Background: Electronic surveillance for hospital-onset sepsis using CDC’s Adult Sepsis Event definition could provide an efficient and objective method to identify a broad array of serious healthcare-associated infections, many of which are missed through current reporting processes. We developed risk adjustment models of varying complexity to support facility-level comparison of hospital-onset sepsis rates, evaluated trade-offs between model performance and feasibility, and quantified residual inter-facility variation that may reflect gaps in care. Methods: We conducted a retrospective study of adults hospitalized for <3 days within 113 community hospitals between 2022-2023. Hospital-onset Adult Sepsis Events (HO-ASEs) occurring on day 4 or later were identified using updated CDC surveillance criteria. We used logistic regression to develop three risk adjustment models of increasing complexity using covariates from administrative and electronic health record data: basic model (hospital and aggregate patient descriptors), intermediate model (replacing aggregate patient descriptors with patient-level descriptors), and maximal model (adding detailed physiologic and clinical data from hospital days 1-3; Figure 1). We evaluated model performance using Area Under Receiver Operating Curve (AUROC), calculated hospital-level Standardized Infection Ratios (SIRs) for each model and assessed concordance in hospital rankings using Kendall’s tau coefficient (τ). Results: The cohort included 1,557,252 hospitalizations of <3 days, of which 24,169 (1.6%) met HO-ASE criteria and 8,500 (35.2%) died in-hospital. The basic model had limited discrimination (AUROC 0.589, 95% CI, 0.585-0.593). Adding patient-level characteristics to form the intermediate model markedly improved performance (AUROC 0.839, 95% CI 0.836-0.841), with further inclusion of detailed clinical data in the maximal model yielding modest additional improvement (AUROC 0.850, 95% CI 0.848-0.853). Concordance between hospital rankings derived from the crude or basic risk-adjusted HO-ASE rates versus rankings derived from the intermediate or maximal models was moderate (τ 0.40-0.51) whereas concordance between rankings derived from the intermediate vs. maximal models was high (τ 0.86). There was a wide distribution of HO-ASE SIRs across facilities even after risk adjustment using the maximal model (Figure 2), with high signal-to-noise ratios and good calibration. Conclusions: Risk adjustment models incorporating hospital characteristics and patient-level data perform well and might explain substantial variability in HO-ASE rates between facilities. The persistence of residual variability after highly detailed adjustment may reflect differences in care processes, suggesting that risk-adjusted HO-ASE comparisons can help identify gaps and opportunities in the prevention of severe healthcare-associated infections. Our findings support the use of HO-ASE as an electronic, scalable, risk-adjusted metric for facility-level benchmarking to inform quality improvement initiatives.

Micronutrient deficiencies are modifiable risk factors for CVD, yet their relative importance and combined effects across populations require further characterisation. We performed a binational analysis integrating cross-sectional data from the US National Health and Nutrition Examination Survey (NHANES, 2007–2018; n 3848) and prospective data from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2018; n 11 391). Associations of specific micronutrient biomarkers (vitamin D, folate, vitamin B12, Ca and Fe) and dietary patterns with CVD were assessed using multivariable-adjusted logistic regression, Cox proportional hazards models and restricted cubic splines. A composite micronutrient deficiency score evaluated cumulative risk. In NHANES, Fe deficiency was independently associated with prevalent CVD after full adjustment (OR 1·49, 95 % CI: 1·09, 2·01), with a population prevalence of 23·4 %. Although most prevalent (33·1 %), vitamin D deficiency showed no independent association. Nonlinear analyses revealed a U-shaped relationship for Fe (P-nonlinearity = 0·003) and an inverse association for vitamin D (P-nonlinearity < 0·001). A dose–response relationship was observed for cumulative deficiencies; participants with ≥ 2 deficiencies had 91 % higher CVD odds (OR 1·91, 95 % CI: 1·20, 2·99). In CHARLS, frequent consumption of fruits/vegetables (adjusted hazard ratio (aHR) 0·81, 95 % CI: 0·70, 0·93), nuts (aHR 0·82, 0·71, 0·95) and fish (aHR 0·85, 0·74, 0·98) was associated with reduced incident CVD risk. Fe deficiency is an underrecognised independent risk factor for CVD, and multiple concurrent deficiencies synergistically increase risk. Plant-based dietary patterns are consistently protective. Findings advocate for Fe status screening in cardiovascular risk assessment and emphasise holistic nutritional approaches.

