Expanding Grey Relational Analysis With the Comparable Degree for Dual Probabilistic Multiplicative Linguistic Term Sets and Its Application on the Cloud Enterprise

Under the cloud trend of enterprises, how do traditional businesses get on the cloud becomes a worth pondering question. To help those traditional businesses that have no experience to dispel the clouds and see the sun as soon as possible, we are planning to choose one corporation with rich experience to take them into the cloud market. The quintessence of dual probabilistic linguistic term sets (DPLTSs) is that it uses the combination of several linguistic terms and their proportions to reveal decision information by opposite angles. This paper proposes the dual probabilistic multiplicative linguistic preference relations (DPMLPRs) based upon the dual probabilistic multiplicative linguistic term sets (DPMLTSs). Then, it de nes the comparable degree between the DPMLPRs and studies the consensus of the group DPMLPR. Moreover, it probes the expanding grey relational analysis (EGRA) under the proposed comparable degree between the DPMLTSs. After that, one example of choosing the experienced cloud cooperative partner is simulated under the dual probabilistic linguistic circumstance. Besides, the comparative analysis is performed by considering the similarity among the EGRA, TODIM, and VIKOR.Postgraduate Research and Practice Innovation Program of Jiangsu Province under Grant KYCX18_0199Scientific Research Foundation of the Graduate School of Southeast University under Grant YBJJ1832FEDER Financial Support under Grant TIN2016-75850-

Metric-aligned Sample Selection and Critical Feature Sampling for Oriented Object Detection

Arbitrary-oriented object detection is a relatively emerging but challenging task. Although remarkable progress has been made, there still remain many unsolved issues due to the large diversity of patterns in orientation, scale, aspect ratio, and visual appearance of objects in aerial images. Most of the existing methods adopt a coarse-grained fixed label assignment strategy and suffer from the inconsistency between the classification score and localization accuracy. First, to align the metric inconsistency between sample selection and regression loss calculation caused by fixed IoU strategy, we introduce affine transformation to evaluate the quality of samples and propose a distance-based label assignment strategy. The proposed metric-aligned selection (MAS) strategy can dynamically select samples according to the shape and rotation characteristic of objects. Second, to further address the inconsistency between classification and localization, we propose a critical feature sampling (CFS) module, which performs localization refinement on the sampling location for classification task to extract critical features accurately. Third, we present a scale-controlled smooth $L_1$ loss (SC-Loss) to adaptively select high quality samples by changing the form of regression loss function based on the statistics of proposals during training. Extensive experiments are conducted on four challenging rotated object detection datasets DOTA, FAIR1M-1.0, HRSC2016, and UCAS-AOD. The results show the state-of-the-art accuracy of the proposed detector

Potential methane and nitrous oxide production and respiration rates from penguin and seal colony tundra soils during freezing–thawing cycles under different water contents in coastal Antarctica

In coastal Antarctica, frequent freezing–thawing cycles (FTCs) and changes to the hydrological conditions may affect methane (CH4) and nitrous oxide (N2O) production and respiration rates in tundra soils, which are difficult to observe in situ. Tundra soils including ornithogenic tundra soil (OAS), seal colony soil (SCS) and emperor penguin colony soil (EPS) were collected. In laboratory, we investigated the effects of FTCs and water addition on potential N2O and CH4 production and respiration rates in the soils. The CH4 fluxes from OAS and SCS were much less than that from EPS. Meanwhile, the N2O fluxes from OAS and EPS were much less than that from SCS. The N2O production rates from all soils were extremely low during freezing, but rapidly increased following thawing. In all cases, FTC also induced considerably enhanced soil respiration, indicating that soil respiration response was sensitive to the FTCs. The highest cumulative rates of CH4, N2O and CO2 were 59.5 mg CH4-C·kg−1 in EPS, 6268.8 μg N2O-N·kg−1 in SCS and 3522.1 mg CO2-C·kg−1 in OAS. Soil water addition had no significant effects on CH4 production and respiration rates, but it could reduce N2O production in OAS and EPS, and it stimulated N2O production in SCS. Overall, CH4 and N2O production rates showed a trade-off relationship during the three FTCs. Our results indicated that FTCs greatly stimulated soil N2O and CO2 production, and water increase has an important effect on soil N2O production in coastal Antarctic tundra

Association of age and night flight duration with sleep disorders among Chinese airline pilots

