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This study first demonstrates that the random distributed feedback fiber laser (RDFL) can be implemented for sensing detection by using the self-mixing effect as a sensing mechanism. By constructing a compact self-mixing velocimeter based on Er-doped RDFL with the integration of a laser, sensing element, and transmission platform, we successfully measured the minimum detectable feedback intensity of 38.65 fW for the velocity signal, corresponding to 0.55 photons per Doppler cycle, exhibiting ultra-high sensitivity dynamics characteristics. In addition, the velocity measurement of a non-cooperative target at a single-channel distance of 100 km is accomplished because of the natural feature of long-distance transmission for the random distributed feedback fiber lasers, which greatly improves the ultra-long detection range in the field of self-mixing sensing. The proposed sensing scheme not only unveils a fresh perspective on the exploration of random fiber laser sensing but also showcases its diverse and wide-ranging applications within the realm of remote sensing measurements.

Turbulence is a significant operational aviation safety hazard during all phases of flight. There is an urgent need for a method of airline turbulence identification in aviation systems to avoid turbulence hazards to aircraft during flight. Integrating flight data and machine learning significantly enhances the efficacy of turbulence identification. Nevertheless, present studies encounter issues including unstable model performance, challenges in data feature extraction, and parameter optimization. Hence, it is imperative to propose a superior approach to enhance the accuracy of turbulence identification along airline. The paper presents a combined swarm intelligence and machine learning model based on data mining for identifying airline turbulence. Based on the theory of swarm-intelligence-based optimization algorithm, the optimal parameters of Categorical Boosting (CatBoost) are obtained by introducing the whale optimization algorithm (WOA), and the corresponding WOA-CatBoost fusion model is established. Then, the Recursive Feature Elimination algorithm (RFE) is used to eliminate the data with lower feature weights, extract the effective features of the data, and the combination with the WOA brings robust optimization effects, whereby the accuracy of CatBoost increased by 11%. The WOA-CatBoost model can perform accurate turbulence identification from QAR data, comparable to that with established EDR approaches and outperforms traditional machine learning models. This discovery highlights the effectiveness of combining swarm intelligence and machine learning algorithms in turbulence monitoring systems to improve aviation safety.

The multidrug and toxic compound extrusion (MATE) family plays pivotal roles in the detoxification process in plants, while no information has been provided for this gene family in melon (Cucumis melo L.) thus far, limiting our understanding of its functions in melon acclimation to stressful environments. In this study, a total of 39 MATEs (CmMATE1–CmMATE39) were observed in the melon genome; these were unevenly distributed in all chromosomes, with the most on Chromosome 1. Based on their orthologous relationship with those from Arabidopsis, rice, and sorghum, melon MATEs were clustered into three subfamilies of Clades I, II, and III, wherein 23, 9, and 7 members were included, respectively. Variable exon number was observed in CmMATEs, and the most were harbored by CmMATE8. Gene ontology (GO) term and cis-regulatory element (CRE) analyses pointed to the potential roles of CmMATEs in both the regulation of melon development and acclimation to various abiotic and biotic stressors. The RNA-seq and qRT-PCR (quantitative real-time PCR) results demonstrated that under normal growth conditions, CmMATEs were expressed in a tissue- and development-specific manner, while their abundance apparently varied in a stress-dependent manner when melon plants were exposed to unfavorable environmental conditions. Altogether, these observations could expand our knowledge about the plant MATE family and benefit functional genomics analysis for CmMATEs in the future.

In the production process of low permeability condensate gas reservoir, the reverse condensate action and stress sensitivity will lead to the decrease of reservoir permeability and gas well productivity. However, there are few studies on the effect of retrograde condensation and stress sensitivity on permeability. In this study, the stress-sensitive experiments using the method with constant confining pressure but variable inner pressure were carried out on three cores from the BZ gas field in southwestern Bohai Sea. The test result that use the nitrogen as the experimental fluid represent the effect of core skeleton damage on reservoir permeability during formation pressure drop, and the test result that use the condensate gas as the experimental fluid represent the effect of core skeleton damage and retrograde condensation on reservoir permeability. The results reveal that when the formation pressure drops to the dew point pressure, retrograde condensation damage is the main cause of permeability decline. And the effects of core skeleton damage will increase as the formation pressure drops and exceed the retrograde condensation damage when the net stress is greater 7 MPa. When the net stress reaches 20 MPa, the core skeleton damage accounts for more than 90% of the total damage. Furthermore, the gas well production was calculated considering stress sensitivity and retrograde condensation based on the experimental results, the result shows that gas well production decreases by 97.65% when retrograde condensation is considered. This study can provide a reference for quantitative evaluation of the retrograde condensation and stress sensitivity in the production process of condensate gas reservoir.