The citrus industry is vital to regional economies, and scientific land suitability evaluation is essential for spatial optimisation. This study focuses on Tongcheng County in Hubei Province, which exemplifies a typical red soil hilly region that is ecologically vulnerable in southeastern China. This study integrates Geographic Information System (GIS), Analytic Hierarchy Process (AHP), and fuzzy mathematics to develop a comprehensive land suitability evaluation model that balances factor continuity with evaluation uncertainty. Multi-source spatial data (climate, topography, soil) were used to quantify fuzzy membership functions for citrus growth. Subsequently, we conducted a weighted overlay analysis based on this quantification, which resulted in the creation of a citrus cultivation suitability evaluation map for Tongcheng County. The evaluation results categorised the study area into three distinct zones: (1) The Optimal Zone (CAI ≥ 0.75), which comprises 75.8% (857 km2) of the total study area; (2) The Suitable Zone (0.55 ≤ CAI ≤ 0.75), which constitutes 19.6% (222 km2) of the total study area; and (3) The Unsuitable Zone (CAI ≤ 0.55), which represents 4.6% (52 km2) of the total study area. The research findings aligned with the actual conditions in Tongcheng County, thereby confirming the feasibility of the employed research methodology. These outcomes address research gaps and provide a replicable methodological framework for land evaluation in mountainous regions. This approach can be directly utilised in local agricultural spatial planning and the development of land-use policies. It carries substantial practical implications for advancing sustainable agricultural development and revitalising rural areas in mountainous regions.

Parking in narrow spaces presents significant challenges, often resulting in deviations between vehicle’s final parking state (FPS) and normal FPS. These deviations can cause partial intrusion into the target parking spots, increasing the risk of collisions in the parking process and potentially making the parking spots unusable. To address these issues, this paper proposes an optimization method for both the parking path and FPS in narrow and non-ideal vertical parking scenarios. Initially, a partition-based calculation method for the minimum distance between the vehicle and obstacles (DBVO) was developed to quantitatively assess the impact of intrusion on parking safety. Following this, a predefined geometric set of clothoids is used to smooth curvature discontinuities in the parking path, and a four-phase parking path pattern is devised. Subsequently, a preferred method for parking manners is proposed by analyzing the effects of parking direction and maneuvers on spatial requirements. Finally, a two-step parking path optimization method is presented with the framework integrating both online and offline calculations, using the minimum DBVO field and a predefined path pattern. Comparative experiments demonstrate that this method could enhance the proportion of available intrusion scenarios, increase the success rate of effective path acquisition, and improve the overall quality of parking paths.

We investigate heat kernel-based and other p-energy norms ($1\lt p\lt\infty$) on bounded and unbounded metric measure spaces, in particular, on nested fractals and their blow-ups. With the weak-monotonicity properties for these semi-norms, we generalise the celebrated Bourgain–Brezis-Mironescu (BBM) type characterisation for $p\neq2$. When the underlying space admits a heat kernel satisfying the sub-Gaussian estimates, we establish the equivalence of various p-energy semi-norms and weak-monotonicity properties, and show that these weak-monotonicity properties hold when $p=2$ (that is the case of Dirichlet form). Our paper’s key results concern the equivalence and verification of various weak-monotonicity properties on fractals. Consequently, many classical results on p-energy norms hold on nested fractals and their blow-ups, including the BBM type characterisation and Gagliardo-Nirenberg inequality.

Goosegrass [Eleusine indica (L.) Gaertn.], one of the most troublesome weeds threatening global crop production, has developed resistance to glyphosate, a widely used herbicide that targets 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS; HRAC Group 9). The most commonly reported EPSPS mutations in glyphosate-resistant E. indica include Pro-106-Leu (P106L), Pro-106-Ser (P106S), and a double mutation of Thr-102-Ile with Pro-106-Ser (T102I + P106S). Although conventional methods such as polymerase chain reaction (PCR) and (derived) cleaved amplified polymorphic sequence [(d)CAPS] are widely used for mutation detection, their applicability remains limited because they are expensive, time-consuming, and technically complex. We developed a dual-mode loop-mediated isothermal amplification (LAMP) genotyping system for the rapid detection of three key glyphosate-resistance mutations (P106L, P106S, and T102I) in E. indica. The system integrated a closed-tube colorimetric assay for on-site screening and a real-time fluorescence ΔCt threshold analysis for laboratory-based quantification. Dual primer sets were designed to distinguish homozygous, heterozygous, and susceptible genotypes while preventing aerosol contamination. Validation using 150 samples demonstrated accuracies of 90% (colorimetric) and 92% (fluorescent). Sensitivity analysis revealed a 1,000-fold improvement over conventional PCR (detection limit: 5 × 10−4 vs. 5 × 10−1 ng μl−1). The closed-tube design eliminated contamination risks, and the ΔCt threshold enabled precise heterozygote identification. This cost-effective, time-saving, and high-precision system provides a robust tool for the early monitoring of glyphosate resistance in E. indica, guiding efficient control and mitigation of resistance spread in crop fields.

