Search Results (1,170)

Morels (Morchella spp.) are highly valued edible fungi, but their cultivation remains largely dependent on seasonal field conditions. In this study, we established a stable off-season industrial cultivation system for Morchella eximia G10 through precise environmental regulation, including substrate and ambient temperature, humidity, and irrigation management. Based on this controllable cultivation platform, integrated transcriptomic and metabolomic analyses were conducted to explore developmental reprogramming from vegetative growth to fruiting body maturation. Metabolomic profiling was performed only at the reproductive stages, whereas transcriptomic analysis covered both vegetative and reproductive stages. Low-temperature stimulation was associated with marked increases in the expression of genes involved in cell wall remodeling, carbohydrate metabolism and stress responses, together with downregulation of genes annotated with oxidoreductase- and hydrolase-related functions. The transition to the primordium stage represented the most dynamic developmental transition and was associated with transcriptional regulation, cytoskeletal organization, and signal transduction pathways. Subsequently, distinct developmental trajectories were observed between pileus and stipe tissues, suggesting tissue-specific regulatory programs during fruiting body maturation. In the pileus, early amino acid- and nitrogen-related metabolism was coordinated with transcriptional changes, followed by a shift toward carbon and lipid metabolism. In the stipe, early development featured elevated aminoacyl-tRNA biosynthesis and translational capacity, followed by later adjustments in carbon, sulfur and lipid metabolism. These results provide a tissue-resolved multi-omics view of developmental reprogramming during the cold-treatment-associated transition from vegetative growth to fruiting in Morchella under controlled off-season cultivation. By linking environmental cues with stage- and tissue-specific transcriptional and metabolic programs, this work provides additional insights into reproductive development and extends previous descriptive omics studies, while offering a molecular basis for optimizing environmental regulation and improving the stability of industrial morel production. Full article

Fruit drying transforms a living, water-rich tissue into a stable food through coupled changes in moisture distribution, structure, color, nutrients, and aroma. Although drying technologies and non-destructive sensing have advanced rapidly, these fields have largely developed in parallel, leaving the relationship between quality formation and measurable process signals insufficiently resolved. Here, physical and chemical changes during drying are connected to the signals that can support quality prediction. Current evidence shows that moisture loss and surface appearance are the most tractable real-time targets. Texture, bioactive retention, and flavor remain less accessible because their signals depend more strongly on internal structure, reference chemistry, or sensory response. Optical, magnetic-resonance, thermal, volatile-sensing, and electrical approaches consequently provide complementary rather than interchangeable views of the product. Multimodal models improve prediction when the added signals resolve different aspects of drying, but redundant inputs can increase complexity without improving transferability. Progress toward intelligent fruit drying therefore depends on matching sensors to the evolving product state, validating models beyond individual batches and instruments, and linking predictions to practical process decisions. This process–quality perspective provides a basis for moving from retrospective quality assessment toward reliable monitoring and controlled drying. Full article

Vehicle-mounted vision sensing, in which cameras on patrol vehicles acquire ground-level imagery across a road network, offers a low-cost, scalable approach to large-scale road surface monitoring and pothole detection. However, its real-world effectiveness depends not only on detector accuracy on curated imagery but also on two rarely reported factors: field acquisition conditions and pipeline architecture. We present a multi-year field study of a vehicle-based pothole-monitoring system. First, controlled field comparisons show how acquisition geometry and device thermal behavior affect detection yield: across three vehicle types, an actively cooled windshield mount achieved yields of 41.7–58.3%, versus 0–4.2% for a rear-view-mirror baseline. Second, we identify a shared-bias limitation in a homogeneous two-stage detector cascade, in which a second You Only Look Once (YOLO) detector reproduces rather than eliminates first-stage false positives. Third, we redesign the pipeline as a heterogeneous detection-to-classification cascade: an edge-deployed YOLOv12-large detector proposes candidate regions, and a server-side EfficientNet-B2 classifier verifies each. On field data, this raised the detection mean average precision (mAP) from 32.7% to 69.5% and reduced the false-positive rate from 99.96% to 32.26%, cutting manual inspection workload by about 70%. Unlike prior work that mainly optimizes detector architectures, this study shows that acquisition geometry and heterogeneous verification dominate real-world sensing performance under operational deployment. Full article

