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Despite a hypothesized link between family conflict and school bullying perpetration, previous findings on the direct association have been inconsistent. It has been argued that affiliation with delinquent peers may be a potential psychosocial mechanism mediating the relationships between family conflict and school perpetration. However, such a proposition has not been examined employing longitudinal panel data. Using longitudinal panel data (two waves with a 9-month interval) from Hong Kong collected from 424 lower secondary students (grades 7–9), this study examined how affiliation with delinquent peers mediates the relationships between family conflict and adolescent school perpetration. The results of the half-longitudinal mediation model indicated no significant link between family conflict at T1 and school bullying perpetration at T2. However, family conflict at T1 was indirectly linked to school bullying perpetration at T2 through affiliation with delinquent peers. The findings imply that affiliation with delinquent peers mediates the association between family conflict and adolescent school bullying perpetration. The findings provide insights into the future development of policies and interventions to reduce school bullying perpetration. [ABSTRACT FROM AUTHOR]

Although the associations between adolescent psychological distress (i.e., depression, anxiety, and somatization), school violence, and cyberbullying have been examined using cross-sectional data, little evidence from longitudinal panel data exists to determine the temporal association. A two-wave longitudinal panel data with a 10-month interval were obtained from a random sample of 487 Chinese junior high school students (grades 7–9) in Tianjin in Mainland China. The cross-lagged panel analysis showed that adolescent psychological distress in the first wave of the survey was significantly associated with subsequent school violence against peers and teachers in the second wave but not in the opposite direction. Student psychological distress in the first wave of the survey was not significantly associated with cyberbullying perpetration in the second wave and vice versa. The findings suggested that adolescent psychological distress was a risk factor rather than the consequence of violence against peers and teachers in school. However, the temporal associations between adolescent psychological distress and cyberbullying perpetration were non-significant. These findings were relevant to both sex groups. The findings may imply that potential interventions and policies to prevent students from committing school violence should consider reducing students' psychological distress as one of the core elements. [ABSTRACT FROM AUTHOR]

[Display omitted] • PFS addition accelerated OP conversion to orthophosphates and promoted DOP bioavailability. • PAC addition inhibited the mineralization of OP and decreased DOP bioavailability. • PFS conditioning mainly facilitated phosphorus-related reactions at the thermophilic stage. • PFS addition enhanced oxidation and dehydrogenation reaction of OP molecules. Phosphorus is enriched in waste activated sludge (WAS) during wastewater treatment, and organic phosphorus (OP) is a potential slow-release P fertilizer. The chemical coagulants used in sludge dewatering leave numerous residues in WAS that affect sludge composting. In this study, the effects of polyaluminum chloride (PAC) and polyferric sulfate (PFS) on the bioconversion of dissolved OP (DOP) during sludge composting were investigated. The results revealed that PFS conditioning promoted the transformation and bioavailability of DOP, whereas PAC conditioning inhibited. Results indicated that PFS conditioning enhanced the transformation of OP molecules in the thermophilic phase. Through oxidation and dehydrogenation reactions, 1-hydroxy-pentane-3,4-diol-5-phosphate and D-ribofuranose 5-phosphate with high bioactivity were generated in the PFS-conditioned compost. Enzymatic hydrolysis experiments further verified that PFS conditioning enhanced the DOP bioavailability in the compost, whereas PAC conditioning inhibited it. The study has provided molecular insights into the effects of chemical conditioning on DOP conversion during sludge composting. [ABSTRACT FROM AUTHOR]