Bemisia tabaci is one of the most important agricultural pests worldwide, and the combined application of multiple natural enemies such as predators and parasitoids can potentially control B. tabaci. The study examined whether the predator Orius similis and the parasitoid Encarsia formosa can synergistically control B. tabaci (crop: kidney bean). The greenhouse cage method was used to release O. similis and E. formosa alone or in combination in different ratios. The combined release of O. similis and E. formosa synergistically decreased the B. tabaci population when compared with O. similis or E. formosa alone. Additionally, O. similis + E. formosa decreased the number of E. formosa black pupae and adults in each crop stage. However, the niche overlap index of E. formosa with B. tabaci nymphs in the O. similis + E. formosa group was higher than in the E. formosa group. Grey correlation analysis revealed that the correlation degree between natural enemies and B. tabaci was the highest when the O. similis and E. formosa release ratio was 1:3. These findings indicate that the combined release of O. similis and E. formosa synergistically controlled B. tabaci with the release ratio 1:3 being optimal for field application.

African swine fever (ASF) is a highly contagious animal disease caused by African swine fever virus (ASFV). It is listed by the World Organization for Animal Health (WOAH) as an animal disease subject to statutory reporting. ASFV, a large, enveloped double-stranded DNA virus with high genomic complexity, exhibits a case fatality rate of up to 100%, posing a significant threat to the global pig industry and food safety. To date, the absence of a safe commercial ASFV vaccine primarily stems from challenges in identifying immunogenic viral antigens, insufficient characterization of ASFV pathogenesis, and limited understanding of the virus’s immune evasion mechanisms. Here, we review the pathogenic characteristics (morphological structure, clinical symptoms, and epidemiological characteristics), molecular biological characteristics, and infection mechanism of ASFV, as well as the immune response mechanism, vaccine research, and the latest information on ASFV in other areas. This review will be in favour of understanding the current state of knowledge of ASF and developing effective vaccines to control this disease.

Computer vision–based precision weed control has proven effective in reducing herbicide usage, lowering weed management costs, and enhancing sustainability in modern agriculture. However, developing deep learning models remains challenging due to the effort required for weed dataset annotation and the difficulty of identifying weeds at different stages and densities in complex field conditions. To address these challenges, this study introduces an indirect weed detection method that combines deep learning and image processing techniques. The proposed approach first employs an object detection network to identify and label crops within the images. Subsequently, image processing techniques are applied to segment the remaining green pixels, thereby enabling indirect detection of weeds. Furthermore, a novel detection network—CD-YOLOv10n (You Only Look Once version 10 nano)—was developed based on the YOLOv10 framework to optimize computational efficiency. Redesigning the backbone (C2f-DBB) and integrating an optimized upsampling module (DySample) permitted the network to achieve higher detection accuracy while maintaining a lightweight structure. Specifically, the model achieved a mean average precision (mAP50) of 98.1%, which is a 1.4% percentage-point increase compared with the YOLOv10n baseline, a relevant improvement given the already strong baseline performance. At the same time, compared with YOLOv10n, its GFLOPs (giga floating-point operations per second) were reduced by 22.62%, and the number of parameters decreased by 15.87%. These innovations make CD-YOLOv10n highly suitable for deployment on resource-constrained platforms.

