Search

To investigate neutralizing antibody levels in symptomatic and asymptomatic patients with coronavirus disease 2019 (COVID-19) at 6 and 10 months after disease onset.Blood samples were collected at three different time points from 27 asymptomatic individuals and 69 symptomatic patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Virus-neutralizing antibody titers against SARS-CoV-2 in both groups were measured and statistically analyzed.The symptomatic and asymptomatic groups had higher neutralizing antibodies at 3 months and 1-2 months post polymerase chain reaction confirmation, respectively. However, neutralizing antibodies in both groups dropped significantly to lower levels at 6 months post-PCR confirmation.Continued monitoring of symptomatic and asymptomatic individuals with COVID-19 is key to controlling the infection.

Orthogonal time frequency space (OTFS) modulation has superior performance than traditional orthogonal frequency division multiplexing (OFDM) in fast time-varying scenarios. However, due to the effect of Doppler shift, higher pilot overhead and pilot power are required to estimate the channel state information. According to the sparseness of the channel in the delay-Doppler domain, this letter proposes a new pilot pattern and a sparse Bayesian learning (SBL)-based channel estimation algorithm. There is no guard pilot in the pilot pattern, and the pilot has the same power as the data. Based on the new pilot pattern, we first convert the channel estimation problem to a sparse signal recovery problem. Then, we introduce a sparse Bayesian learning framework and construct a sparse signal prior model as a hierarchical Laplace prior. Finally, the expected maximum (EM) algorithm is used to update the parameters in the prior model. Numerical simulation highlights the superiority of the proposed algorithm in terms of pilot overhead, pilot power consumption, and anti-noise interference.

Contraposing the problem of high pilot overhead and poor estimation performance for OFDM system in fast time-varying channels, a novel channel estimation method based on joint-sparse basis expansion model is proposed. In order to resist the inter-carrier interference (ICI) of OFDM system over fast time-varying channel, we introduce the OTFS (Orthogonal Time Frequency Space) technique and propose an implementation scheme of OTFS system based on time-frequency domain channel estimation. Simulation results demonstrate that the proposed OTFS system has higher reliability and better adaptability than the OFDM system in high dynamic scenarios.

The history has usually been deemed a useless subject for many years. Meanwhile, most students have lost the passion of learning history-related subjects. Thus, this study is taking steps for introducing the method of Team-Assisted Individualisation (TAI) to attempt improving the teaching environment of art history course. Finally, this study has discovered problems in the art history teaching process due to we closely follow the current educational difficulties. Therefore, it can provide its contributions for the relevant researches in the future.The history has usually been deemed a useless subject for many years. Meanwhile, most students have lost the passion of learning history-related subjects. Thus, this study is taking steps for introducing the method of Team-Assisted Individualisation (TAI) to attempt improving the teaching environment of art history course. Finally, this study has discovered problems in the art history teaching process due to we closely follow the current educational difficulties. Therefore, it can provide its contributions for the relevant researches in the future.

This chapter looks at the mould industry to illustrate the challenges of the emerging business model that can be found in many other industries as well. It presents a background and literature review about leasing, and examples of papers within the context of remanufacturing. The chapter introduces the problem and the model formulation, and outlines the implement environment. A growing number of advocates have claimed adopting leasing as a business model as being more ‘green'. The practice of leasing products, rather than selling them, is a strategy for increasing resource productivity by moving to a pattern of closed-loop material use by manufacturers. Several firms that are actively involved in remanufacturing recover their used products by leasing their new products. Product leasing results in the most predictable return stream and is a popular option for firms that also remanufacture. Leasing provides both a motive for product take-back and a structure to aid in the logistics involved with closed material loops.

