Iterative Qubits Management for Quantum Index Searching in a Hybrid System

Recent advances in quantum computing systems attract tremendous attention. Commercial companies, such as IBM, Amazon, and IonQ, have started to provide access to noisy intermediate-scale quantum computers. Researchers and entrepreneurs attempt to deploy their applications that aim to achieve a quantum speedup. Grover's algorithm and quantum phase estimation are the foundations of many applications with the potential for such a speedup. While these algorithms, in theory, obtain marvelous performance, deploying them on existing quantum devices is a challenging task. For example, quantum phase estimation requires extra qubits and a large number of controlled operations, which are impractical due to low-qubit and noisy hardware. To fully utilize the limited onboard qubits, we propose IQuCS, which aims at index searching and counting in a quantum-classical hybrid system. IQuCS is based on Grover's algorithm. From the problem size perspective, it analyzes results and tries to filter out unlikely data points iteratively. A reduced data set is fed to the quantum computer in the next iteration. With a reduction in the problem size, IQuCS requires fewer qubits iteratively, which provides the potential for a shared computing environment. We implement IQuCS with Qiskit and conduct intensive experiments. The results demonstrate that it reduces qubits consumption by up to 66.2%

Corticosteroids for the prevention of bronchopulmonary dysplasia in preterm infants: a network meta-analysis

Objective: To determine the comparative efficacy and safety of corticosteroids in the prevention of bronchopulmonary dysplasia (BPD) in preterm infants.  Study design: We systematically searched PubMed, EMBASE and the Cochrane Library. Two reviewers independently selected randomised controlled trials (RCTs) of postnatal corticosteroids in preterm infants. A Bayesian network meta-analysis and subgroup analyses were performed.  Results: We included 47 RCTs with 6747 participants. The use of dexamethasone at either high dose or low dose decreased the risk of BPD (OR 0.29, 95% credible interval (CrI) 0.14 to 0.52; OR 0.58, 95% CrI 0.39 to 0.76, respectively). High-dose dexamethasone was more effective than hydrocortisone, beclomethasone and low-dose dexamethasone. Early and long-term dexamethasone at either high dose or low dose decreased the risk of BPD (OR 0.11, 95% CrI 0.02 to 0.4; OR 0.37, 95% CrI 0.16 to 0.67, respectively). There were no statistically significant differences in the risk of cerebral palsy (CP) between different corticosteroids. However, high-dose and long-term dexamethasone ranked lower than placebo and other regimens in terms of CP. Subgroup analyses indicated budesonide was associated with a decreased risk of BPD in extremely preterm and extremely low birthweight infants (OR 0.60, 95% CrI 0.36 to 0.93).  Conclusions: Dexamethasone can reduce the risk of BPD in preterm infants. Of the different dexamethasone regimens, aggressive initiation seems beneficial, while a combination of high-dose and long-term use should be avoided because of the possible adverse neurodevelopmental outcome. Dexamethasone and inhaled corticosteroids need to be further evaluated in large-scale RCTs with long-term follow-ups

Iterative Qubits Management for Quantum Search

Recent advances in quantum computing systems attract tremendous attention. Commercial companies, such as IBM, Amazon, and IonQ, have started to provide access to noisy intermediate-scale quantum computers. Researchers and entrepreneurs attempt to deploy their applications that aim to achieve a quantum speedup. Grover\u27s algorithm and quantum phase estimation are the foundations of many applications with the potential for such a speedup. While these algorithms, in theory, obtain marvelous performance, deploying them on existing quantum devices is a challenging task. For example, quantum phase estimation requires extra qubits and a large number of controlled operations, which are impractical due to low-qubit and noisy hardware. To fully utilize the limited onboard qubits, we develop a distributed application with a key-value data structure based on Grover\u27s algorithm called IQuCS. Consider a database with duplicates. By encoding each element to a binary type with a unique key and forming a key-value pair, we can count the number of occurrences of each element in the database based on quantum computing. We have optimized the operation process by filtering data points to make it more efficient. To determine the effect of this optimization, we evaluate it with datasets of different sizes and with different numbers of duplicates. With the assistance of classical computers, IQuCS can reduce the problem set for each query. Due to this reduction, IQuCS requires fewer . Through the iterative management, IQuCS achieves a reduction of qubit virtualized consumption, up to 66.2%, with reasonable accuracy

