Search

Through-wall radar systems with single transmitter and receiver have the advantages of portability, simplicity, and independent operation; however, they cannot accomplish two-dimensional (2D) localization and tracking of targets. This paper proposes distributed wireless networking for through-wall radar systems based on a portable single transmitter and single receiver radar. Moreover, a target joint positioning method is proposed in this study, which can balance system portability, low cost, and target 2D information estimation. First, a complementary Gray code transmission waveform is utilized to overcome the issue of mutual interference when multiple radars operate simultaneously in the same frequency band, and each radar node communicates with the processing center via wireless modules, forming a distributed wireless networking radar system. In addition, a data synchronization method combines the behavioral cognition theory and template matching, which identifies identical motion states in data obtained from different radars, realizing slow-time synchronization among distributed radars and thereby eliminating the strict hardware requirements of conventional synchronization methods. Finally, a joint localization method based on Levenberg-Marquardt is proposed, which can simultaneously estimate the positions of radar nodes and targets without requiring prior radar position information. Simulation and field experiments are performed, and the results reveal that the distributed wireless networking radar system developed in this study can obtain 2D target positions and track moving targets in real time. The estimation accuracy of the radar’s own position is less than 0.06 m, and the positioning accuracy of moving human targets is less than 0.62 m.

Abstract The overgeneralisation may happen because most studies on data publishing for multiple sensitive attributes (SAs) have not considered the personalised privacy requirement. Furthermore, sensitive information disclosure may also be caused by these personalised requirements. To address the matter, this article develops a personalised data publishing method for multiple SAs. According to the requirements of individuals, the new method partitions SAs values into two categories: private values and public values, and breaks the association between them for privacy guarantees. For the private values, this paper takes the process of anonymisation, while the public values are released without this process. An algorithm is designed to achieve the privacy mode, where the selectivity is determined by the sensitive value frequency and undesirable objects. The experimental results show that the proposed method can provide more information utility when compared with previous methods. The theoretic analyses and experiments also indicate that the privacy can be guaranteed even though the public values are known to an adversary. The overgeneralisation and privacy breach caused by the personalised requirement can be avoided by the new method.

The biological activity of plant polysaccharides can be enhanced by sulfated modification. In this study, the immunomodulatory effect of sulfated Cyclocarya paliurus polysaccharides (SCP3) on macrophages RAW264.7 and its potential molecular mechanism were investigated. Results showed that SCP3 at 25−100 μg/mL increased viability and improved phagocytosis of RAW264.7 cells. Meanwhile, SCP3 could activate mitogen-activated protein kinase (MAPK) and nuclear factor kappa B (NF-κB) signaling pathways, which increased the phosphorylation of Erk1/2, JNK, p38 and NF-κB p65, promoting secretion of cytokines tumor necrosis factor α (TNF-α), interleukin 6 (IL-6) and nitric oxide (NO) as well as the production of reactive oxygen species (ROS). In addition, Toll-like receptor 4 (TLR4) receptor inhibitors were able to block the production of NO and TNF-α by SCP3-stimulated macrophages. Based on Western blot analysis and validation using specific inhibitors against MAPK and NF-κB signaling pathways, the results demonstrated that SCP3 induced macrophages activation and enhanced TNF-α and NO production via TLR4-mediated MAPK and NF-κB pathways. In summary, SCP3 has significant immunomodulatory potential. The underlying molecular mechanism was that SCP3 activates macrophages via TLR4 receptors to promote ROS production, which in turn activates the downstream MAPK/NF-κB signaling pathway and then increases the secretion levels of cytokines and NO.

IntroductionAcupuncture is a Traditional Chinese Medicine (TCM) method that achieves therapeutic effects through the interaction of neurotransmitters and neural regulation. It is generally carried out manually, making the related process expert-biased. Meanwhile, the neural stimulation effect of acupuncture is difficult to track objectively. In recent years, virtual reality (VR) in medicine has been on the fast lane to widespread use, especially in therapeutic stimulation. However, the use of related technologies in acupuncture has not been reported.MethodsIn this work, a novel acupuncture stimulation technique using VR is proposed. To track the stimulation effect, the electroencephalogram (EEG) is used as the marker to validate brain activities under acupuncture.Results and discussionAfter statistically analyzing the data of 24 subjects during acupuncture at the “Zusanli (ST36)” acupoint, it has been determined that Virtual Acupuncture (VA) has at least a 63.54% probability of inducing similar EEG activities as in Manual Acupuncture (MA). This work may provide a new solution for researchers and clinical practitioners using Brain-Computer Interface (BCI) in acupuncture.