The fluid flow is closely related to the reservoir microstructure. And the pore throat is small, and the pore structure is complex of tight reservoirs, so the fluid flow mechanism is different from the conventional sandstones. In this paper, the sample size and scanning accuracy are determined by mercury-pressure experiments, and the gray-scale images of core samples are enhanced, filtered, and noise reduced, segmented by Avizo software, and finally 3D digital cores are constructed to realize quantitative characterization of pore throat parameters. The results show the following: (i) It is highly accurate to determine 3D digital core by comparing porosity measurement and calculating porosity; (ii) the average pore radius of the three samples is more than 7 μm, the pore number is less than 651, the average throat length is greater than 159 μm, and the percentage of connected pore volume is above 95%; (iii) large pores are mainly developed in the reservoir, while a certain number of isolated pores exist, and the connected pores are distributed in sheets and strips; (iv) the pores of tight reservoir are mainly micron pores, and the distribution frequency histogram of pore radius is single peak; (v) porosity is related to connectivity, pore radius, and pore number; and (vi) the influence of throat on porosity and permeability is greater than that of pore. This paper is helpful for quantitative evaluation of reservoir microscopic parameters and provides technical support for visualization and quantitative characterization of rock microstructure.

Fusarium oxysporum f. sp. radicis-lycopersici (Forl) is a destructive soil-borne phytopathogenic fungus that causes Fusarium crown and root rot (FCRR) of tomato, leading to considerable field yield losses. In this study, we explored the antifungal capability of linalool, a natural plant volatile organic component, against Forl and its role in controlling FCRR symptoms in tomatoes. Our results showed that Forl mycelial growth was inhibited by the linalool treatment and that the linalool treatment damaged cell membrane integrity, enhanced reactive oxygen species levels, depleted glutathione, and reduced the activities of many antioxidant enzymes in Forl. Transcriptomic and proteomic analyses demonstrated that linalool also downregulated metabolic biosynthetic pathways at the transcript and protein levels, including redox, transporter activity, and carbohydrate metabolism in Forl. Moreover, linalool significantly decreased the expression of many Forl pathogenic genes, such as cell wall degrading enzymes (CWDEs) and G proteins, which is likely how a Forl infection was prevented. Importantly, exogenously applied linalool activated the salicylic acid (SA) and jasmonic acid (JA) defensive pathways to improve disease resistance and relieved the negative effects of Forl on plant growth. Taken together, we report that linalool is an effective fungicide against Forl and will be a promising green chemical agent for controlling FCRR.

Gray mold is a disease that often occurs in postharvest tomato fruits, leading to a significant decline in the fruits’ quality. In production, pesticides are mostly used to control gray mold, which negatively affect both the environment and food safety. The purpose of this article is to study the effects of linalool fumigation on controlling gray mold in tomato fruits and to further investigate the mechanism of linalool function, so as to provide technical support and a theoretical basis for the application of linalool in the green control of tomato gray mold. The results of the in vitro experiments showed that linalool fumigation had a strong inhibitory effect on the mycelial growth of Botrytis cinerea and that the fumigation of linalool inhibited the expansion of pathogens on tomato fruits. The disease index of tomato fruits in the linalool treatment was always lower than that of the control within 72 h after inoculation with Botrytis cinerea; at the end of experiment, the disease index of the control reached 100.0, which was only 8.0 with the 30 μL/L linalool treatment. Linalool fumigation increased the antioxidant capacity of the tomato fruits under Botrytis cinerea infection through regulating activities of SOD, POD and CAT as well as the ascorbic acid (AsA) content, which could be responsible for the lower malondialdehyde (MDA) accumulation. Linalool fumigation increased the activities of polyphenol oxidase (PPO), and phenylalanine ammonia-lyase (PAL) in tomato fruits, indicating that secondary metabolism was involved in the function of linalool in the regulation of tomato fruit resistance to pathogens. As the main enzymes related to cell structure, polygalacturonase (PG), cellulase (CL) and β-galactosidase (β-GAL) were inhibited by linalool, which could protect cell wall structures from damage, and strengthened the mechanical barrier against pathogen access to fruit flesh.

