correlation network (DGCN) to develop automated diabetic retinopathy grading without any professional annotations. DGCN involves the novel deep learning algorithm of a graph convolutional network to exploit inherent correlations from independent retinal image features learned by a convolutional neural network. Three designed loss functions of graph-center, pseudo-contrastive, and transformation-invariant constrain the optimisation and application of the DGCN model in an automated diabetic retinopathy grading task.ResultsTo evaluate the DGCN model, this study employed EyePACS-1 and Messidor-2 sets to perform grading results. It achieved an accuracy of 89.9% (91.8%), sensitivity of 88.2% (90.2%), and specificity of 91.3% (93.0%) on EyePACS-1 (Messidor-2) data set with a confidence index of 95% and commendable effectiveness on receiver operating characteristic (ROC) curve and t-SNE plots.ConclusionThe grading capability of this study is close to that of retina specialists, but superior to that of trained graders, which demonstrates that the proposed DGCN provides an innovative route for automated diabetic retinopathy grading and other computer-aided diagnostic systems.

Diabetic Retinopathy (DR) causes a significant health threat to the patient's vision with diabetic disease, which may result in blindness in severe situations. Various automatic DR diagnosis models have been proposed along with the development of deep learning, while there always relies on a large scale annotated data to train the network. However, annotating medical fundus images is cost-expensive and requires well-trained professional doctors to identity the DR grades. To overcome this drawback, this paper focuses on utilizing the easily-obtained unlabeled data with the help of limited annotated data to identify DR grades accurately. Hence we proposes a semi-supervised retinal image classification method by a Hybrid Graph Convolutional Network (HGCN). This HGCN network designs a modularity-based graph learning module and integrates Convolutional Neural Network (CNN) features into the graph representation by graph convolutional network. The synthesized hybrid features are optimized by a semi-supervised classification task which is assisted by a similarity-based pseudo label estimator. Through the proposed HGCN method, the retinal image classification model can be trained efficiently by partially labeled samples and the complicated annotating work is not required for the most retinal images. The experimental results on MESSIDOR dataset demonstrate the favorable performance of HGCN on semi-supervised retinal image classification, and the fully labeled data training also achieves an obvious superiority to the state-of-the-art supervised learning methods.

Diabetic retinal image classification aims to conduct diabetic retinopathy automatically diagnosing, which has achieved considerable improvement by deep learning models. However, these methods all rely on sufficient network training by large scale annotated data, which is very labor-expensive in medical image labeling. Aiming to overcome these drawbacks, this paper focuses on embedding self-supervised framework into unsupervised deep learning architecture. Specifically, we propose a Self-supervised Fuzzy Clustering Network (SFCN) by a feature learning module, reconstruction module, and a fuzzy self-supervision module. The feature learning and reconstruction modules ensure the representative ability of the network, and fuzzy self-supervision module is in charge of further providing the training direction for the whole network. Furthermore, three losses of reconstruction, self-supervision, and fuzzy supervision jointly optimize the SFCN under an unsupervised manner. To evaluate the effectiveness of the proposed method, we implement the network on three widely used retinal image datasets, which results demonstrate the satisfied performance on unsupervised retinal image classification task.

Since there are not many studies on the application of polymeric surfactants in viscosity reduction emulsification of heavy oil, a series of polyether carboxylic acid–sulfonate polymeric surfactants were synthesized. The viscosity reduction performance and the effect of different chain lengths on the viscosity reduction effect were also investigated. The viscosity reduction, emulsification, wetting, and foaming performance tests showed that the viscosity reduction performance of this series of polymeric surfactants was excellent, with the viscosity reduction rate exceeding 95%, and the viscosity was reduced to 97 mPa·s by the polymeric surfactant with a molecular weight of 600 polyethers. It was also concluded that among the three surfactants with different side chains, the polymeric surfactant with a polyether molecular weight of 600, which is the medium side-chain length, had the best viscosity reduction performance. The study showed that the polyether carboxylic acid–sulfonate polymer surfactant had a promising application in the viscosity reduction of heavy oil.

