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This article uses acoustic emission (AE) analysis to diagnose an industrial-scale and slow-speed wind turbine blade bearing. The main challenge for AE analysis is that the fault signals are mingled with heavy noise. As a result, the objective of this article is to filter the raw AE signals and extract weak fault signals. To achieve this goal, a general linear and nonlinear auto-regressive (GLNAR) model is first developed to exploit the nonlinear characteristics of the AE signals. Then, the sparse augmented Lagrangian (SAL) algorithm is applied to learn the built GLNAR model and filter the raw AE signals. The characteristics of SAL is that it is a novel sparse representation technique which can convert the original filtering problem into a number of suboptimization problems, and these subproblems can be solved separately. Finally, as the blade bearing rotates at fluctuating speeds, the filtered signals are resampled so that the bearing fault type can be diagnosed in the order domain. The proposed diagnostic framework was validated in several experiments under time-varying low-speed and heavy-blade-load conditions. The results indicate that our proposed methods are effective and accurate. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) prediction has been increasingly considered in many industrial fields for the reliability and safety of their systems. As a data analysis tool of deep learning, deep convolutional neural network (CNN) shows great potential for RUL prediction. This paper proposes an intelligent RUL prediction method based on a double-CNN model architecture. Given the powerful feature extraction capability of CNN, the proposed method is fed with original vibration signals with no need to resort to any feature extractor, which can also retain the useful information in maximum. The prediction includes two stages: first, incipient fault point is identified by the first CNN model and a proposed “3/5” principle; then, the second CNN model is constructed for RUL prediction. In practice, RULs of identical components are different from each other, which poses a major challenge in RUL prediction. To overcome this problem, an intermediate reliability variable is first calculated in this paper, instead of directly predicting the RUL value. Then, a mapping algorithm is proposed to map reliability to RUL. To demonstrate the effectiveness of the proposed method, data of four tests of bearing degradation are utilized for RUL prediction. Compared with state-of-the-art methods, the proposed method shows higher prediction accuracy and robustness. The prediction results and evaluation indexes demonstrated the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Fault diagnosis under time-varying operational conditions is always the challenge in industrial systems. The chirplet transform (CT) provides an effective first-order approximation approach, but the slow operation speed and the drawback for high-order signal analysis limit its uses. Focusing on this problem, a fast nonlinear chirplet dictionary-based sparse decomposition (FNC-SD) method for nonlinear signal analysis is proposed. By replacing the time–frequency inclination parameter in the CT with a frequency bending parameter, the proposed method can track the nonstationary signals by arbitrary order polynomial law with time by an additional degree. Considering that too many parameters will slow down the operation speed, a frequency bending operator estimation algorithm is also proposed in this paper based on the calculated rotating frequency. Therefore, a laser vibrometer is needed in the experiment to collect the impulse signals, which can represent rotating speed signals. Then, the FNC-SD method is used to construct a redundant dictionary for signal sparse time–frequency representation. The high similarity between signals and atoms can make the method prefer to choose the right atoms, instead of being influenced by noise. Therefore, the proposed method also shows a strong noise robustness. Two simulation experiments are constructed to verify the performance of the proposed method. Finally, the FNC-SD method is used to diagnose a faulty motor with broken bar. The analyzed results and comparisons with respect to the state-of-the-art methods are illustrated in detail, as well as the convergence accuracy and speed, which highlight the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

