Search Results (13,375)

As global agriculture shifts to intelligence and precision, crop attribute detection has become foundational for intelligent systems (harvesters, UAVs, sorters). It enables real-time monitoring of key indicators (maturity, moisture, disease) to optimize operations—reducing crop losses by 10–15% via precise cutting height adjustment—and boosts resource-use efficiency. This review targets harvesting-stage and in-field monitoring for grains, fruits, and vegetables, highlighting practical technologies: near-infrared/Raman spectroscopy (non-destructive internal attribute detection), 3D vision/LiDAR (high-precision plant height/density/fruit location measurement), and deep learning (YOLO for counting, U-Net for disease segmentation). It addresses universal field challenges (lighting variation, target occlusion, real-time demands) and actionable fixes (illumination compensation, sensor fusion, lightweight AI) to enhance stability across scenarios. Future trends prioritize real-world deployment: multi-sensor fusion (e.g., RGB + thermal imaging) for comprehensive perception, edge computing (inference delay < 100 ms) to solve rural network latency, and low-cost solutions (mobile/embedded device compatibility) to lower smallholder barriers—directly supporting scalable precision agriculture and global sustainable food production. Full article

Rapid and accurate diagnostics for influenza viruses are essential for preventing future epidemics. Surface-enhanced Raman spectroscopy (SERS) presents a promising alternative to conventional techniques, offering a rapid, cost-effective, and highly sensitive platform for influenza virus detection. It is a highly sensitive analytical technique that enables the detection of minute chemical substances through significant signal enhancement. It operates by illuminating a sample with a laser and analyzing the scattered light to generate a unique molecular Raman spectrum. The sensitivity of SERS is derived from its use of metal nanoparticles, which amplify the weak Raman signals, making it particularly effective for detecting low-concentration targets such as viruses. Avian influenza (AI) is a major threat to domestic poultry, leading to large-scale culling during outbreaks. It leads to economic losses globally and can also infect pigs and humans, potentially causing a pandemic. Migratory birds spread various strains, leading to the development of highly pathogenic viruses. Viral monitoring is crucial for prevention strategies and understanding the virus evolution. This review outlines the challenges in detecting AI virus in chickens and critically assesses the established and emerging diagnostic technologies, with a specific focus on the factors influencing detection and recent advances in SERS-based AI detection. Ultimately, this review aims to provide insights that will assist the influenza research community in developing novel strategies for monitoring and preventing AI outbreaks in chickens and mitigating zoonotic transmission. Full article

Lymantria xylina is a major pest in coastal casuarina shelterbelts and a species subject to quarantine regulations by countries to which it is non-native. Phototaxis is fundamental to the insect’s surveillance and risk assessment analysis, and it exhibits pronounced sexual dimorphism in compound eye structure. This dimorphism was investigated using scanning and transmission electron microscopy. Males displayed significantly larger compound eyes, characterized by greater ommatidial areas and a higher total number of facets per eye compared to females. From the distal to proximal end, the ommatidium consists of the cornea, primary and secondary pigment cells, crystalline cones, retinula cells, a rhabdom bundle, and basal retinal cells (in a “7 + 1” arrangement). The internal ultrastructure of the ommatidia is similar in both sexes. However, males possess significantly thinner cornea and extremely elongated crystalline cones. Based on external morphology, both sexes generally exhibit a parallel-symmetrical compound eye form, minimizing optical asymmetry to optimize nocturnal vision. These differences are attributed to the distinct visual demands of males for mate-searching in low-light environments, while females, being more stationary, have reduced visual needs. Paraffin sections of Lymantria xylina compound eyes further revealed that, during dark adaptation, pigment granules aggregated within the crystalline cone region to enhance low-light capture. Conversely, following intense light stimulation, these granules translocated to the perinuclear region of photoreceptor cells, forming a light-shielding configuration. Full article