High-redshift protoclusters are crucial for understanding the formation of galaxy clusters and the evolution of galaxies in dense environments. The James Webb Space Telescope (JWST), with its unprecedented near-infrared sensitivity, enables the first exploration of protoclusters beyond $ z \gt 10 $. Among JWST surveys, COSMOS-Web Data Release 0.5 offers the largest area ($\sim 0.27$ deg$^2$), making it an optimal field for protocluster searches. In this study, we searched for protoclusters at $ z \sim 9-10 $ using 366 F115W dropout galaxies. We evaluated the reliability of our photometric redshift by validation tests with the JADES DR3 spectroscopic sample, obtaining the likelihood of falsely identifying interlopers as $\sim25\%$. Overdensities ($\delta$) are computed by weighting galaxy positions with their photometric redshift probability density functions, using a 2.5 cMpc aperture and a redshift slice of $\pm 0.5$. We selected the most promising core galaxies of protocluster candidate galaxies with an overdensity greater than the 95th percentile of the distribution of 366 F115W dropout galaxies. The member galaxies are then linked within an angular separation of 7.5 cMpc to the core galaxies, finding seven protocluster candidates. These seven protocluster candidates have inferred halo masses of $ M_{\text{halo}} \sim 10^{11}\,{\rm M}_{\odot} $. The detection of such overdensities at these redshifts provides a critical test for current cosmological simulations. However, confirming these candidates and distinguishing them from low-redshift dusty star-forming galaxies or Balmer-break galaxies will require follow-up near-infrared spectroscopic observations.

Small-scale topography can significantly influence large-scale motions in geophysical flows, but the dominant mechanisms underlying this complicated process are poorly understood. Here, we present a systematic experimental study of the effect of small-scale topography on zonal jets. The jet flows form under the conditions of fast rotation, a uniform background $\beta$-effect, and sink–source forcing. The small-scale topography is produced by attaching numerous small cones on the curved bottom plate, and the height of the cones is much smaller than the water depth. It is found that for all tested cases, the energy fraction in the zonal mean flow consistently follows a scaling $E_{uZ}/E_{uT}=C_1 l_f^2\epsilon _{\textit{up}}^{-2/5}\beta _{\textit{eff}}^{6/5}$, where $l_f$ is the forcing scale, $\epsilon _{\textit{up}}$ is the upscale energy transfer rate, and $\beta _{\textit{eff}}$ measures the effective $\beta$-effect in the presence of topography. The presence of the small-scale topography weakens the jet strength notably. Moreover, the effect of topography on energy transfers depends on the topography magnitude $\beta _\eta$, and there exist three regimes. At small $\beta _\eta$, the inverse energy transfers are remarkably diminished while the jet pattern remains unchanged. When $\beta _\eta$ increases, a blocked flow pattern forms, and the jet width reaches saturation, becoming independent of the forcing magnitude and $\beta$. At moderate $\beta _\eta$, the inverse energy fluxes are surprisingly enhanced. A further increase of $\beta _\eta$ leads to a greater reduction of the energy fluxes. We finally examine the effect of topography from the perspective of turbulence–topography interaction.

Previous studies highlighted the health benefits of coffee and tea, but they only focused on the comparisons between different consumptions. Consequently, the association estimate lacked a clear interpretation, as the substitution of beverages and distribution of doses were not explicitly prescribed. We focused on the ‘relative association’ to ascertain the optimal consumption strategy (including total intake and optimal allocation strategy) for coffee, tea and plain water associated with decreased mortality. Self-reported coffee, tea and plain water intake were used from the UK Biobank. Within a compositional data analysis framework, a multivariate Cox model was used to assess the relative associations after adjusting for a range of potential confounders. The lower mortality risk was observed with at least approximately 7–8 drinks/d of total consumption. When the total intake > 4 drinks/d, substituting plain water with coffee or tea was linked to reduced mortality; nevertheless, the benefit was not seen for ≤ 4 drinks/d. Besides, a balanced consumption of coffee and tea (roughly a ratio of 2:3) associated with the lowest hazard ratios of 0·55 (95 % CI 0·47, 0·64) for all-cause mortality, 0·59 (95 % CI 0·48, 0·72) for cancer mortality, 0·69 (95 % CI 0·49, 0·99) for CVD mortality, 0·28 (95 % CI 0·15, 0·52) for respiratory disease mortality and 0·35 (95 % CI 0·15, 0·82) for digestive disease mortality than other combinations. These results highlight the importance of the rational combination of coffee, tea and plain water, with particular emphasis on ensuring adequate total intake, offering more comprehensive and explicit guidance for individuals.

Magnetohydrodynamic turbulence with Hall effects is ubiquitous in heliophysics and plasma physics. Direct numerical simulations reveal that, when the forcing scale is comparable to the ion inertial scale, the Hall effects induce remarkable cross-helicity. It then suppresses the cascade efficiency, leading to the accumulation of large-scale magnetic energy and helicity. The process is accompanied by the disruption of current sheets through the entrainment by vortex tubes or the excitation of whistler waves. Using the solar wind data from the Parker Solar Probe, the numerical findings are separately confirmed. These findings provide new insights into the emergence of large-scale solar wind turbulence driven by helical fields and Hall effects.