Development of novel approaches for biofouling mitigation is of crucial importance for membrane-based technologies. d-amino acids (d-AAs) have been proposed as a potential strategy to mitigate biofouling. However, the effect of bacterial cell-wall properties and d-AAs type on biofouling mitigation remains unclear. This study assesses the effect of d-AAs type on membrane biofouling control, towards Gram positive (G+) and Gram negative (G-) bacteria. Three kinds of d-AAs were found to inhibit both G+ and G- bacterial attachment in short-term attachment and dead-end filtration experiments. The existence of d-AAs reduces extracellular polysaccharides and proteins on the membrane, which may decrease membrane biofouling. Cross-flow filtration tests further indicated that d-AAs could effectively reduce membrane biofouling. The permeate flux recovery post chemical cleaning, improved for both P. aeruginosa and B. subtilis treated with d-AAs. The results obtained from this study enable better understanding of the role of d-AAs species on bacterial adhesion and biofilm formation. This may provide a new way to regulate biofilm formation by manipulating the species of d-AAs membrane systems.

Energy levels, line strengths, oscillator strengths, radiative decay rates and fine structure collision strengths are presented for six-times ionized krypton (Kr VII). The atomic data are calculated with the AUTOSTRUCTURE code, where relativistic corrections are introduced according to the Breit–Pauli distorted wave approach. We present calculations of atomic data for 40 fine-structure levels generated from nine configurations ((1s22s22p63s23p63d10)4s2, 4lnl′, n = 4, 5; l = 0, 1; and l′ ≤ 3) of the Zn-like Kr ion. Fine structure collision strengths for transitions from the ground and the first four excited levels are presented at six electron energies: 8, 15, 30, 45, 60, and 80 Ryd. Our atomic structure data are compared with the available experimental and theoretical results.

We have calculated highly excited fine-structure energy levels and their autoionizing width of 3pns3P1 (n = 11–26), 3pns1P1 (n = 10–22), 3pnd3D1 (n = 11–26), 3pnd3P1 (n = 10–21), 3pnd1P1(n = 10–21), 3dnp3D1 (n = 7–30), 3dnp3P1 (n = 7–28), 3dnp1P1 (n = 7–28), 3dnf3D1 (n = 7, 9–27), 3dnf3P1 (n = 7, 9–27), and 3dnf1P1 (n = 7, 9–27) for magnesium-like Ni XVII. The calculations are based upon the relativistic Breit-Pauli R-matrix approximation combining with the QB method of Quigley-Berrington (L. Quigley, K. A. Berrington, Pelan J. Comput. Phys. Commun. 114, 225 (1998)). We have reported the many unpublished energy values and autoionizing width of the J = 1 odd states of magnesium-like Ni XVII.

Remanufacturing has become a superior option for product recovery management system. It mainly consists of three stages: retrieval, reproduction, and redistribution. So far, many different approaches have been followed in order to improve the efficiency of a remanufacturing process. However, as the complexity increases, the use of computational intelligence (CI) in those problems is becoming a unique tool of imperative value. In this paper, different CI methods, such as artificial neural network (ANN), ant colony optimization (ACO), biogeography-based optimization (BBO), cuckoo search (CS) and fuzzy logic (FL), are utilized to solve the problems involved in retrieval and reproduction stages for remanufacturing. The key issues in implementing the proposed approaches are discussed, and finally the applicability of the proposed methods are illustrated through different examples.

Relativistic calculations of photoionization cross sections of Ne-like Cu XX using the Breit–Pauli R-matrix method in close-coupling approximation and fully relativistic distorted-wave method are reported. In the present work, the relativistic distorted-wave method is employed to calculate the fine-structure energy levels and radial functions, and the Breit–Pauli R-matrix method is employed to calculate photoionizaton cross section for photon energy from 124.2 Ryd to 208 Ryd. Resonance structure in the photoionization of Ne-like Cu XX for ejection of 2p or 2s electron from the ground 2s22p6 1S0 has been studied.

Thermogravimetric curves of micro nanometer iron powder’s combustion were studied in different heating rates that were 10K/min, 20 K/min, 30 K/min and 40 K/min, and the particle sizes of iron were 100nm and 20μm. The iron’s kinetic parameters of combustion reaction were calculated by Coats-Redfern integral, differential and Kissinger methods, then the kinetic model was determined and the most probable mechanism function was verified by Popescu method. The results show that different heating rates, particle sizes and calculations can affect kinetic parameters and reaction mechanism. The activation energy of 100nm and 20μm iron powder’s combustion is 122.48 KJ/mol and 161.64 KJ/mol respectively. The combustion reaction of micro nanometer iron powder is controlled by random nucleation and subsequent growth model which is in agreement with Avrami-Erofeev equation. Rational control for the temperature and reaction time are conducive to optimize the combustion reaction of micro nanometer iron powder.