Critical nitrogen concentration (Nc) and accumulation (Na) throughout the entire growth period are key indicators for diagnosing N status and implementing precision N management in cut chrysanthemum. However, direct measurement of these two parameters is both time-consuming and destructive, and establishing accurate predictive models is fundamental to their practical application. From May 2021 to July 2022, five N-gradient experiments (ranging from 14 to 574 mgf·plant⁻¹) were conducted on the cut chrysanthemum cultivar ‘Nannong Xiaojinxing’. Predictive models for Nc and Na were developed using environmental light and temperature data during growth as driving variables. The results showed that the aboveground dry matter (DM) prediction model, which utilized the cumulative photo-thermal effect (PTE) derived from these environmental factors, demonstrated superior accuracy compared to models relying on conventional driving variables. Subsequently, the Nc and Na prediction models were established with DM as the driving variable. These models indicated that at a DM level of 1 g·plant⁻¹, Nc and Na values were 4.53% and 45.30 mg·plant⁻¹, respectively. The Na reached a maximum of 236.50 mg·plant⁻¹ at the flower harvesting stage, representing the minimum N accumulation required for optimal floral quality. Using the dry matter model as a process-based model, we successfully developed predictive models for Nc and Na driven by PTE. Validation using independent experimental data confirmed the models’ high predictive accuracy, with coefficients of determination of 0.9378 and 0.9612, and low errors—root mean square errors of 0.2736% and 19.18 mg·plant⁻¹, and normalized RMSE of 10.79% and 14.94%, respectively. These models provide a foundation for implementing precision N management and reducing fertilizer application in cut chrysanthemum production. Full article

Aim: Nephrotic syndrome (NS) represents a complex glomerular disorder with significant clinical heterogeneity across pediatric and adult populations. Although glucocorticosteroids have constituted the mainstay of therapeutic intervention for more than six decades, primary treatment resistance manifests in approximately 20% of pediatric patients and 50% of adult cohorts. Steroid-resistant nephrotic syndrome (SRNS) is associated with substantially greater morbidity compared to steroid-sensitive nephrotic syndrome (SSNS), characterized by both iatrogenic glucocorticoid toxicity and progressive nephron loss with attendant decline in renal function. Based on this, the current study aims to develop a robust machine learning (ML) model integrated with explainable artificial intelligence (XAI) to distinguish SRNS and identify important biomarker candidate metabolites. Methods: In the study, biomarker candidate compounds obtained from proton nuclear magnetic resonance (1 H NMR) metabolomics analyses on plasma samples taken from 41 patients with NS (27 SSNS and 14 SRNS) were used. We developed ML models to predict steroid resistance in pediatric NS using metabolomic data. After preprocessing with MICE-LightGBM imputation for missing values (<30%) and standardization, the dataset was randomly split into training (80%) and testing (20%) sets, repeated 100 times for robust evaluation. Four supervised algorithms (XGBoost, LightGBM, AdaBoost, and Random Forest) were trained and evaluated using AUC, sensitivity, specificity, F1-score, accuracy, and Brier score. XAI methods including SHAP (for global feature importance and model interpretability) and LIME (for individual patient-level explanations) were applied to identify key metabolomic biomarkers and ensure clinical transparency of predictions. Results: Among four ML algorithms evaluated, Random Forest demonstrated superior performance with the highest accuracy (0.87 ± 0.12), sensitivity (0.90 ± 0.18), AUC (0.92 ± 0.09), and lowest Brier score (0.20 ± 0.03), followed by LightGBM, AdaBoost, and XGBoost. The superiority of the Random Forest model was confirmed by paired t-tests, which revealed significantly higher AUC and lower Brier scores compared to all other algorithms (p < 0.05). SHAP analysis identified key metabolomic biomarkers consistently across all models, including glucose, creatine, 1-methylhistidine, homocysteine, and acetone. Low glucose and creatine levels were positively associated with steroid resistance risk, while higher propylene glycol and carnitine concentrations increased SRNS probability. LIME analysis provided patient-specific interpretability, confirming these metabolomic patterns at individual level. The XAI approach successfully identified clinically relevant metabolomic signatures for predicting steroid resistance with high accuracy and interpretability. Conclusions: The present study successfully identified candidate metabolomic biomarkers capable of predicting SRNS prior to treatment initiation and elucidating critical molecular mechanisms underlying steroid resistance regulation. Full article

Apple juice is widely consumed across global beverage markets. Its colour and cloudy appearance play a crucial role in consumer perception. Various physical treatments are available to modify juice turbidity and preserve colour, among which filtration and high-pressure homogenisation are considered the most respectful to the raw juice composition. In this study, red apple juice from the Kissabel^® Rouge cultivar was filtered using membranes ranging from 100.0 to 0.2 μm and homogenised at pressures from 20 to 60 MPa. The physicochemical properties were then evaluated after 205 days of storage at two different temperatures. Microfiltration (<5.0 μm) increased juice lightness (87 vs. 66), but compromised cloud and colour stability by reducing cloudiness by 15 days compared with the unfiltered juices. Homogenisation increased turbidity, both in absolute value and during storage, which is typically appreciated in 100% apple juices. Finally, colloidal stability was affected by both treatments; combining mild filtration with low homogenisation pressure yielded the highest colloidal repulsion (−16.3 mV). Elevated storage temperatures generally diminished juice quality in terms of colour tone and intensity, and accelerated particle sedimentation. Full article

