Performance Analysis and Optimal Allocation of Layered Defense M/M/N Queueing Systems

One important mission of strategic defense is to develop an integrated layered Ballistic Missile Defense System (BMDS). Motivated by the queueing theory, we presented a work for the representation, modeling, performance simulation, and channels optimal allocation of the layered BMDS M/M/N queueing systems. Firstly, in order to simulate the process of defense and to study the Defense Effectiveness (DE), we modeled and simulated the M/M/N queueing system of layered BMDS. Specifically, we proposed the M/M/N/N and M/M/N/C queueing model for short defense depth and long defense depth, respectively; single target channel and multiple target channels were distinguished in each model. Secondly, we considered the problem of assigning limited target channels to incoming targets, we illustrated how to allocate channels for achieving the best DE, and we also proposed a novel and robust search algorithm for obtaining the minimum channel requirements across a set of neighborhoods. Simultaneously, we presented examples of optimal allocation problems under different constraints. Thirdly, several simulation examples verified the effectiveness of the proposed queueing models. This work may help to understand the rules of queueing process and to provide optimal configuration suggestions for defense decision-making

Evaluation of myocardial work in patients with hypertrophic cardiomyopathy and hypertensive left ventricular hypertrophy based on non-invasive pressure-strain loops

BackgroundThe capacity to distinguish hypertrophic cardiomyopathy (HCM) from hypertensive left ventricular hypertrophy (H-LVH) based on morphological features obtained by conventional echocardiography is limited. We investigated the global myocardial work of the left ventricle in two types of hypertrophies using the non-invasive myocardial work index (NMWI).MethodsConventional echocardiography was performed on 107 subjects with preserved left ventricular ejection fraction (LVEF ≥ 50%), who comprised patients with HCM (n = 40), H-LVH (n = 35), and healthy people with normal blood pressure and left ventricular structure (n = 32). Except for the conventional echocardiographic parameters, the left ventricular myocardial work parameters based on pressure-strain loops, including global myocardial work index (GWI), global constructive work (GCW), global wasted work (GWW), and global work efficiency (GWE), were evaluated in three groups. Multivariate discriminant analysis and receiver operating characteristic (ROC) curve were used to evaluate the incremental value of NMWI for distinguishing HCM from H-LVH.ResultsCompared to the control group, GWI and GCW were significantly lower in HCM patients (P < 0.05), whereas GWI was significantly higher in H-LVH patients. GWW was higher and GWE was significantly decreased in both HCM and H-LVH patients than in the control group (P < 0.05). Multivariate discriminant analysis and ROC curve revealed that the inter-ventricular septum thickness (IVST)/left ventricular posterior wall thickness (LVPWT) and GCW were each able to distinguish HCM from H-LVH. The combination of IVST/LVPWT and GCW discriminated HCM and H-LVH with a higher predictive accuracy of 94.7%.ConclusionNMWI may provide additional information in evaluating the myocardial function in patients with HCM and H-LVH. Myocardial work combined with conventional echocardiography could improve the clinical diagnostic accuracy of distinguishing HCM and H-LVH

Treatment of Rheumatoid Arthritis Using Combination of Methotrexate and Tripterygium Glycosides Tablets—A Quantitative Plasma Pharmacochemical and Pseudotargeted Metabolomic Approach

Rheumatoid arthritis (RA) is a chronic inflammatory disease characterized by chronic destructive synovitis and is associated with progressive disability, systemic difficulties, premature death, and socioeconomic costs. Early intervention with disease-modifying antirheumatic drugs (DMARDs) like methotrexate (MTX) and its combination regimen would provide obvious benefits to patients, healthcare systems and society. MTX and tripterygium glycosides tablets (TGTS) are most frequently prescribed medicines for RA, and the combination of them occurs frequently in anti-RA prescriptions. While the underlying combination mechanisms and the affected variation of drug blood level remain unclear. According to the American College of Rheumatology criteria for improvement, clinical evaluation following three treatment groups (i.e., MTX and TGTS mono- and combined groups) were carried out at baseline and at the end of 12 weeks in a randomized controlled clinical trial. To monitor the affected variation of drug blood level and perturbation of metabolites caused by MTX plus TGTS combined to treat active RA, the collected plasma samples were analyzed using RRLC-QqQ-MS and UHPLC-QE Orbitrap HRMS instruments. As a result, 39 metabolites including 7 MTX-related metabolites, 13 TGTS-related migratory ingredients and 19 characteristic endogenous metabolites, were quantitatively determined in plasma samples of RA patients after oral administration. The potential mechanism of MTX and TGTS combination were preliminarily elucidated on the aspect of clinical biochemical test indicators integrated with quantitative plasma pharmacochemistry and the pseudotargeted metabolomics