Background: Tumor-infiltrating lymphocytes (TILs) are clinically significant in triple-negative breast cancer (TNBC). Although a standardized methodology for visual TILs assessment (VTA) exists, it has several inherent limitations. We established a deep learning-based computational TIL assessment (CTA) method broadly following VTA guideline and compared it with VTA for TNBC to determine the prognostic value of the CTA and a reasonable CTA workflow for clinical practice. Methods: We trained three deep neural networks for nuclei segmentation, nuclei classification and necrosis classification to establish a CTA workflow. The automatic TIL (aTIL) score generated was compared with manual TIL (mTIL) scores provided by three pathologists in an Asian (n = 184) and a Caucasian (n = 117) TNBC cohort to evaluate scoring concordance and prognostic value. Findings: The intraclass correlations (ICCs) between aTILs and mTILs varied from 0.40 to 0.70 in two cohorts. Multivariate Cox proportional hazards analysis revealed that the aTIL score was associated with disease free survival (DFS) in both cohorts, as either a continuous [hazard ratio (HR)=0.96, 95% CI 0.94–0.99] or dichotomous variable (HR=0.29, 95% CI 0.12–0.72). A higher C-index was observed in a composite mTIL/aTIL three-tier stratification model than in the dichotomous model, using either mTILs or aTILs alone. Interpretation: The current study provides a useful tool for stromal TIL assessment and prognosis evaluation for patients with TNBC. A workflow integrating both VTA and CTA may aid pathologists in performing risk management and decision-making tasks.

The complete mitochondrial genome of Locastra muscosalis (Walker, 1866) was sequenced and characterized in this study, which was the first reported complete mitogenome of the genus Locastra. The mitogenome of L. muscosalis has a total length of 15,177 bp, encompassing 13 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs), 2 ribosomal RNA genes (rRNAs), and an A-T rich region. Phylogenetic analysis revealed that L. muscosalis is closely associated with Orthaga euadrusalis. These data will serve as a valuable foundation for future investigations into the Epipaschiinae and Pyralidae evolutionary history.

This research examines how the shear properties of glass fiber-reinforced polymer (GFRP) and steel single-lap joints react under various temperatures (−25, 0, 25, 50, and 100℃) and loading speeds (0.625, 1.25, 2.5, and 5.0 m/s). The GFRP sheets, aligned unidirectionally, were produced using vacuum-assisted resin transfer molding and were then adhesively attached to steel plates with epoxy resin. The assemblies were subjected to a high-speed servo-hydraulic testing apparatus for evaluation. The results indicate that the joint's performance is relatively unaffected by changes in the loading speed, but the overall toughness tends to decrease at higher speeds. However, the adhesive bond strength increases when temperatures are between −25 and 50℃ but diminishes as temperatures rise from 50 to 100℃. Most samples exhibited debonding at the steel-adhesive interface under varied speeds and colder temperatures, whereas debonding at the GFRP-adhesive interface was more prevalent at elevated temperatures. These insights are vital for both theoretical analyses and practical implementations of GFRP-steel single-lap joints in environments subject to extreme loading conditions and temperatures.

Evolutionary algorithm is an effective strategy for solving many-objective optimization problems. At present, most evolutionary many-objective algorithms are designed for solving many-objective optimization problems where the objectives conflict with each other. In some cases, however, the objectives are not always in conflict. It consists of multiple independent objective subsets and the relationship between objectives is unknown in advance. The classical evolutionary many-objective algorithms may not be able to effectively solve such problems. Accordingly, we propose an objective set decomposition strategy based on the partial set covering model. It decomposes the objectives into a collection of objective subsets to preserve the nondominance relationship as much as possible. An optimization subproblem is defined on each objective subset. A coevolutionary algorithm is presented to optimize all subproblems simultaneously, in which a nondominance ranking is presented to interact information among these sub-populations. The proposed algorithm is compared with five popular many-objective evolutionary algorithms and four objective set decomposition based evolutionary algorithms on a series of test problems. Numerical experiments demonstrate that the proposed algorithm can achieve promising results for the many-objective optimization problems with independent and harmonious objectives.

Background: Organisms may develop into multiple phenotypes under different nutritional environments by developmental plasticity, whereas the potential costs and mechanisms of such plasticity are poorly understood. Here we examined the fitness and gene expression of nutrition-induced phenotypes in the ladybeetle, Cryptolaemus montrouzieri after having experienced varying larval food regimes. Results: We found that C. montrouzieri adults undergoing a variable larval food regime achieved a similar developmental time, survival, body mass and egg production as those undergoing a high larval food regime. The survival, developmental time, body mass and fecundity of the adults from a restricted larval food regime were inferior to those from the high and variable larval food regimes. However, the adults from this restricted larval food regime had a higher expression level of genes encoding immune- and antioxidant-related enzymes than those from the high and variable larval food regimes when exposed to starvation and pesticide conditions in adult life. Conclusions: These results suggest that larval food availability in C. montrouzieri not only triggers adult phenotypic differences but also affects reproduction and expression level of genes in adult life, indicating that the larval nutritional conditions can affect adult fitness and resistance to stressful conditions through developmental plasticity. [ABSTRACT FROM AUTHOR]