In this study, the digital image correlation (DIC) method and acoustic emission (AE) technology were combined to study the evolution of rock deformation localization in detail. The second-order spatial–temporal subset DIC (STS-DIC) algorithm was proposed and used for measuring strongly heterogeneous deformation fields of red sandstone specimens under uniaxial compression. The evolution of the deformation field was analyzed with a focus on the deformation localization stage. The length and width of the deformation localization band (DLB) were measured, and the relationships between the relative sliding rate of the DLB, the relative opening rate of the DLB, and the AE counts were identified. Deformation localization was found to result from the rapid evolution of the strain concentration before the peak stress. The complete development of the DLB is an inducing factor for catastrophic rock failure, and the failure modes of the rock specimens were consistent with the final state of the DLB. A good correlation was identified between the AE counts and the relative displacement rate of the DLB, and the sliding rate was found to have a significant influence on the AE counts.

A nylon-like polyester tire cord, which combined the characteristics of nylon and polyester tire cords, was designed as the carcass reinforcement material used to meet the increasing demands of the tire industry for performance and impact on the environment. Tires’ carcass construction plays a crucial role in affecting handling performance and ride comfort. Small changes in the carcass component can lead to significant improvements in the total tire/vehicle performance. This study evaluated the performance of nylon-like polyester and nylon 6 motorcycle tires. The results showed that the nylon-like polyester tire passed all indoor tests, and post-cure inflation (PCI) could be eliminated, resulting in energy and cost savings. The rolling resistance coefficient of the nylon-like polyester tire was reduced by 6.8% compared to that of the nylon 6 control tire, which could save fuel and have a positive impact on the environment. Nylon-like polyester tire cord extracted from the experimental tire possessed a higher modulus compared to that of nylon 6 tire cord, which could lead to better handling and ride comfort performance. Morphological pictures showed that both nylon-like polyester and nylon 6 cords extracted from tires had a good rubber coverage and comparable adhesion properties.

Abstract Data publishing methods can provide available information for analysis while preserving privacy. The multiple sensitive attributes data publishing, which preserves the relationship between sensitive attributes, may keep many records from being grouped and bring in a high record suppression ratio. Another category of multiple sensitive attributes data publishing, which reduces the possibility of record suppression by breaking the relationship between sensitive attributes, cannot provide the sensitive attributes association for analysis. Hence, the existing multiple sensitive attributes data publishing fails to fully account for the comprehensive information utility. To acquire a guaranteed information utility, this article defines comprehensive information loss that considers both the suppression of records and the relationship between sensitive attributes. A heuristic method is leveraged to discover the optimal anonymity scheme that has the lowest comprehensive information loss. The experimental results verify the practice of the proposed data publishing method with multiple sensitive attributes. The proposed method can guarantee information utility when compared with previous ones.

Abstract Background Chronic kidney disease (CKD) is a global public health issue. The diagnosis of CKD would be considerably enhanced by discovering novel biomarkers used to determine the glomerular filtration rate (GFR). Small molecule metabolites related to kidney filtration function that might be utilized as biomarkers to measure GFR more accurately could be found via a metabolomics analysis of blood samples taken from individuals with varied glomerular filtration rates. Methods An untargeted metabolomics study of 145 plasma samples was performed using ultrahigh-performance liquid chromatography tandem mass spectrometry (UPLC–MS/MS). The 145 samples were divided into four groups based on the patient’s measured glomerular filtration rates (mGFRs) determined by the iohexol plasma clearance rate. The data were analyzed using random forest analyses and six other unique statistical analyses. Principal component analysis (PCA) was conducted using R software. Results A large number of metabolites involved in various metabolic pathways changed significantly between groups with different GFRs. These included metabolites involved in tryptophan or pyrimidine metabolism. The top 30 metabolites that best distinguished between the four groups in a random forest plot analysis included 13 amino acids, 9 nucleotides, and 3 carbohydrates. A panel of metabolites (including hydroxyaparagine, pseudouridine, C-glycosyltryptophan, erythronate, N-acetylalanine, and 7-methylguanidine) for estimating GFR was selected for future testing in targeted analyses by combining the candidate lists with the six other statistical analyses. Both hydroxyasparagine and N,N-dimethyl-proline-proline are unique biomarkers shown to be inversely associated with kidney function that have not been reported previously. In contrast, 1,5-anhydroglucitol (1,5-AG) decreases with impaired renal function. Conclusions This global untargeted metabolomics study of plasma samples from patients with different degrees of renal function identified potential metabolite biomarkers related to kidney filtration. These novel potential metabolites provide more insight into the underlying pathophysiologic processes that may contribute to the progression of CKD, lead to improvements in the estimation of GFR and provide potential therapeutic targets to improve kidney function.