As wind power attracts increasing attention and wind turbines (WTs) capacity expands, fault diagnosis of WT is playing a more and more important role in improving reliability, minimizing down time, reducing maintenance costs, and providing reliable power generation. In this paper, a novel sparse time-frequency representation (STFR) method is proposed to increase the diagnostic precision of incipient faults. The proposed method can be applied once the condition is detected as abnormal according to the VDI3834 vibration threshold standard in WT fault diagnosis systems. The proposed method is a novel signal representation method based on the sparse representation theory and Wigner–Ville distribution (WVD), which can overcome the limitations of traditional basis functions expansion and time-frequency analysis methods. In this method, a union of redundant dictionary (URD) is constructed on the basis of the underlying prior information of the oscillate characteristics with multicomponent coupling effect and different morphological waveforms. Therefore, the vibration signal can be sparsely represented over the URD. Then, the sparse coefficients and corresponding atoms can be obtained by solving the basis pursuit denoising problem via alternating direction method of multipliers. Based on the combination of the WVD of each atom and corresponding sparse coefficient, the time-frequency distribution of the vibration signal can be obtained. To verify the effectiveness of the STFR method, a simulation and two field tests in the wind farm are performed. The comparison results with state-of-the-art methods illustrate the superiority and robustness of the proposed method in the engineering applications. [ABSTRACT FROM AUTHOR]

Urban flooding and waterlogging are becoming increasingly serious due to rapid urbanization and climate change. The stormwater management philosophy of low-impact development (LID) has been applied in urban construction to alleviate these problems. The selection and placement of LID designs are the most important tasks. In this study, LID experiments were performed to calibrate the Storm Water Management Model (SWMM). Then, a multi-objective optimization model, which adopted the minimum surface runoff coefficient, surcharge time, and investment cost as objectives, was established by coupling the SWMM and non-dominated sorting genetic algorithm-II (NSGA-II). Hydrological simulations were performed with the SWMM, and optimal calculations were conducted with NSGA-II. Real-coded optimal variables containing detailed size and location information of multiple LID measures were generated, and a decision space for LID design selection was obtained. The optimization designs reduced the surface runoff coefficient from 0.7 to approximately 0.5, the conduit surcharge duration was reduced from 1.62 h to 0.04–0.47 h, and the total investment cost only ranged from 395,000–872,000 ¥. Thus, the optimization model could achieve synchronous optimization of all objectives. This study could provide valuable information for LID design with the aim of urban flooding and waterlogging control. [Display omitted] • Calibrating SWMM parameters based on LID experiments in the area without measured data. • The surcharge status of drainage systems is taken into account in LID optimization. • The structure of optimal variables is suitable for optimization in small-scale communities. [ABSTRACT FROM AUTHOR]

[Display omitted] • The maximum removal rate of residual lincomycin in LMRs was up to 98.38 % at 200 °C. • Protein and lignin/CRAMs compounds displayed the highest reactivity during AHT. • Humic acids were produced via a novel "Radicals-based polymerization" pathway. • Fatty acid and indole derivative were regarded as plant-growth-promoting compounds. Fermentation-based antibiotic production results in the generation of nutrient-rich antibiotic mycelial residues with significant recycling potential. However, the high concentration of residual antibiotics restricts their application in high-value contexts. This study employs an alkali-thermal humification treatment (AHT) to safely manage lincomycin mycelial residues (LMRs) and assesses the feasibility of the resulting LMRs liquid as organic fertilizer. The removal efficiency for lincomycin attains 98.38 % at 200 °C under AHT. Density functional theory calculations combined with liquid chromatography tandem mass spectrometry (LC-MS/MS) reveal that hydrothermal degradation of lincomycin occurs through processes such as hydrolyzation, N-demethylation, hydroxylation, and desulfurization. Post AHT, the LMRs liquid contains substantial quantities of amino acids (2.9 g/L) and humic acid (17.29 g/L) at 200 °C. Molecular networking identifies fatty acids and indole derivatives as plant-growth-promoting compounds in LMRs liquid, rendering it an appealing option for organic fertilization. Furthermore, a new pathway termed "Radicals-based polymerization" is proposed, which augments humic acid yield and organic stabilization. This study provides valuable insights into the comprehensive removal of antibiotics while concurrently recovering plant-growth-promoting compounds from antibiotic mycelial residues through the application of the AHT process. [ABSTRACT FROM AUTHOR]

