Component design optimization of green roof substrate layer based on the assessment of multifunctional performance.

A high-quality substrate layer is the cornerstone of supporting that green roofs (GRs) can become an efficient and sustainable nature-based solution to urban environmental problems. In the present study, three lightweight substrate materials commonly used in GRs of peat soil, vermiculite and pumice with four appropriate proportions of the nutrient substrate and the mineral substrates were selected to install twelve substrate modules. The lightweight property, water-holding, nutrient retention and rainwater reduction performance of the substrate modules were investigated by the laboratory determination methods and the simulated rainfall experiment. An assessment model based on the multifunctional performance established by analytic hierarchy process (AHP) was used for the component design optimization of GR substrate layer. The results showed that the substrate modules based on peat soil and vermiculite (PV) as the mineral substrate, which the dry volumetric weights and the average water content were 1.40-1.70 kN m-3 and 47.80%-49.06%, always exhibited better lightweight properties and water-holding performance compared to those composed of pumice. PV-40 had the highest value of the multifunction index even while none of its functional performance was optimal among all the substrate modules. The present study emphasizes the necessity of optimizing the GR substrate layer component based on the assessment of multifunctional performance to better promote the sustainable development of GRs in urban areas.

Selective Memory Recursive Least Squares: Recast Forgetting Into Memory in RBF Neural Network-Based Real-Time Learning.

In radial basis function neural network (RBFNN)-based real-time learning tasks, forgetting mechanisms are widely used such that the neural network can keep its sensitivity to new data. However, with forgetting mechanisms, some useful knowledge will get lost simply because they are learned a long time ago, which we refer to as the passive knowledge forgetting phenomenon. To address this problem, this article proposes a real-time training method named selective memory recursive least squares (SMRLS) in which the classical forgetting mechanisms are recast into a memory mechanism. Different from the forgetting mechanism, which mainly evaluates the importance of samples according to the time when samples are collected, the memory mechanism evaluates the importance of samples through both temporal and spatial distribution of samples. With SMRLS, the input space of the RBFNN is evenly divided into a finite number of partitions, and a synthesized objective function is developed using synthesized samples from each partition. In addition to the current approximation error, the neural network also updates its weights according to the recorded data from the partition being visited. Compared with classical training methods including the forgetting factor recursive least squares (FFRLS) and stochastic gradient descent (SGD) methods, SMRLS achieves improved learning speed and generalization capability, which are demonstrated by corresponding simulation results.

Deterministic learning-based neural network control with adaptive phase compensation.

Under the persistent excitation (PE) condition, the real dynamics of the nonlinear system can be obtained through the deterministic learning-based radial basis function neural network (RBFNN) control. However, in this scheme, the learning speed and accuracy are limited by the tradeoff between the PE levels and the approximation capabilities of the neural network (NN). Inspired by the frequency domain phase compensation of linear time-invariant (LTI) systems, this paper presents an adaptive phase compensator employing the pure time delay to improve the performance of the deterministic learning-based adaptive feedforward control with the reference input known a priori. When the adaptive phase compensation is applied to the hidden layer of the RBFNN, the nonlinear approximation capability of the RBFNN is effectively improved such that both the learning performance (learning speed and accuracy) and the control performance of the deterministic learning-based control scheme are improved. Theoretical analysis is conducted to prove the stability of the proposed learning control scheme for a class of systems which are affine in the control. Simulation studies demonstrate the effectiveness of the proposed phase compensation method.

Seroprevalence and infection attack rate of COVID-19 in Indian cities.

Objectives: Serological surveys were used to infer the infection attack rate in different populations. The sensitivity of the testing assay, Abbott, drops fast over time since infection which makes the serological data difficult to interpret. In this work, we aim to solve this issue. Methods: We collect longitudinal serological data of Abbott to construct a sensitive decay function. We use the reported COVID-19 deaths to infer the infections, and use the decay function to simulate the seroprevalence and match to the reported seroprevalence in 12 Indian cities. Results: Our model simulated seroprevalence matchs the reported seroprevalence in most of the 12 Indian cities. We obtain reasonable infection attack rate and infection fatality rate for most of the 12 Indian cities. Conclusions: Using both reported COVID-19 deaths data and serological survey data, we infer the infection attack rate and infection fatality rate with increased confidence.

A Novel Review of Homocysteine and Pregnancy Complications.

Homocysteine (Hct) is a substance produced in the metabolism of methionine. It is an essential type of amino acid gained from the daily diet. Methylenetetrahydrofolate reductase (MTHFR) gene mutation is related to elevated total homocysteine (tHct) expressions, in particular, among women with low folate intake. Hyperhomocysteinemia (HHct) is caused by numerous factors, such as genetic defects, lack of folic acid, vitamin B6 and B12 deficiency, hypothyroidism, drugs, aging, and renal dysfunction. Increased Hct in peripheral blood may lead to vascular illnesses, coronary artery dysfunction, atherosclerotic changes, and embolic diseases. Compared to nonpregnant women, the Hct level is lower in normal pregnancies. Recent studies have reported that HHct was associated with numerous pregnancy complications, including recurrent pregnancy loss (RPL), preeclampsia (PE), preterm delivery, placental abruption, fetal growth restriction (FGR), and gestational diabetes mellitus (GDM). Besides, it was discovered that neonatal birth weight and maternal Hct levels were negatively correlated. However, a number of these findings lack consistency. In this review, we summarized the metabolic process of Hct in the human body, the levels of Hct in different stages of normal pregnancy reported in previous studies, and the relationship between Hct and pregnancy complications. The work done is helpful for obstetricians to improve the likelihood of a positive outcome during pregnancy complications. Reducing the Hct level with a high dosage of folic acid supplements during the next pregnancy could be helpful for females who have suffered pregnancy complications due to HHct.