Summary: Background: Lymph node status is an important factor for the patients with non-functional pancreatic neuroendocrine tumors (NF-PanNETs) with respect to the surgical methods, prognosis, recurrence. Our aim is to develop and validate a combination model based on contrast-enhanced CT images to predict the lymph node metastasis (LNM) in NF-PanNETs. Methods: Retrospective data were gathered for 320 patients with NF-PanNETs who underwent curative pancreatic resection and CT imaging at two institutions (Center 1, n = 236 and Center 2, n = 84) between January 2010 and March 2022. RDPs (Radiomics deep learning signature) were developed based on ten machine-learning techniques. These signatures were integrated with the clinicopathological factors into a nomogram for clinical applications. The evaluation of the model’s performance was conducted through the metrics of the area under the curve (AUC). Findings: The RDPs showed excellent performance in both centers with a high AUC for predicting LNM and disease-free survival (DFS) in Center 1 (AUC, 0.88; 95% CI: 0.84–0.92; DFS, p 2 cm: AUC, 0.86) and Center 2 (≤2 cm: AUC, 0.93 and >2 cm: AUC, 0.91). Interpretation: Our RDPs may have the potential to preoperatively predict LNM in NF-PanNETs, address the insufficiency of clinical guidelines concerning the 2-cm threshold for tumor lymph node dissection, and provide precise therapeutic strategies. Funding: This work was supported by JSPS KAKENHI Grant Number JP22K20814; the Rare Tumor Research Special Project of the National Natural Science Foundation of China (82141104) and Clinical Research Special Project of Shanghai Municipal Health Commission (202340123).

Extended target tracking estimates the centroid and shape of the target in space and time. In various situations where extended target tracking is applicable, the presence of multiple targets can lead to interference, particularly when they maneuver behind one another in a sensor like a camera. Nonetheless, when dealing with multiple extended targets, there’s a tendency for them to share similar shapes within a group, which can enhance their detectability. For instance, the coordinated movement of a cluster of aerial vehicles might cause radar misdetections during their convergence or divergence. Similarly, in the context of a self-driving car, lane markings might split or converge, resulting in inaccurate lane tracking detections. A well-known joint probabilistic data association coupled (JPDAC) filter can address this problem in only a single-point target tracking. A variation of JPDACF was developed by introducing a nonparametric Spatio-Temporal Joint Probabilistic Data Association Coupled Filter (ST-JPDACF) to address the problem for extended targets. Using different kernel functions, we manage the dependency of measurements in space (inside a frame) and time (between frames). Kernel functions are able to be learned using a limited number of training data. This extension can be used for tracking the shape and dynamics of nonparametric dependent extended targets in clutter when targets share measurements. The proposed algorithm was applied to lane tracking and compared with other well-known supervised methods in an interfering case, yielding promising results.

In this paper, we demonstrate the significant impact of the solution flow and electrical field on the homogeneity of large-scale ZnO nanorod electrodeposition from an aqueous solution containing zinc nitrate and ammonium nitrate, primarily based on the X-ray fluorescence results. The homogeneity can be enhanced by adjusting the counter electrode size and solution flow rate. We have successfully produced relatively uniform nanorod arrays on an 8 × 10 cm2 i-ZnO-coated fluorine-doped tin oxide (FTO) substrate using a compact counter electrode and a vertical stirring setup. The as-grown nanorods exhibit similar surface morphologies and dominant, intense, almost uniform near-band-edge emissions in different regions of the sample. Additionally, the surface reflectance is significantly reduced after depositing the ZnO nanorods, achieving a moth-eye effect through subwavelength structuring. This effect of the nanorod array structure indicates that it can improve the utilization efficiency of light reception or emission in various optoelectronic devices and products. The large-scale preparation of ZnO nanorods is more practical to apply and has an extremely broad application value. Based on the research results, it is feasible to prepare large-scale ZnO nanorods suitable for antireflective coatings and commercial applications by optimizing the electrodeposition conditions.

Abstract Most of the data publishing methods have not considered sensitivity protection, and hence the adversary can disclose privacy by sensitivity attack. Faced with this problem, this paper presents a medical data publishing method based on sensitivity determination. To protect the sensitivity, the sensitivity of disease information is determined by semantics. To seek the trade‐off between information utility and privacy security, the new method focusses on the protection of sensitive values with high sensitivity and assigns the highly sensitive disease information to groups as evenly as possible. The experiments are conducted on two real‐world datasets, of which the records include various attributes of patients. To measure sensitivity protection, the authors define a metric, which can evaluate the degree of sensitivity disclosure. Besides, additional information loss and discernability metrics are used to measure the availability of released tables. The experimental results indicate that the new method can provide better privacy than the traditional one while the information utility is guaranteed. Besides value protection, the proposed method can provide sensitivity protection and available releasing for medical data.

