Search Results (3,634)

To address compacted soils with high power consumption and waterlogging risks in rice–rapeseed rotation areas of the Yangtze River, this study designed a ditching machine combining a stepped cutter head and trapezoidal cleaning blade, where the mechanical synergy between components minimizes energy loss during soil-cutting and -throwing processes. We mathematically modeled soil cutting–throwing dynamics and blade traction forces, integrating soil rheological properties to refine parameter interactions. Discrete Element Method (DEM) simulations and single-factor experiments analyzed impacts of the inner/outer blade widths, blade group distance, and blade opening on power consumption. Results indicated that increasing the inner/outer blade widths (200–300 mm) by expanding the direct cutting area significantly reduced the cutter torque by 32% and traction resistance by 48.6% from reduced soil-blockage drag; larger blade group distance (0–300 mm) initially decreased but later increased power consumption due to soil backflow interference, with peak efficiency at 200 mm spacing; the optimal blade opening (586 mm) minimized the soil accumulation-induced power loss, validated by DEM trajectory analysis showing continuous soil flow. Box–Behnken experiments and genetic algorithm optimization determined the optimal parameters: inner blade width: 200 mm; outer blade width: 300 mm; blade group distance: 200 mm; and blade opening: 586 mm, yielding a simulated power consumption of 27.07 kW. Field tests under typical 18.7% soil moisture conditions confirmed a <10% error between simulated and actual power consumption (28.73 kW), with a 17.3 ± 0.5% reduction versus controls. Stability coefficients for the ditch depth, top/bottom widths exceeded 90%, and the backfill rate was 4.5 ± 0.3%, ensuring effective drainage for rapeseed cultivation. This provides practical theoretical and technical support for efficient ditching equipment in rice–rapeseed rotations, enabling resource-saving design for clay loam soils. Full article

Understanding the spatial dynamics and drivers of armed conflict is crucial for anticipating risk and informing targeted interventions. However, current research rarely considers the spatio-temporal clustering characteristics of armed conflicts. Here, we assess the distribution dynamics and driving factors of armed conflict from the perspective of armed conflict clusters, employing complex network dynamic community detection methods and interpretable machine learning approaches. The results show that conflict clusters vary in terms of regional distribution. Sub-Saharan Africa boasts the highest number of conflict clusters, accounting for 37.9% of the global total and covering 40.4% of the total cluster area. In contrast, South Asia and Afghanistan, despite having a smaller proportion of clusters at 12.1%, hold the second-largest cluster area, which is 18.1% of the total. The characteristics of different conflict networks are influenced by different factors. Historical exposure, socio-economic deprivation, and spatial structure are the primary determinants of conflict patterns, while climatic variables contribute less prominently as part of a broader system of environmental vulnerability. Moreover, the influence of driving factors shows spatial heterogeneity. By integrating cluster-level analysis with interpretable machine learning, this study offers a novel perspective for understanding the multidimensional characteristics of armed conflicts. Full article

To ensure the sustainability of alloy-based strategies, both compositional design and processing routes must be simplified. Metal additive manufacturing (AM), with its exceptionally rapid, non-equilibrium solidification, offers a unique platform to produce tailored microstructures in simple alloys that deliver superior mechanical properties. In this study, we employ laser powder bed fusion (LPBF) to fabricate 1080 plain carbon steel, a binary alloy comprising only iron and carbon. Deviating from conventional process optimization focusing primarily on density, we optimize LPBF parameters for mechanical performance. We systematically varied key parameters (laser power and scan speed) to produce batches of tensile specimens, which were then evaluated on a high-throughput mechanical testing platform (HTP). Using response surface methodology (RSM), we developed predictive models correlating these parameters with yield strength (YS) and elongation. The RSM models identified optimal and suboptimal parameter sets. Specimens printed under the predicted optimal conditions achieved YS of 1543.5 MPa and elongation of 7.58%, closely matching RSM predictions (1595.3 MPa and 8.32%) with deviations of −3.25% and −8.89% for YS and elongation, respectively, thus validating model accuracy. Comprehensive microstructural characterization, including metallographic analysis and fracture surface examination, revealed the microstructural origins of performance differences and the underlying strengthening mechanisms. This methodology enables rapid evaluation and optimization of LPBF parameters for 1080 carbon steel and can be generalized as an efficient framework for robust LPBF process development. Full article

