Luminescent films have received great interest for chemo-/bio-sensing applications due to their distinct advantages over solution-based probes, such as good stability and portability, tunable shape ...and size, non-invasion, real-time detection, extensive suitability in gas/vapor sensing, and recycling. On the other hand, they can achieve selective and sensitive detection of chemical/biological species using special luminophores with a recognition moiety or the assembly of common luminophores and functional materials. Nowadays, the extensively used assembly techniques include drop-casting/spin-coating, Langmuir-Blodgett (LB), self-assembled monolayers (SAMs), layer-by-layer (LBL), and electrospinning. Therefore, this review summarizes the recent advances in luminescent films with these assembly techniques and their applications in chemo-/bio-sensing. We mainly focused on the discussion of the relationship between the sensing properties of the films and their architecture. Furthermore, we discussed some critical challenges existing in this field and possible solutions that have been or are being developed to overcome these challenges.
Due to its practicality, hybrid flowshop scheduling problem (HFSP) with productivity objective has been extensively explored. However, studies on HFSP considering green objective in distributed ...production environment are quite limited. Moreover, the current manufacturing mode is gradually evolving toward distributed co-production mode. Thus, this paper investigated a distributed hybrid flowshop scheduling problem (DHFSP) with objectives of minimization the makespan and total energy consumption (TEC). To address this problem, this paper designed a Pareto-based multi-objective hybrid iterated greedy algorithm (MOHIG) by integrating the merits of genetic operator and iterated greedy heuristic. In this MOHIG, firstly, one cooperative initialization strategy is proposed to boost initial solutions’ quality based on the previous experience and rules. Secondly, one knowledge-based multi-objective local search method is invented to enhance the exploitation capability according to characteristics of problem. Thirdly, an energy-saving technique is developed to decrease the idle energy consumption of machine tools. Furthermore, the effectiveness of each improvement component of MOHIG is assessed by three common indicators. Finally, the proposed MOHIG algorithm is compared with other multi-objective optimization algorithms, including SPEA2, MOEA/D, and NSGAII. Experimental results indicate that the proposed MOHIG outperforms its compared algorithms in solving this problem. In addition, this research can better guide practical production in some certain environments.
•Considering green scheduling in distributed hybrid flowshop environment.•Designing a new energy-saving strategy into this problem.•Proposing a Pareto-based multi-objective hybrid iterated greedy algorithm (MOHIG).•Evaluating performance of the proposed MOHIG by conducting comparison experiments.
Objectives
Coronavirus disease 2019 (COVID‐19) is rapidly spreading worldwide. Lianhua Qingwen capsule (LQC) has shown therapeutic effects in patients with COVID‐19. This study is aimed to discover ...its molecular mechanism and provide potential drug targets.
Materials and Methods
An LQC target and COVID‐19–related gene set was established using the Traditional Chinese Medicine Systems Pharmacology database and seven disease‐gene databases. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and protein‐protein interaction (PPI) network were performed to discover the potential mechanism. Molecular docking was performed to visualize the patterns of interactions between the effective molecule and targeted protein.
Results
A gene set of 65 genes was generated. We then constructed a compound‐target network that contained 234 nodes of active compounds and 916 edges of compound‐target pairs. The GO and KEGG indicated that LQC can act by regulating immune response, apoptosis and virus infection. PPI network and subnetworks identified nine hub genes. The molecular docking was conducted on the most significant gene Akt1, which is involved in lung injury, lung fibrogenesis and virus infection. Six active compounds of LQC can enter the active pocket of Akt1, namely beta‐carotene, kaempferol, luteolin, naringenin, quercetin and wogonin, thereby exerting potential therapeutic effects in COVID‐19.
Conclusions
The network pharmacological strategy integrates molecular docking to unravel the molecular mechanism of LQC. Akt1 is a promising drug target to reduce tissue damage and help eliminate virus infection.
A Lianhua Qingwen capsule (LQC) target and COVID‐19 related gene set is established to construct compound‐target pharmacology network. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis indicate the regulating effect of LQC on apoptosis, antivirus, immune defense, and inflammatory response. Protein‐protein interaction network and critical subnetworks are constructed to identify hub gene target. The most significant gene, Akt 1, is selected to perform molecular docking with active compounds of LQC. Six compounds are finally recognized as potential anti‐COVID‐19 agents.
Methylation of cytosine in CpG dinucleotides and histone lysine and arginine residues is a chromatin modification that critically contributes to the regulation of genome integrity, replication, and ...accessibility. A strong correlation exists between the genome‐wide distribution of DNA and histone methylation, suggesting an intimate relationship between these epigenetic marks. Indeed, accumulating literature reveals complex mechanisms underlying the molecular crosstalk between DNA and histone methylation. These in vitro and in vivo discoveries are further supported by the finding that genes encoding DNA‐ and histone‐modifying enzymes are often mutated in overlapping human diseases. Here, we summarize recent advances in understanding how DNA and histone methylation cooperate to maintain the cellular epigenomic landscape. We will also discuss the potential implication of these insights for understanding the etiology of, and developing biomarkers and therapies for, human congenital disorders and cancers that are driven by chromatin abnormalities.
