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Traditional mechanized chestnut harvesting is too costly for small producers, non-selective, and prone to damaging nuts. Accurate, reliable detection of chestnuts on the orchard floor is crucial for developing low-cost, vision-guided automated harvesting technology. However, developing a reliable chestnut detection system faces challenges in complex environments with shading, varying natural light conditions, and interference from weeds, fallen leaves, stones, and other foreign on-ground objects, which have remained unaddressed. This study collected 319 images of chestnuts on the orchard floor, containing 6524 annotated chestnuts. A comprehensive set of 29 state-of-the-art real-time object detectors, including 14 in the YOLO (v11–v13) and 15 in the RT-DETR (v1–v4) families at various model scales, was systematically evaluated through replicated modeling experiments for chestnut detection. Experimental results show that the YOLOv12m model achieved the best mAP@0.5 of 95.1% among all the evaluated models, while RT-DETRv2-R101 was the most accurate variant among the RT-DETR models, with mAP@0.5 of 91.1%. In terms of mAP@[0.5:0.95], the YOLOv11x model achieved the best accuracy of 80.1%. All models demonstrated significant potential for real-time chestnut detection, and YOLO models outperformed RT-DETR models in terms of both detection accuracy and inference, making them better suited for on-board deployment. This work lays a foundation for developing AI-based, vision-guided intelligent chestnut harvest systems.

Orbital angular momentum (OAM) multiplexing holography has emerged as a pivotal technology for high-capacity optical communication, encryption and display, but it requires multiple inputs for decoding and its security remain constrained due to the rotational symmetry of topological charge (TC) distribution in conventional OAM modes. Here, we introduce a general paradigm of OAM multiplexing holography that enables multi-channel holographic encoding using a single incident light. Our methodology leverages a discontinuous OAM with a spatially varying TC across the azimuth, which breaks the rotational symmetry and imposes angular selectivity for information retrieval. Notably, by rationally designing the TC distribution, the discontinuous OAM exhibits self-orthogonality at different rotation angles, laying the foundation for multiplexed holography. A modified weighted Gerchberg-Saxton algorithm is developed to calculate the holographic phase profile, which can then be encoded onto a pure geometry-phase metasurface. By further integrating different pairs of discontinuous OAMs, we successfully expand the channel capacity for holographic multiplexing, significantly advancing high-security and high-capacity optical information encryption. Our work establishes discontinuous OAM as a versatile platform for secure optical communications, high-density data storage, and dynamic holographic displays, bridging the gap between structured light manipulation and cryptographic robustness. Gao et al. realized a discontinuous orbital angular momentum metasurface holography, which enhances the channel capacity for holographic multiplexing and makes significant strides in high-security optical information encryption.

Exceptional points (EPs) are spectral singularities of non-Hermitian systems and represent the coalescence of eigenvalues and eigenstates. Traditional photonic systems typically exhibit coalesced eigenstates that correspond to circular polarizations of a specific handedness, thereby restricting their applicability to only the poles of the Poincaré sphere. Here, by judiciously combining optical anisotropy, chirality and non-Hermiticity of diffractive plasmonic metasurfaces with basis transformation, we achieve a continuum of EPs for which the corresponding coalesced eigenstates can access any point on the Poincaré sphere, greatly alleviating the strict requirement of approaching EP degeneracy. Our theoretical proposal and experimental implementation overcome the main practical limitation of EPs, extending the applicability of the topological phase to arbitrarily polarized state within the diffraction region. The emergence of these non-conventional EPs not only contributes to applications in wavefront engineering and optical multiplexing, but also brings in new fundamental properties of topological systems in general. Qin et al. realized a plasmonic exceptional point distribution that covers full Poincaré sphere based on extrinsic chirality and basis transformation, extending the application of singularity induced topological phase to arbitrary polarization states.

With the increasing interest in the application of electrochromism to flexible and wearable electronics in recent years, flexible electrochromic devices (ECDs) that can function at extreme temperatures are required. However, the functionalities of flexible ECDs are severely hampered by the inadequate choice of electrolytes, which might ultimately result in performance fading during low‐ and high‐temperature operations. Here, we develop a deep eutectic solvent (DES)‐based gel electrolyte that can maintain its optical, electrical, and mechanical properties over a wide range of temperatures (−40 to 150°C), exhibiting an extremely high visible‐range transmittance over 90%, ion conductivity of 0.63 mS cm−1, and fracture strain exceeding 2000%. Owing to the excellent processability of the DES‐based electrolytes, provided by dynamic interactions such as the lithium and hydrogen bonding between the DES and polymer matrix, a directly written patterning in ECDs is realized for the first time. The fabricated ECDs exhibit an excellent electrochromic behavior superior to the behavior of the ECDs fabricated with traditional gel electrolytes. The introduction of such DES‐based electrolytes is expected to pave the way for a widespread application of electrochromic products. The gel electrolyte constructed with deep eutectic solvent (DES) and acrylate‐based copolymer enables a direct writing electrolyte layer for patterning in flexible electrochromic devices (ECDs), which can maintain its excellent optical transparency, electrical, and mechanical properties over a wide span of temperature ranging from −40 to 150°C.