Privacy-sensitive electricity scene classification requires robust models under data localization constraints, making federated learning (FL) a suitable framework. Existing FL frameworks face two critical challenges in multi-label electricity scene classification: (1) Label correlations and their strengths significantly impact classification performance. (2) Electricity scene data and labels show distributional inconsistencies across regions. However, current FL frameworks lack explicit modeling of label correlation strengths, and locally trained regional models naturally capture these differences, leading to regional differences in their model parameters. In this scenario, the server’s standard single-stage aggregation often over-averages the global model’s parameters, reducing its discriminative ability. To address these issues, we propose FMMAN, a federated multi-stage attention neural network for multi-label electricity scene classification. The main contributions of this FMMAN lie in label correlation learning and the stepwise model aggregation. It splits the client–server interaction into multiple stages: (1) Clients train models locally to encode features and label correlation strengths after receiving the server’s initial model. (2) The server clusters these locally trained models into K groups to ensure that models within a group have more consistent parameters and generates K prototype models via intra-group aggregation to reduce over-averaging. The K models are then distributed back to the clients. (3) Clients refine their models using the K prototypes with contrastive group-specific consistency regularization to further mitigate over-averaging, and sends the refined model back to the server. (4) Finally, the server aggregates the models into a global model. Experiments on multi-label benchmarks verify that FMMAN outperforms baseline methods. Full article

Early blight, caused by the pathogen Alternaria solani, is a major fungal disease impacting potato production globally, with reported yield losses of up to 40% in susceptible varieties. As one of the most common diseases affecting potatoes, its incidence has been steadily increasing year after year. This study aimed to elucidate the molecular mechanisms underlying resistance to early blight by comparing gene expression profiles in resistant (B1) and susceptible (D30) potato seedlings. Transcriptome sequencing was conducted at three time points post-infection (3, 7, and 10 dpi) to identify differentially expressed genes (DEGs). Weighted Gene Co-expression Network Analysis (WGCNA) and pathway enrichment analyses were performed to explore resistance-associated pathways and hub genes. Over 11,537 DEGs were identified, with the highest number observed at 10 dpi. Genes such as LOC102603761 and LOC102573998 were significantly differentially expressed across multiple comparisons. In the resistant B1 variety, upregulated genes were enriched in plant–pathogen interaction, MAPK signaling, hormonal signaling, and secondary metabolite biosynthesis pathways, particularly flavonoid biosynthesis, which likely contributes to biochemical defense against A. solani. WGCNA identified 24 distinct modules, with hub transcription factors (e.g., WRKY33, MYB, and NAC) as key regulators of resistance. These findings highlight critical molecular pathways and candidate genes involved in early blight resistance, providing a foundation for further functional studies and breeding strategies to enhance potato resilience. Full article

Due to post-cementing hydraulic fracturing and other operational stresses, inadequate mechanical properties or suboptimal design of the cement sheath can lead to tensile failure and microcrack development, compromising both hydrocarbon recovery and well integrity. In this study, three field-deployed cement slurry systems were compared on the basis of their basic mechanical properties such as compressive and tensile strength. Laboratory-scale physical simulations of hydraulic fracturing during shale oil production were conducted, using dynamic permeability as a quantitative indicator of integrity loss. The experimental results show that evaluating only basic mechanical properties is insufficient for cement slurry system design. A more comprehensive mechanical assessment is re-quired. Incorporation of an expansive agent into the cement slurry system can alleviate the damage caused by the microannulus to the interfacial sealing performance of the cement sheath, while adding a toughening agent can alleviate the damage caused by tensile cracks to the sealing performance of the cement sheath matrix. Through this research, a microexpansive and toughened cement slurry system, modified with both expansive and toughening agents, was optimized. The expansive agent and toughening agent can significantly enhance the shear strength, the flexural strength, and the interfacial hydraulic isolation strength of cement stone. Moreover, the expansion agents mitigate the detrimental effects of microannulus generation on the interfacial sealing, while the toughening agents alleviate the damage caused by tensile cracking to the bulk sealing performance of the cement sheath matrix. This system has been successfully implemented in over 100 wells in the GL block of Daqing Oilfield. Field application results show that the proportion of high-quality well sections in the horizontal section reached 88.63%, indicating the system’s high performance in enhancing zonal isolation and cementing quality. Full article