Biochar (BC) combined with humic acid (HA) and wood vinegar (WV) was designed and prepared as an inexpensive, effective, and environmentally friendly immobilization material (BHW) for metal-polluted soil. The influences of the wood vinegar and humic acid on the immobilization properties and adsorption mechanism of this new material were also investigated. The remediation performance was evaluated using a laboratory-made, nickel-contaminated soil with a Ni2+ concentration of 200 mg per kg surface soil (top 20 cm from agricultural land). The results indicated that the immobilization ratio sequence of nickel (II) in the soil was BC

Drought area expansion has a great impact on the growth and development of plants. To contribute to the water management of strawberry, this work aims to study the chronological relationship between the electrical signals and representative physiological parameters of strawberry seedlings under drought stress. This study analyzed the characteristic variables of the electrical signals; physiological parameters under drought; and control treatments. Moreover, we compared the chronological sequence of the appearance of significant differences between drought and control treatment in terms of their physiological parameters and electrical signals. The results showed that with the increase of drought treatment, the time domain parameters (peak-to-peak value, standard deviation) and frequency domain parameters (spectral of central gravity, power spectrum entropy) of the drought-treated electrical signals showed significant differences from the control on Day 2 and Day 6, respectively (p < 0.05). The root vitality of the drought treatment was significantly different from the control on Day 4 (p < 0.05); the Fv/Fm and the SPAD were significantly different (p < 0.05) on Day 7. Electrical signals first start to show a significant difference between drought and control treatment, followed by physiological parameters. Therefore, the electrical signal can be used as an early indicator of drought stress conditions. This will provide a scientific basis for the actual water management of strawberry seedlings. It also provides a methodological and theoretical basis for other studies analyzing the relationship between plant physiological parameters and electrical signals under other stress conditions.

Developers of parallel programming are faced with choices of using various Java locks. Choosing the best lock is a challenging task because a multithreaded application implemented using different locks may end up with uncertain performance. There is a strong need for automated tool support that helps Java programmers choose the best lock for each specific application. This paper presents an automated transformation approach to convert an implementation using a synchronized lock to a ReentrantLock or a ReentrantReadWriteLock at the bytecode level. For the ReentrantLock, our approach runs analysis for global monitors using the visitor pattern in the Joeq compiler. For the ReentrantReadWriteLock, a read or write lock is chosen after a side-effect analysis. Then, the proposed system validates the consistency of the analysis results to ensure a correct sequence of lock usage. Finally, lock operations and related thread communication operations are transformed into a ReentrantLock or a ReentrantReadWriteLock using a bytecode transformation framework Javassist. We validate our approach on several benchmarks including RBTree, PC, SPECjbb2005, and HSQLDB. The experimental results show that the proposed automated transformation approach can transform these benchmarks successfully in a timely fashion.

In this work, a novel amphoteric copolymer named Poly(sodium p-styrenesulfonate–co-acrylic acid-co-diallyldimethylammonium chloride) (P(SS-co-AA-co-DMDAAC)) was synthesized via free radical polymerization. Afterwards, P(SS-co-AA-co-DMDAAC) was explored for use as a dispersant in coal water slurry (CWS) preparation. The structure of P(SS-co-AA-co-DMDAAC) was verified by Fourier transform infrared spectroscopy and nuclear magnetic resonance. The synthetic conditions were optimized as the feed ratio of AA to SS was 1 : 1 (for Yulin coal) or 1.5 : 1 (for Yili coal), and DMDAAC dosage was 4.0 wt% (for Yulin coal) and 6.0 wt% (for Yili coal) toward total monomers. The performances of P(SS-co-AA-co-DMDAAC) as a dispersant for CWS were evaluated by various technologies, such as apparent viscosity, zeta potential, static stability and contact angle measurements. The results revealed that the optimized dosage of P(SS-co-AA-co-DMDAAC) in CWS preparation was 0.3 and 0.4 wt% for Yulin coal and Yili coal respectively. In this optimum condition, CWS prepared using P(SS-co-AA-co-DMDAAC) as dispersant showed a typical shear thinning behaviour and excellent stability, which are desired in industries. The rheological models also confirmed the pseudo-plastic characteristics of CWS. Finally, compared with the widely used anionic dispersant naphthalene sulphonate formaldehyde condensate (NSF) and poly(sodium p-styrenesulfonate) (PSS), P(SS-co-AA-co-DMDAAC) developed in this work exhibited better slurry making performance. The introduction of cationic functional groups promoted the adsorption of the dispersant, which further enhanced the electrostatic repulsion and steric hindrance among coal particles. Accordingly, the viscosity of CWS decreased and static stability enhanced.

