Effect of impeller submergence on power dissipation and solids suspension in mixing systems equipped with pitch-blade turbines

Mixing and dispersion of solids in a liquid is a process frequently encountered in the pharmaceutical industry and often conducted in cylindrical baffled tanks stirred by mechanical impellers. In operations where the liquid level is decreased (as often which emptying the tank) the process must be stopped when the solids are no longer suspended. In this work, the minimum agitation to suspend solids (Njs) when the liquid level was lowered, and the impeller submergence Sb changed as a result were determined for the case of a six-blade, pitch-blade turbine (6-PBT) impeller. The power consumed by the impeller was also measured. It was found that when a critical impeller submerge level was reached it was impossible to suspend the solids and the impeller power decreased significantly, although the impeller was still full submerged. The results are important to ensure that mixing systems are operated properly

On A Simpler and Faster Derivation of Single Use Reliability Mean and Variance for Model-Based Statistical Testing

Markov chain usage-based statistical testing has proved sound and effective in providing audit trails of evidence in certifying software-intensive systems. The system end-toend reliability is derived analytically in closed form, following an arc-based Bayesian model. System reliability is represented by an important statistic called single use reliability, and defined as the probability of a randomly selected use being successful. This paper continues our earlier work on a simpler and faster derivation of the single use reliability mean, and proposes a new derivation of the single use reliability variance by applying a well-known theorem and eliminating the need to compute the second moments of arc failure probabilities. Our new results complete a new analysis that could be shown to be simpler, faster, and more direct while also rendering a more intuitive explanation. Our new theory is illustrated with three simple Markov chain usage models with manual derivations and experimental results

On bases of g-invariant endomorphism algebras

Let g be a complex simple Lie algebra. Let Z(g) be the center of the universal enveloping algebra U(g). Let Vλ be the finite-dimensional irreducible g-module with highest weight λ. Our main result is a criterion of the existence of Z(g)-bases for the g-invariant endomorphism algebra Rλ=: Homg(End Vλ,U(g)). Then we obtain a Clifford algebra analogue, namely a criterion of the existence C(g)g-bases for RλC =: Homg(End Vλ,C(g)). We also describe a criterion of the existence of bases generated by powers of the Casimir element for R{λ,ν} =: Homg(End Vλ, End Vν)

A Simpler and More Direct Derivation of System Reliability Using Markov Chain Usage Models

Markov chain usage-based statistical testing has been around for more than two decades, and proved sound and effective in providing audit trails of evidence in certifying software-intensive systems. The system end-to-end reliability is derived analytically in closed form, following an arc-based Bayesian model. System reliability is represented by an important statistic called single use reliability, and defined as the probability of a randomly selected use being successful. This paper reviews the analytical derivation of the single use reliability mean, and proposes a simpler, faster, and more direct way to compute the expected value that renders an intuitive explanation. The new derivation is illustrated with two examples

Associations of Muscle Mass and Strength with All-Cause Mortality among US Older Adults

INTRODUCTION: Recent studies suggested that muscle mass and muscle strength may independently or synergistically affect aging-related health outcomes in older adults; however, prospective data on mortality in the general population are sparse. METHODS: We aimed to prospectively examine individual and joint associations of low muscle mass and low muscle strength with all-cause mortality in a nationally representative sample. This study included 4449 participants age 50 yr and older from the National Health and Nutrition Examination Survey 1999 to 2002 with public use 2011 linked mortality files. Weighted multivariable logistic regression models were adjusted for age, sex, race, body mass index (BMI), smoking, alcohol use, education, leisure time physical activity, sedentary time, and comorbid diseases. RESULTS: Overall, the prevalence of low muscle mass was 23.1% defined by appendicular lean mass (ALM) and 17.0% defined by ALM/BMI, and the prevalence of low muscle strength was 19.4%. In the joint analyses, all-cause mortality was significantly higher among individuals with low muscle strength, whether they had low muscle mass (odds ratio [OR], 2.03; 95% confidence interval [CI], 1.27-3.24 for ALM; OR, 2.53; 95% CI, 1.64-3.88 for ALM/BMI) or not (OR, 2.66; 95% CI, 1.53-4.62 for ALM; OR, 2.17; 95% CI, 1.29-3.64 for ALM/BMI). In addition, the significant associations between low muscle strength and all-cause mortality persisted across different levels of metabolic syndrome, sedentary time, and LTPA. CONCLUSIONS: Low muscle strength was independently associated with elevated risk of all-cause mortality, regardless of muscle mass, metabolic syndrome, sedentary time, or LTPA among US older adults, indicating the importance of muscle strength in predicting aging-related health outcomes in older adults