Abstract Objective Whether preoperative serum carbohydrate antigen 19–9 (CA19-9) is an independent prognostic factor and there are interactions of serum CA19-9 with carcinoembryonic antigen (CEA) on the risk of recurrence in colorectal cancer (CRC) patients are still not clarified. Methods Consecutive patients with CRC who underwent curative resection for stage II-III colorectal adenocarcinoma at five hospitals were collected. Based on Cox models, associations of preoperative CA19-9 with recurrence-free survival (RFS) and overall survival (OS) were evaluated in patients with or without elevated CEA, and interactions between CEA and CA19-9 were also calculated. Restricted cubic spline (RCS) curves were used to evaluate the associations between preoperative CA19-9 and CRC outcomes on a continuous scale. Results A total of 5048 patients (3029 [60.0%] men; median [interquartile range, IQR] age, 61.0 [51.0, 68.0] years; median [IQR] follow-up duration 46.8 [36.5–62.4] months) were included. The risk of recurrence increased with the elevated level of preoperative CA19-9, with the slope steeper in patients with normal CEA than those with elevated CEA. Worse RFS was observed for elevated preoperative CA19-9 (> 37 U/mL) (n = 738) versus normal preoperative CA19-9 (≤ 37 U/mL) (n = 4310) (3-year RFS rate: 59.4% versus 78.0%; unadjusted hazard ratio [HR]: 2.02; 95% confidence interval [CI]:1.79 to 2.28), and significant interaction was found between CA19-9 and CEA (P for interaction = 0.001). Increased risk and interaction with CEA were also observed for OS. In the Cox multivariable analysis, elevated CA19-9 was associated with shorter RFS and OS regardless of preoperative CEA level, even after adjustment for other prognostic factors (HR: 2.08, 95% CI:1.75 to 2.47; HR: 2.25, 95% CI:1.80 to 2.81). Subgroup analyses and sensitivity analyses yielded largely similar results. These associations were maintained in patients with stage II disease (n = 2724). Conclusions Preoperative CA19-9 is an independent prognostic factor in CRC patients. Preoperative CA19-9 can be clinically used as a routine biomarker for CRC patients, especially with preoperative normal serum CEA.

Objective: Grain-sized moxibustion is an effective treatment for hyperlipidemia, but how it regulates dyslipidemia and liver lipid deposits still needs to be fully understood. This study explored the molecular biological mechanism of grain-sized moxibustion to regulate hepatic autophagy in hyperlipidemic rats by affecting ULK1 and TFEB through the AMPK/mTOR signaling pathway. Methods: Thirty male Sprague-Dawley (SD) rats were fed a high-fat diet for eight weeks to induce hyperlipidemia. Hyperlipidemic rats were divided into the HFD group, HFD + Statin group, HFD + CC + Moxi group, and grain-sized moxibustion intervention group (HFD + Moxi group). The control (Blank) group consisted of normal rats without any intervention. Grain-sized moxibustion and drug interventions were initiated eight weeks after high-fat diet induction and continued for ten weeks. Serum total cholesterol (TC), triglyceride (TG), low-density lipoprotein (LDL), and high-density lipoprotein (HDL), as well as hepatic triglyceride (TG), were measured after treatment. Hepatic steatosis and the expression of LC3I, LC3II, p62, p-AMPK, AMPK, p-mTOR, mTOR, ULK1, p-ULK1, and TFEB in the liver were analyzed. Results: Compared with the HFD group, grain-sized moxibustion improved hyperlipidemia and hepatocyte steatosis, increased the LC3, p-AMPK, p-ULK1, and nuclear TFEB expression in the liver, but decreased the p62 and p-mTOR expression. Conclusion: Grain-sized moxibustion at ST36 acupoints could regulate the blood lipid level of SD rats with hyperlipidemia, increase the expression level of ULK1 and TFEB by activating the AMPK/mTOR signaling pathway in liver tissues, and initiate the transcription of autophagy genes such as LC3.

Unmanned aerial seeding (UAS) is an efficient unmanned rice planting method with broad application prospects. But its optimum basic seedling number and yield and quality characteristics remain unclear. Field experiments were conducted to compare UAS, unmanned dry direct seeding (UDDS), and unmanned carpet transplanting (UCT) methods using inbred japonica rice variety Nanjing 5718. The UAS method was subdivided into four planting density treatments (UAS105, UAS150, UAS195, and UAS240 = 105, 150, 195, and 240 seedlings/m2, respectively). The results showed that increasing the basic seedling density first increased the yield but then it decreased, and UAS195 produced a significantly higher yield. On the other hand, the grain processing, appearance, and taste quality deteriorated with improved nutritional quality. Among the three planting methods, UAS (UAS195) yielded less than UCT due to there being fewer spikelets per panicle, a lower grain-filling rate, and less photosynthetic activity after heading. However, UAS (UAS195) produced more yields than UDDS by having more panicles, more spikelets per panicle, and more biomass accumulation. Both UAS195 and UDDS had poorer grain processing, appearance, and nutritional quality than UCT, but increased amylose content and taste values. Therefore, UAS195 could be an alternative planting method for inbred japonica rice with coordinated yield, quality, and production efficiency.