This paper analyzes the current real-time monitoring system based on grey-related IoT security sensors for the detection of risk factors in the production environment of the Internet of Things and proposes a design plan for the Internet of Things environment monitoring based on the grey-related Internet of Things security sensor network, and according to the reliability guarantee mechanism of the system, a three-dimensional uniform IoT node deployment method suitable for IoT security monitoring is proposed. Based on the grey correlation analysis, it can provide a quantitative measurement analysis for the development and change state of a system, which is very suitable for the analysis of dynamic operating systems. As a real-time dynamic system of the Internet of Things, the use of grey correlation method to analyze its network security status has good operability and practical value. According to the multisource information processing technology, the monitoring data are preprocessed by dynamic limiting filtering, and then the data are fused at the data level with the optimal weighting algorithm. Through the use of grey correlation analysis to quantify the relative impact of cyberattacks on the network within a certain period of time, the quantitative assessment of the security environment and status of the entire network is realized. Finally, the characteristics of grey relational analysis and rough set theory attribute reduction are used to form the basis of grey correlation decision-level fusion algorithm, to achieve effective processing of the data of the Internet of Things security monitoring system.

In recent years, fifth-generation communication technology has begun to experiment successfully. As an indoor positioning technology of the Internet of things, it changes with each passing day and shows great vitality in the development of smart cities. Aiming at the problem that existing radio frequency identification indoor positioning algorithm is prone to environmental interference and poor positioning accuracy, a LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network is proposed. In this article, the signal intensity value is processed by Gaussian filter, and the noise points and boundary points are removed by density-based clustering algorithm. The threshold and weight of radial basis function neural network were optimized by genetic algorithm. With less data information, the relationship between the value of label signal strength and position coordinate could be established to improve the positioning accuracy of LANDMARC positioning algorithm. Experimental research shows that the average positioning error of the proposed LANDMARC algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network is about 0.9 m, which is 64% lower than the average positioning error of the traditional LANDMARC algorithm and improves the indoor positioning accuracy.

A high sensitivity refractive index sensor based on surface plasmon resonance (SPR) with topological insulator (Bi2Te3) has been proposed theoretically. It is demonstrated that the presented sensor exhibits an extremely great sensitivity enhancement with angular and wavelength-interrogation modes compared to conventional SPR sensor by covering the topological insulator (Bi2Te3) on the gold thin film. In particular, the maximum sensitivity of wavelength-interrogated sensor can be further increased above 25% with 3 QL Bi2Te3 sheet. Moreover, in order to further increase the sensing performance, the few layers black phosphorus (BP) and cytop film are covered on 1 QL (a single Bi2Te3 crystal quintuple layer containing five layers of Bi and Te) Bi2Te3 to form a highly angular sensitive Bi2Te3-BP heterojunction, the maximum angular sensitivity can reach about 175 deg/RIU. Compared to the aforementioned Au-Bi2Te3 based SPR sensor, the sensitivity of this heterojunction-based SPR sensor is improved by 29%. With such improving performance, we believe that the configuration can be potentially used in the field of chemical and biological sensors in the future. Keywords: Surface plasmon resonance (SPR), Hybrid sensor, Sensitivity, Angular and wavelength-interrogation modes

Raltegravir, an inhibitor of human immunodeficiency virus-1 (HIV-1) integrase, has been used to treat HIV/acquired immunodeficiency syndrome; however, its therapeutic effects on pulmonary fibrosis have not been investigated. In this study, the in vitro effects of raltegravir (RAV) on transforming growth factor beta 1 (TGF-β1)-induced pulmonary fibrosis on L929 mouse fibroblasts were investigated. In addition, the effects of RAV on an in vivo pulmonary fibrosis model induced by intratracheal instillation of bleomycin were investigated. The proliferation of L929 cells was inhibited after RAV treatment. Meanwhile, the in vitro and in vivo protein expression of nucleotide-binding oligomerization domain-like receptor 3 (NLRP3), high-mobility group box 1 (HMGB1), toll-like receptor 4 (TLR4), prolyl hydroxylase domain protein 2, phosphorylated nuclear factor-κB (p-NF-κB), hypoxia-inducible factor-1α (HIF-1α), collagens I and III was reduced relative to TGF-β1 or the bleomycin group. Raltegravir ameliorated pulmonary fibrosis by reducing the pathology score, collagen deposition, and expression of α-smooth muscle actin, NLRP3, HMGB1, TLR4, inhibitor of kappa B, p-NF-κB, HIF-1α, collagen I, and collagen III. The results of this study demonstrate that RAV attenuated experimental attenuates pulmonary fibrosis by inhibiting NLRP3 activation.

