Assembly Quality Research Articles

Structural deformation and clamping force monitoring of reconfigurable tooling motivated by strain data in aircraft assembly

Abstract The quality of aircraft assembly is mainly guaranteed by toolings which are vital to the geometrical accuracy and service performance of aviation products. In this research, a real-time monitoring system that determines the structural deformation and clamping force of reconfigurable toolings using strain data is developed to perceive the service state of the toolings. By laying fiber Bragg gratings on positioning beam and baseplate of the reconfigurable tooling, strain data of the tooling structures are gauged and transferred to curvatures. The beam and baseplate are modeled as one and two-dimensional objects respectively and shape reconstruction algorithms are established to obtain their deflection curve and surface using curvature information. Distribution of fibers is optimized to minimize the conversion error from strain to curvature. An estimation that reveals the mathematical relationship between the shape reconstruction error and measurement interval is implemented, and a mapping model from strains to clamping force of the beam is established. These algorithms are integrated into the self-developed monitoring software and undergo simulating and experimental tests. The maximum relative errors of deformation and force are 4.53% and 4.12% respectively in simulation, and 9.21% and 7.29% individually in experiment, which validates the efficiencies of the method. Tests of the monitoring system suggest that it can provide a timely and accurate sensing of the deformation and force of the tooling.

Automating the Illumina DNA library preparation kit for whole genome sequencing applications on the flowbot ONE liquid handler robot.

Whole-genome sequencing (WGS) is currently making its transition from research tool into routine (clinical) diagnostic practice. The workflow for WGS includes the highly labor-intensive library preparations (LP), one of the most critical steps in the WGS procedure. Here, we describe the automation of the LP on the flowbot ONE robot to minimize the risk of human error and reduce hands-on time (HOT). For this, the robot was equipped, programmed, and optimized to perform the Illumina DNA Prep automatically. Results obtained from 16 LP that were performed both manually and automatically showed comparable library DNA yields (median of 1.5-fold difference), similar assembly quality values, and 100% concordance on the final core genome multilocus sequence typing results. In addition, reproducibility of results was confirmed by re-processing eight of the 16 LPs using the automated workflow. With the automated workflow, the HOT was reduced to 25min compared to the 125min needed when performing eight LPs using the manual workflow. The turn-around time was 170 and 200min for the automated and manual workflow, respectively. In summary, the automated workflow on the flowbot ONE generates consistent results in terms of reliability and reproducibility, while significantly reducing HOT as compared to manual LP.

Improving the Principles of Identifying Critical Requirements for the Assembly of High-Precision Products

Introduction. The problem of improving the manufacturing of high-precision products is currently becoming a key one, since the requirements for them are constantly being tightened. Maintaining assembly quality and accuracy is an important aspect of manufacturing precision products. Standard approaches to this process do not always have sufficient versatility. Existing studies that aim to develop universal approaches, such as end-to-end production design or the application of parallel engineering principles, also have a number of shortcomings. These include the fact that the given approaches do not fully take into account information about the technology capabilities of a particular production when making design decisions, and do not consider the relationship between the manufacturing preparation of machining and mechanical assembly industries. That is why studies aimed at developing such universal approaches have high applicability. To solve these problems, the authors conceptually developed a set of formalized design procedures for a system of accounting requirements for the assembly of high-precision products in the design of machining processes. However, to effectively identify the numerous requirements for the assembly of high-precision products (output parameters) and select those that cannot be provided by the method of complete interchangeability (critical elements), additional research is needed. The research objective is to develop principles for identifying the output parameters of high-precision products and detecting critical elements. To achieve this goal, it is required to solve the following problems: to formulate principles for constructing generalized surface graphs of high-precision products; to develop standards for classifying output parameters and identifying critical ones.Materials and Methods. To conduct the research, a high-precision assembly unit was selected — “Stator Package 2”. The research was carried out under real conditions of the existing multiproduct manufacture. For this assembly, a generalized surface graph was constructed, including information about the nature and sequence of surfaces, requirements for the assembly, dimensional tolerances and tolerances of shape and location, with its subsequent analysis.Results. This paper presents the results of research on improving the enlarged block of design procedures for analyzing requirements for the assembly of high-precision products of the designed system. The paper established the relationship between the accuracy of dimensional tolerances and the tolerances of the shape and location of the surfaces of the product to which these dimensions belonged. Based on the relationship obtained, an order was determined for the unambiguous identification of critical elements.Discussion and Conclusion. The application of this technique makes it possible to increase the reliability of the source information obtained during the implementation of an enlarged block of design procedures, as well as the validity and efficiency of identifying rational manufacturing technologies at subsequent stages of the system implementation, while providing the specified quality, accuracy of products, and reducing the complexity and cost of their manufacture.