Vitamin C (VC, l-ascorbic acid) is an essential nutrient that plays a key role in metabolism and functions as a potent antioxidant in regulating the S-nitrosylation and denitrosylation of target proteins. The precise function of VC deprivation in glucose homeostasis is still unknown. In the absence of L-gulono-1,4-lactone oxidoreductase, an essential enzyme for the last step of VC synthesis, VC deprivation resulted in persistent hypoglycemia and subsequent impairment of cognitive functions in female but not male mouse pups. The cognitive disorders caused by VC deprivation were largely reversed when these female pups were given glucose. VC deprivation-induced S-nitrosylation of glycogen synthase kinase 3β (GSK3β) at Cys14, which activated GSK3β and inactivated glycogen synthase to decrease glycogen synthesis and storage under the feeding condition, while VC deprivation inactivated glycogen phosphorylase to decrease glycogenolysis under the fasting condition, ultimately leading to hypoglycemia and cognitive disorders. Treatment with Nω-Nitro-l-arginine methyl ester (l-NAME), a specific inhibitor of nitric oxide synthase, on the other hand, effectively prevented S-nitrosylation and activation of GSK3β in female pups in response to the VC deprivation and reversed hypoglycemia and cognitive disorders. Overall, this research identifies S-nitrosylation of GSK3β and subsequent GSK3β activation as a previously unknown mechanism controlling glucose homeostasis in female pups in response to VC deprivation, implying that VC supplementation in the prevention of hypoglycemia and cognitive disorders should be considered in the certain groups of people, particularly young females.

Abstract Accurate detection of chronic kidney disease (CKD) plays a pivotal role in early diagnosis and treatment. Measured glomerular filtration rate (mGFR) is considered the benchmark indicator in measuring the kidney function. However, due to the high resource cost of measuring mGFR, it is usually approximated by the estimated glomerular filtration rate, underscoring an urgent need for more precise and stable approaches. With the introduction of novel machine learning methodologies, prediction performance is shown to be significantly improved across all available data, but the performance is still limited because of the lack of models in dealing with ultra‐high dimensional datasets. This study aims to provide a two‐stage neural network approach for prediction of GFR and to suggest some other useful biomarkers obtained from the blood metabolites in measuring GFR. It is a composite of feature shrinkage and neural network when the number of features is much larger than the number of training samples. The results show that the proposed method outperforms the existing ones, such as convolutionneural network and direct deep neural network.

BackgroundDefects in the pelvic floor connective tissue may underlie the etiology of pelvic organ prolapse (POP). We hypothesized that the expression of proteins regulating extracellular matrix turnover is altered in the uterosacral ligament of women with POP. We compared the expression of CD44, transforming growth factor (TGF)-β, and matrix metalloproteinases (MMPs) 2/9 in women with and without POP.Methods and ResultsThis matched case-control study included 30 postmenopausal women, with POP stage 2 and higher according to the POP quantification system, and 30 postmenopausal women without POP. Immunohistochemical analyses of the uterosacral ligament specimens obtained after hysterectomy were performed to determine CD44, TGF-β, MMP-2, and MMP-9 expression. The expression was quantified using ImageJ software, and the association between prolapse occurrence and risk factors was evaluated using Spearman's correlation analysis. CD44 expressions were significantly lower (p

Background. Chronic kidney disease (CKD) is a global public health problem. Identifying new biomarkers that can be used to calculate the glomerular filtration rate (GFR) would greatly improve the diagnosis and understanding of CKD at the molecular level. A metabolomics study of blood samples derived from patients with widely divergent glomerular filtration rates could potentially discover small molecule metabolites associated with varying kidney function. Methods. Using ultrahigh-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), serum was analyzed from 53 participants with a spectrum of measured GFR (by iohexol plasma clearance) ranging from normal to severe renal insufficiency. An untargeted metabolomics assay (N ¼ 214) was conducted at the Calibra-Metabolon Joint Laboratory. Results. From a large number of metabolomics-derived metabolites, the top 30 metabolites correlated to increasing renal insufficiency according to mGFR were selected by the random forest method. Significant differences in metabolite profiles with increasing stages of CKD were observed. Combining candidate lists from six other unique statistical analyses, six novel, potential metabolites that were reproducibly strongly associated with mGFR were selected, including erythronate, gulonate, C-glycosyltryptophan, N-acetylserine, N6-carbamoylthreonyladenosine, and pseudouridine. In addition, hydroxyasparagine were strongly associated with mGFR and CKD, which were unique to this study. Conclusions. Global metabolite profiling of serum yielded potentially valuable biomarkers of different stages of CKD. Additionally, these potential biomarkers might provide insight into the underlying pathophysiologic processes that contribute to the progression of CKD as well as improve GFR estimation.