Emerging applications, such as improved all-weather safety for self-driving vehicles, require public positioning precision and reliability beyond the capability of open global navigation satellite system (GNSS) services. To meet these requirements, next-generation public positioning, navigation, and time (PNT) services should: 1) deliver ubiquitously available differential corrections and assistance information for massive numbers of concurrent users with fixed delay, indoor penetration and stable bit rate signals and 2) have navigation signals that are extremely enriched against challenging scenarios. The dilemma is that the free-of-charge public PNT service makes it a formidable challenge to build a dedicated information infrastructure. In this context, a natural question arises: where should we seek next-generation public PNT services in the age of Internet of Things? In this paper, we propose GNSS augmentation by FM radio symbiosis. Specifically, we propose GNSS augmentation-optimized digital data broadcasting for which the signal symbiotically co-exists with the analog FM radio in band and in channel without sensibly affecting the existing FM service. With this symbiotic digital data broadcasting, we then propose a GNSS augmentation system that can: 1) broadcast real-time kinematics correction to massive numbers of concurrent GNSS receivers to achieve outdoor precision at the centimeter-level; 2) deliver fine time GNSS assistance for significantly improved receiver sensitivity; and 3) provide common-view timing to enable GNSS independent ground positioning beacon signals. A prime challenge therein is to separate the symbiotic digital signal from the much stronger, spectrum-overlapping co-channel FM signal; this challenge represents a non-orthogonal de-multiplexing problem that is solved by a novel modulation structure. A prototype was designed, and field tests by authorized third parties were conducted with excellent results.

This paper briefly introduces two classic algorithms for fingerprint image processing, which include the soft threshold denoise algorithm of wavelet domain based on wavelet domain and the fingerprint image enhancement algorithm based on Gabor function. Contourlet transform has good texture sensitivity and can be used for the segmentation enforcement of the fingerprint image. The method proposed in this paper has attained the final fingerprint segmentation image through utilizing a modified denoising for a high-frequency coefficient after Contourlet decomposition, highlighting the fingerprint ridge line through modulus maxima detection and finally connecting the broken fingerprint line using a value filter in direction. It can attain richer direction information than the method based on wavelet transform and Gabor function and can make the positioning of detailed features more accurate. However, its ridge should be more coherent. Experiments have shown that this algorithm is obviously superior in fingerprint features detection.

In order to improve the positioning accuracy and reduce the impact of indoor complex environment on WiFi positioning results, an improved fusion positioning algorithm based on WiFi–pedestrian dead reckoning is proposed. The algorithm uses extended Kalman filter as the fusion positioning filter of WiFi–pedestrian dead reckoning. Aiming at the problem of WiFi signal strength fluctuation, Bayesian estimation matching algorithm based on K -nearest neighbor is proposed to reduce the impact of the dramatic change of received signal strength indicator value on the positioning result effectively. For the cumulative error problem in pedestrian dead reckoning positioning algorithm, a post-correction module is used to reduce the error. The experimental results show that the algorithm can improve the shortcomings of these two algorithms and control the positioning accuracy within 1.68 m.

Glucocorticoid insensitivity is an important barrier to the treatment of several inflammatory diseases, including acute lung injury (ALI). Saquinavir (SQV) is an inhibitor of the human immunodeficiency virus protease, and the therapeutic effects of SQV in ALI accompanied with glucocorticoid insensitivity have not been previously investigated. In this study, the effects of SQV on lipopolysaccharide (LPS)-mediated injury in human pulmonary microvascular endothelial cells (HPMECs), human type I alveolar epithelial cells (AT I), and alveolar macrophages were determined. In addition, the effects of SQV on an LPS-induced ALI model with or without methylprednisolone (MPS) were studied. In LPS-stimulated HPMECs, SQV treatment resulted in a decrease of high mobility group box 1 (HMGB1), phospho-NF-κB (p-NF-κB), and toll-like receptor 4 (TLR4), and an increase of VE-cadherin. Compared to MPS alone, MPS plus SQV attenuated the decrease of glucocorticoid receptor alpha (GRα) and IκBα in LPS-stimulated HPMECs. HMGB1, TLR4, and p-NF-κB expression were also lessened in LPS-stimulated alveolar macrophages with SQV treatment. In addition, SQV reduced the injury in human AT I with a decrease of HMGB1 and p-NF-κB, and with an increase of aquaporin 5 (AQP 5). SQV ameliorated the lung injury caused by LPS in rats with reductions in vascular permeability, myeloperoxidase activity (MPO) and histopathological scores, and with lowered HMGB1, TLR4, and p-NF-κB expression, but with enhanced VE-cadherin expression. By comparison, SQV plus MPS increased GRα and IκBα in lung tissues of rats with ALI. This study demonstrated that SQV prevented experimental ALI and improved glucocorticoid insensitivity by modulating the HMGB1/TLR4 pathway.