Background Due to the importance of chicken production and the remarkable influence of the gut microbiota on host health and growth, tens of thousands of metagenome-assembled genomes (MAGs) have been constructed for the chicken gut microbiome. However, due to the limitations of short-read sequencing and assembly technologies, most of these MAGs are far from complete, are of lower quality, and include contaminant reads. Results We generated 332 Gb of high-fidelity (HiFi) long reads from the 5 chicken intestinal compartments and assembled 461 and 337 microbial genomes, of which 53% and 55% are circular, at the species and strain levels, respectively. For the assembled microbial genomes, approximately 95% were regarded as complete according to the "RNA complete" criteria, which requires at least 1 full-length ribosomal RNA (rRNA) operon encoding all 3 types of rRNA (16S, 23S, and 5S) and at least 18 copies of full-length transfer RNA genes. In comparison with the short-read-derived chicken MAGs, 384 (83% of 461) and 89 (26% of 337) strain-level and species-level genomes in this study are novel, with no matches to previously reported sequences. At the gene level, one-third of the 2.5 million genes in the HiFi-derived gene catalog are novel and cannot be matched to the short-read-derived gene catalog. Moreover, the HiFi-derived genomes have much higher continuity and completeness, as well as lower contamination; the HiFi-derived gene catalog has a much higher ratio of complete gene structures. The dominant phylum in our HiFi-assembled genomes was Firmicutes (82.5%), and the foregut was highly enriched in 5 genera: Ligilactobacillus, Limosilactobacillus, Lactobacillus, Weissella , and Enterococcus , all of which belong to the order Lactobacillales. Using GTDB-Tk, all 337 species-level genomes were successfully classified at the order level; however, 2, 35, and 189 genomes could not be classified into any known family, genus, and species, respectively. Among these incompletely classified genomes, 9 and 49 may belong to novel genera and species, respectively, because their 16S rRNA genes have identities lower than 95% and 97% to any known 16S rRNA genes. Conclusions HiFi sequencing not only produced metagenome assemblies and gene structures with markedly improved quality but also recovered a substantial portion of novel genomes and genes that were missed in previous short-read-based metagenome studies. The novel genomes and species obtained in this study will facilitate gut microbiome and host–microbiota interaction studies, thereby contributing to the sustainable development of poultry resources. [ABSTRACT FROM AUTHOR]

Agricultural intensification has increased crop productivity but simplified production and reduced cropping system diversity. In recent decades, the intensified wheat–maize rotation in the North China Plain has sharply decreased the groundwater table, with associated environmental and biodiversity issues. Understanding whether increasing cropping system diversity stabilizes productivity, improves resilience, and reduces adverse environmental impacts is critical. This study quantified the water requirements of nine staple crops from 1960 to 2020, established 15 alternative crop rotations, and evaluated the resilience of each rotation in the Cangzhou area, a typical groundwater deletion funnel area. The results showed that reducing cropping density (harvests per year) from 2 to 1.5 decreased the average annual water requirement and irrigation demand by 14 % and 33 %, respectively. Summer soybean alternated with maize and rotated with wheat did not reduce groundwater use but increased profitability and protein production. Spring mung bean–summer millet-based multi-rotations had higher precipitation coupling degrees (8 % in wet years, 17 % in normal years, and 56 % in dry years) and profitability (1.1–2.4 times) than the wheat–maize rotation. The spring potato–summer millet rotation in one year had the greatest profitability, the highest equivalent yield to wheat, and the highest water use efficiency (WUE), while spring maize rotated with winter wheat–summer soybean performed best for protein content, energy output, and WUEs. This study identified 11 alternative rotations with a higher comprehensive evaluation index than the conventional wheat–maize rotation based on entropy-TOPSIS considering 12 indicators. Spring mungbean is not suitable for inclusion in the crop rotation when solely cultivated in one year due to mismatched rainfall. Beyond wheat and maize, soybean, millet, and potato are promising crops for innovative multi-year multi-crop rotations to enhance crop diversification, maximize system outputs, and minimize groundwater and energy depletion. This study's analysis could be extended to develop robust and diverse crop rotations with multiple co-benefits in other water-stressed agricultural regions. • Water requirements of nine staple crops from 1960 to 2020 was quantified. • 15 diversified crop rotations were redesigned and established. • Multi-dimensional outcomes of each rotation were evaluated for synergies and tradeoff. • Reducing cropping density mitigated groundwater table decline. • Wheat-maize rotations involving soybean, millet and potato achieved multiple co-benefits. [ABSTRACT FROM AUTHOR]