Metasurfaces excel at local, spatially varying control of wavefronts, whereas photonic crystals (PhCs) are admired for their nonlocal resonances such as bound states in the continuum (BICs). These two regimes—local control and nonlocal collective response—have long been viewed as difficult to integrate within a single platform. Here, we introduce local-nonlocal assisted multifunctional PhCs unifying wavefront shaping and BICs by embedding meta-notches within PhC pillars. The locally tunable notches generate spectral-zero-assisted topological phase for efficient 2π coverage, while the strongly confined BIC modes remain largely unperturbed, preserving high- Q nonlocal resonances. This constructive local-nonlocal integration synthesizes the design freedom of metasurfaces with the dispersive resonance of PhCs in a single planar device. Our approach extends the capabilities of flat optics, enabling multifunctional PhCs and opening pathways toward higher-order topologies, advanced imaging, communication, and analogue optical computing.

Computer vision with artificial intelligence (AI) offers a promising tool for blueberry growers to accomplish orchard tasks such as harvest maturity assessment and yield estimation, which otherwise would be labor-intensive and prone to error. However, blueberry detection in natural environments remains challenging due to variable natural lighting, frequent occlusions by leaves and branches, and motion blur due to environmental factors and imaging devices. AI models such as deep learning-based object detectors promise to address these challenges, but they are data-driven, demanding a large-scale, diverse dataset that captures the complexities of real-world orchard conditions. Deployment of these models in practical scenarios often faces limited computing resources, highlighting the importance of achieving the right accuracy/speed/memory trade-off in model selection. This study presents a novel comparative benchmark analysis of advanced real-time object detectors, including YOLO (You Only Look Once) (v8–v12) and RT-DETR (Real-Time Detection Transformers) (v1–v2) families, consisting of 36 model variants, evaluated on a newly curated large dataset for blueberry detection. This dataset contained 661 canopy images collected with smartphones during the 2022–2023 seasons, consisting of 85,879 manually annotated instances (including 36,256 ripe and 49,623 unripe blueberries) that represent a broad range of lighting conditions, occlusions, and fruit maturity stages. Among the YOLO models, YOLOv12m achieved the best accuracy with a mAP@50 of 93.3%, while RT-DETRv2-X obtained a mAP@50 of 93.6%, the highest among all RT-DETR variants. The inference time varied with the model scale and complexity, and the mid-sized models appeared to offer a good balance between accuracy and speed. To further improve fruit detection performance, all models were fine-tuned using Unbiased Mean Teacher-based semi-supervised learning (SSL) with 1644 cross-source unlabeled canopy images acquired from ground-based machine vision platforms. SSL resulted in accuracy improvements of up to 2.0%, with RT-DETR-v2-X achieving the highest mAP@50 of 95.5%. These findings highlight the efficacy of SSL for leveraging cross-domain unlabeled data, although further research is needed to fully exploit its benefits. The curated dataset and developed software programs are publicly available to facilitate further research and practical deployment.

Traditional mechanized chestnut harvesting is too costly for small producers, non-selective, and prone to damaging nuts. Accurate, reliable detection of chestnuts on the orchard floor is crucial for developing low-cost, vision-guided automated harvesting technology. However, developing a reliable chestnut detection system faces challenges in complex environments with shading, varying natural light conditions, and interference from weeds, fallen leaves, stones, and other foreign on-ground objects, which have remained unaddressed. This study collected 319 images of chestnuts on the orchard floor, containing 6524 annotated chestnuts. A comprehensive set of 29 state-of-the-art real-time object detectors, including 14 in the YOLO (v11-13) and 15 in the RT-DETR (v1-v4) families at varied model scales, was systematically evaluated through replicated modeling experiments for chestnut detection. Experimental results show that the YOLOv12m model achieves the best mAP@0.5 of 95.1% among all the evaluated models, while the RT-DETRv2-R101 was the most accurate variant among RT-DETR models, with mAP@0.5 of 91.1%. In terms of mAP@[0.5:0.95], the YOLOv11x model achieved the best accuracy of 80.1%. All models demonstrate significant potential for real-time chestnut detection, and YOLO models outperformed RT-DETR models in terms of both detection accuracy and inference, making them better suited for on-board deployment. Both the dataset and software programs in this study have been made publicly available at https://github.com/AgFood-Sensing-and-Intelligence-Lab/ChestnutDetection.