Accurately simulating the asymmetric spectral profiles of blue LEDs is crucial for photobiological research, yet it remains a challenge for traditional symmetric models. This study proposes a novel spectral simulation model that effectively captures these asymmetries. The proposed model structure is partly motivated by known broadening and dispersion mechanisms observed in real LED spectra; it employs a ‘base model + correction’ framework, where an Exponentially Modified Gaussian (EMG) function captures the primary spectral shape and falling edge and an Asymmetric Pseudo-Voigt (APV) function corrects the deviations on the rising edge. Requiring only the central wavelength and bandwidth as user inputs, the simulation results exhibit a high degree of agreement with the experimental data spectra. The model provides a rapid and robust tool for pre-evaluating light sources against regulatory criteria (e.g., >99% of the spectral intensity is in the 400–500 nm band), thereby enhancing the efficiency of experimental design in blue light protection studies. Full article

The advancement of Artificial Intelligence (AI) has significantly accelerated progress across various research domains, with growing interest in plant science due to its substantial economic potential. However, the integration of AI with digital vegetation analysis remains underexplored, largely due to the absence of large-scale, real-world plant datasets, which are crucial for advancing this field. To address this gap, we introduce the PP3D dataset—a meticulously labeled collection of about 500 potted plants represented as 3D point clouds, featuring fine-grained annotations for approximately 20 species. The PP3D dataset provides 3D phenotypic data for about 20 plant species spanning model organisms (e.g., Arabidopsis thaliana), potted plants (e.g., Foliage plants, Flowering plants), and horticultural plants (e.g., Solanum lycopersicum), covering most of the common important plant species. Leveraging this dataset, we propose the panoptic plant recognition task, which combines semantic segmentation (stems and leaves) with leaf instance segmentation. To tackle this challenge, we present SCNet, a novel dual-representation learning network designed specifically for plant point cloud segmentation. SCNet integrates two key branches: a cylindrical feature extraction branch for robust spatial encoding and a sequential slice feature extraction branch for detailed structural analysis. By efficiently propagating features between these representations, SCNet achieves superior flexibility and computational efficiency, establishing a new baseline for panoptic plant recognition and paving the way for future AI-driven research in plant science. Full article

Ecotribology focuses on both saving energy resources and reducing environmental pollution. Considering environmental concerns, water-based nanolubricants have gained significant attention over conventional oil-based ones. Non-ecotoxic and highly environmentally friendly nanoadditives were chosen for nanolubricant synthesis, especially considering their use at elevated temperatures. In this study, hexagonal boron nitride nanosheets (hBNNSs) and titanium dioxide nanoparticles (TiO₂ NPs) were used to prepare water-based lubricants with glycerol and surfactant sodium dodecyl benzene sulfonate (SDBS) in water under ultrasonication. An Rtec ball-on-disk tribometer was used to investigate the tribological performance of the synthesised water-based lubricants containing different nano-hBN/TiO₂ concentrations, with dry and water conditions used as benchmarks. The results indicated that the water-based nanolubricant containing 0.5 wt% hBN and 0.5 wt% TiO₂ exhibited the best tribological performance at both ambient (25 °C) and elevated (500 °C) temperatures. This optimal concentration leads to a reduction in the coefficient of friction (COF) by 72.9% and 37.5%, wear of disk by 62.5% and 49%, and wear of ball by 74% and 69% at ambient and elevated temperatures, respectively, compared to that of distilled water. Lubrication mechanisms were attributed to the rolling, mending, tribofilm, solid layer formation, and synergistic effects of hBNNSs and TiO₂ NPs. Full article