• PAC conditioning inhibited composting microbiomes with a decreased enzymatic reaction. • PFS conditioning increased the content of humification intermediate-indole. • PFS conditioning increased humic acid and indoleacetic acid content in the compost. • PFS conditioning changed the dominant humification occurrence stage during composting. • PAC conditioning declined compost quality, while PFS dosage increased compost quality. Inorganic coagulants (aluminum and iron salt) are widely used to improve sludge dewaterability, resulting in numerous residues in dewatered sludge. Composting refers to the controlled microbial process that converts organic wastes into fertilizer, and coagulant residues in dewatered sludge can affect subsequent compost efficiency and resource recycling, which remains unclear. This work investigated the effects of two typical metal salt coagulants (poly aluminum chloride [PAC] and poly ferric sulfate [PFS]) conditioning on sludge compost. Our results revealed that PAC conditioning inhibited composting with decreased peak temperature, microbial richness, enzymatic reaction intensities, and compost quality, associated with decreased pH and microbial toxicity of aluminum. Nevertheless, PFS conditioning selectively enriched Pseudoxanthomonas sp. and resulted in more fertile compost with increased peak temperature, enzymatic reaction intensities, and humification degree. Spectroscopy and mass difference analyses indicated that PFS conditioning enhanced reaction intensities of labile biopolymers at the thermophilic stage, mainly comprising hydrolyzation (H 2 O), dehydrogenation (-H 2 , -H 4), oxidation (+O 1 H 2), and other reactions (i.e., +C H 2 , C 2 H 4 O 1 , C 2 H 6 O 1). Unlike the common composting process primarily conducts humification at the cooling stage, PFS conditioning changed the main occurrence stage to the thermophilic stage. Non-targeted metabolomics revealed that indole (a humification intermediate) is responsible for the increased humification degree and indoleacetic acid content in the PFS-conditioned compost, which then promoted compost quality. Plant growth experiments further confirmed that the dissolved organic matter (DOM) in PFS-conditioned compost produced the maximum plant biomass. This study provided molecular-level evidence that PFS conditioning can promote humification and compost fertility during sludge composting, enabling chemical conditioning optimization for sustainable management of sludge. [Display omitted] [ABSTRACT FROM AUTHOR]

In this study, comprehensive energy, exergy, and economic modeling, and optimization of a flash-binary geothermal system, were carried out for power and hydrogen production. The binary system is a combination of a dual-pressure organic Rankine cycle and proton exchange membrane electrolyzer. For performance improvement of dual-pressure organic Rankine cycle, zeotropic mixtures were used as the working fluids. The multi-objective Genetic algorithm was employed to optimize the system performance. According to the obtained results, the highest rate of exergy destruction was obtained for the steam turbine with a value of 16.42 kW. For the base case simulation, the energy and exergy efficiencies of the proposed system were obtained by 16.66 % and 58.03 % , respectively; also, the generated power, overall exergy destruction, and hydrogen production capacity were calculated by 114.53 kW , 82.755 kW , and 0.306 kg · hr − 1 , respectively. For the multi-objective optimization mode, the best performance was calculated for Pentane (0.467)/Butane (0.533) with energy and exergy efficiencies and hydrogen production of 16.67 % , 58.14 % , and 0.37 kg · hr − 1 . Moreover, the optimized payback period and NPV were around 5.52 years and 402600 $ , respectively. • Proposal of a flash-binary geothermal system for power and hydrogen production. • Using zeotropic mixtures for performance improvement of dual-pressure ORC. • Analyzing economic aspects of the proposed system for probable investment. • Energy and exergy efficiencies of 16.67 % and 58.14 % for the optimized mode. • Optimized payback period and total profit of 5.52 years and 402800 $. [ABSTRACT FROM AUTHOR]

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]

Fault diagnosis of rotating machinery plays a significant role for the reliability and safety of modern industrial systems. As an emerging field in industrial applications and an effective solution for fault recognition, artificial intelligence (AI) techniques have been receiving increasing attention from academia and industry. However, great challenges are met by the AI methods under the different real operating conditions. This paper attempts to present a comprehensive review of AI algorithms in rotating machinery fault diagnosis, from both the views of theory background and industrial applications. A brief introduction of different AI algorithms is presented first, including the following methods: k -nearest neighbour, naive Bayes, support vector machine, artificial neural network and deep learning. Then, a broad literature survey of these AI algorithms in industrial applications is given. Finally, the advantages, limitations, practical implications of different AI algorithms, as well as some new research trends, are discussed. [ABSTRACT FROM AUTHOR]