Natural fibers have been widely used for reinforcing polymers, attributed to their sustainable nature, light weight, biodegradability, and low cost compared with synthetic fibers, for example, carbon or glass fibers. The objective of this research was to promote the use of natural resource-blended polypropylene (PP) to reduce greenhouse gas emissions and to explore the potential of using grain by-products, such as coconut shell (CS), as fillers for thermoplastic materials. CS (30 wt%) is embedded in the PP matrix of the composite. Thereafter, CS/PP composites were produced utilizing a hot press compounding machine to produce the specimens and a high-speed mixer set at 3000 rpm for five minutes. The impact of coconut shell content on the mechanical and thermal properties of CS/PP composites was examined. The results show the CS/PP composite’s tensile strength and tensile modulus improved by 36% and 30%, respectively. In the meantime, the CS/PP composite’s flexural strength and flexural modulus increased by 16% and 13%, respectively. At a maximum temperature of 260 °C, the CS/PP composite demonstrated thermal stability. Due to the unprocessed particles, the coconut fiber appeared on the surface as homogenous particles. Researchers and industry professionals can use these results to help create new products. Full article

To address issues such as low visual SLAM (Simultaneous Localization and Mapping) positioning accuracy and poor map construction robustness caused by light variations, foliage occlusion, and texture repetition in unstructured orchard environments, this paper proposes an orchard robot navigation method based on an improved RTAB-Map algorithm. By integrating ORB-SLAM3 as the visual odometry module within the RTAB-Map framework, the system achieves significantly improved accuracy and stability in pose estimation. During the post-processing stage of map generation, a height filtering strategy is proposed to effectively filter out low-hanging branch point clouds, thereby generating raster maps that better meet navigation requirements. The navigation layer integrates the ROS (Robot Operating System) Navigation framework, employing the A* algorithm for global path planning while incorporating the TEB (Timed Elastic Band) algorithm to achieve real-time local obstacle avoidance and dynamic adjustment. Experimental results demonstrate that the improved system exhibits higher mapping consistency in simulated orchard environments, with the odometry’s absolute trajectory error reduced by approximately 45.5%. The robot can reliably plan paths and traverse areas with low-hanging branches. This study provides a solution for autonomous navigation in agricultural settings that balances precision with practicality. Full article

This study investigates the temperature-driven transmission switching behavior of our proposed smart glass, which utilizes a biphasic liquid crystal system under continuous application of a distinctive homeotropic (H) state voltage (V_H). By ascertaining V_H at temperatures near the phase transition point, the minimum voltage required to sustain the H state in the smectic A* (SmA*) phase is identified. Interestingly, this minimum V_H is unable to induce the H state in the chiral nematic (N*) phase, thereby maintaining a low-transmission scattering state; i.e., the focal conic (FC) state. This empowers passive, bidirectional optical switching between the transparent H state (in the SmA* phase) and the scattering FC state (in the N* phase) in an unaligned liquid crystal cell. This work employs two dissimilar chiral dopants, R811/S811 and CB7CB/R5011, both capable of inducing the SmA* phase. Neither resulting cell system underwent surface orientation treatment, and a black dye was incorporated to enhance the contrast ratio. The results indicate that the more efficacious CB7CB/R5011 system achieves a contrast ratio of 17 between the transparent and scattering states, with a corresponding haze level of 78%. To further reduce energy consumption, the experimental framework was transitioned from a continuous-voltage to a variable-voltage mode, giving rise to an increased haze level of 88%. The proposed switching scheme holds promise for diverse applications, notably in smart windows and light shutters. Full article