ObjectiveNight flights might aggravate sleep disorders among aging airline pilots, posing a threat to flight safety. In this study, we assess the prevalence of sleep disorders as well as the combined effects of night flight duration and aging on sleep disorders.MethodA cross-sectional study was conducted between July and December, 2021. Participants were recruited from a commercial airline. Sleep disorders were evaluated using the Pittsburgh Sleep Quality Index (PSQI). The interaction effect of night flight duration and age on sleep disorders and their correlates were examined using logistic regression models.ResultsIn total, 1,208 male airline pilots were included in the study, with a median age of 34 (interquartile range [IQR]: 29–39) years. The overall prevalence of sleep disorders was 42.6%. The multivariate logistic regression identified an interaction between night flight duration and age on sleep disorders (adjusted odds ratio [aOR] of the interaction term was 5.85 95% CI: 2.23–15.34 for age ≥ 45 years; 1.96 95% CI:1.01–3.81 for the age group 30–44 years). Longer night flight duration (aOR: 4.55; 95%CI: 1.82–11.38) and body mass index (BMI) ≥28.0 kg/m2 (aOR: 0.16; 95% CI: 0.03–0.91) were significantly associated with sleep disorders in participants aged ≥45 years. Hyperuricemia (aOR: 1.54; 95% CI: 1.09–2.16) and regular exercise (aOR: 0.23; 95% CI: 0.08–0.70) were significantly associated with sleep disorders in the 30–44 years age group.ConclusionThe mean monthly night flight duration and aging had a synergistic effect on airline pilots’ sleep disorders, implying an aging and work-related mechanistic pathogenesis of sleep disorders in airline pilots that requires additional exploration and intervention

Robust Kernel-Based Tracking with Multiple Subtemplates in Vision Guidance System

The mean shift algorithm has achieved considerable success in target tracking due to its simplicity and robustness. However, the lack of spatial information may result in its failure to get high tracking precision. This might be even worse when the target is scale variant and the sequences are gray-levels. This paper presents a novel multiple subtemplates based tracking algorithm for the terminal guidance application. By applying a separate tracker to each subtemplate, it can handle more complicated situations such as rotation, scaling, and partial coverage of the target. The innovations include: (1) an optimal subtemplates selection algorithm is designed, which ensures that the selected subtemplates maximally represent the information of the entire template while having the least mutual redundancy; (2) based on the serial tracking results and the spatial constraint prior to those subtemplates, a Gaussian weighted voting method is proposed to locate the target center; (3) the optimal scale factor is determined by maximizing the voting results among the scale searching layers, which avoids the complicated threshold setting problem. Experiments on some videos with static scenes show that the proposed method greatly improves the tracking accuracy compared to the original mean shift algorithm

An Efficient Approach to Solve the Large-Scale Semidefinite Programming Problems

Solving the large-scale problems with semidefinite programming (SDP) constraints is of great importance in modeling and model reduction of complex system, dynamical system, optimal control, computer vision, and machine learning. However, existing SDP solvers are of large complexities and thus unavailable to deal with large-scale problems. In this paper, we solve SDP using matrix generation, which is an extension of the classical column generation. The exponentiated gradient algorithm is also used to solve the special structure subproblem of matrix generation. The numerical experiments show that our approach is efficient and scales very well with the problem dimension. Furthermore, the proposed algorithm is applied for a clustering problem. The experimental results on real datasets imply that the proposed approach outperforms the traditional interior-point SDP solvers in terms of efficiency and scalability

Characterization of the Metabolic Fate of Datura metel Seed Extract and Its Main Constituents in Rats

Datura metel L. has been frequently used in Chinese traditional medicine. However, little is known on the chemical composition and in vivo metabolism of its seeds. In this study, using the strategy “chemical analysis, metabolism of single representative compounds, and metabolism of extract at clinical dosage” that we propose here, 42 constituents were characterized from D. metel seeds water extract. Furthermore, the metabolic pathways of 13 representative bioactive compounds of D. metel seeds were studied in rats after the oral administration of D. metel seeds water extract at a clinical dosage (0.15 g/kg). These included three withanolides, two withanolide glucosides, four amides, one indole, one triterpenoid, one steroid, and one sesquiterpenoid, and with regard to phase II metabolism, hydroxylation, (de)methylation, and dehydrogenation reactions were dominant. Furthermore, the metabolism of D. metel seeds water extract provided to rats at a clinical dosage was investigated by liquid chromatography-tandem mass spectrometry based on the above metabolic pathways. Sixty-one compounds were detected in plasma, 83 in urine, and 76 in fecal samples. Among them, withanolides exhibited higher plasma exposure than the other types. To our knowledge, this is the first systematic study on the chemical profiling and metabolite identification of D. metel seeds, including all compounds instead of single constituents