Fluorescent nanomaterials, such as quantum dots, have developed rapidly in recent years and have been significantly developed. Herein, we demonstrate a facile, one-pot, and in-situ synthesis strategy to obtain fluorescent silver nanoclusters (AgNCs) coated with eight-armed poly (ethylene glycol) polymers (8PEG-AgNCs) via a direct gel-mediated process. During the synthesis, ammonium (NH3) served as the crosslinker for the gel formation via a amine-type Michael addition reaction. This hydrogel can be used as a template to synthesize AgNCs using its volume-limiting effect. The in-situ generation of AgNCs takes place inside the nanocages of the formed gels, which guarantees the homogenous distribution of AgNCs in the gel matrix, as well as the efficient coating of PEG on the nanoclusters. After the degradation of gels, the released 8PEG-AgNCs nanohybrids showed strong blue fluorescence and exhibited long-term stability in aqueous solution for nearly one year. Results showed that the fabricated sensor revealed excellent fluorescent sensitivity for the selective detection of Cu2+ with a detection limit of 50 nM and a wide linear detection range of 5–100 μM. It is proposed that the greater cross-linking density leads to smaller gel pores and allows the synthesis of AgNCs with fluorescent properties. These results indicate that this novel hydrogel with certain biodegradation has the potential to be applied as a fluorescent sensor for catalytic synthesis, fluorescence tracing in cells, and fluorescence detection fields. Meanwhile, the novel design principle has a certain versatility to accelerate the development and application of other kinds of metal nanoclusters and quantum dots.

A finer direction-of-arrival (DOA) estimation result needs a large and dense array; it may, however, encounter the mutual coupling effect, which degrades the performance of DOA estimation. There is a new approach to mitigating this effect by using a nonuniform array to achieve DOA estimation. In this paper, we consider a priori DOA estimation, which is easily obtained from tracking results. The a priori DOA requires us to pay close attention to the high possibility of where the DOA will appear; then, a weight according to the prior probability distribution of DOA is added to each direction, which leads the sensing matrix of DOA estimation to be near low-rank. Thus, according to the low-rank matrix approximation theory, an optimal low-rank approximate matrix is obtained and an algorithm is proposed to select the elements of the original array according to right singular vectors of the approximate matrix. After that, the impacts of different weights are analyzed, and a mixed weight is presented which has flexibility for common use. Finally, a number of numerical simulations are carried out, and the results verify the effectiveness of the proposed methods.

A multifunctional fluorescent polymer based on coumarin (MFPC) was synthesized using a four-step synthetic route. To obtain the target compound, 7-amino-4-methylcoumarin, coumarin fluorescent monomer (CFM), and ultraviolet absorber monomer (UVAM) were prepared. By the reaction of CFM, UVAM, trimethyl-2-methacroyloxyethylammonium chloride (DMC), and acrylamide (AM) as a linker, a new multifunctional fluorescent polymer was synthesized. The structure of the MFPC was characterized by Fourier transform infrared (FTIR) and nuclear magnetic resonance (1H-NMR) spectroscopies. The optical properties of MFPC were measured by ultraviolet-visible (UV-vis) and fluorescence spectroscopies, in aqueous solution. The integration effect between MFPC and paper was tested by scanning electron microscopy. The application performance of MFPC on paper was evaluated by measuring the surface strength, smoothness, and whiteness using the printability tester and the UV accelerated aging test. Results indicated that the MFPC had good solubility in water. It also exhibited a positive effect on the light stability and surface strength of paper as a light stabilizer, fluorescent brightener, and surface sizing agent.