MDENet: Multi-modal Dual-embedding Networks for Malware Open-set Recognition

Malware open-set recognition (MOSR) aims at jointly classifying malware samples from known families and detect the ones from novel unknown families, respectively. Existing works mostly rely on a well-trained classifier considering the predicted probabilities of each known family with a threshold-based detection to achieve the MOSR. However, our observation reveals that the feature distributions of malware samples are extremely similar to each other even between known and unknown families. Thus the obtained classifier may produce overly high probabilities of testing unknown samples toward known families and degrade the model performance. In this paper, we propose the Multi-modal Dual-Embedding Networks, dubbed MDENet, to take advantage of comprehensive malware features (i.e., malware images and malware sentences) from different modalities to enhance the diversity of malware feature space, which is more representative and discriminative for down-stream recognition. Last, to further guarantee the open-set recognition, we dually embed the fused multi-modal representation into one primary space and an associated sub-space, i.e., discriminative and exclusive spaces, with contrastive sampling and rho-bounded enclosing sphere regularizations, which resort to classification and detection, respectively. Moreover, we also enrich our previously proposed large-scaled malware dataset MAL-100 with multi-modal characteristics and contribute an improved version dubbed MAL-100+. Experimental results on the widely used malware dataset Mailing and the proposed MAL-100+ demonstrate the effectiveness of our method.Comment: 14 pages, 7 figure

Sodium-glucose co-transporter-2 inhibitors and risk of adverse renal outcomes among patients with type 2 diabetes: A network and cumulative meta-analysis of randomized controlled trials

Aim To compare the associations of individual sodium-glucose co-transporter-2 (SGLT2) inhibitors with adverse renal outcomes in patients with type 2 diabetes mellitus (T2DM). Methods PubMed, EMBASE, CENTRAL and ClinicalTrials.gov were searched for studies published up to May 24, 2016, without language or date restrictions. Randomized trials that reported at least 1 renal-related adverse outcome in patients with T2DM treated with SGLT2 inhibitors were included. Pairwise and network meta-analyses were carried out to calculate the odds ratios (ORs) with 95% confidence intervals (CIs), and a cumulative meta-analysis was performed to assess the robustness of evidence. Results In total, we extracted 1334 composite renal events among 39 741 patients from 58 trials, and 511 acute renal impairment/failure events among 36 716 patients from 53 trials. Dapagliflozin was significantly associated with a greater risk of composite renal events than placebo (OR 1.64, 95% CI 1.26-2.13). Empagliflozin seemed to confer a lower risk than placebo (OR 0.63, 95% CI 0.54-0.72), canagliflozin (OR 0.48, 95% CI 0.29-0.82) and dapagliflozin (OR 0.38, 95% CI 0.28-0.51). With regard to acute renal impairment/failure, only empagliflozin was significantly associated with a lower risk than placebo (OR 0.72, 95% CI 0.60-0.86). The cumulative meta-analysis indicated the robustness of our significant findings. Conclusions The present meta-analysis indicated that dapagliflozin may increase the risk of adverse renal events, while empagliflozin may have a protective effect among patients with T2DM. Further data from large well-conducted randomized controlled trials and a real-world setting are warranted

The longitudinal association between possible new sarcopenia and the depression trajectory of individuals and their intimate partners