We develop a design-conditional limit theory for kernel estimators of conditional U-functionals based on locally stationary functional random fields observed at irregular random locations and under incomplete response observation. The covariates take values in a separable Hilbert space, the responses are allowed to take values in a general Polish space, and the target is indexed by a class of symmetric kernels of a fixed order. Functional localization is induced by single-index semi-metrics, while spatial localization is performed on the rescaled observation domain. Missing responses are incorporated through a complete-case construction under a Missing At Random condition and a uniform-positivity assumption. The resulting estimator is a ratio of spatially weighted U-statistics with random tuplewise observation indicators. The asymptotic analysis must account simultaneously for four sources of complexity: dependence within the spatial field, nonstationarity across an expanding domain, concentration in an infinite-dimensional covariate space, and the random thinning generated by missing responses. Conditioning on the sampling locations removes the randomness of the spatial design weights but does not eliminate dependence among the observations. We therefore derive a design-conditional projection decomposition adapted to the triangular-array structure of the model. The leading component is represented by a spatially dependent complete-case empirical process, whereas the higher-order canonical terms are controlled uniformly over the response kernels, functional-target points, single-index directions, and rescaled spatial locations. The proofs combine stationary tangent-field approximations for locally stationary random fields, large-block–small-block decompositions, coupling arguments under spatial absolute regularity, small-ball probability estimates, and entropy bounds for the joint indexing class. These arguments yield a uniform stochastic expansion in which the empirical fluctuation, the spatial–functional smoothing bias, and the local-stationarity approximation error appear as distinct contributions. In particular, the local-stationarity remainder has no counterpart in the strictly stationary theory and quantifies the cost of replacing the observed nonstationary field with its stationary tangent approximation. Under the MAR and positivity conditions, complete-case sampling reduces the effective local information and modifies the covariance structure, but it does not change the formal order of the uniform-convergence rate. Under strengthened moment, mixing, entropy, and negligibility conditions, we establish weak convergence of the normalized conditional U-process in the corresponding supremum-norm function space to a tight centered Gaussian process. The limiting covariance is determined by the complete-case first-order projection and consequently retains the effect of the observation propensity and the spatial dependence structure. We also introduce a complete-case leave-tuple-out spatial prediction criterion for bandwidth selection and prove oracle optimality over admissible bandwidth families. The general theory applies to conditional rank association, discrimination probabilities, set-indexed conditional distribution functionals, and related pairwise statistical-learning criteria. Simulation experiments and applications to spatial environmental and epidemiological data illustrate the finite-sample implications of the theory and the stabilizing role of single-index localization. Viewed through the lens of data-driven science, the framework addresses a fundamental asymmetry between the information carried by irregular, locally heterogeneous functional covariates and the selectively observed response tuples. By combining design conditioning, complete-case normalization, tangent-field localization, and single-index dimension reduction, the proposed approach resolves this inferential asymmetry at the level of the model by matching estimation and uncertainty quantification to the information actually available locally, without imposing artificial stationarity or complete-data symmetry. Full article

Lensless coded ptychography (CP) acquires multiple intensity measurements by translating either the object or the coded sensor, and reconstructs high-resolution, large field-of-view images via iterative phase retrieval. However, conventional CP suffers from slow reconstruction due to two limitations. First, periodic uniform scanning requires dense measurements to maintain sufficient measurement diversity, resulting in a large number of raw measurements and a high computational burden for iterative phase retrieval. Second, random initialization leads to slow convergence and increases the risk of stagnation in local minima. To address these limitations, we propose a joint optimization of the sampling strategy and initialization for accelerated reconstruction. Specifically, a continuous non-uniform scanning strategy preserves measurement diversity while reducing the number of required measurements. In addition, a low-resolution regularized ptychographic iterative engine (rPIE)-based initialization provides a more accurate starting point and accelerates the convergence of iterative phase retrieval. Both simulations and experiments demonstrate that the proposed approach achieves approximately three-fold faster convergence while maintaining high reconstruction fidelity. The proposed approach offers an effective solution for high-throughput imaging applications, including digital pathology and label-free quantitative phase imaging. Full article

Automated wildlife monitoring systems are essential for studying bat populations in natural environments, where nocturnal behavior, high flight speeds, and limited illumination make conventional observation difficult. This paper presents a modular multi-sensor vision system that integrates RGB, near-infrared (NIR), and depth cameras for automated bat monitoring. The proposed architecture consists of one main module and two secondary modules that can be configured into multiple operating modes according to monitoring requirements. The main module operates independently to perform real-time habitat reconstruction using an integrated depth camera or bat detection using a YOLO-based model. When combined with one secondary module, it forms a stereo vision system for three-dimensional localization; when combined with both secondary modules, it generates panoramic images that substantially expand the field of view for monitoring large cave entrances and other complex environments. The proposed modular architecture enables flexible deployment while supporting multiple sensing configurations within a single platform. The modular design provides scalability, geometric consistency through multi-sensor calibration, and flexible deployment, enabling accurate bat detection, habitat reconstruction, and wide-area monitoring within a unified sensing framework. The proposed system provides a versatile and scalable solution for adapting wildlife monitoring to different environmental conditions and observation scenarios. Experimental results demonstrate a detection precision of 0.893, a panoramic field of view of 119°, and real-time processing at 60 fps, validating the effectiveness of the proposed modular architecture. Full article