In a variety of UAV applications, visual–inertial navigation systems (VINSs) play a crucial role in providing accurate positioning and navigation solutions. However, traditional VINS struggle to adapt flexibly to varying environmental conditions due to fixed covariance matrix settings. This limitation becomes especially acute during high-speed drone operations, where motion blur and fluctuating image clarity can significantly compromise navigation accuracy and system robustness. To address these issues, we propose an innovative adaptive covariance matrix estimation method for UAV-based VINS using Gaussian formulas. Our approach enhances the accuracy and robustness of the navigation system by dynamically adjusting the covariance matrix according to the quality of the images. Leveraging the advanced Laplacian operator, detailed assessments of image blur are performed, thereby achieving precise perception of image quality. Based on these assessments, a novel mechanism is introduced for dynamically adjusting the visual covariance matrix using a Gaussian model according to the clarity of images in the current environment. Extensive simulation experiments across the EuRoC and TUM VI datasets, as well as the field tests, have validated our method, demonstrating significant improvements in navigation accuracy of drones in scenarios with motion blur. Our algorithm has shown significantly higher accuracy compared to the famous VINS-Mono framework, outperforming it by 18.18% on average, as well as the optimization rate of RMS, which reaches 65.66% for the F1 dataset and 41.74% for F2 in the field tests outdoors. Full article

An oat protein isolate is an ideal raw material for producing a wide range of plant-based products. However, oat protein exhibits weak functional properties, particularly in emulsification. Casein-based ingredients are commonly employed to enhance emulsifying properties as a general practice in the food industry. pH-shifting processing is a straightforward method to partially unfold protein structures. This study modified a mixture of an oat protein isolate (OPI) and casein by combining a pH adjustment (adjusting the pH of two solutions to 12, mixing them at a 3:7 ratio, and maintaining the pH at 12 for 2 h) with an atmospheric cold plasma (ACP) treatment to improve the emulsifying properties. The results demonstrated that the ACP treatment significantly enhanced the solubility of the OPI/casein mixtures, with a maximum solubility of 82.63 ± 0.33%, while the ζ-potential values were approximately −40 mV, indicating that all the samples were fairly stable. The plasma-induced increase in surface hydrophobicity supported greater protein adsorption and redistribution at the oil/water interface. After 3 min of treatment, the interfacial pressure peaked at 8.32 mN/m. Emulsions stabilized with the modified OPI/casein mixtures also exhibited a significant droplet size reduction upon extending the ACP treatment to 3 min, decreasing from 5.364 ± 0.034 μm to 3.075 ± 0.016 μm. The resulting enhanced uniformity in droplet size distribution signified the formation of a robust interfacial film. Moreover, the ACP treatment effectively enhanced the emulsifying activity of the OPI/casein mixtures, reaching (179.65 ± 1.96 m²/g). These findings highlight the potential application value of OPI/casein mixtures in liquid dairy products. In addition, dairy products based on oat protein are more conducive to sustainable development than traditional dairy products. Full article

Recombinant vesicular stomatitis virus (rVSV) is a promising viral vaccine vector for addressing the COVID-19 pandemic. Inducing mucosal immunity via the intranasal route is an ideal strategy for rVSV-based vaccines, but it requires extremely stringent safety standards. In this study, we constructed two rVSV variants with amino acid mutations in their M protein: rVSV-M2 with M33A/M51R mutations and rVSV-M4 with M33A/M51R/V221F/S226R mutations, and developed COVID-19 vaccines based on these attenuated vectors. By comparing viral replication capacity, intranasal immunization, intracranial injection, and blood cell counts, we demonstrated that the M protein mutation variants exhibit significant attenuation effects both in vitro and in vivo. Moreover, preliminary investigations into the mechanisms of virus attenuation revealed that these attenuated viruses can induce a stronger type I interferon response while reducing inflammation compared to the wild-type rVSV. We developed three candidate vaccines against SARS-CoV-2 using the wildtype VSV backbone with either wild-type M (rVSV-JN.1) and two M mutant variants (rVSV-M2-JN.1 and rVSV-M4-JN.1). Our results confirmed that rVSV-M2-JN.1 and rVSV-M4-JN.1 retain strong immunogenicity while enhancing safety in hamsters. In summary, the rVSV variants with M protein mutations represent promising candidate vectors for mucosal vaccines and warrant further investigation. Full article