Circulating microbial content in myeloid malignancy patients is associated with disease subtypes and patient outcomes

Although recent work has described the microbiome in solid tumors, microbial content in hematological malignancies is not well-characterized. Here we analyze existing deep DNA sequence data from the blood and bone marrow of 1870 patients with myeloid malignancies, along with healthy controls, for bacterial, fungal, and viral content. After strict quality filtering, we find evidence for dysbiosis in disease cases, and distinct microbial signatures among disease subtypes. We also find that microbial content is associated with host gene mutations and with myeloblast cell percentages. In patients with low-risk myelodysplastic syndrome, we provide evidence that Epstein-Barr virus status refines risk stratification into more precise categories than the current standard. Motivated by these observations, we construct machine-learning classifiers that can discriminate among disease subtypes based solely on bacterial content. Our study highlights the association between the circulating microbiome and patient outcome, and its relationship with disease subtype

Cardio-Protection of Salvianolic Acid B through Inhibition of Apoptosis Network

Targeting cellular function as a system rather than on the level of the single target significantly increases therapeutic potency. In the present study, we detect the target pathway of salvianolic acid B (SalB) in vivo. Acute myocardial infarction (AMI) was induced in rats followed by the treatment with 10 mg/kg SalB. Hemodynamic detection and pathological stain, 2-dimensional electrophoresis, MALDI-TOF MS/MS, Western blot, pathway identification, apoptosis assay and transmission electron microscope were used to elucidate the effects and mechanism of SalB on cardioprotection. Higher SalB concentration was found in ischemic area compared to no-ischemic area of heart, correlating with improved heart function and histological structure. Thirty-three proteins regulated by SalB in AMI rats were identified by biochemical analysis and were classified as the components of metabolism and apoptosis networks. SalB protected cardiomyocytes from apoptosis, inhibited poly (ADP-ribose) polymerase-1 pathway, and improved the integrity of mitochondrial and nucleus of heart tissue during AMI. Furthermore, the protective effects of SalB against apoptosis were verified in H9c2 cells. Our results provide evidence that SalB regulates multi-targets involved in the apoptosis pathway during AMI and therefore may be a candidate for novel therapeutics of heart diseases

Incorporation of a machine learning pathological diagnosis algorithm into the thyroid ultrasound imaging data improves the diagnosis risk of malignant thyroid nodules

ObjectiveThis study aimed at establishing a new model to predict malignant thyroid nodules using machine learning algorithms.MethodsA retrospective study was performed on 274 patients with thyroid nodules who underwent fine-needle aspiration (FNA) cytology or surgery from October 2018 to 2020 in Xianyang Central Hospital. The least absolute shrinkage and selection operator (lasso) regression analysis and logistic analysis were applied to screen and identified variables. Six machine learning algorithms, including Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Naive Bayes Classifier (NBC), Random Forest (RF), and Logistic Regression (LR), were employed and compared in constructing the predictive model, coupled with preoperative clinical characteristics and ultrasound features. Internal validation was performed by using 10-fold cross-validation. The performance of the model was measured by the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, F1 score, Shapley additive explanations (SHAP) plot, feature importance, and correlation of features. The best cutoff value for risk stratification was identified by probability density function (PDF) and clinical utility curve (CUC).ResultsThe malignant rate of thyroid nodules in the study cohort was 53.2%. The predictive models are constructed by age, margin, shape, echogenic foci, echogenicity, and lymph nodes. The XGBoost model was significantly superior to any one of the machine learning models, with an AUC value of 0.829. According to the PDF and CUC, we recommended that 51% probability be used as a threshold for determining the risk stratification of malignant nodules, where about 85.6% of patients with malignant nodules could be detected. Meanwhile, approximately 89.8% of unnecessary biopsy procedures would be saved. Finally, an online web risk calculator has been built to estimate the personal likelihood of malignant thyroid nodules based on the best-performing ML-ed model of XGBoost.ConclusionsCombining clinical characteristics and features of ultrasound images, ML algorithms can achieve reliable prediction of malignant thyroid nodules. The online web risk calculator based on the XGBoost model can easily identify in real-time the probability of malignant thyroid nodules, which can assist clinicians to formulate individualized management strategies for patients