In this paper, we take a water quality testing Unmanned Surface Vessel (USV) as the research object. We propose a heading keeping strategy based on Human Simulated Intelligent Control (HSIC) algorithm and a trajectory tracking strategy under line-of-sight (LOS) algorithm. The practicality of the proposed control strategies was verified by combining simulations and experiments. The main contents were constructed with three parts: Firstly, we designed a complete control system of a water quality inspection unmanned boat with Arduino microcontroller as the core processor. Secondly, we derived the mathematical model of motion after reasonable simplification. Combined with the cycle experiment, the mapping relation between virtual rudder angle and motor speed was established. Then, the USV heading direction control strategy of HSIC was presented and the reliability of the proposed strategy was verified by the course control experiment of USV. Finally, aiming at the defects and shortcomings of the upper-level trajectory tracking LOS algorithm in practical application, we propose the trajectory correction and precise steering control strategies, and the practicality of the improved algorithm was verified by multi-point trajectory tracking experiments. The autonomous fixed-point water quality testing experiment was designed and verified the effectiveness of the proposed strategies.

Radial vortex-based spin torque nano-oscillators (RV-STNOs) have attracted extensive attention as potential nano microwave signal generators due to their advantages over other topological states, such as their higher oscillation, higher microwave power, and lower power consumption. However, the current driving the oscillation frequency of the STNOs must be limited in a small range of adjustment, which means less data transmission channels. In this paper, a new RV-STNO system is proposed with a multiferroic nanostructure, which consists of an ultrathin magnetic multilayer and a piezoelectric layer. Phase diagrams of oscillation frequency and amplitude with respect to piezostrain and current are obtained through micromagnetic simulation. The results show that the threshold current density of −4000-ppm compressive strain-assisted RV-STNOs is reduced from 2 × 109 A/m2 to 2 × 108 A/m2, showing one order of magnitude lower than that of conventional current-driven nano-oscillators. Meanwhile, the range of oscillation frequency adjustment is significantly enhanced, and there is an increased amplitude at the low oscillation point. Moreover, a promising digital binary frequency-shift key (BFSK) and binary amplitude-shift key (BASK) modulation technique is proposed under the combined action of current pulse and piezostrain pulse. They can transmit bit signals and show good modulation characteristics with a minimal transient state. These results provide a reference for developing the next generation of spintronic nano-oscillators with a wide frequency range and low power consumption, showing potential for future wireless communication applications.

The popularity of cloud computing has fueled the growth in multiprovision cloud service composition (MPCSC), where each cloud service provider (CSP) can fulfill multiple tasks, i.e., offer multiple services, simultaneously. In the MPCSC, users would rather disclose some private data for more benefits (e.g., personalized services). However, the more private data is released, the more serious the privacy risk faced by users. In particular, the multiservice provision characteristic of MPCSC further exacerbates the privacy risk. Therefore, how to balance the privacy risk and benefit in service selection for MPCSC is a challenging research problem. In this paper, firstly we explore the service selection problem of balancing privacy risk and benefit in MPCSC (SSBM), then we propose an improved Kuhn–Munkres (KM) algorithm solution to the SSBM problem. Furthermore, we conduct a series of simulation experiments to evaluate the proposed approach. The experimental results show that the proposed approach is both efficient and effective for solving the SSBM problem.

Spontaneous reporting systems (SRSs) are used to collect adverse drug events (ADEs) for their evaluation and analysis. Periodical SRS data publication gives rise to a problem where sensitive, private data can be discovered through various attacks. The existing SRS data publishing methods are vulnerable to Medicine Discontinuation Attack(MD-attack) and Substantial symptoms-attack(SS-attack). To remedy this problem, an improved periodical SRS data publishing-PPMS(k, θ, ɑ)-bounding is proposed. This new method can recognize MD-attack by ensuring that each equivalence group contains at least k new medicine discontinuation records. The SS-attack can be thwarted using a heuristic algorithm. Theoretical analysis indicates that PPMS(k, θ, ɑ)-bounding can thwart the above-mentioned attacks. The experimental results also demonstrate that PPMS(k, θ, ɑ)-bounding can provide much better protection for privacy than the existing method and the new method dose not increase the information loss. PPMS(k, θ, ɑ)-bounding can improve the privacy, guaranteeing the information usability of the released tables.