• The Interfacial Transition Zone (ITZ) is a known weak part in concrete. • The nature of aggregate influences mechanical behaviors of the interface. • Limestone shows a very high bonding strength with mortar. • Water absorption ability of aggregate impacts the mechanical behaviors of concrete. • Microscopic observation can provide physical and chemical insights to evaluate ITZ. This work aims to study the mechanical properties of the interfacial transition zone (ITZ) through both experimental tests and microstructural observations. The study considers a mortar with water-cement ratio of 0.58 and four types of aggregates: sandstone, granite, and two different types of limestone with varying water absorption coefficients. Chemical compositions, physical characteristics, and surface micromorphology of the four aggregates were evaluated using X-ray diffraction (XRD), thermogravimetric analysis (TGA), and water absorption measurements as well as SEM observations. Experimental setups were developed to investigate the bonding behavior of the aggregate-mortar interface. The bonding strength between granite and mortar through direct tensile test varied from 51% to 69% of tensile strength of mortar from young age to mature, while 61% to 74% for sandstone, 48% to greater than 90% and 81% to greater than 102% averagely for black and white limestone respectively. The fact of failure in the bulk mortar indicates ITZ adhesion for limestones may equal or exceed the mortar tensile strength generally from 28-day age. This conclusion is consistent with the results obtained by slant shear test. The significant difference of adhesion behavior of ITZ between two limestones due to their water absorption capability. High-water absorption turns out to be beneficial in forming a strong chemical bond at early age, however, the mineralogy remains the pivot under same surface condition. This chemical bond is evidenced by the failure pattern and morphology of the aggregate surface after failure and the presence of a dense accumulation of portlandite and C-S-H under SEM. [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]

Redesigned multifunctional biopolymers represent a novel building bridge for interdisciplinary collaborations in biomaterials development. We prepared fluorescein-grafted polyurethane scaffolds (PU-C1, PU-C5, and PU-B1) to meet both clinical needs and biological safety evaluations, using different contents of calcein and different synthesis procedures for potential biomedical tracing. X-ray diffraction, infrared, X-ray photoelectron spectroscopy, nuclear magnetic resonance, scanning electron microscopy, and light microscopy were used to analyze the composition and structure of polyurethanes, as well as to observe their morphology with and without biomarkers. Fluorescence spectrophotometer and fluorescence microscopy were used to detect the fluorescence characteristics. The results showed that the grafting of calcein significantly affected the chemical structure and fluorescence sensitivities of copolymers. When compared to calcein, which was added before synthesis (PU-C1), the marker that was added during the extender process (PU-B1) presented higher fluorescence efficiency. Both PU-C5 and PU-B1 exhibited strong fluorescent response and good cytocompatibility in vitro and in vivo, with no interference from the autofluorescence of tissues after 4 weeks of implantation. The fluorescence-marked material can be used to continuously and noninvasively monitor the dynamic changes in polymers, which provides a way to clearly trace the material or to distinguish between the material and tissue in vivo. [ABSTRACT FROM AUTHOR]

Bearing plays an essential role in the performance of mechanical system and fault diagnosis of mechanical system is inseparably related to the diagnosis of the bearings. However, it is a challenge to detect weak fault from the complex and non-stationary vibration signals with a large amount of noise, especially at the early stage. To improve the anti-noise ability and detect incipient fault, a novel fault detection method based on a short-time matching method and Support Vector Machine (SVM) is proposed. In this paper, the mechanism of roller bearing is discussed and the impact time frequency dictionary is constructed targeting the multi-component characteristics and fault feature of roller bearing fault vibration signals. Then, a short-time matching method is described and the simulation results show the excellent feature extraction effects in extremely low signal-to-noise ratio (SNR). After extracting the most relevance atoms as features, SVM was trained for fault recognition. Finally, the practical bearing experiments indicate that the proposed method is more effective and efficient than the traditional methods in weak impact signal oscillatory characters extraction and incipient fault diagnosis. [ABSTRACT FROM AUTHOR]