This study takes the Ankang area in the Qinling–Dabashan Mountains on the northern margin of the Yangtze Block as an example to investigate whether effective shale gas preservation conditions can exist in large-scale thrust nappe belts at convergent plate margins—a critical scientific question. The aim is to provide new concepts and models for shale gas exploration in tectonically complex regions. An integrated approach combining surface geological mapping, geophysical surveying (2D seismic and wide-field electromagnetic method), calibration of a key borehole (ZBDR01), and geochemical analysis was employed to reconstruct the deep geological structure and evaluate the hydrocarbon generation potential and reservoir characteristics of the target shale interval. The results reveal a relatively gentle, weakly deformed “structural stability window” beneath the Zhongbao Fault, a major thrust nappe surface. Within this window, strata dip at low angles and faults are sparse, exhibiting a significant stress-shielding effect. The Lower Cambrian Niutitang Formation shale within this window is well preserved, characterized by high total organic carbon (average TOC: 4.26%) and moderate thermal maturity (average R_o = 3.02%), falling within the effective shale gas generation window. In contrast, the Lujiaping Formation shale in the hanging wall of the fault, though widely distributed, shows excessive thermal maturity and poor reservoir properties. The study demonstrates that the “stress-shielding” effect is the core mechanism controlling the formation of this stability window and proposes a new “tectonic shielding” accumulation model. This model elucidates how the thrust nappe body itself acts as a thick regional caprock, which together with lateral sealing by the fault zone forms a composite seal-cap system, ensuring in situ preservation of shale gas under a strongly tectonic background. It is concluded that local preservation units can form in the footwalls of thrust nappe belts at convergent plate margins due to stress shielding, challenging the conventional view that intensely deformed zones are unfavorable for shale gas preservation. This research not only provides a new direction and model for shale gas exploration in the tectonically complex Qinling–Dabashan region but also offers important theoretical and technical insights for unconventional hydrocarbon exploration in similar tectonic settings globally. Full article

Carried out with 30 students enrolled in the Computer Technologies Department of a vocational school at a public university in Türkiye, this qualitative study examines university students’ perceptions and institutional expectations regarding the integration of sustainable artificial intelligence (AI) in universities. Data were collected through semi-structured interviews. The interview protocol was developed by the researchers and refined based on feedback from three field experts to ensure content validity. Before the main data collection, the interview form was reviewed and refined through an initial exploratory application with a small group of participants to assess the clarity and comprehensibility of the questions and to identify potential issues in the data collection process. The data analysis was conducted using qualitative content analysis supported by MAXQDA software. The findings indicate that students generally associate sustainable AI with energy efficiency, environmental responsibility, and ethical use of technology. Participants demonstrated awareness of large-scale AI systems’ environmental impacts. Although students viewed the development of environmentally friendly AI systems positively, they expressed concerns about ethical boundaries, excessive energy consumption, and the absence of clear regulatory frameworks. Overall, the findings suggest that structured content on the environmental, ethical, and social dimensions of AI should be integrated into vocational higher education curricula. Full article

Holographic stereograms record large sets of multi-view projection images into elementary hologram units (hogels), and their visual quality is governed primarily by the quality and inter-view consistency of the input multi-view imagery. Conventional photogrammetry-based pipelines for generating such imagery rely on surface meshes and require burdensome post-processing, which limits the fidelity of thin structures and view-dependent reflections. This paper proposes an integrated workflow that replaces the photogrammetric stage with Gaussian splatting and presents a controlled comparison of volumetric 3D Gaussian splatting (3DGS) and surface-aligned 2D Gaussian splatting (2DGS) for hogel-based digital hologram production. Using 397 drone-captured images of a 3 m bronze Pegasus statue, both representations were trained under identical input data, camera poses, and hyperparameters, and 119,808 full-parallax multi-view images (768 × 156 grid; 120° × 52° field of view) were rendered from each model, converted into hogel arrays by an identical ray-tracing transform, and printed onto Ultimate U04 silver halide plates under identical optical conditions. In the digital domain, 3DGS outperformed 2DGS on all three standard novel-view-synthesis metrics (PSNR 32.68 vs. 31.03 dB; SSIM 0.9281 vs. 0.9139; and LPIPS 0.1150 vs. 0.1406) while using approximately 1.67 times more Gaussians. Conversely, 2DGS produced superior results at viewpoints outside the training distribution, on thin structures such as wings and mane, and at the extremes (±60°) of the virtual camera array, and these differences propagated consistently to the printed holograms, whose edge sharpness was higher for 2DGS at every measured viewpoint (mean 39.6 vs. 23.8). Notably, 2DGS achieved this superior printed-output quality while using approximately 40% fewer Gaussians than 3DGS, combining model efficiency with the multi-view consistency that proved decisive for hogel-based printing. The results demonstrate that single-view metrics such as PSNR do not capture the multi-view consistency that dominates hogel-based output quality and provide practical guidance for representation selection in holographic-printing workflows. Full article