With the continuous progress of science and technology, intelligent wireless sensor network (IWSN) communication has become indispensable in its role in production and life because of its convenient network settings and flexible use. However, with the widespread availability of intelligent wireless sensor networks, the use of many wireless sensor nodes constitutes a multi-node wireless communication system, which turns the accuracy and low complexity of multi-node detection in sensor networks into a problem. Although the traditional algorithm has excellent performance, it cannot give consideration to both accuracy and complexity. Therefore, a maximum logarithm message passing algorithm based on serial and threshold (S-T-Max-log-MPA) for multi-mode detection in IWSN is proposed in this paper. In this algorithm, the threshold is used to determine the necessary conditions of sensor node stability first, and then the sensor node information updating is integrated into the resource node information updating, so that the system can maintain good accuracy, performance, and change the situation of poor system accuracy at low threshold. Compared with the traditional algorithm, the proposed algorithm significantly changes the algorithm complexity reduction rate of the system multi-node detection. Simulation results show that the algorithm has a good balance between accuracy and complexity reduction rate.

Sparse Code Multiple Access (SCMA) technology is a new multiple access scheme based on non-orthogonal spread spectrum technology, which was proposed by Huawei in 2014. In the algorithm application of this technology, the original Message Passing Algorithm (MPA) has slow convergence speed and high algorithm complexity. The threshold-based MPA has a high Bit Error Ratio (BER) when the threshold is low. In the Maximum logarithm Message Passing Algorithm (Max-log-MPA), the approximation method is used, which will cause some messages to be lost and the detection performance to be poor. Therefore, in order to solve the above problems, a Threshold-Based Max-log-MPA (T-Max-log-MPA) low complexity multiuser detection algorithm is proposed in this paper. The Maximum logarithm (Max-log) algorithm is combined with threshold setting, and the stability of user nodes is considered as a necessary condition for decision in the algorithm. Before message updating, the user information nodes are judged whether the necessary conditions for the stability of the user node have been met, and then the threshold is determined. Only users who meet the threshold condition and pass the necessary condition of user node stability can be decoded in advance. In the whole process, the logarithm domain MPA algorithm is used to convert an exp operation and a multiplication operation into a maximum value and addition operation. The simulation results show that the proposed algorithm can effectively reduce the computational complexity while ensuring the BER, and with the increase of signal-to-noise ratio, the effect of the Computational Complexity Reduction Ratio (CCRR) is more obvious.

The detection of p-nitrophenol (PNP) is of great significance for assessment of environment pollution and potential health risks. In this study, based on inverse opal polymeric photonic crystals (IOPPCs), a selective and visual sensor for high-performance PNP detection is developed. Due to their unique optical properties, IOPPCs report events by change of color, which can easily be observed by the naked eye. Hydroxyethyl methacrylate (HEMA) was selected as the functional monomer with which to fabricate the IOPPCs. By precisely adjusting the molar ratio between the functional monomer and the crosslinker, the sensors were only able to be sensitive to a specific solution, thus realizing the visual, selective, and semi-quantitative detection of PNP. When the sensors were immersed in different concentrations of PNP solution, their Bragg diffraction wavelengths showed different redshifts. The color of the IOPPCs changed from green to red as the peak shift of Bragg diffraction occurred. In addition, the IOPPCs displayed good interference immunity and reusability.

Polylactic electrospun porous fibres have been widely used in tissue engineering scaffolds. However, the application of linear polylactic is limited due to its poor hydrophilicity, which leads to phase separation and has been seldom used in porous fibre preparation. Instead, branching polylactic acts as a new effective method to prepare porous fibres because it can increase polylactic polar property and make it easy to be formulated in the following application. In the current study, we prepared an ultra-high molecular weight of high branching polylactic with glycerol as the initiator by controlling the ring-opening polymerization time, adding amount of catalyst and glycerol. The structure, molecular weight and thermal properties of copolymers were tested subsequently. The result showed that the surface of the high branching polylactic films is smooth, hydrophilic and porous. This branching polylactic formed electrospun porous fibres and possessed a strong adsorption of silver ion. Our study provided a simple and efficient way to synthesize branching polylactic polymer and prepare electrospun porous fibres, which may provide potential applications in the field of biomaterials for tissue engineering or antibacterial dressing compared with the application of linear polylactic and 3-arm polylactic materials.