Analysis of Energy Consumption of Robotic Welding Stations

Analysis of the energy consumption of industrial robots during the implementation of a technological task can increase efficiency and minimize production costs, as well as extend the service life of robots, taking into account the rational use of energy sources. An important challenge in designing sustainable production processes is the selection of energy-saving technological equipment supporting the work of robots. Additionally, optimization of robot program codes, including parameters and robot movement paths, determines the energy efficiency of the robotic station. Welding is one of the most popular areas of process robotization due to its ability to increase production efficiency and improve the quality of part assembly compared to manual work. In these studies, an exemplary process of robotic welding of aluminum alloy parts was designed, analyzed, and optimized with an orientation towards sustainable development guidelines. This work also presents a review of current publications discussing the authors’ contribution to achieving energy efficiency in robotic welding processes. The purpose of this analysis is also to indicate directions for the development of future research work in order to minimize energy consumption in production processes with an appropriate level of product quality and increase production efficiency.

Assembly collapsing versus heterozygosity oversizing: detection of homokaryotic and heterokaryotic Laccaria trichodermophora strains by hybrid genome assembly.

Genome assembly and annotation using short-paired reads is challenging for eukaryotic organisms due to their large size, variable ploidy and large number of repetitive elements. However, the use of single-molecule long reads improves assembly quality (completeness and contiguity), but haplotype duplications still pose assembly challenges. To address the effect of read length on genome assembly quality, gene prediction and annotation, we compared genome assemblers and sequencing technologies with four strains of the ectomycorrhizal fungus Laccaria trichodermophora. By analysing the predicted repertoire of carbohydrate enzymes, we investigated the effects of assembly quality on functional inferences. Libraries were generated using three different sequencing platforms (Illumina Next-Seq, Mi-Seq and PacBio Sequel), and genomes were assembled using single and hybrid assemblies/libraries. Long reads or hybrid assemby resolved the collapsing of repeated regions, but the nuclear heterozygous versions remained unresolved. In dikaryotic fungi, each cell includes two nuclei and each nucleus has differences not only in allelic gene version but also in gene composition and synteny. These heterokaryotic cells produce fragmentation and size overestimation of the genome assembly of each nucleus. Hybrid assembly revealed a wider functional diversity of genomes. Here, several predicted oxidizing activities on glycosyl residues of oligosaccharides and several chitooligosaccharide acetylase activities would have passed unnoticed in short-read assemblies. Also, the size and fragmentation of the genome assembly, in combination with heterozygosity analysis, allowed us to distinguish homokaryotic and heterokaryotic strains isolated from L. trichodermophora fruit bodies.

AFCN: An attention‐directed feature‐fusion ConvNeXt network for low‐voltage apparatus assembly quality inspection

AbstractIn the production of low‐voltage apparatus, assembly quality inspection is of great relevance for ensuring the final quality of the entire product. With the continuous improvement of production efficiency and people's requirements for production quality, traditional manual inspection methods can no longer meet the quality inspection requirements. In this paper, an Attention‐guided Feature‐fusion ConvNeXt Network (AFCN) for the automated visual inspection is proposed. By embedding the attention mechanism of the Coordinate Attention block into the residual channel of the ConvNeXt block, the position‐aware information and features of the low‐voltage apparatus images can be effectively captured to locate the quality problems. Then, an improved attention feature fusion module is adopted to merge the output features at different stages, which introduces a 3D non‐parameter attention SimAM block and adapts output accordingly. Therefore, this model can capture the key information of the feature map in a coordinated way in terms of channel and position, fully integrating multiscale features and obtaining contour texture information and semantic information of the low‐voltage apparatus. Experiments show the proposed approach can effectively classify defective and normal products.

