State of AI-based monitoring in smart manufacturing and introduction to focused section

Over the past few decades, intelligentization, supported by artificial intelligence (AI) technologies, has become an important trend for industrial manufacturing, accelerating the development of smart manufacturing. In modern industries, standard AI has been endowed with additional attributes, yielding the so-called industrial artificial intelligence (IAI) that has become the technical core of smart manufacturing. AI-powered manufacturing brings remarkable improvements in many aspects of closed-loop production chains from manufacturing processes to end product logistics. In particular, IAI incorporating domain knowledge has benefited the area of production monitoring considerably. Advanced AI methods such as deep neural networks, adversarial training, and transfer learning have been widely used to support both diagnostics and predictive maintenance of the entire production process. It is generally believed that IAI is the critical technologies needed to drive the future evolution of industrial manufacturing. This article offers a comprehensive overview of AI-powered manufacturing and its applications in monitoring. More specifically, it summarizes the key technologies of IAI and discusses their typical application scenarios with respect to three major aspects of production monitoring: fault diagnosis, remaining useful life prediction, and quality inspection. In addition, the existing problems and future research directions of IAI are also discussed. This article further introduces the papers in this focused section on AI-based monitoring in smart manufacturing by weaving them into the overview, highlighting how they contribute to and extend the body of literature in this area

Additive-Free, Low-Temperature Crystallization of Stable α-FAPbI3 Perovskite

Formamidinium lead triiodide (FAPbI3) is attractive for photovoltaic devices due to its optimal bandgap at around 1.45 eV and improved thermal stability compared with methylammonium‐based perovskites. Crystallization of phase‐pure α‐FAPbI3 conventionally requires high‐temperature thermal annealing at 150 °C whilst the obtained α‐FAPbI3 is metastable at room temperature. Here, aerosol‐assisted crystallization (AAC) is reported, which converts yellow δ‐FAPbI3 into black α‐FAPbI3 at only 100 °C using precursor solutions containing only lead iodide and formamidinium iodide with no chemical additives. The obtained α‐FAPbI3 exhibits remarkably enhanced stability compared to the 150 °C annealed counterparts, in combination with improvements in film crystallinity and photoluminescence yield. Using X‐ray diffraction, X‐ray scattering, and density functional theory simulation, it is identified that relaxation of residual tensile strains, achieved through the lower annealing temperature and post‐crystallization crystal growth during AAC, is the key factor that facilitates the formation of phase‐stable α‐FAPbI3. This overcomes the strain‐induced lattice expansion that is known to cause the metastability of α‐FAPbI3. Accordingly, pure FAPbI3 p–i–n solar cells are reported, facilitated by the low‐temperature (≤100 °C) AAC processing, which demonstrates increases of both power conversion efficiency and operational stability compared to devices fabricated using 150 °C annealed films

Spinal Astrocytic Activation Is Involved in a Virally-Induced Rat Model of Neuropathic Pain

Postherpetic neuralgia (PHN), the most common complication of herpes zoster (HZ), plays a major role in decreased life quality of HZ patients. However, the neural mechanisms underlying PHN remain unclear. Here, using a PHN rat model at 2 weeks after varicella zoster virus infection, we found that spinal astrocytes were dramatically activated. The mechanical allodynia and spinal central sensitization were significantly attenuated by intrathecally injected L-α-aminoadipate (astrocytic specific inhibitor) whereas minocycline (microglial specific inhibitor) had no effect, which indicated that spinal astrocyte but not microglia contributed to the chronic pain in PHN rat. Further study was taken to investigate the molecular mechanism of astrocyte-incudced allodynia in PHN rat at post-infection 2 weeks. Results showed that nitric oxide (NO) produced by inducible nitric oxide synthase mediated the development of spinal astrocytic activation, and activated astrocytes dramatically increased interleukin-1β expression which induced N-methyl-D-aspartic acid receptor (NMDAR) phosphorylation in spinal dorsal horn neurons to strengthen pain transmission. Taken together, these results suggest that spinal activated astrocytes may be one of the most important factors in the pathophysiology of PHN and “NO-Astrocyte-Cytokine-NMDAR-Neuron” pathway may be the detailed neural mechanisms underlying PHN. Thus, inhibiting spinal astrocytic activation may represent a novel therapeutic strategy for clinical management of PHN