BackgroundIt is currently unknown whether the dynamic nature of depression affects the development of sarcopenia. Herein, this study aims to assess the association between possible new sarcopenia and the depression trajectory of individuals and their intimate partners through a 4-year longitudinal cohort study.MethodsOur study included 784 pairs of individuals without possible sarcopenia and their spouses from the China Health and Retirement Longitudinal Study (CHARLS) 2011. All individuals and their spouses received three assessments of the Center for Epidemiologic Studies Depression 10-item (CESD-10) scale in 2011, 2013, and 2015. According to the diagnostic algorithm recommended by the Asian Working Group for Sarcopenia (AWGS) 2019, we evaluated the incidence of possible sarcopenia in individuals in 2015. Latent class analysis (LCA) was used to identify a longitudinal depression trajectory of individuals and their spouses during a 4-year follow-up. Subsequently, we assessed the relationship between possible sarcopenia and depression trajectory using three generalized additive models.ResultsIn 2015, 24.87% (195/784) of individuals were diagnosed with possible sarcopenia. LCA identified five depression trajectories: a persistently high risk of depression in individuals and their spouses (reference; class 1 = 34 [4.3%]); a persistently low risk of depression in individuals and their spouses (class 2 = 526 [67.1%]); a high risk of depression in individuals and a low risk of depression in spouses (class 3 = 46 [5.9%]); a low risk of depression in individuals and a high risk of depression in spouses (class 4 = 116 [14.8%]); and a reduced risk of depression in individuals and their spouses (class 5 = 62 [7.9%]). The highest incidence of possible sarcopenia was shown in class 1, followed by classes 3 and 5. Classes 2 (adjusted relative risk (RR) = 0.44, 95% confidence interval (CI): 0.20–0.97) and 4 (adjusted RR = 0.40, 95%CI: 0.17–0.96) had a significantly lower incidence of possible sarcopenia than class 1. Subgroup analysis demonstrated that the incidence of possible sarcopenia in class 4 was obviously higher in women (38.89%) than in men (18.4%).ConclusionsOur study indicates a persistently high risk of depression in individuals to develop possible sarcopenia. In addition, a persistently high risk of depression in intimate partners potentially increases the risk of possible new sarcopenia, especially in female individuals who are at low risk of depression

Elevated serum magnesium associated with SGLT2 inhibitor use in type 2 diabetes patients: a meta-analysis of randomised controlled trials

Aims/hypothesis By analysing available evidence from randomised controlled trials (RCTs), we aimed to examine whether and to what extent sodium–glucose cotransporter 2 (SGLT2) inhibitors affect serum electrolyte levels in type 2 diabetes patients. Methods We searched PubMed, EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL) and ClinicalTrials.gov up to 24 May 2016 for published RCTs of SGLT2 inhibitors that reported changes in serum electrolyte levels. Weighted mean differences (WMD) between each SGLT2 inhibitor and placebo were calculated using a random-effects model. Dose-dependent relationships for each SGLT2 inhibitor were evaluated using meta-regression analysis. Results Eighteen eligible RCTs, including 15,309 patients and four SGLT2 inhibitors (canagliflozin, dapagliflozin, empagliflozin and ipragliflozin) were evaluated. In patients without chronic kidney disease, each SGLT2 inhibitor significantly increased serum magnesium levels compared with placebo (canagliflozin: WMD 0.06 mmol/l for 100 mg and 0.09 mmol/l for 300 mg; dapagliflozin: WMD 0.1 mmol/l for 10 mg; empagliflozin: WMD 0.04 mmol/l for 10 mg and 0.07 mmol/l for 25 mg; and ipragliflozin: WMD 0.05 mmol/l for 50 mg). Canagliflozin increased serum magnesium in a linear dose-dependent manner (p = 0.10). Serum phosphate was significantly increased by dapagliflozin. Serum sodium appeared to significantly differ by SGLT2 inhibitor type. No significant changes in serum calcium and potassium were observed. Findings were robust after including trials involving patients with chronic kidney disease. Conclusions/interpretation SGLT2 inhibitors marginally increased serum magnesium levels in type 2 diabetes patients indicating a drug class effect. Further investigations are required to examine the clinical significance of elevated magnesium levels in individuals with type 2 diabetes