Search Results (490)

Spotted wing drosophila (Drosophila suzukii; SWD) has become a globally invasive pest by ovipositing in ripening, intact fruit rather than decaying material, a niche distinct from most other drosophilids. An expanding body of work implicates microbes and microbially derived chemistry as key drivers of this ecology, shaping fly biology across life stages. However, much of this evidence is derived from microbiome surveys and observational comparisons, further constrained by uncontrolled diet history, laboratory rearing, and insufficient ecological context. We examine how the SWD microbiome differs in which taxa are present (composition), how flies pick up those taxa from fruit and maternal sources (acquisition), how long those taxa are retained across life stages (persistence), and how each of these varies with diet, geography, season, and host crops. We then address how microbial cues and fermentation state function as context-dependent drivers of adult attraction, avoidance, and oviposition, and how microbe-mediated interspecific interactions reshape substrate suitability and competition among drosophilids. Throughout, we critically evaluate experimental designs and identify gaps that impede causal inference. These include limited strain-level resolution, incomplete fungal characterization, and weak linkages between microbial community structure and host phenotypes. Key unresolved questions include how SWD maintains performance across diverse hosts, how microbes modulate sensory processing during seasonal shifts, and which microbial metabolites drive attraction, avoidance, and competition. Resolving these questions is a direct prerequisite for field-stable integrated pest management (IPM), including microbially informed behavioral lures, oviposition deterrents derived from pathogen- and competitor-associated volatiles, and competitor-mediated suppression strategies. The experimental priorities identified here translate directly into a roadmap for the next generation of mechanistically grounded, ecologically realistic SWD management tools. Full article

Green finance (GF) is increasingly seen as an important policy tool for promoting sustainable urban development; however, its role in facilitating the green transformation of urban land remains insufficiently understood, particularly from the perspectives of land use efficiency and spatial interactions. This study takes China’s Green Finance Reform and Innovation Pilot Zones as a quasi-natural experiment and employs a spatial difference-in-differences framework to examine whether and how GF affects urban land green use efficiency (LGUE). The results indicate that GF significantly improves LGUE in pilot cities, and this finding remains robust across a range of alternative specifications and robustness checks. The mechanism analysis further suggests that GF enhances LGUE primarily by optimizing resource allocation, promoting green innovation, and strengthening information disclosure. In addition, digital development is found to reinforce the positive effects of GF. Compared with existing studies, this paper integrates mechanism analysis with spatial econometric methods to provide a more comprehensive understanding of both the transmission channels and spatial spillover effects of GF. In particular, it provides new evidence on geographically constrained negative spillover effects across cities. The results further indicate that such spillover effects are most pronounced within a 250 km radius, suggesting that GF induces localized inter-city competition and resource reallocation. This finding offers empirical support for understanding the effects of GF from a spatial competition perspective. This study highlights the necessity of coordinating regional policy design to mitigate spatial spillover effects and improve the overall effectiveness of green finance policies. Full article

This paper presents the design and simulation of a dual-redundant broadband low-noise amplifier (LNA) module for inter-satellite communication links operating in the V-band (59–71 GHz). The growing demand for high-capacity space communication systems requires highly reliable, low-noise front-end architectures capable of maintaining performance over long mission lifetimes. To address these needs, a selectable dual-input receiver architecture is proposed, integrating a waveguide dual-probe, redundant switching, and a two-stage LNA within a single Gallium Arsenide (GaAs) MMIC. The design methodology accounts for the non-ideal behavior of the redundant branch and its impact on noise figure and insertion loss. The front-end is implemented using a 70 nm GaAs mHEMT technology optimized for millimeter-wave low-noise applications. Simulations show an insertion gain higher than 15 dB across the operational band, with gain ripple below 1.3 dB peak-to-peak. The simulated system noise figure is approximately 3.0 dB, closely matching the target specification. The results demonstrate that the proposed architecture provides improved reliability through redundancy while maintaining competitive noise and gain performance for future V-band inter-satellite links. Full article