Blueberry detection in natural environments remains challenging due to variable lighting, occlusions, and motion blur due to environmental factors and imaging devices. Deep learning-based object detectors promise to address these challenges, but they demand a large-scale, diverse dataset that captures the real-world complexities. Moreover, deploying these models in practical scenarios often requires the right accuracy/speed/memory trade-off in model selection. This study presents a novel comparative benchmark analysis of advanced real-time object detectors, including YOLO (You Only Look Once) (v8-v12) and RT-DETR (Real-Time Detection Transformers) (v1-v2) families, consisting of 36 model variants, evaluated on a newly curated dataset for blueberry detection. This dataset comprises 661 canopy images collected with smartphones during the 2022-2023 seasons, consisting of 85,879 labelled instances (including 36,256 ripe and 49,623 unripe blueberries) across a wide range of lighting conditions, occlusions, and fruit maturity stages. Among the YOLO models, YOLOv12m achieved the best accuracy with a mAP@50 of 93.3%, while RT-DETRv2-X obtained a mAP@50 of 93.6%, the highest among all the RT-DETR variants. The inference time varied with the model scale and complexity, and the mid-sized models appeared to offer a good accuracy-speed balance. To further enhance detection performance, all the models were fine-tuned using Unbiased Mean Teacher-based semi-supervised learning (SSL) on a separate set of 1,035 unlabeled images acquired by a ground-based machine vision platform in 2024. This resulted in accuracy gains ranging from -1.4% to 2.9%, with RT-DETR-v2-X achieving the best mAP@50 of 94.8%. More in-depth research into SSL is needed to better leverage cross-domain unlabeled data. Both the dataset and software programs of this study are made publicly available to support further research.

Background Fatty liver hemorrhagic syndrome (FLHS), a fatty liver disease in laying hens, poses a grave threat to the layer industry, stemming from its ability to trigger an alarming plummet in egg production and usher in acute mortality among laying hens. Increasing evidence suggests that the onset and progression of fatty liver was closely related to mitochondria dysfunction. Sodium butyrate was demonstrated to modulate hepatic lipid metabolism, alleviate oxidative stress and improve mitochondrial dysfunction in vitro and mice models. Nevertheless, there is limited existing research on coated sodium butyrate (CSB) to prevent FLHS in laying hens, and whether and how CSB exerts the anti-FLHS effect still needs to be explored. In this experiment, the FLHS model was induced by administering a high-energy low-protein (HELP) diet in laying hens. The objective was to investigate the effects of CSB on alleviating FLHS with a focus on the role of CSB in modulating mitochondrial function. Methods A total of 288 healthy 28-week-old Huafeng laying hens were arbitrarily allocated into 4 groups with 6 replicates each, namely, the CON group (normal diet), HELP group (HELP diet), CH500 group (500 mg/kg CSB added to HELP diet) and CH750 group (750 mg/kg CSB added to HELP diet). The duration of the trial encompassed a period of 10 weeks. Results The result revealed that CSB ameliorated the HELP-induced FLHS by improving hepatic steatosis and pathological damage, reducing the gene levels of fatty acid synthesis, and promoting the mRNA levels of key enzymes of fatty acid catabolism. CSB reduced oxidative stress induced by the HELP diet, upregulated the activity of GSH-Px and SOD, and decreased the content of MDA and ROS. CSB also mitigated the HELP diet-induced inflammatory response by blocking TNF-α, IL-1β , and F4/80. In addition, dietary CSB supplementation attenuated HELP-induced activation of the mitochondrial unfolded protein response (UPRmt), mitochondrial damage, and decline of ATPase activity. HELP diet decreased the autophagosome formation, and downregulated LC3B but upregulated p62 protein expression, which CSB administration reversed. CSB reduced HELP-induced apoptosis, as indicated by decreases in the Bax / Bcl-2 , Caspase-9 , Caspase-3, and Cyt C expression levels. Conclusions Dietary CSB could ameliorate HELP diet-induced hepatic dysfunction via modulating mitochondrial dynamics, autophagy, and apoptosis in laying hens. Consequently, CSB, as a feed additive, exhibited the capacity to prevent FLHS by modulating autophagy and lipid metabolism. Graphical Abstract

During the aging process of laying hens, hepatic oxidative stress damage and lipid accumulation are prone to occur, leading to the deterioration of egg quality and a decline in production properties. This research was designed to explore the effects of different levels of coated sodium butyrate (CSB) addition on oxidation resistance, inflammatory reaction, lipid metabolism and hepatic oxidative damage-related gene expression in aged laying hens. A total of 720 healthy 52 weeks old Huafeng laying hens were arbitrarily divided into 5 groups of 6 replicates with 24 birds each and fed a basal diet supplemented with 0, 250, 500, 750 and 1000 mg/kg CSB for 8 weeks, respectively. The CSB quadratically upgraded GSH-Px activities and downgraded MDA content in the liver and serum. The LDL-C, NEFA and TG contents decreased quadratically in CSB groups and significantly reduced the fatty vacuoles as well as the formation of fat granules in the liver (p < 0.05). Meanwhile, the CSB quadratically upregulated the gene expression of IL-10, Nrf2 and HO1, but downregulated the gene expression of IFN-γ, TNF-α and Keap1 in a quadratic manner (p < 0.05). Moreover, the CSB quadratically degraded the mRNA level of fatty acid synthesis but increased the gene level of key enzymes of fatty acid catabolism (p < 0.05). In conclusion, dietary CSB supplementation has a favorable effect in protecting against liver injury and alleviating lipid accumulation and inflammation by enhancing hepatic antioxidative function in aged laying hens.