We have calculated highly excited fine-structure energy levels and their autoionizing widths for the 3pns 3P1(n=9–33), 3pns 1P1(n=9–27), 3pnd 3D1(n=9–35), 3pnd 3P1(n=8–26), 3pnd 1P1(n=8–26), 3dnp3D1(n=6,8–29), 3dnp3P1(n=6,8–29), 3dnp 1P1(n=6,8–28), 3dnf 3D1(n=7–28), 3dnf 3P1(n=7–27), and 3dnf 1P1(n=7–27) states of the magnesium-like ion Ti XI and for the 3pns 3P1(n=10–35), 3pns 1P1(n=10–25), 3pnd 3D1(n=10–35),3pnd 3P1(n=10–25),3pnd 1P1(n=10–24),3dnp 3D1(n=7,9–31),3dnp 3P1(n=7,9–31),3dnp 1P1(n=7,9–28),3dnf 3D1(n=9–30),3dnf 3P1(n=9–28), and 3dnf 1P1(n=9–28) states of the magnesium-like ion Fe XV. The calculations are based on the relativistic Breit–Pauli R -matrix approximation combined with the QB method [L. Quigley, K. Berrington, J. Pelan, Comput. Phys. Commun. 144 (1998) 225]. We report many previously unpublished energy values and autoionizing widths for these magnesium-like ions.

As an energy-intensive sector, industrial sector is the subject of carbon emissions. The changes of the sector profoundly affect the pattern of China's overall carbon productivity. Based on DEA method, this paper calculated the generalized carbon-productivity from 2004-2009 of China’s sub-industrial sectors, and then decomposed their changes. The results indicate: 1. the overall carbon-productivity of China’s sub-industrial sectors was low, and there is an internal imbalance; 2.the low carbon-productivity is mainly due to the low technical efficiency and scale inefficiency.

In our continuing search for camptothecin (CPT)-derived antitumor drugs, novel structurally diverse PEG-based 20(S)-CPT sulfonylamidine derivatives were designed, synthesized via a Cu-multicomponent reaction (MCR), and evaluated for cytotoxicity against four human tumor cell lines (A-549, MDA-MB-231, KB, and KBvin). All of the derivatives showed promising in vitro cytotoxic activity against the tested tumor cell lines, and were more potent than irinotecan. Significantly, these derivatives exhibited comparable cytotoxicity against KBvin, while irinotecan was less active against this cell line. With a concise efficient synthesis and potent cytotoxic profiles, especially significant activity towards KBvin, these compounds merit further development as a new generation of CPT-derived PEG-conjugated drug candidates.

Remanufacturing has become a superior option for product recovery management system. It mainly consists of three stages: retrieval, reproduction, and redistribution. So far, many different approaches have been followed in order to improve the efficiency of a remanufacturing process. However, as the complexity increases, the use of computational intelligence (CI) in those problems is becoming a unique tool of imperative value. In this paper, different CI methods, such as artificial neural network (ANN), ant colony optimization (ACO), biogeography-based optimization (BBO), cuckoo search (CS) and fuzzy logic (FL), are utilized to solve the problems involved in retrieval and reproduction stages for remanufacturing. The key issues in implementing the proposed approaches are discussed, and finally the applicability of the proposed methods are illustrated through different examples.

In this chapter, an emerging mathematics-based CI category called matheuristics is introduced. We first, in Sect. 28.1, describe the background knowledge regarding the metaheuritics. Then, the fundamentals and representative application of matheuristics are briefed in Sect. 28.2. Finally, Sect. 28.3 draws the conclusions of this chapter.