Motor imagery electroencephalography (MI-EEG) decoding remains challenging in cross-subject scenarios due to pronounced inter-subject variability and signal non-stationarity, which often lead to performance degradation on unseen subjects. Existing prototype-based and domain adaptation methods typically rely on global feature alignment or single-level class representation, limiting their ability to capture both channel-wise spatial variability and high-level semantic structure. To address these limitations, we propose a dual-stage prototype representation framework for cross-subject MI-EEG decoding. The framework models spatial and semantic variability in a hierarchical manner by introducing channel prototypes and feature prototypes, enabling more consistent representations across subjects. Furthermore, a prototype-guided pairwise similarity learning strategy is employed to enhance intra-class compactness and inter-class separability in the embedding space. To mitigate cross-subject distribution shifts, we integrate a lightweight statistical perturbation method (StyleMix) with Wasserstein-based domain alignment, helping reduce subject-specific distribution variations. Experiments on the BCI Competition IV 2a and 2b datasets show that the proposed method achieves competitive performance under the evaluated target-assisted few-shot setting, reaching average accuracies of 79.12% and 87.31%, respectively, and improving over the strongest baseline by up to 2.99 percentage points. Full article

Accurate residential load forecasting is crucial for enhancing the efficiency and reliability of energy systems in smart grid and demand response applications. However, residential load data are characterized by strong stochasticity, high volatility, and pronounced multi-scale temporal dynamics while being highly susceptible to noise and outliers. These challenges hinder existing methods from effectively capturing complex temporal patterns and learning reliable inter-variable dependencies, thereby limiting forecasting accuracy and stability. To address these issues, this paper proposes a Prior-Guided Multi-Scale Neural Network (PG-MSNN) for multi-step residential load forecasting. The proposed framework integrates prior-guided dependency modeling with multi-scale temporal representation learning in an end-to-end trainable architecture. Specifically, a learnable periodic prior space is constructed, within which a Prior-Guided Module (PGM) is designed to learn cross-variable dependencies and provide structured global periodic guidance. In parallel, a Multi-Scale Patch-LSTM Encoder (MS-PLE) is developed to model temporal dynamics across multiple scales through patch-based sequence representation and adaptive cross-scale fusion. Extensive experiments on three real-world datasets, including IHEPC, REC, and CN-OBEE, demonstrate that, under within-household temporal forecasting settings, the proposed method achieves consistent and competitive performance across various forecasting horizons. Full article

Multi-personpose estimation is a fundamental technology for deep learning-based intelligent sensing systems, enabling downstream understanding of human action in applications such as surveillance, robotics, healthcare, and sports analytics. Most two-stage multi-person pose estimation algorithms suffer from low efficiency due to their decoupled representation between body and keypoints, resulting in a complex inference process. Single-stage algorithms such as AdaptivePose that represent body parts as semantic proxy points can substantially simplify the pipeline while achieving competitive results; however, the limited receptive field of convolutional features still makes it difficult to localize proxy points for long-range extremities in semantically informative regions. IHPP addresses this issue as a task-specific refinement of AdaptivePose; it enriches the proxy-point perceiver with directional context modeling along the horizontal and vertical axes and redesigns the second-step regression stage with a part-wise branch to reduce inter-part feature interference while keeping the overall pipeline lightweight. Here, we present IHPP in three sizes (IHPP-S, IHPP-M, and IHPP-L), optimizing the tradeoffs between efficiency and accuracy through fine-tuning of the channel dimensions for each bodypart feature. IHPP-L achieves 72.3 AP at 28 fps on COCO test-dev, surpassing DEKR-W48 and SWAHR-W48 by 1.3 AP and 0.3 AP, respectively, while IHPP-M reaches 69.0 AP at 35 fps and IHPP-S runs at 42 fps with only 9.4 M parameters. On CrowdPose, IHPP-M outperforms AdaptivePose-W48 by 0.2 AP with about one-third the parameters. Comprehensive experiments on the MS COCO and CrowdPose datasets validate the effectiveness of this design. Full article