Digital twin-driven assembly accuracy prediction method for high performance precision assembly of complex products

The high performance precision assembly (HPPA) of complex products such as aerospace, aircraft and high-end machine tool has demanding requirements for assembly accuracy. Achieving the accurate prediction of assembly accuracy for these complex products before assembling is the premise of improving the assembly quality and performance, and also has always been a challenge. Existing assembly accuracy prediction methods focus on acquiring the assembly deviation based on CAD model and manufacturing errors of parts, but rarely involve the multidimensional error coupling of parts and the influencing factors in the assembly process, which inevitably cause a certain gap between the prediction result and the actual condition, affecting the reliability of the prediction result. To address the above problems, this paper presents a digital twin (DT)-driven assembly accuracy prediction method for the HPPA of complex products. Firstly, this paper introduces the methodology overview and proposes an overall framework for DT-driven assembly accuracy prediction. Secondly, three key enabling technologies realizing the DT-driven assembly accuracy prediction, including the construction of part digital twin model, the generation of DT-based assembly process model, and assembly deviation propagation and accuracy analysis are introduced in detail. Finally, an application implementation of a prototype system and a case study involving a simplified satellite structure panel assembly process are used to verify the effectiveness and feasibility of the proposed method.

High quality assemblies of four indigenous chicken genomes and related functional data resources

Many lines of evidence indicate that red jungle fowl (RJF) is the primary ancestor of domestic chickens. Although multiple versions of RJF (galgal2-galgal5 and GRCg6a) and commercial chickens (GRCg7b/w and Huxu) genomes have been assembled since 2004, no high-quality indigenous chicken genomes have been assembled, hampering the understanding of chicken domestication and evolution. To fill the gap, we sequenced the genomes of four indigenous chickens with distinct morphological traits in southwest China, using a combination of short, long and Hi-C reads. We assembled each genome (~1.0 Gb) into 42 chromosomes with chromosome N50 90.5–90.9 Mb, amongst the highest quality of chicken genome assemblies. To provide resources for gene annotation and functional analysis, we also sequenced transcriptomes of 10 tissues for each of the four chickens. Moreover, we corrected many mis-assemblies and assembled missing micro-chromosomes 29 and 34–39 for GRCg6a. Our assemblies, sequencing data and the correction of GRCg6a can be valuable resources for studying chicken domestication and evolution.

Development of Genome-Wide Intron Length Polymorphism (ILP) Markers in Tea Plant (Camellia sinensis) and Related Applications for Genetics Research.

The market value of tea is largely dependent on the tea species and cultivar. Therefore, it is important to develop efficient molecular markers covering the entire tea genome that can be used for the identification of tea varieties, marker-assisted breeding, and mapping important quantitative trait loci for beneficial traits. In this study, genome-wide molecular markers based on intron length polymorphism (ILP) were developed for tea trees. A total of 479, 1393, and 1342 tea ILP markers were identified using the PCR method in silico from the 'Shuchazao' scaffold genome, the chromosome-level genome of 'Longjing 43', and the ancient tea DASZ chromosome-level genome, respectively. A total of 230 tea ILP markers were used to amplify six tea tree species. Among these, 213 pairs of primers successfully characterize products in all six species, with 112 primer pairs exhibiting polymorphism. The polymorphism rate of primer pairs increased with the improvement in reference genome assembly quality level. The cross-species transferability analysis of 35 primer pairs of tea ILP markers showed an average amplification rate of 85.17% through 11 species in 6 families, with high transferability in Camellia reticulata and tobacco. We also used 40 pairs of tea ILP primers to evaluate the genetic diversity and population structure of C. tetracocca with 176 plants from Puan County, Guizhou Province, China. These genome-wide markers will be a valuable resource for genetic diversity analysis, marker-assisted breeding, and variety identification in tea, providing important information for the tea industry.