Multisensory integration is crucial for effective cognitive functioning, especially in complex tasks such as those requiring rapid audiovisual information processing. High-level martial arts routine athletes, trained in integrating visual and auditory cues for performance, may exhibit superior abilities in cross-audiovisual integration. This study aimed to explore whether these athletes demonstrate an expert advantage effect in audiovisual integration, particularly focusing on whether this advantage is due to enhanced automatic auditory processing. A total of 165 participants (81 male, 84 female) were included in three experiments. Experiment 1 (n = 63) used a cross-audiovisual Rapid Serial Visual Presentation (RSVP) paradigm to compare the martial arts routine athlete group (n = 31) with a control group (n = 33) in tasks requiring target stimulus identification under audiovisual congruent and incongruent conditions. Experiment 2 (n = 52) manipulated the synchronicity of auditory stimuli to differentiate between audiovisual integration and auditory alerting effects. Experiment 3 (n = 50) combined surprise and post-surprise tests to investigate the role of automatic auditory processing in this expert advantage. Experiment 1 revealed that martial arts routine athletes outperformed the control group, especially in semantically incongruent conditions, with significantly higher accuracy at both lag3 (p < 0.001, 95% CI = [0.165, 0.275]) and lag8 (p < 0.001, 95% CI = [0.242, 0.435]). Experiment 2 found no significant difference between groups in response to the manipulation of auditory stimulus synchronicity, ruling out an alerting effect. In Experiment 3, martial arts routine athletes demonstrated better performance in reporting unexpected auditory stimuli during the surprise test, indicating enhanced automatic processing capacity. Additionally, a significant improvement in working memory re-selection was observed in the martial arts routine group. The expert advantage effect observed in martial arts routine athletes is attributable to enhanced cross-audiovisual integration, independent of an auditory alerting mechanism. Long-term training improves the efficiency of working memory re-selection and the ability to inhibit conflicting information, suggesting that the expanded capacity for automatic auditory processing underpins their multisensory integration advantage. Full article

In this study, a composite film with dual antioxidant and antibacterial properties was prepared by combining 2% chitosan and 7% gelatin (2:1, w:w), with D-carvone (0–4%) as the primary active component. The effect of D-carvone content on the performance of the composite films was systematically investigated. The results showed that adding 1% D-carvone increased the water contact angle by 28%, increased the elongation at break by 35%, and decreased the WVTR by 18%. FTIR and SEM confirmed that ≤2% D-carvone uniformly bonded with the substrate through hydrogen bonds, and the film was dense and non-porous. In addition, the DPPH scavenging rate of the 1–2% D-carvone composite film increased to about 30–40%, and the ABTS⁺ scavenging rate increased to about 35–40%; the antibacterial effect on Escherichia coli and Staphylococcus aureus increased by more than 70%. However, when the addition amount was too high (exceeding 2%), the composite film became agglomerated, microporous, and phase-separated, affecting the film performance, and due to its own taste, it reduced the sensory quality of the noodles. Comprehensively, the composites showed better performance when the content of D-carvone was 1–2% and also the best effect for freshness preservation in Xinjiang ramen. This study provides a broad application prospect for natural terpene compound-based composite films in the field of high-moisture, multi-fat food preservation, and provides a theoretical basis and practical guidance for the development of efficient and safe food packaging materials. In the future, the composite film can be further optimized, and the effect of flavor can be further explored to meet the needs of different food preservation methods. Full article

Larvae of Episyrphus balteatus De Geer (Diptera: Syrphidae) are important natural enemies of common agricultural pests such as aphids (Hemiptera: Aphididae). This well-known aphidophagous flower fly is used as a biological control agent. The predatory functional responses, control efficacy, and oviposition and predatory preferences of E. balteatus against Aphis craccivora Koch, Myzus persicae Sulzer, and Megoura crassicauda Mordvilko were systematically determined through controlled laboratory experiments. The best functional response model of both second- and third-instar E. balteatus larvae to these three aphid species was the Holling type III model, except for the third-instar larvae to A. craccivora, for which the Holling type II model was superior. The A. craccivora population decline rates for ratios of 1:500 and 1:1000 were 94.67% and 100.00% on day 12 after inoculation; the M. persicae population decline rates for ratios of 1:2000 and 1:4000 reached 96.67% and 95.42% by day 12, and the M. crassicauda population at a ratio of 1:250 was completely eliminated by day 9, achieving a 100.00% population decline rate. The oviposition and predatory preferences of E. balteatus were consistent, in that it preferred M. crassicauda for oviposition and had a positive predatory preference for this aphid species. These results provide scientific evidence for the biological control strategy of E. balteatus against these aphids. Full article