The evolution of herbicide resistance can increase, decrease, or have no effect on the growth rate, competitive ability, and fitness of field-selected populations. The growth response of an ACCase-resistant (R) johnsongrass [Sorghum halepense (L.) Pers.] population harboring an Ile1781Leu mutation, and a susceptible (S) population was studied in pot experiments under intraspecific and interspecific competition with corn or sunflower, using a target-neighborhood design. The R population in the intraspecific competition indicated greater fitness-related traits such as height (H), tiller number (TN), aboveground fresh weight (AFW), and rhizome fresh weight (RFW) than the S population. Aggressiveness, competitive ratio, competition intensity index, and relative competition intensity indices confirmed also the superiority of the R population. Similarly, the R population grown in interspecific competition with corn or sunflower produced greater H, TN, and AFW than the S population. In addition, both R and S populations growing in competition with corn produced more H, TN, and AFW than those growing in competition with sunflower. Furthermore, the R population in competition with corn hybrids resulted in a greater reduction in H and AFW in corn plants. These findings strongly support the evidence of fitness advantage in the R population harboring the 1781Leu mutant allele as compared to the S counterpart. Full article

Cardiovascular diseases are among the most prevalent global health conditions, making the accurate diagnosis and classification of cardiac abnormalities crucial for effective treatment and patient management. While the electrocardiogram (ECG) is the primary tool for assessing cardiac electrical activity, its manual analysis is often time-consuming and susceptible to interpretive error. To address these limitations, this work proposes a comprehensive deep learning pipeline for the automated classification of arrhythmias, incorporating specific strategies to mitigate the challenge of imbalanced datasets. Furthermore, we introduce a novel three-dimensional (3D) visualisation framework that provides interactive, anatomically precise renderings of the heart regions implicated by the ECG classification, thereby delivering enhanced diagnostic insight. Our evaluation demonstrates that the proposed data balancing techniques yield significant performance gains, and under our current experimental setup, the results are competitive with or exceed several previously reported methods. We acknowledge that a more rigorous inter-patient cross-validation is needed to fully establish generalisation. The resulting 3D visualisations not only enable precise anatomical localisation of arrhythmia substrates but also serve as a powerful interactive tool for clinical practice and medical education. Full article

Motivation: In modern organizations where remote and hybrid work has become normalized, fostering trust without frequent face-to-face interaction is a critical management challenge. This study aims to explore how non-verbal digital dynamics associate with trust formation within hybrid product development teams from a teamwork science perspective, integrating Big Five traits and established trust scales. Methods: The empirical study observed twelve product development teams (N = 40) participating in a major innovation competition over an eight-month period. Dynamic behavioral data, including speaking time, nodding, smiling, and silence, were extracted from online workshop recordings using synchronized behavioral coding validated by high inter-rater reliability (Cohen’s Kappa k ≥ 0.78). These were integrated with Big Five personality traits, mutual trust scales, and idea value metrics into a Bayesian Network (BN) to model probabilistic dependencies. The structural model was validated using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to ensure predictive robustness. Furthermore, we performed sensitivity analysis on the BN to quantify how specific shifts in non-verbal cues—particularly nodding and the functional categories of silence—disproportionately affect the “Mutual Trust” node. While this exploratory study utilizes a sample of “digital native” student teams, it provides a critical baseline for “high digital fluency” collaboration, which we contextualize against the “asymmetric cues” found in multi-generational corporate environments. Results: Sensitivity analysis identified specific probabilistic associations suggesting that effective role fulfillment is the strongest predictor of idea originality. Crucially, nodding was identified as a behavioral ‘digital reward’ that enhances psychological safety, facilitating divergent thinking. Smiling showed a strong association with feasibility and consensus-building during convergent phases. The model further identifies distinct behavioral ‘fingerprints’: high-trust sequences are characterized by frequent non-verbal backchanneling and deliberate “thinking silences,” whereas low-trust sequences exhibit a disproportionate increase in unproductive lapses (e.g., a 10% increase in lapses correlating with an 18% decrease in trust probability). Furthermore, a probabilistic pathway was identified where teams with highly open members and frequent non-verbal validation exhibit higher mutual support behaviors. Conclusions: This research offers empirical insights into how trust can be modeled in hybrid environments through specific combinations of behavioral and personality traits. Practically, this study proposes “Hybrid Team Protocols”—such as intentional backchanneling and the normalization of deliberative silence—as actionable Organizational Development (OD) interventions. These provide managers with data-driven guidelines to visualize and monitor the quality of digital collaboration while emphasizing the ethical necessity of transparent implementation to prevent “digital performance” and ensure psychological safety across diverse organizational structures. Full article