Desolvation and Dehydrogenation of Solvated Magnesium Salts of Dodecahydrododecaborate: Relationship between Structure and Thermal Decomposition

Attempts to synthesize solvent-free MgB_(12)H_(12) by heating various solvated forms (H_2O, NH_3, and CH_3OH) of the salt failed because of the competition between desolvation and dehydrogenation. This competition has been studied by thermogravimetric analysis (TGA) and temperature-programmed desorption (TPD). Products were characterized by IR, solution- and solid-state NMR spectroscopy, elemental analysis, and single-crystal or powder X-ray diffraction analysis. For hydrated salts, thermal decomposition proceeded in three stages, loss of water to form first hexahydrated then trihydrated, and finally loss of water and hydrogen to form polyhydroxylated complexes. For partially ammoniated salts, two stages of thermal decomposition were observed as ammonia and hydrogen were released with weight loss first of 14 % and then 5.5 %. Thermal decomposition of methanolated salts proceeded through a single step with a total weight loss of 32 % with the release of methanol, methane, and hydrogen. All the gaseous products of thermal decomposition were characterized by using mass spectrometry. Residual solid materials were characterized by solid-state 11B magic-angle spinning (MAS) NMR spectroscopy and X-ray powder diffraction analysis by which the molecular structures of hexahydrated and trihydrated complexes were solved. Both hydrogen and dihydrogen bonds were observed in structures of [Mg(H_2O_6B_(12)H_(12)]⋅6 H_2O and [Mg(CH_3OH)_(6)B_(12)H_(12)]⋅6 CH_3OH, which were determined by single-crystal X-ray diffraction analysis. The structural factors influencing thermal decomposition behavior are identified and discussed. The dependence of dehydrogenation on the formation of dihydrogen bonds may be an important consideration in the design of solid-state hydrogen storage materials

Transferring Pre-Trained Deep CNNs for Remote Scene Classification with General Features Learned from Linear PCA Network

Deep convolutional neural networks (CNNs) have been widely used to obtain high-level representation in various computer vision tasks. However, in the field of remote sensing, there are not sufficient images to train a useful deep CNN. Instead, we tend to transfer successful pre-trained deep CNNs to remote sensing tasks. In the transferring process, generalization power of features in pre-trained deep CNNs plays the key role. In this paper, we propose two promising architectures to extract general features from pre-trained deep CNNs for remote scene classification. These two architectures suggest two directions for improvement. First, before the pre-trained deep CNNs, we design a linear PCA network (LPCANet) to synthesize spatial information of remote sensing images in each spectral channel. This design shortens the spatial “distance” of target and source datasets for pre-trained deep CNNs. Second, we introduce quaternion algebra to LPCANet, which further shortens the spectral “distance” between remote sensing images and images used to pre-train deep CNNs. With five well-known pre-trained deep CNNs, experimental results on three independent remote sensing datasets demonstrate that our proposed framework obtains state-of-the-art results without fine-tuning and feature fusing. This paper also provides baseline for transferring fresh pretrained deep CNNs to other remote sensing tasks

Recent Advances in Sorbicillinoids from Fungi and Their Bioactivities (Covering 2016–2021)

Sorbicillinoids are a family of hexaketide metabolites with a characteristic sorbyl side chain residue. Sixty-nine sorbicillinoids from fungi, newly identified from 2016 to 2021, are summarized in this review, including their structures and bioactivities. They are classified into monomeric, dimeric, trimeric, and hybrid sorbicillinoids according to their basic structural features, with the main groups comprising both monomeric and dimeric sorbicillinoids. Some of the identified sorbicillinoids have special structures such as ustilobisorbicillinol A, and sorbicillasins A and B. The majority of sorbicillinoids have been reported from fungi genera such as Acremonium, Penicillium, Trichoderma, and Ustilaginoidea, with some sorbicillinoids exhibiting cytotoxic, antimicrobial, anti-inflammatory, phytotoxic, and α-glucosidase inhibitory activities. In recent years, marine-derived, extremophilic, plant endophytic, and phytopathogenic fungi have emerged as important resources for diverse sorbicillinoids with unique skeletons. The recently revealed biological activities of sorbicillinoids discovered before 2016 are also described in this review