An intelligent inspection method for body-in-white weld quality based on vibration excitation response signals

To enhance the assembly quality in Body-in-White (BIW) assembly, this paper proposes an intelligent detection method for the nugget quality of Resistance Spot Weld (RSW) based on weld joint vibration excitation response signals. The method proposes a novel deep learning model, the Local-Global Hierarchical Graph Neural Network (LGHGNN). LGHGNN can automatically construct graph structures and, by introducing a newly designed upgrade pooling operation, extends the traditional flat structure of graph networks into a hierarchical structure within three-dimensional space. Therefore, LGHGNN achieves layered interaction of local-global information, enabling the model to focus on local details while gaining a broader learning perspective. Additionally, this paper proposes a strategy for multi-label unsupervised anomaly detection that involves layered interaction and collaborative decision-making for local and global graphs. The effectiveness of LGHGNN is demonstrated through its application in the BIW right front door assembly, achieving a remarkable 97.5% average accuracy in multi-region parallel anomaly detection.

The Complex Landscape of Structural Divergence Between the Drosophila pseudoobscura and D. persimilis Genomes.

Structural genomic variants are key drivers of phenotypic evolution. They can span hundreds to millions of base pairs and can thus affect large numbers of genetic elements. Although structural variation is quite common within and between species, its characterization depends upon the quality of genome assemblies and the proportion of repetitive elements. Using new high-quality genome assemblies, we report a complex and previously hidden landscape of structural divergence between the genomes of Drosophila persimilis and D. pseudoobscura, two classic species in speciation research, and study the relationships among structural variants, transposable elements, and gene expression divergence. The new assemblies confirm the already known fixed inversion differences between these species. Consistent with previous studies showing higher levels of nucleotide divergence between fixed inversions relative to collinear regions of the genome, we also find a significant overrepresentation of INDELs inside the inversions. We find that transposable elements accumulate in regions with low levels of recombination, and spatial correlation analyses reveal a strong association between transposable elements and structural variants. We also report a strong association between differentially expressed (DE) genes and structural variants and an overrepresentation of DE genes inside the fixed chromosomal inversions that separate this species pair. Interestingly, species-specific structural variants are overrepresented in DE genes involved in neural development, spermatogenesis, and oocyte-to-embryo transition. Overall, our results highlight the association of transposable elements with structural variants and their importance in driving evolutionary divergence.

Comparisons between Assembly Technologies by Welding and Brazing of Aluminium Alloys from the Sight of Assembly Quality

Aluminum and its alloys are finding use in more and more industrial fields. Since the early 1970s, when many companies in the automotive, pharmaceutical and aeronautical industries replaced the classic components of iron alloys with those of aluminum alloys, they faced various difficulties in the non-removable assembly of designed components.The first assembly technologies used were welding, followed in the 1980s by brazing. Due to its economic advantages, in many cases brazing technologies are preferred by manufacturers. The present paper aims to make a comparative study, based on experimental data, between the non-removable assembly technology by welding and brazing for aluminum alloys of group 6000. Due to the high costs of materials and labor, it is preferable to use relevant non-destructive control methods that can indicate any discontinuities that may occur in welded or brazed structures. These defects can provide valuable information and may indicate either non-compliance with the required technological conditions or the wrong choice of assembly method. Non-destructive testing methods that can highlight interior defects, such as radiographic control with penetrating radiation and ultrasound will be used, as well as less expensive equipment and labor methods that may highlight surface defects or which communicates with the surface such as the method of control with penetrating liquids. Thus, the joint areas of the base and filler materials will be analyzed, as well as the areas in the immediate vicinity that have been thermally influenced. The experimental results will be able to lead to pertinent conclusions in the way of choosing the non-removable assembly technology, results that can be easily generalized in the industrial fields. Thus, each company will be able to choose according to the conditions imposed by the product specifications a certain technology that will satisfy its requirements.

The effect of tightening torque on the strength of composite-metal hybrid joints

In the manufacturing and assembly process of aircraft composite-metal hybrid structures, tightening torque is one of the important factors that control assembly quality and affect connection strength. This paper experimentally studies the mechanical properties of composite-metal single-bolt single-lap joints under different tightening torques, reveals the impact mechanism of different tightening torques on the manufacturing and assembly of composite-metal hybrid structures, and also establishes a finite element analysis model of hybrid connection structure considering the influence of drilling and assembly processes. The results show that the ultimate load of the composite-metal connection structure increased with the increase of tightening torque. When the tightening torques were 3 N·m, 6 N·m, and 9 N·m, the ultimate failure loads of the composite-metal hybrid joints were 12.9 kN, 13.8 kN, and 14.6 kN respectively.