The response of tomato plants, Ailsa Craig and the carotenoid mutant tangerine, to five days of treatment by low light intensity at normal and low temperature with respect to the photosynthetic performance as well as their capacity to recover after three days under normal conditions was evaluated. Tangerine plants are characterized by defective prolycopene isomerase (CRTISO) and accumulate tetra-cis lycopene instead of all-trans lycopene. The gas exchange parameters were evaluated on intact plants and the pigment content in leaves was estimated. The photosynthetic competence of photosystem II (PSII) and photosystem I (PSI) and the effectiveness of the energy dissipation were assessed by pulse-amplitude-modulated (PAM) fluorometry. The abundance of reaction center proteins of PSII and PSI was estimated by immunoblotting. The application of low light alone or low light and low temperature reduced the chlorophyll content in both types of plants, which was more strongly expressed in Ailsa Craig. The net photosynthetic rate and photochemical activities of PSII and PSI were negatively affected by low light and much more strongly decreased when low light was applied at low temperature. The low-light-induced increase in excitation pressure on PSII and the effectiveness of non-photochemical quenching were not temperature-dependent. The negative effect of the combined treatment in tangerine was more strongly expressed in comparison with Ailsa Craig with respect to the abundance of reaction center proteins of both photosystems. Most probably, the differential photosynthetic response of the carotenoid mutant tangerine and Ailsa Craig to the combined treatment by low light and low temperature is related to the accumulation of tetra-cis-lycopene instead of all-trans-lycopene. Full article

Fucoxanthin is the predominant carotenoid in diatoms, playing a central role in light harvesting and photoprotection, and is increasingly valued for its potential in pharmaceutical, nutraceutical, and cosmetic applications. In this study, we investigated the influence of high nitrate supplementation, low-light exposure, and combined treatment, on fucoxanthin content and on the expression of key genes involved in its biosynthetic pathway in the marine diatom Thalassiosira rotula. Fucoxanthin content was quantified using HPLC-based and spectrophotometric methods. Control culture at the exponential growth phase showed a fucoxanthin content of 4.7 mg g⁻¹ DW, reaching 5.2 mg g⁻¹ DW under low-light conditions at the late exponential phase. Gene expression analysis revealed condition-dependent modulation of major biosynthetic genes (PSY, PDS, ZCIS, CRTISO, ZEP, VDL, DDE). Early biosynthetic genes, PSY and PDS, were upregulated under low light, whereas ZCIS and CRTISO responded to high nitrate availability. ZEP exhibited treatment-specific induction and VDL isoforms showed differential regulation, highlighting distinct xanthophyll cycle gene expression patterns across treatments. These results demonstrate that both light and nitrate availability modulate fucoxanthin content and biosynthetic gene expression in T. rotula, providing insights into the regulatory mechanisms underlying carotenoid metabolism in diatoms and proposing T. rotula as a potential candidate for fucoxanthin production. Full article

Low-light image enhancement in architectural scenes presents a considerable challenge for computer vision applications in construction engineering. Images captured in architectural settings during nighttime or under inadequate illumination often suffer from noise interference, low-light blurring, and obscured structural features. Although low-light image enhancement and deblurring are intrinsically linked when emphasizing architectural defects, conventional image restoration methods generally treat these tasks as separate entities. This paper introduces an efficient and robust Frequency-Space Recovery Network (FSRNet), specifically designed for low-light image enhancement in architectural contexts, tailored to the unique characteristics of such scenes. The encoder utilizes a Feature Refinement Feedforward Network (FRFN) to achieve precise enhancement of defect features while dynamically mitigating background redundancy. Coupled with a Frequency Response Module, it modifies the amplitude spectrum to amplify high-frequency components of defects and ensure balanced global illumination. The decoder utilizes InceptionDWConv2d modules to capture multi-directional and multi-scale features of cracks. When combined with a gating mechanism, it dynamically suppresses noise, restores the spatial continuity of defects, and eliminates blurring. This method also reduces computational costs in terms of parameters and MAC operations. To assess the effectiveness of the proposed approach in architectural contexts, this paper conducts a comprehensive study using low-light defect images from indoor concrete walls as a representative case. Experimental results indicate that FSRNet not only achieves state-of-the-art PSNR performance of 27.58 dB but also enhances the mAP of the downstream YOLOv8 detection model by 7.1%, while utilizing only 3.75 M parameters and 8.8 GMACs. These findings fully validate the superiority and practicality of the proposed method for low-light image enhancement tasks in architectural settings. Full article