A Primer on Regression Methods for Decoding cis-Regulatory Logic

The rapidly emerging field of systems biology is helping us to understand the molecular determinants of phenotype on a genomic scale [1]. Cis-regulatory elements are major sequence-based determinants of biological processes in cells and tissues [2]. For instance, during transcriptional regulation, transcription factors (TFs) bind to very specific regions on the promoter DNA [2,3] and recruit the basal transcriptional machinery, which ultimately initiates mRNA transcription (Figure 1A). Learning cis-Regulatory Elements from Omics Data A vast amount of work over the past decade has shown that omics data can be used to learn cis-regulatory logic on a genome-wide scale [4-6]--in particular, by integrating sequence data with mRNA expression profiles. The most popular approach has been to identify over-represented motifs in promoters of genes that are coexpressed [4,7,8]. Though widely used, such an approach can be limiting for a variety of reasons. First, the combinatorial nature of gene regulation is difficult to explicitly model in this framework. Moreover, in many applications of this approach, expression data from multiple conditions are necessary to obtain reliable predictions. This can potentially limit the use of this method to only large data sets [9]. Although these methods can be adapted to analyze mRNA expression data from a pair of biological conditions, such comparisons are often confounded by the fact that primary and secondary response genes are clustered together--whereas only the primary response genes are expected to contain the functional motifs [10]. A set of approaches based on regression has been developed to overcome the above limitations [11-32]. These approaches have their foundations in certain biophysical aspects of gene regulation [26,33-35]. That is, the models are motivated by the expected transcriptional response of genes due to the binding of TFs to their promoters. While such methods have gathered popularity in the computational domain, they remain largely obscure to the broader biology community. The purpose of this tutorial is to bridge this gap. We will focus on transcriptional regulation to introduce the concepts. However, these techniques may be applied to other regulatory processes. We will consider only eukaryotes in this tutorial

Peregrine and saker falcon genome sequences provide insights into evolution of a predatory lifestyle

As top predators, falcons possess unique morphological, physiological and behavioral adaptations that allow them to be successful hunters: for example, the peregrine is renowned as the world's fastest animal. To examine the evolutionary basis of predatory adaptations, we sequenced the genomes of both the peregrine (Falco peregrinus) and saker falcon (Falco cherrug), and we present parallel, genome-wide evidence for evolutionary innovation and selection for a predatory lifestyle. The genomes, assembled using Illumina deep sequencing with greater than 100-fold coverage, are both approximately 1.2 Gb in length, with transcriptome-assisted prediction of approximately 16,200 genes for both species. Analysis of 8,424 orthologs in both falcons, chicken, zebra finch and turkey identified consistent evidence for genome-wide rapid evolution in these raptors. SNP-based inference showed contrasting recent demographic trajectories for the two falcons, and gene-based analysis highlighted falcon-specific evolutionary novelties for beak development and olfaction and specifically for homeostasis-related genes in the arid environment–adapted saker

Genetic dissection of the relationships between grain yield components by genome-wide association mapping in a collection of tetraploid wheats

Increasing grain yield potential in wheat has been a major target of most breeding programs. Genetic advance has been frequently hindered by negative correlations among yield components that have been often observed in segregant populations and germplasm collections. A tetraploid wheat collection was evaluated in seven environments and genotyped with a 90K SNP assay to identify major and stable quantitative trait loci (QTL) for grain yield per spike (GYS), kernel number per spike (KNS) and thousand-kernel weight (TKW), and to analyse the genetic relationships between the yield components at QTL level. The genome-wide association analysis detected eight, eleven and ten QTL for KNS, TKW and GYS, respectively, significant in at least three environments or two environments and the mean across environments. Most of the QTL for TKW and KNS were found located in different marker intervals, indicating that they are genetically controlled independently by each other. Out of eight KNS QTL, three were associated to significant increases of GYS, while the increased grain number of five additional QTL was completely or partially compensated by decreases in grain weight, thus producing no or reduced effects on GYS. Similarly, four consistent and five suggestive TKW QTL resulted in visible increase of GYS, while seven additional QTL were associated to reduced effects in grain number and no effects on GYS. Our results showed that QTL analysis for detecting TKW or KNS alleles useful for improving grain yield potential should consider the pleiotropic effects of the QTL or the association to other QTLs