Precision weed detection and mapping in vegetable crops are beneficial for improving the effectiveness of weed control. This study proposes a novel method for indirect weed detection and mapping using a detection network based on the You-Only-Look-Once-v8 (YOLOv8) architecture. This approach detects weeds by first identifying vegetables and then segmenting weeds from the background using image processing techniques. Subsequently, weed mapping was established and innovative path planning algorithms were implemented to optimize actuator trajectories along the shortest possible path. Experimental results demonstrated significant improvements in both precision and computational efficiency compared with the original YOLOv8 network. The mean average precision at 0.5 (mAP50) increased by 0.2, while the number of parameters, giga floating-point operations per second (GFLOPS), and model size decreased by 0.57 million, 1.8 GFLOPS, and 1.1 MB, respectively, highlighting enhanced accuracy and reduced computational costs. Among the analyzed path planning algorithms, including Christofides, Dijkstra, and dynamic programming (DP), the Dijkstra algorithm was the most efficient, producing the shortest path for guiding the weeding system. This method enhances the robustness and adaptability of weed detection by eliminating the need to detect diverse weed species. By integrating precision weed mapping and efficient path planning, mechanical actuators can target weed-infested areas with optimal precision. This approach offers a scalable solution that can be adapted to other precision weeding applications.

Visual exploration is a task in which a camera-equipped robot seeks to efficiently visit all navigable areas of an environment within the shortest possible time. Most existing visual exploration methods rely on a static camera fixed to the robot’s body to control its own movements. However, coupling the orientation of camera with robot’s body limits the extra degrees of freedom to obtain more visual information. In this work, we adjust the camera orientation during robot motion by using a novel camera view planning (CVP) policy to improve the exploration efficiency. Specifically, we reformulate the CVP problem as a reinforcement learning problem. However, two new challenges need to be addressed: 1) determining how to learn an effective CVP policy in complex indoor environments and 2) figuring out how to synchronize it with the robot motion. To solve the above issues, we create a reward function considering factors such as exploration area, observed semantic objects, and the motion conflicts between the camera and the robot’s body. Moreover, to better coordinate the policies of the camera and the robot’s body, the CVP policy takes the body actions and the egocentric 2D spatial maps with exploration, occupancy, and trajectory information into account to make motion decisions. Experimental results show that after using the proposed CVP policy, the exploration area is expanded by 21.72% and 25.6% on average in the small-scale indoor scene with few structured obstacles and large-scale indoor scene with cluttered obstacles, respectively.

In small-plot experiments, weed scientists have traditionally estimated herbicide efficacy through visual assessments or manual counts with wooden frames—methods that are time-consuming, labor-intensive, and error-prone. This study introduces a novel mobile application (app) powered by convolutional neural networks (CNNs) to automate the evaluation of weed coverage in turfgrass. The mobile app automatically segments input images into 10 by 10 grid cells. A comparative analysis of EfficientNet, MobileNetV3, MobileOne, ResNet, ResNeXt, ShuffleNetV1, and ShuffleNetV2 was conducted to identify weed-infested grid cells and calculate weed coverage in bahiagrass (Paspalum notatum Flueggé), dormant bermudagrass [Cynodon dactylon (L.) Pers.], and perennial ryegrass (Lolium perenne L.). Results showed that EfficientNet and MobileOne outperformed other models in detecting weeds growing in bahiagrass, achieving an F1 score of 0.988. For dormant bermudagrass, ResNet performed best, with an F1 score of 0.996. Additionally, app-based coverage estimates (11%) were highly consistent with manual assessments (11%), showing no significant difference (P = 0.3560). Similarly, ResNeXt achieved the highest F1 score of 0.996 for detecting weeds growing in perennial ryegrass, with app-based and manual coverage estimates also closely aligned at 10% (P = 0.1340). High F1 scores across all turfgrass types demonstrate the models’ ability to accurately replicate manual assessments, which is essential for herbicide efficacy trials requiring precise weed coverage data. Moreover, the time for weed assessment was compared, revealing that manual counting with 10 by 10 wooden frames took an average of 39.25, 37.25, and 42.25 s per instance for bahiagrass, dormant bermudagrass, and perennial ryegrass, respectively, whereas the app-based approach reduced the assessment times to 8.23, 7.75, and 14.96 s, respectively. These results highlight the potential of deep learning–based mobile tools for fast, accurate, scalable weed coverage assessments, enabling efficient herbicide trials and offering labor and cost savings for researchers and turfgrass managers.