The purpose of this study was to examine sex differences in upper-limb neuromuscular performance in competitive volleyball players across task-specific shoulder positions. Twenty-four athletes (12 males, 12 females) competing at the national level participated in the study. Upper-limb neuromuscular performance was assessed using the Athletic Shoulder (ASH) test performed in three positions (I, Y, and T). Peak force, rate of force development (RFD), and time to peak force were analyzed. A significant main effect of test position and a Test × Sex interaction were observed for peak force (p < 0.05), with males demonstrating higher values than females across all positions. In contrast, no significant sex differences or interaction effects were found for RFD (p > 0.05). For time to peak force, no main effect of test position or interaction was observed; however, post hoc comparisons indicated higher values in males across individual positions. No significant inter-limb differences were detected for any variable. These findings suggest that sex-related differences in upper-limb neuromuscular performance may depend on the specific variable and shoulder position assessed. The results provide preliminary insight into sex-related characteristics of shoulder neuromuscular performance in volleyball players. However, given the cross-sectional design and limited sample size, the findings should be interpreted with caution and cannot be generalized beyond the studied population. Further research is needed to confirm these observations and to explore their potential relevance in applied settings. Full article

Ecotypic variation in tall fescue (Lolium arundinaceum (Schreb.) Darbysh.), with differences in phenology, may affect the performance of mixtures with alfalfa (Medicago sativa L.). However, the effects of ecotypic variation within mixtures remain largely unexplored. The aim of this study was to evaluate the aerial dry matter (ADM) production and radiation model components of alfalfa–tall fescue mixtures, with particular emphasis on their implications for radiation interception and radiation use efficiency (RUE) at the canopy level. We evaluated from March 2017 to May 2018 in the Pampas (Argentina) monocultures of alfalfa and tall fescue Mediterranean and Continental ecotypes, and their mixtures with a sowing ratio 1:1 under frequent defoliation without fertilization. ADM was higher in alfalfa monoculture and mixture with the Mediterranean ecotype than the mixture with the Continental ecotype (+20%; 3225 kg ha⁻¹). Alfalfa monoculture exhibited the highest radiation interception, whereas the mixture with the Mediterranean ecotype compensated for reduced interception through increased RUE (≈10%). The Continental mixture exhibited lower interception indicating stronger interspecific competition. Tall fescue monocultures were the least productive due to low interception and RUE. These findings highlight the potential of Mediterranean tall fescue ecotype and the importance of species/ecotype selection for grassland productivity. Full article

Pseudomonas aeruginosa is a common Gram-negative bacterium in hospital infections and one of the main pathogens causing opportunistic infections in humans. In recent years, the drug resistance of P. aeruginosa has become increasingly severe. Therefore, it is urgent to explore new targets for antibacterial therapy. In P. aeruginosa, iron is an essential element not only for cell growth but also for successful infection. Two siderophores are produced by P. aeruginosa: pyoverdine and pyochelin. They help P. aeruginosa to obtain iron and play an important role in interspecific competition, anti-oxidative stress, and virulence. Furthermore, siderophores have been used to design “Trojan horse” antibiotics. These antibiotic–siderophore conjugates enter the cytoplasm of P. aeruginosa via siderophore uptake systems for pyoverdine and pyochelin, releasing antibacterial substances and exerting corresponding effects against P. aeruginosa. This review discusses the synthesis, secretion, and uptake of siderophores in P. aeruginosa as well as the role of the “Trojan horse” strategy in treating P. aeruginosa infections. Full article