Deep learning-based augmented reality work instruction assistance system for complex manual assembly

The manual assembly process of complex products is lengthy, the assembly requirements are difficult to recall, and the assembly quality requirements are high. The separation of the operation guidance information from the real physical object in the traditional paper manual easily distracts the operator, increasing their cognitive burden. To address this issue, we integrate augmented reality (AR) and artificial intelligence (AI) technologies for manual assembly assistance. A novel encoding and decoding convolutional neural network (CNN) is built to realize accurate AR registration under mark-less assembly environment. An assembly quality inspection method integrating a neural network and virtual model matching was proposed for AR systems. The specific process of the proposed method includes two stages: offline and online. In the first stage, a monocular RGB image dataset was built for assembling object keypoints and assembly object detection. CNN models were designed and trained for deployment in an AR-assisted assembly system. Second, the deployed CNN models were used to perform AR registration and assembly quality inspection. Experimental results demonstrate that the proposed method can accurately present AR work instruction guidance and assembly quality inspection for manual assembly. The proposed AR registration method can achieve a frame rate of 25FPS, which satisfies the timeliness requirements of the AR system. The accuracy rate of the proposed assembly quality inspection method for the MONA component assembly reached 92.5%.

Research on intelligent auxiliary assembly technology based on deep learning

Background Auxiliary assembly refers to guiding and prompting the assembly process to help operators complete complex assembly operations. Due to the complex structure of products, the similar shape of parts and human factors, the misassembly and missing assembly of parts still occur in the process of product assembly, so it is of great significance to detect the assembly correctness of complex products. Methods Aiming at the problem that manual inspection is inefficient and depends heavily on the level of inspectors in the process of complex product assembly inspection, this paper proposes an assembly correctness detection method based on deep learning. Through the three steps of view transformation, semantic segmentation and template matching, the automatic judgment of assembly errors such as wrong assembly, missing assembly and redundancy is realized, and the method is verified by the computer motherboard. Results Taking the computer motherboard as the verification object to test the correctness of assembly, the experimental re sults show that the perspective adjustment of the image after homography transformation is very obvious. The evaluation index of the semantic segmentation network detection object is calculated, and each accuracy meets the requirements of assembly correctness detection. A visualization module is also used to visually display the results of assembly correctness detection based on template matching. Conclusions The assembly correctness detection method can provide a guarantee for the manual assembly process and reduce the error rate of assembly. The machine vision detection technology can be used for automatic detection of assembly quality to improve the efficiency and automation level of detection.

MAC-ErrorReads: machine learning-assisted classifier for filtering erroneous NGS reads.

The rapid advancement of next-generation sequencing (NGS) machines in terms of speed and affordability has led to the generation of a massive amount of biological data at the expense of data quality as errors become more prevalent. This introduces the need to utilize different approaches to detect and filtrate errors, and data quality assurance is moved from the hardware space to the software preprocessing stages. We introduce MAC-ErrorReads, a novel Machine learning-Assisted Classifier designed for filtering Erroneous NGS Reads. MAC-ErrorReads transforms the erroneous NGS read filtration process into a robust binary classification task, employing five supervised machine learning algorithms. These models are trained on features extracted through the computation of Term Frequency-Inverse Document Frequency (TF_IDF) values from various datasets such as E. coli, GAGE S. aureus, H. Chr14, Arabidopsis thaliana Chr1 and Metriaclima zebra. Notably, Naive Bayes demonstrated robust performance across various datasets, displaying high accuracy, precision, recall, F1-score, MCC, and ROC values. The MAC-ErrorReads NB model accurately classified S. aureus reads, surpassing most error correction tools with a 38.69% alignment rate. For H. Chr14, tools like Lighter, Karect, CARE, Pollux, and MAC-ErrorReads showed rates above 99%. BFC and RECKONER exceeded 98%, while Fiona had 95.78%. For the Arabidopsis thaliana Chr1, Pollux, Karect, RECKONER, and MAC-ErrorReads demonstrated good alignment rates of 92.62%, 91.80%, 91.78%, and 90.87%, respectively. For the Metriaclima zebra, Pollux achieved a high alignment rate of 91.23%, despite having the lowest number of mapped reads. MAC-ErrorReads, Karect, and RECKONER demonstrated good alignment rates of 83.76%, 83.71%, and 83.67%, respectively, while also producing reasonable numbers of mapped reads to the reference genome. This study demonstrates that machine learning approaches for filtering NGS reads effectively identify and retain the most accurate reads, significantly enhancing assembly quality and genomic coverage. The integration of genomics and artificial intelligence through machine learning algorithms holds promise for enhancing NGS data quality, advancing downstream data analysis accuracy, and opening new opportunities in genetics, genomics, and personalized medicine research.