Background: The application of PacBio HiFi and ultra-long ONT reads have enabled huge progress in the contig-level assembly, but it is still challenging to assemble large contigs into chromosomes with available Hi-C scaffolding tools, which count Hi-C links between contigs using the whole or a large part of contig regions. As the Hi-C links of two adjacent contigs concentrate only at the neighbor ends of the contigs, larger contig size will reduce the power to differentiate adjacent (signal) and non-adjacent (noise) contig linkages, leading to a higher rate of mis-assembly. Results: We design and develop a novel Hi-C based scaffolding tool EndHiC, which is suitable to assemble large contigs into chromosomal-level scaffolds. The core idea behind EndHiC, which distinguishes it from other Hi-C scaffolding tools, is using Hi-C links only from the most effective regions of contig ends. By this way, the signal neighbor contig linkages and noise non-neighbor contig linkages are separated more clearly. Benefiting from the increased signal to noise ratio, the reciprocal best requirement, as well as the robustness evaluation, EndHiC achieves higher accuracy for scaffolding large contigs compared to existing tools. EndHiC has been successfully applied in the Hi-C scaffolding of simulated data from human, rice and Arabidopsis, and real data from human, great burdock, water spinach, chicory, endive, yacon, and Ipomoea cairica, suggesting that EndHiC can be applied to a broad range of plant and animal genomes. Conclusions: EndHiC is a novel Hi-C scaffolding tool, which is suitable for scaffolding of contig assemblies with contig N50 size near or over 10 Mb and N90 size near or over 1 Mb. EndHiC is efficient both in time and memory, and it is interface-friendly to the users. As more genome projects have been launched and the contig continuity constantly improved, we believe EndHiC has the potential to make a great contribution to the genomics field and liberate the scientists from labor-intensive manual curation works. [ABSTRACT FROM AUTHOR]

Background The globe skimmer dragonfly (Pantala flavescens) is a notable Odonata insect distributed in nature fields and farmlands worldwide, and it is commonly recognized as a natural enemy because it preys on agricultural pests and health pests. As one of the sister groups of winged insects, odonatan species are key to understanding the evolution of insect wings. Findings We present a high-quality reference genome of P. flavescens , which is the first chromosome-level genome in the Palaeoptera (Odonata and Ephemeroptera). The assembled genome size was 662 Mb, with a contig N50 of 16.2 Mb. Via Hi-C scaffolding, 648 Mb (97.9%) of contig sequences were clustered, ordered, and assembled into 12 large scaffolds, each corresponding to a natural chromosome. The X chromosome was identified by sequence coverage depth. The repetitive sequences and gene density of the X chromosome are similar to those of autosomal sequences, but the X chromosome shows a much lower degree of heterozygosity. Our analysis shows that the effective population size experienced 3 declining events, which may have been caused by climate change and environmental pollution. Conclusions The genome of P. flavescens provides more information on the biology and evolution of insects and will help for the use of this species in pest control. [ABSTRACT FROM AUTHOR]