In this chapter, a set of cuckoo inspired (CS) optimization algorithms are introduced. We first, in Sect. 7.1, describe the general knowledge of cuckoos. Then, the fundamentals and performance of CS are introduced in Sect. 7.2. Next, the selected variants of CS are outlined in Sect. 7.3 which is followed by a presentation of representative CS application in Sect. 7.4. Right after this, Sect. 7.5 introduces an emerging algorithm, i.e., cuckoo optimization algorithm (COA), which also falls within this category. Finally, Sect. 7.6 draws the conclusions of this chapter.

In this chapter, we present a set of algorithms that are inspired by the different bacteria behavioural patterns, i.e., bacterial foraging algorithm (BFA), bacterial colony chemotaxis (BCC) algorithm, superbug algorithm (SuA), bacterial colony optimization (BCO) algorithm, and viral system (VS) algorithm. We first describe the general knowledge of bacteria foraging behaviour in Sect. 2.1. Then, the fundamentals and performances of BFA, BCC algorithm, SuA, BCO algorithm, and VS algorithm are introduced in Sects. 2.2 and 2.3, respectively. Finally, Sect. 2.4 summarises this chapter.

In this chapter, we present two frog inspired computational intelligence (CI) algorithms, namely, shuffled frog leaping algorithm (SFLA) and frog calling algorithm (FCA). We first provide a brief introduction in Sect. 10.1. Then, the fundamentals and performance of SFLA are introduced in Sect. 10.2. Next, Sect. 10.3 outlines some core working principles and preliminary experimental studies relative to FCA. Finally, Sect. 10.4 summarises this chapter.

In this chapter, we present a novel optimization approach named chemical-reaction optimization (CRO) algorithm. The main idea behind CRO is that a simulation of the molecules’ movements and their resultant chemical reactions. We first describe the general knowledge of the chemical-reaction in Sect. 25.1. Then, the fundamentals and performance of CRO are introduced in Sect. 25.2. Next, a selected variation of CRO is explained in Sect. 25.3. Right after this, Sect. 25.4 presents a representative CRO application. Finally, Sect. 25.5 summarises in this chapter.

In this chapter, the big bang–big crunch (BB–BC), a global optimization method inspired from one of the cosmological theories known as closed universe, is introduced. We first, in Sect. 18.1, describe the background knowledge regarding the big bang and big crunch. Then, Sect. 18.2 details the fundamentals of BB–BC, the selected variants of BB–BC, and the representative BB–BC application, respectively. Finally, Sect. 18.3 draws the conclusions of this chapter.

In this chapter, we present several fish algorithms that are inspired by some key features of the fish school/swarm, namely, artificial fish school algorithm (AFSA), fish school search (FSS), group escaping algorithm (GEA), and shark-search algorithm (SSA). We first provide a short introduction in Sect. 9.1. Then, the detailed descriptions regarding AFSA and FSS can be found in Sects. 9.2 and 9.3, respectively. Next, Sect. 9.4 briefs two emerging fish inspired algorithms, i.e., GEA and SSA. Finally, Sect. 9.5 summarises in this chapter

In this chapter, we present a new population-based method, called cat swarm optimization (CSO) algorithm, which imitates the natural behaviour of cats. We first describe the general knowledge of the behaviour of cats in Sect. 6.1. Then, the fundamentals and performance of CSO are introduced in Sect. 6.2. Next, some selected variations of CSO are explained in Sect. 6.3. Right after this, Sect. 6.4 presents a representative CSO application. Finally, Sect. 6.5 summarises this chapter.

In this chapter, we present three algorithms that are inspired by the flashing behaviour of luminous insects, i.e., firefly algorithm (FA), glowworm swarm optimization (GlSO) algorithm, and bioluminescent swarm optimization (BiSO) algorithm. We first describe the general knowledge of the luminous insects in Sect. 8.1. Then, the fundamentals, performances and selected applications of FA, GlSO algorithm and BiSO algorithm are introduced in Sects. 8.2, 8.3 and 8.4, respectively. Finally, Sect. 8.5 summarises this chapter.