First report of Pectobacterium aroidearum causing soft rot on leaf mustard in China.

Leaf mustard (Brassica juncea [L.] Czern. et Coss.) is widely planted in China as an important leaf vegetable. In March 2022, water-soaked and mushy rot symptoms were observed on leaf mustard plants in the field of Zhaotong (27.85°N; 105.05°E), Yunnan, China. The incidence of symptomatic leaf mustard was approximately 10%. The isolation of the causal agent followed the method of Peng et al. (2023). Briefly, infected tissues from four diseased plants were mixed and teased apart, and homogenized by vortex shaking. The bacterial suspension was diluted and spread on nutrient agar (NA). About 10 single colonies exhibiting different colony morphologies were picked and purified separately by successive streaking. A pinprick method was used for pathogenicity tests with an inoculum concentration of 108 CFU/ml (Singh et al. 2013). Among 10 isolates, only strain YKX exhibited soft rot symptoms on detached mustard leaves. In addition, ten two-month-old leaf mustard plants grown in the greenhouse were used for in vivo pathogenicity tests. Briefly, sterilized pins were dipped in the bacterial suspension, and then leaf mustard petioles were pricked with these pins. After inoculation, each plant was kept in a plastic bag for 12 hours to maintain high humidity. As expected, strain YKX caused obvious rot symptoms on eight plants at 1-2 days post-inoculation while the control group including two plants treated with sterile water showed no symptoms. The colonies of strain YKX on NA were white, roughly circular, and convex. For a preliminary identification, total DNA was extracted and used as the template in PCR amplification of 16S rDNA with the universal PCR primer pair 27F/1492R (Weisburg et al. 1991). The quality-filtered DNA sequence (871 bp) showed 100% query coverage and 99.47% identity to the 16S rDNA sequences of type strain Pectobacterium aroidearum SCRI 109T (GenBank: NR_159926) found in the NCBI rRNA/ITS database. Whole-genome sequencing of strain YKX was then performed using the Illumina and Nanopore sequencing platforms by Tsingke Biotechnology Co., Ltd. (Beijing, China). A single contig (GenBank: CP129239) with a length of approximately 4.9 Mb was obtained by de novo hybrid assembly using Unicycler v0.5.0 (Wick et al. 2017). The quality of the genomic data was evaluated by BUSCO v5.4.7 (Manni et al. 2021) against the gammaproteobacteria_odb10 dataset. A BUSCO complete score of 99.5% indicated high assembly quality. The genome sequence of strain YKX was uploaded to the Type Strain Genome Server for a genome-based taxonomic analysis (Meier-Kolthoff et al. 2022). The distance-based phylogeny showed that strain YKX and P. aroidearum L6 (GenBank: CP065044) and P. aroidearum PC1 (GenBank: NC_012917) form a clade. When comparing strain YKX with L6 or PC1, the digital DNA-DNA hybridization value (83.5-83.8%) was above the species delineation threshold (70% for DDH), clearly indicating that strain YKX should be classified as P. aroidearum. Additionally, P. aroidearum was reisolated from inoculated leaves and identified based on morphological similarities and 16S rDNA sequencing, thus fulfilling Koch's postulates. It is worth noting that a previous study reported occurrences of soft rot disease on leaf mustard attributed to Rhizopus microsporus var. chinensis (Wang et al. 2020). To our knowledge, this is the first report of P. aroidearum causing soft rot on leaf mustard in China, which expands the known host range of this pathogen and benefits the control of this disease.