Numerical simulation has become a pivotal method in geotechnical engineering for analyzing rock failure. However, traditional model lacks comprehensive elastoplastic rock characterization. Therefore, a new finite-discrete element model using the Drucker-Prager criterion and cohesive elements based on a traction-separation law to accurately simulate elastoplastic rock breaking has been built. A scratch test simulation is conducted to describe the mechanical response and failure characteristics of the rock during the cutting process. Rock failure characteristics derived from numerical simulations align closely with experimental observations. These simulations successfully replicate both the ductile and brittle regimes observed during scratch tests. Furthermore, they provide insights into the primary driving factors behind rock failure under different regimes. Variation of cutting force and specific energy obtained from the model are entirely consistent with experimental results, with the specific energy decreasing following a −0.5 power law, thus explaining the energy dissipation rule of the specific energy decrease. In brittle regime, crush zones and the resulting rough shearing surface are observed, plastic damage is also found, revealing that the cutter tip is the key structure influencing them. The model established in this paper holds significant importance for rock mechanical property evaluation and the optimization design of cutter structures. [ABSTRACT FROM AUTHOR]

[Display omitted] • Non-targeted mass difference analysis was used to clarify DON transformation pathways. • Nucleic acid (mainly RNA) contributed to deamidization and deamination at 30 °C–70 °C. • Aspartyl, serine, histidine, and glutamyl units were representative precursors at 160 °C–200 °C. • HT liquor at 70–120 °C showed the highest DON bioavailability (∼57.6 %). Hydrothermal (HT) treatment has been considered effectively to recover nitrogen-containing organic nutrients from wastewater activated sludge (WAS), and understanding the fate and distribution of dissolved organic nitrogen (DON) during HT process is of great importance to process optimization. Thus, this study aims to investigate molecular transformation pathway of DON during HT process using non-targeted mass difference network analysis and evaluate the bioavailability of DON with chlorella. Results suggested that HT treatment was superior in N -containing organic matter solubilization (DON /TN > 60 %), and DON in WAS underwent extensive transformation processes at various HT temperatures. From room temperature to 70 °C, nucleic acids were solubilized to HT liquor, accomplishing with deamidation (–CONH + H 2 /H 2 O). And a representative ribose nucleic acid with a 'PA–pentose (ribose)–uracil (U)' structure was identified. With temperature rising to 120 °C, large amounts of stable protein were solubilized and hydrolyzed into HT liquor. The non-targeted mass difference network analysis suggested that, from 160 °C to 200 °C, aspartyl, serine, histidine, and glutamyl units were representative precursors for a variety of reactions including Maillard reactions, deamidation, deamination and so on. Amino acids were deaminated (–NH) to produce organic acids and ammonia (ammonium) or decarboxylated (–COO) to produce amines and CO 2. Dicarbonyl compounds (–C 2 H 2 O 2) were identified as the most abundant fragments which could either form humic substances or Maillard reaction products. Chlorella growth bioassay indicated that org-N in HT liquor produced at 120 °C showed the highest bioavailability for Chlorella , while that generated in HT liquor above 160 °C inhibited the growth of Chlorella. This study provided molecular insights for DON transformation following HT process and promoted high-quality resource utilization of WAS. [ABSTRACT FROM AUTHOR]

Machine fault diagnosis collects massive amounts of vibration data about complex mechanical systems. Performing feature detection from these data sets has already led to a major challenge. Compressive sensing theory is a new sampling framework that provides an alternative to the well-known Shannon sampling theory. This theory enables the recovery of sparse or compressible signals from a small set of nonadaptive linear measurements. However, it is suboptimal to recover the whole signals from the compressive measurements and then solve feature identification problems through traditional DSP techniques. Thus, a novel mechanical feature identification method is proposed in this paper. Its main advantage is that fault features are extracted directly in the compressive measurement domain without sacrificing accuracy, while a significant reduction in the dimensionality of the measurement data is achieved. Moreover, Gaussian white noises are significantly alleviated, which dramatically enhances the reliability of machine fault diagnosis. Parameter analysis is also profoundly investigated through a set of numerical experiments. Numerical simulations and experiments are further performed to prove the reliability and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]