Substantial tracts of fallow farmland remain unutilized across southwestern China throughout winter and spring. To explore a high-yield planting pattern for utilizing such fallow land, a cereal–legume intercropping experiment was conducted in Chengdu in 2021–2022 and in 2022–2023. This involved five different intercropping ratios of oat (Avena sativa) and common vetch (Vicia sativa) including 100:0, 75:25, 50:50, 25:75, and 0:100 based on seed number per unit area. The relative density, LER (land equivalent ratio), hay yield, nutritional composition and in vitro fermentation characteristics were assessed. The study revealed that the combination of oat and common vetch led to a significant enhancement in the production performance over the monocultures. At the flowering stage, the most balanced interspecific competition was observed at a ratio of 50:50. The ratio of 50:50 had the higher LER in the mixture—from 1.018 to 1.873—which was significantly higher than the other two intercropping ratios in 2021–2022. At the flowing development stage in 2021–2022, the harvesting of mixed crops at the 50:50 ratio resulted in a significant higher crude protein yield, 1454.7 kg/hm², than the other intercropping ratios. As the growth stage continued, the mixture hay neutral detergent fiber and acid detergent fiber contents increased, while the relative feed value and crude fat content decreased. The soluble sugar content increased with the prolongation of the growth stage and peaked at the jointing stage, and decreased with the decrease in the proportion of oat in the mixture. Additionally, the gas production showed an overall decreasing trend with the increase in the proportion of common vetch. The dry matter degradation rate in the mixture hay was overall higher than that of the monocultures, and the NH₃-N content showed an overall trend of increasing with the decrease with the intercropping ratio of oat. Consequently, the 50:50 ratio may be recommended as an oat-common vetch intercropping ratio suitable for utilizing fallow fields in southwestern China from October to April to produce high-quality forage. Full article

Habitat destruction is a major driver of biodiversity decline, yet how it reshapes multispecies coexistence by altering interaction structure remains unclear. We adopt a spatially explicit metacommunity model framework under a homogeneity assumption and introduce a tunable parameter controlling intransitive competition. Within this framework, we represent the system using a generalized Lotka–Volterra model to examine how coexistence mechanisms respond to habitat destruction. Our findings demonstrate that (1) coexistence is not driven by a single mechanism: under transitive competition, it highly relies on niche differentiation, whereas in intransitive structures, coexistence can be maintained even with low niche differentiation. (2) Habitat destruction compresses the feasible coexistence space, but regions dominated by different mechanisms respond asymmetrically, with niche-difference-driven coexistence shrinking and intransitive-dominated coexistence expanding under certain conditions. (3) The difference stems from habitat destruction, altering the relative proportions of intraspecific and interspecific competition, driving the community beyond the coexistence threshold. This reduces the probability of coexistence and reshapes the relative importance of several coexistence mechanisms. This finding provides a new theoretical perspective for biodiversity in fragmented landscapes. Full article

Optimizing neuromuscular strength and balance is essential for performance and injury prevention in elite Paralympic sport. However, limited evidence describes how these parameters change over time during specific phases of the training season in athletes with lower limb deficiencies. This retrospective case series aimed to describe longitudinal changes in neuromuscular and balance performance during the fundamental preparation period in elite athletes using prosthetic devices. Routinely collected performance data from five international-level Paralympic athletes (Para-swimming and Para-athletics) were retrospectively analyzed across two preparatory observation windows conducted in consecutive competitive seasons. Neuromuscular performance was assessed using countermovement jump variables, while static balance was evaluated through Inertial Measurement Unit-derived sway metrics. Within-athlete changes were examined using descriptive and exploratory analyses. At the group level, changes were observed in selected neuromuscular and balance outcomes over time, including jump height and path length. Individual analyses revealed substantial inter-athlete variability in the magnitude and direction of changes across all outcomes. Overall, the findings indicate that neuromuscular and postural performance may fluctuate meaningfully during preparatory phases in elite athletes with lower limb deficiencies. This study provides exploratory insights derived from real-world training settings and highlights the value of longitudinal monitoring to support individualized performance management in Paralympic sport. Full article