A mixture of controlled-release nitrogen (N) fertilizers (CRNFs) and conventional urea (CU) as a single application of basal fertilizer could simplify fertilization in rice cultivation from the traditional and more labor-intensive fertilization strategy of multiple applications of nitrogen. However, the reported benefits of this combined approach in increasing rice yield have varied substantially for various reasons, including that various types of rice are characterized by different N requirements to obtain high yield. In this study, two late japonica rice cultivars, Jia58 (J58) and Jia67(J67), were used to determine the best combination of one of two short-acting CRNFs (release periods were 40 and 60 days) and one of three long-acting CRNFs (release periods were 80, 100 and 120 days) to apply with the CU as a one-time application of basal fertilizer. Six combinations of CRNFs were established based on their release periods: A1, 40 + 80 days; A2, 40 + 100 days; A3, 40 + 120 days; B1, 60 + 80 days; B2, 60 + 100 days; and B3, 60 + 120 days. CU applied split at basal, tillering and panicle differentiation stages, respectively as control (CK). The effects of the different treatment combinations of CRNFs on late-rice grain yield, N accumulation and N-use efficiency in a two-year field experiment were determined. Results showed that, the A2 treatment achieved the same yield as that of CK, and yield of the B2 treatment exceeded the yield of CK. Yield of J58 applied with B2 was 7.35% higher in 2018 and 7.40% higher in 2019 than that of the corresponding yield of CK; yield of J67 applied with B2 was 6.05% higher in 2018 and 6.87% higher in 2019 than that of CK. Compared with other CRNF treatments, the release of N from A2 and B2 was most synchronized with nitrogen uptake by the two cultivars, which indicates that fertilizer combination completely met the nitrogen demands during each growth stage of rice. Rice of the A2 and B2 treatments had higher N accumulation, higher aboveground biomass accumulation and LAI (leaf area index) at the heading and maturity stages and higher photosynthetic activity than those of other CRNF treatments. In conclusion, for late japonica rice in China, the application of the A2 and B2 treatments as optimal type of CRNF can achieve labor saving and yield increasing simultaneously in rice production.

To prevent students from learning risks and improve teachers' teaching quality, it is of great significance to provide accurate early warning of learning performance to students by analyzing their interactions through an e-learning system. In existing research, the correlations between learning risks and students' changing cognitive abilities or learning states are still underexplored, and the personalized early warning is unavailable for students at different levels. To accurately identify the possible learning risks faced by students at different levels, this article proposes a personalized early warning approach to learning performance for college students via cognitive ability and learning state modeling. In this approach, students' learning process data and historical performance data are analyzed to track students' cognitive abilities in the whole learning process, and model their learning states from four dimensions, i.e., learning quality, learning engagement, latent learning state, and historical learning state. Then, the Adaboost algorithm is used to predict students' learning performance, and an evaluation rule with five levels is designed to dynamically provide multilevel personalized early warning to students. Finally, the comparative experiments based on real-world datasets demonstrate that the proposed approach could effectively predict all students' learning performance, and provide accurate early warning services to them.

Natural polysaccharides and their metabolites’ short chain fatty acids (SCFAs) have attracted much attention. Recently, they have shown great potential in attenuating systemic inflammation activities, especially in inflammatory bowel disease (IBD). IBD is a complex pathological process and is related to epithelial damage and microbiota imbalance in the gut. Recent studies have indicated that natural polysaccharides could improve IBD recovery by different mechanisms. They could not only influence the ratio of intestine microbiota, but also regulate the secretion levels of immunity cytokines through multiple pathways, the latter including modulation of the TLR/MAPK/NF-κB signaling pathways and stimulation of G-protein-coupled receptors. Moreover, they could increase intestinal integrity and modulate oxidative stress. In this review, recent research about how natural polysaccharides impact the pathogenesis of IBD are summarized to prove the association between polysaccharides and disease recovery, which might contribute to the secretion of inflammatory cytokines, improve intestine epithelial damage, reduce oxidative stress, sustain the balanced microenvironment of the intestines, and finally lower the risk of IBD.

As an erosional landform, the formation processes of Danxia landform are controlled by internal and external forces as well as lithologic properties. Using field data, we studied the role of lithologic properties on the formation of Danxia landform in Kongtongshan National Geopark, northwest China, through a series of experiments, including uniaxial compressive strength, identification analysis under polarizing microscope, X-ray diffraction analysis, inductively coupled plasma-mass spectrometry analysis, and scanning electron microscopy. The results show that the diagenesis degree, mineral composition, cement composition, degree of cementation, geochemical composition and element contents, and micro-structure influenced the structure and anti-weathering and anti-erosion abilities of the Danxia rock mass. Differential weathering of rock in different environments was an important force shaping the different types of Danxia landform. Weathering failure of the Danxia rock mass was the result of multiple combined factors; as well as lithology, other factors, such as those induced during tectonic uplift (i.e., faulting, jointing, and fracturing) and climate, cannot be neglected. Therefore, lithology played an important role in the structural development of Danxia landform, and different lithologies influenced its weathering rate and formation processes. Our findings can provide a reference for revealing the microscopic development of Danxia landform in arid and semi-arid areas.