This study aims to determine the effects of coating category and degradation on chloride resistance and service life of concrete coatings. Four typical coatings were applied on concrete specimens, and aged under outdoor natural climate conditions and indoor artificial accelerated experiments using ultraviolet light radiation and wetting/drying cycle. Coulomb electric fluxes of the specimens were periodically tested to determine their chloride resistance before and during aging. The chloride resistance of concrete is remarkably improved with the use of coatings, and organic film coatings provide superior improvement to infiltrating coatings. The chloride resistance of coatings is time-dependent, and organic film coatings exhibit faster aging than infiltrating coatings. The experimental time needed for coating degradation can be shortened through artificial accelerated aging experiments. The service lives of concrete coatings against chloride resistance are closely related and can be predicted through sunlight irradiance of the service environment. [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]

It is a challenging problem to design excellent dictionaries to sparsely represent diverse fault information and simultaneously discriminate different fault sources. Therefore, this paper describes and analyzes a novel multiple feature recognition framework which incorporates the tight frame learning technique with an adaptive subspace recognition strategy. The proposed framework consists of four stages. Firstly, by introducing the tight frame constraint into the popular dictionary learning model, the proposed tight frame learning model could be formulated as a nonconvex optimization problem which can be solved by alternatively implementing hard thresholding operation and singular value decomposition. Secondly, the noises are effectively eliminated through transform sparse coding techniques. Thirdly, the denoised signal is decoupled into discriminative feature subspaces by each tight frame filter. Finally, in guidance of elaborately designed fault related sensitive indexes, latent fault feature subspaces can be adaptively recognized and multiple faults are diagnosed simultaneously. Extensive numerical experiments are sequently implemented to investigate the sparsifying capability of the learned tight frame as well as its comprehensive denoising performance. Most importantly, the feasibility and superiority of the proposed framework is verified through performing multiple fault diagnosis of motor bearings. Compared with the state-of-the-art fault detection techniques, some important advantages have been observed: firstly, the proposed framework incorporates the physical prior with the data-driven strategy and naturally multiple fault feature with similar oscillation morphology can be adaptively decoupled. Secondly, the tight frame dictionary directly learned from the noisy observation can significantly promote the sparsity of fault features compared to analytical tight frames. Thirdly, a satisfactory complete signal space description property is guaranteed and thus weak feature leakage problem is avoided compared to typical learning methods. [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]

It is a fundamental task in the machine fault diagnosis community to detect impulsive signatures generated by the localized faults of bearings. The main goal of this paper is to exploit the low-rank physical structure of periodic impulsive features and further establish a weighted low-rank sparse model for bearing fault detection. The proposed model mainly consists of three basic components: an adaptive partition window, a nuclear norm regularization and a weighted sequence. Firstly, due to the periodic repetition mechanism of impulsive feature, an adaptive partition window could be designed to transform the impulsive feature into a data matrix. The highlight of partition window is to accumulate all local feature information and align them. Then, all columns of the data matrix share similar waveforms and a core physical phenomenon arises, i.e., these singular values of the data matrix demonstrates a sparse distribution pattern. Therefore, a nuclear norm regularization is enforced to capture that sparse prior. However, the nuclear norm regularization treats all singular values equally and thus ignores one basic fact that larger singular values have more information volume of impulsive features and should be preserved as much as possible. Therefore, a weighted sequence with adaptively tuning weights inversely proportional to singular amplitude is adopted to guarantee the distribution consistence of large singular values. On the other hand, the proposed model is difficult to solve due to its non-convexity and thus a new algorithm is developed to search one satisfying stationary solution through alternatively implementing one proximal operator operation and least-square fitting. Moreover, the sensitivity analysis and selection principles of algorithmic parameters are comprehensively investigated through a set of numerical experiments, which shows that the proposed method is robust and only has a few adjustable parameters. Lastly, the proposed model is applied to the wind turbine (WT) bearing fault detection and its effectiveness is sufficiently verified. Compared with the current popular bearing fault diagnosis techniques, wavelet analysis and spectral kurtosis, our model achieves a higher diagnostic accuracy. [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]