In this work, a multifunctional yellowing inhibitor was synthesized by the Pechmann method. In order to obtain the target compound, 7-hydroxy-4-methyl coumarin was prepared by using the raw materials of resorcinol and ethyl acetoacetate, with toluene-p-sulfonic acid as the catalyst. New polymeric fluorescent compounds were synthesized by connecting the 7-hydroxy-4-methyl coumarin, the hindered amine light stabilizer 4-amion-2,2,6,6-tetramentylniperidine, and a series of polyethylene glycol segments into the same molecule with cyanuric chloride as a bridge. The structures of the synthesized molecules were confirmed by FT-IR, 1H NMR, and elemental analysis. The luminescent properties of the fluorescent compounds were studied by UV-vis spectroscopy and fluorescence spectroscopy. The integration effect between the fluorescent compounds and paper was tested by a scanning electron microscope. The light stability effect on the paper sheet was tested using an ultraviolet aging apparatus. The results indicate that the polymeric fluorescent compounds had a positive effect on the light stability of the high-yield pulp.

The internet of things (IoT) is becoming more indispensable in modern society as the further development and maturity of information technology progresses. However the exponential growth of IoT devices leads to severe energy consumption. As a technology with broad application prospects, simultaneous wireless information and power transfer (SWIPT) enables IoT devices to harvest energy from receiving radio frequency (RF) signals while ensuring information transmission. In this paper, we investigate the transmission rate optimization problem for a dual-hop multi-relay IoT system, where a decode-and-forward (DF) relay supports the SWIPT technique. We jointly optimize the resource including power and subcarrier allocation, to maximize the system transmission rate. The time-sharing strategy and Lagrange dual method are used to solve this optimization problem. Simulation results reveal that the proposed algorithm has a larger transmission rate than other benchmark algorithms when ensuring each relay has no additional energy supply. Specifically, the proposed algorithm improves the information transmission rate by 2.8%, 3.4% and 43% compared with other algorithms in the case of five relays when the source’s power is equal to 0.5 W, respectively.

Specific to the selective forwarding attack on routing in cognitive radio network, this paper proposes a trust-based secure routing model. Through monitoring nodes' forwarding behaviors, trusts of nodes are constructed to identify malicious nodes. In consideration of that routing selection-based model must be closely collaborative with spectrum allocation, a route request piggybacking available spectrum opportunities is sent to non-malicious nodes. In the routing decision phase, nodes' trusts are used to construct available path trusts and delay measurement is combined for making routing decisions. At the same time, according to the trust classification, different responses are made specific to their service requests. By adopting stricter punishment on malicious behaviors from non-trusted nodes, the cooperation of nodes in routing can be stimulated. Simulation results and analysis indicate that this model has good performance in network throughput and end-to-end delay under the selective forwarding attack.

To address the vulnerability of geographic routing to multiple security threats such as false routing information, selective forwarding and the Sybil attack in wireless sensor networks, this paper proposes a trust-based defending model against above-mentioned multiple attacks. Considering the characteristics of resource-constrained sensor nodes, trust values of neighboring nodes on the routing path can be calculated through the Dirichlet distribution function, which is based on data packets' acknowledgements in a certain period instead of energy-consuming monitoring. Trust is combined with the cost of geographic and energy aware routing for selecting the next hop of routing. At the same time, the initial trust is dynamically determined, service requests are restricted for malicious nodes in accordance with trust values, and the impact of node mobility is weakened by the trust evolution. The simulation results and analysis show that the proposed model under multiple attacks has advantages in packet delivery ratio and network lifetime over the existing models.

Dysthymia is a form of chronic mild depression that has a complex relationship with major depressive disorder (MDD). Here we investigate the role of environmental risk factors, including stressful life events and parenting style, in patients with both MDD and dysthymia. We ask whether these risk factors act in the same way in MDD with and without dysthymia.We examined the clinical features in 5,950 Han Chinese women with MDD between 30-60 years of age across China. We confirmed earlier results by replicating prior analyses in 3,950 new MDD cases. There were no significant differences between the two data sets. We identified sixteen stressful life events that significantly increase the risk of dysthymia, given the presence of MDD. Low parental warmth, from either mother or father, increases the risk of dysthymia. Highly threatening but short-lived threats (such as rape) are more specific for MDD than dysthymia. While for MDD more severe life events show the largest odds ratio versus controls, this was not seen for cases of MDD with or without dysthymia.There are increased rates of stressful life events in MDD with dysthymia, but the impact of life events on susceptibility to dysthymia with MDD differs from that seen for MDD alone. The pattern does not fit a simple dose-response relationship, suggesting that there are moderating factors involved in the relationship between environmental precipitants and the onset of dysthymia. It is possible that severe life events in childhood events index a general susceptibility to chronic depression, rather than acting specifically as risk factors for dysthymia.