Digital image correlation (DIC) is a popular photomechanics method for deformation measurement. The conventional subset-based DIC method obtains the displacement vectors at the subset centers, but cannot calculate the deformation on the specimen edges, which may contain very useful information because specimens usually have greater deformation on edges, especially on curved edges due to stress concentration. In this study, the capability of a mesh-based DIC method using 8-node quadrilateral elements (Q8-mesh-DIC) in boundary deformation measurement was investigated and highlighted for specimens with non-uniform deformation. The results were compared with those obtained by some conventional subset-based DIC methods, and the accuracy of the boundary deformation measurement was verified through simulated and real experiments. The Q8-mesh-DIC appears to be more suitable for the boundary deformation measurement of non-uniform deformation fields.

Characterizing global mechanical behavior accurately is important for a detailed understanding of the deformation mechanism of rock material. In this paper, a new characterization model of the global mechanical behavior of rock is proposed, based on the structural characteristics of rock deformation. Uniaxial compression tests were carried out using the digital image correlation method and acoustic emission to obtain the interrelationship between mechanical behavior and deformation evolution. The test results show that the appearance of deformation localization leads to non-linear evolution of global mechanical behavior in a rock specimen. Further, due to the gradual evolution of deformation localization bands, the rock specimen evolves from a complete whole to a rock structure with a “weak interlayer”. Thus, the global mechanical behavior of the rock specimen depends heavily on the structural evolution process, especially when close to failure. A simplified characterization model was established according to the deformation process. The finite element method was used to verify the rationality of the proposed structural model. The verification result showed that under uniaxial compression, the structural model can reproduce the global mechanical behavior evolution process of the rock specimen.

A thermal-induced measurement error induced by thermal deformation of digital camera in photo mechanics methods that has the same magnitude of the error as that in the resistance strain measurement method reduces the accuracy of high-precision measurement substantially and must, therefore, be compensated. Starting from the underlying mechanism of a thermal-induced measurement error, we investigated the image error introduced by thermal behaviours of digital cameras widely used in photo mechanics. We experimentally determined the relationships between the thermal behaviours and temperature, derived the relationship between the image error and measurement error of photo mechanics methods, and eventually established a physical model to explain the underlying relationship between the thermal-induced measurement error and temperature. Furthermore, based on the investigation of the underlying mechanism, we introduced three types of temperature compensation methods for photo mechanics, namely the model compensation method, the preheating method, and the compensation specimen method. We experimentally demonstrated the feasibility of these compensation methods. The model compensation method only needs the data regarding the environmental temperature during operation of the digital camera to implement the correction of the measurement results and enhance the measurement accuracy of photo mechanics methods. The preheating method is suitable for indoor photo mechanics measurements wherein the environmental temperature is almost constant, which ensures that the appearance of thermal balance of the digital camera after a period of self-heating. The compensation specimen method reduces the effective resolution of the images and it also requires that the image error forms in the region of tested specimen are the same as that in the region of the compensation specimen.

Digital cameras represented by industrial cameras are widely used as image acquisition sensors in the field of image-based mechanics measurement, and their thermal effect inevitably induces thermal-induced errors of the mechanics measurement. To deeply understand the errors, the research for digital camera’s thermal effect is necessary. This study systematically investigated the heat transfer processes and temperature characteristics of a working digital camera. Concretely, based on the temperature distribution of a typical working digital camera, the heat transfer of the working digital camera was investigated, and a model describing the temperature variation and distribution was presented and verified experimentally. With this model, the thermal equilibrium time and thermal equilibrium temperature of the camera system were calculated. Then, the influences of thermal parameters of digital camera and environmental temperature on the temperature characteristics of working digital camera were simulated and experimentally investigated. The theory analysis and experimental results demonstrate that the presented model can accurately describe the temperature characteristics and further calculate the thermal equilibrium state of working digital camera, all of which contribute to guiding mechanics measurement and thermal design based on such camera sensors.

Recent prediction on the heavy statistical correlation between the mechanical properties of fiber reinforced composite (FRP) raises new concerns on the accurate reliability evaluation of composite structures, but such statistical correlation still lacks experimental verification. In this work, an experimental methodology is proposed to determine the statistical correlation between mechanical properties of unidirectional FRP composite. A rectangular shaped carbon fiber reinforced plastic (CFRP) specimen with a circular hole is loaded by tension, and 3D digital image correlation (DIC) is employed to characterize the heterogeneous strain field around the hole. Virtual field method (VFM) is used to derive E11, E22, ν12, and G12 of specimens. Specimen configuration considering fiber angle and hole diameter is optimized to achieve accurate determination of correlation coefficients. Experimental results on the linear correlation coefficients between E11, E22, ν12, and G12 agree well with previous theoretical predictions.