Diesel engine quality abnormal patterns recognition based on feature fusion and adaptive decision fusion

The current assembly process of marine diesel engines is low in intelligence and the control chart pattern classifier with unstable performance, which makes it difficult to control and identify the quality control chart pattern. This paper proposes a new assembly quality control diagram recognition method based on an adaptive decision model to address these problems. Through characteristics and changes of the diesel engine assembly process analyses, the triangular norm is used to fuse the extracted shape features and statistical features to reduce the influence of data fluctuation and imbalance on pattern recognition. An adaptive decision fusion model of the assembly process is established by defining multiple weights with considering the complexity and uncontrollability of the diesel engine assembly process. Based on these, the fusion coefficients within the adaptive decision model are optimized by the Ant Lion Optimization algorithm (ALO) to improve the decision efficiency and classification precision. To validate the proposed model, diesel engine exhaust pressure is selected as a case for abnormal pattern recognition, and the ability of the model is discussed in terms of recognition accuracy and stability.

Three Rounds of Read Correction Significantly Improve Eukaryotic Protein Detection in ONT Reads.

Eukaryotes' whole-genome sequencing is crucial for species identification, gene detection, and protein annotation. Oxford Nanopore Technology (ONT) is an affordable and rapid platform for sequencing eukaryotes; however, the relatively higher error rates require computational and bioinformatic efforts to produce more accurate genome assemblies. Here, we evaluated the effect of read correction tools on eukaryote genome completeness, gene detection and protein annotation. Reads generated by ONT of four eukaryotes, C. albicans, C. gattii, S. cerevisiae, and P. falciparum, were assembled using minimap2 and underwent three rounds of read correction using flye, medaka and racon. The generates consensus FASTA files were compared for total length (bp), genome completeness, gene detection, and protein-annotation by QUAST, BUSCO, BRAKER1 and InterProScan, respectively. Genome completeness was dependent on the assembly method rather than on the read correction tool; however, medaka performed better than flye and racon. Racon significantly performed better than flye and medaka in gene detection, while both racon and medaka significantly performed better than flye in protein-annotation. We show that three rounds of read correction significantly affect gene detection and protein annotation, which are dependent on assembly quality in preference to assembly completeness.

Performance of a Double-Filter-Medium Tandem Membrane Bioreactor with Low Operating Costs in Domestic Wastewater Treatment

To reduce the operating costs of conventional membrane bioreactors (MBRs) and improve the stability and quality of the dynamic membrane bioreactor (DMBR) effluent, a homemade inexpensive filter cloth assembly was connected to an up-flow ultra-lightweight-medium filter (UUF) in lieu of expensive membrane modules to form a double-filter-medium tandem (DT)-MBR. DT-MBR was used to treat domestic wastewater, and its removal efficiencies for chemical oxygen demand, ammonia nitrogen, total nitrogen, and total phosphorus were similar to those of aerobic MBR, with average removal rates of 91.1%, 98.4%, 15.1%, and 50.7%, respectively. The average suspended solid (SS) of the final effluent was 5.6 mg∙L−1, and the filter cloth assembly played a leading role in SS removal, with an average removal rate of 86.0% and a relatively stable removal effect with little impact via backwashing. The activated sludge zeta potential, flocculation and sedimentation properties, particle size distribution, microbial compositions, extracellular polymeric substances (EPS), and filtration resistance of the cake layer were analyzed; it was found that the cake layer, which can also be called the dynamic membrane (DM), had an excellent filtration performance. However, the DM theory could not reasonably explain why the effluent quality of the filter cloth assembly maintained good stability even after backwashing. The real reason must be related to the sieving of cloth pores. Therefore, the concept of an in situ autogenous static membrane (ISASM) was proposed. With low operating costs and good and stable effluent quality, DT-MBR is a desirable alternative to the traditional MBR.