Temperature correction method for dielectric response of high moisture content and aging degree oil impregnated paper based on segmented activation energy

Frequency Domain Spectroscopy (FDS) is widely used to estimate oil–paper insulation. The high moisture and long aging change the FDS characteristics of the insulating oil–paper, so normalization processing is required. Still, the choice of single activation energy needs further research. This paper studies the FDS characteristic of oil–paper insulation samples with different moisture and aging degrees and improves the traditional temperature correction method. The enhanced Arrhenius model uses the segmented activation energy to make the normalized FDS curve coincide better with the target curve, and the error is reduced. In addition, to verify the method’s effectiveness, this paper proposes an iterative correction process. It corrects the tan δ-f curve of the bushing with an aging time of 800 h based on segmented activation energy, and the overall normalization effect is improved

Usefulness of Triglyceride-glucose index for detecting prevalent atrial fibrillation in a type 2 diabetic population

Atrial fibrillation (AF) is the most common arrhythmias, which significantly jeopardizes global cardiovascular health through the complicated heart failure and stroke. Published studies have demonstrated the impact of insulin resistance on the genesis of AF. Hence, monitoring insulin resistance may be a possible way to improve the detection of early-stage AF. Accordingly, our work aimed to investigate the association between TyG, a surrogate of insulin resistance, and the prevalent AF, and to evaluate the potential of TyG to refine the detection of prevalent AF in a diabetic population. This cross-sectional study was derived from the National Metabolic Management Center Program and included 3244 diabetic patients between September 2017 and December 2020. TyG was calculated as ln[fasting TG (mg/ dL)× FPG (mg/dL)/2]. AF was diagnosed according to electrocardiography and subjects’ self-reports. The prevalence of AF was 6.57%. In the fully adjusted model, each SD elevation of TyG cast a 40.6% additional risk for prevalent AF. In the quartile analysis, the top quartile showed a 2.120 times risk of prevalent AF compared with the bottom quartile. Smooth curve fitting demonstrated that the association was linear in the full range of TyG, and subgroup analysis suggested that the association was robust in several common subpopulations of AF. Furthermore, ROC results displayed an improvement for the detection of prevalent AF when adding TyG into conventional cardiovascular risk factors (0.812vs.0.825, P = 0.019), and continuous net reclassification index (0.227, 95% CI: 0.088–0.365, P = 0.001) and integrated discrimination index (0.007, 95% CI: 0.001–0.012, P = 0.026) also showed the improvement achieved by TyG. Our data supported a linear and robust correlation between TyG and the prevalent AF in a diabetic population. Moreover, our results implicated the potential usefulness of TyG to refine the detection of prevalent AF in a diabetic population.</p

Polydopamine-enabled distribution of polysiloxane domains in polyamide thin-film nanocomposite membranes for organic solvent nanofiltration

© 2018 Elsevier B.V. Thin-film nanocomposite (TFN) membranes have attracted growing interests for improving the energy efficiency of many chemical separation processes, while well-designed microstructures are essential to acquire high permeation flux, high selectivity and high stability for different types of permeates. Herein, a novel strategy to regulate the microstructures and solvent permeation properties of TFN membranes is developed. Hydrophobic polysiloxane domains are proposed to be evenly distributed within hydrophilic polyamide matrix with the mediation of polydopamine nanoparticles (PDNPs). To be specific, PDNPs treated by 3-(triethoxysilyl)- propylamine (APTES) allow PDMS converge on its surface, so as to form nano-sized poly(dimethylsiloxane) (PDMS) domains within the active layer of TFN membranes. With polyethyleneimine (PEI) as the aqueous phase monomer during conventional interfacial polymerization (IP), trimesoyl chloride not only acts as the oil phase monomer, but also reacts with the terminal hydroxyl groups of PDMS, facilitating the uniform dispersion of the nanoparticles within the PEI matrix. By tuning the ratio of PDNPs to PDMS, PDMS could be uniformly dispersed within the active layer together with PDNPs, which effectively construct hydrophobic pathways for nonpolar solvents. A maximum permeate flux for n-heptane of 7.9 L m-2h-1bar-1at 10 bar is achieved, along with moderate area swelling (3.16%) and rather low MWCOs (below 400). Meanwhile, these TFN membranes containing PDMS domains still display appropriate permeate fluxes for polar solvents due to the maintenance of hydrophilic pathways, as well as enhanced rejection ability and potential long-term operation stability than the control membranes