Weather radar reflectivity plays a critical role in precipitation estimation and convective storm identification. However, due to terrain limitations and the uneven spatial distribution of radar stations, oceanic regions have long suffered from a lack of radar observations, resulting in extensive monitoring gaps. Geostationary meteorological satellites have wide-area coverage and near-real-time observation capability, offering a viable solution for synthesizing radar reflectivity in these regions. Most previous synthesis studies have adopted fixed time-window data partitioning, which introduces significant noise into visible-light observations under large-scale, low-illumination conditions, thereby degrading synthesis quality. To address this issue, we propose an integrated deep-learning method that combines illumination-based classification and reflectivity synthesis to enhance the accuracy of radar reflectivity synthesis from geostationary meteorological satellites. This approach integrates a classification network with a synthesis network. First, visible-light observations from the Himawari-8 satellite are classified based on illumination conditions to separate valid signals from noise; then, noise-free infrared observations and multimodal fused data are fed into dedicated synthesis networks to generate composite reflectivity products. In experiments, the proposed method outperformed the baseline approach in regions with strong convection (≥35 dBZ), with a 9.5% improvement in the critical success index, a 7.5% increase in the probability of detection, and a 6.1% reduction in the false alarm rate. Additional experiments confirmed the applicability and robustness of the method across various complex scenarios. Full article

Formamidinium lead bromide (FAPbBr₃) quantum dots (QDs) have shown potential in light-emitting diodes (LEDs). However, their performance is constrained by surface defects and the limitations of charge transport. Zwitterionic ligands, owing to their twin functions of Lewis base coordination and electrostatic compensation, passivate surface defects of perovskite QDs. Some other zwitterionic ligands are high-cost, while amino acids, as zwitterionic ligands, are inexpensive, readily available, and have efficient passivation capabilities. Their short main chain and programmable side chain can control the volume and dipole at Å-scale range through functional group selection and feed ratio regulation, achieving interface energy level engineering. This work adopts green-emitting FAPbBr₃ QDs as the model, tuning ligand properties by modifying side-chain functional groups, thereby achieving PLQY of 87.2%. Experimental results and DFT reveal that amino acids preferentially undergo coordination and can be further fine-tuned through conjugated contacts. Without severe site competition and without affecting coordination occupation and ligand uniformity, the EQE reaches 5.6% and the luminance exceeds 9000 cd/m². This low-cost technology is easily scalable and broadly manufacturable, providing a replicable material and interface design route for green zone perovskite LEDs. Full article

Thrombosis is a common complication in multiple myeloma (MM) patients treated with immunomodulatory drugs (IMiDs), including thalidomide, lenalidomide, and pomalidomide. When combined with anti-CD38 monoclonal antibodies, these agents are highly effective but may increase thrombotic events (TE), potentially delaying therapy. This exploratory, hypothesis-generating analysis, conducted within the MMVision mono-institutional prospective study, included 53 MM patients who initiated IMiD plus anti-CD38 therapy between May 2021 and December 2022 (median follow-up: 18 months). Treatment regimens comprised lenalidomide (n = 36) or thalidomide (n = 15) with daratumumab, and pomalidomide (n = 2) with isatuximab. Most patients (n = 38) received frontline therapy, and all were given thromboprophylaxis according to guidelines, mainly aspirin (73%). Five patients (9.4%) developed VTE after a median of 48 days, managed with short-term low-molecular-weight heparin (LMWH). Exploratory analysis of 27 clinical/laboratory parameters suggested possible associations between VTE and low levels of beta-2 microglobulin, ferritin, intact/free lambda light chains, and monocyte-to-lymphocyte ratio. Notably, four of the five VTEs occurred in patients without lytic bone disease, typically associated with bone-driven inflammation in MM. Although all patients received aspirin prophylaxis from treatment initiation, it remains unclear whether thrombosis would also have occurred among those with higher inflammatory burden. These preliminary observations may indicate that in patients with relatively lower inflammation, aspirin prophylaxis could be less effective, potentially favoring VTE onset. In two VTE cases, cytokine profiling showed decreased M-CSF, SCLF-β, and MIP-1α, with increased G-CSF, raising the hypothesis of distinct immune-inflammatory pathways contributing to TEs. Given the limited number of patients and thrombotic events, and the cytokine data available for only two VTE cases, these associations should be regarded as exploratory and interpreted with caution. Overall, these exploratory findings warrant validation in larger, independent cohorts and may help generate hypotheses on how inflammatory signatures influence thrombotic risk and prophylaxis efficacy in MM patients receiving IMiD/anti-CD38-based regimens. Full article