Methylation of cytosines and histones regulates genome integrity, replication and chromatin accessibility. This review summarizes how DNA and histone methylation cooperate to maintain cellular epigenomic landscapes and provides links to congenital disorders and cancers.
In terms of machine learning-based power system dynamic stability assessment, it is feasible to collect learning data from massive synchrophasor measurements in practice. However, the fact that ...instability events rarely occur would lead to a challenging class imbalance problem. Besides, short-term feature extraction from scarce instability seems extremely difficult for conventional learning machines. Faced with such a dilemma, this paper develops a systematic imbalance learning machine for online short-term voltage stability assessment. A powerful time series shapelet (discriminative subsequence) classification method is embedded into the machine for sequential transient feature mining. A forecasting-based nonlinear synthetic minority oversampling technique is proposed to mitigate the distortion of class distribution. Cost-sensitive learning is employed to intensify bias toward those scarce yet valuable unstable cases. Furthermore, an incremental learning strategy is put forward for online monitoring, contributing to adaptability and reliability enhancement along with time. Simulation results on the Nordic test system illustrate the high performance of the proposed learning machine and of the assessment scheme.
Driven primarily by cloud service and data-center applications, short-reach optical communication has become a key market segment and growing research area in recent years. Short-reach systems are ...characterized by direct detection-based receiver configurations and other low-cost and small form factor components that induce transmission impairments unforeseen in their coherent counterparts. Innovative signaling and digital signal processing (DSP) play a pivotal role in enabling these components to realize their ultimate potentials and meet data rate requirements in cost-effective manners. This paper presents an overview of recent DSP developments for short-reach communications systems and discusses future trends.
Machine learning (ML) has disrupted a wide range of science and engineering disciplines in recent years. ML applications in optical communications and networking are also gaining more attention, ...particularly in the areas of nonlinear transmission systems, optical performance monitoring, and cross-layer network optimizations for software-defined networks. However, the extent to which ML techniques can benefit optical communications and networking is not clear and this is partly due to an insufficient understanding of the nature of ML concepts. This paper aims to describe the mathematical foundations of basic ML techniques from communication theory and signal processing perspectives, which in turn will shed light on the types of problems in optical communications and networking that naturally warrant ML use. This will be followed by an overview of ongoing ML research in optical communications and networking with a focus on physical layer issues.
In a multiple-input multiple-output (MIMO) system, the availability of channel state information (CSI) at the transmitter is essential for performance improvement. Recent convolutional neural network ...(NN)-based techniques show competitive ability in realizing CSI compression and feedback. By introducing a new NN architecture, we enhance the accuracy of quantized CSI feedback in MIMO communications. The proposed NN architecture invokes a module named long short-term memory that admits the NN to benefit from exploiting temporal and frequency correlations of wireless channels. Compromising performance with complexity, we further modify the NN architecture with a significantly reduced number of parameters to be trained. Finally, experiments show that the proposed NN architectures achieve better performance in terms of both CSI compression and recovery accuracy.
Permutation flow shop scheduling problems (PFSPs) have been extensively studied because of its broad industrial applications. However, setup and transportation time are usually ignored in most ...research, which causes a huge gap between the theoretical research and practical application. Meanwhile, energy saving has attracted growing attention due to the advent of sustainable manufacturing. Thus, we investigate an energy-efficient PFSP with sequence-dependent setup and controllable transportation time from a real-world manufacturing enterprise. First of all, a novel multi-objective mathematical model considering both makespan and energy consumption is formulated based on a comprehensive investigation. Then, a hybrid multi-objective backtracking search algorithm (HMOBSA) is proposed to solve this problem. Furthermore, a new energy saving scenario is developed to simultaneously ensure the service span of machines and energy saving. Finally, to evaluate the effectiveness of the proposed HMOBSA and energy saving scenario, we compare our proposal with other two famous multi-objective algorithms including NSGA-II and MOEA/D by conducting a real-world case study. The experimental results indicate that the proposed HMOBSA is superior to NSGA-II and MOEA/D for this case. Additionally, the proposed energy saving scenario also outperforms its competitors.
•An energy-efficient scheduling with controllable transportation times is modeled.•A new multiobjective backtracking search algorithm is proposed for this model.•An effective energy saving strategy is proposed.•The HMOBSA outperforms other well-known MOEAs on solving the studied problem.
PDF