Reliable image-based permeability estimation in tight porous media depends strongly on how pore and throat geometries are resolved across scales. This study investigates the influence of image resolution on pore characterization and permeability estimation in tight sandstone using true multiscale scanning electron microscopy (SEM). A fixed sandstone region was imaged at three resolutions—S1 (0.1 μm/pixel), S4 (0.05 μm/pixel), and S16 (0.025 μm/pixel)—and spatially registered to ensure the same field of view across scales. Porosity, pore roundness, fractal dimension, pore size distribution, and permeability were extracted and compared. With increasing resolution, more fine pores are identified, porosity rises from 4.6% (S1) to 5.55% (S4) and 6.31% (S16), pore roundness and fractal dimension increase, indicating greater complexity and fine-scale heterogeneity. Meanwhile, the pore size distribution narrows and shifts towards smaller pores as large merged pores at low resolution are decomposed into multiple micropores. Permeability derived from individual images becomes more spatially variable at higher resolutions, but the overall permeability decreases, with only a small additional change from S4 to S16. The values at the S4 and S16 scales (1.77 × 10⁻¹⁷ m² and 1.72 × 10⁻¹⁷ m²) agree well with the measured gas permeability of 1.85 × 10⁻¹⁷ m². These results indicate that image resolution exerts systematic control on transport-relevant pore descriptors and image-based permeability. Within the investigated resolution range, further refinement from S4 to S16 reveals additional fine-scale heterogeneity but produces only a limited change in the overall permeability estimate. The findings, therefore, highlight the importance of balancing image resolution and field-of-view representativeness in digital-rock workflows aimed at pore-scale transport analysis and permeability upscaling. Full article

Robot-centric elevation maps built from onboard sensing are always incomplete: occlusions, a limited field of view, and range limits leave large unobserved regions that traversability analysis and motion planning must still reason about. We present a supervised framework that completes these maps and reports a per-cell uncertainty. Its core is a ray-cone augmentation that removes angular sectors anchored at the sensor origin during training; unlike the random masks of image inpainting, these sectors match the coverage gaps of real deployments, such as camera failures or reduced camera configurations. Partial maps generated from four depth cameras along legged-robot trajectories in the TartanGround dataset are paired with dense ground truth, yielding 32,329 samples across five outdoor environments. An encoder–decoder network is trained with a masked $\beta$-NLL loss and evaluated with a five-fold leave-one-environment-out protocol. The augmentation lowers the completion error on missing sensor sectors by 8.3 to 9.7%, depending on the sector width, at no measurable cost on uncorrupted partial inputs. The completed maps reach a hole root-mean-square error of 2.86 m, a 45% improvement over the strongest classical interpolation baseline. Full article

Single-UAV dense visual SLAM is often limited by long trajectory accumulation, incomplete local observations, redundant map growth, and onboard computation constraints. Collaborative mapping can distribute a large mission across several local submaps, but dense 3D Gaussian Splatting (3DGS) maps are expensive to exchange and individual Gaussian primitives are not reliable cross-agent matching units. This paper proposes G³M-SLAM, an anchor-guided Gaussian memory framework for UAV-oriented dense SLAM with representation-level submap fusion. Stable geometric anchors organize local Gaussian primitives and form a compact structural interface for submap exchange, overlap recognition, and correction. A hybrid feature-render tracking strategy combines sparse geometric constraints with Gaussian rendering residuals. A generative completion module predicts candidate Gaussians in weakly observed regions, while multi-view geometric verification and an evidence-aware lifecycle mechanism reject unsupported candidates and control redundant map growth. A dual-graph loop bundle adjustment couples the camera pose graph and the anchor memory graph so that corrections can be propagated to anchor-associated Gaussian structures. Experiments on Replica, ScanNet, TUM RGB-D, and EuRoC MAV evaluate local tracking, dense rendering, runtime, and a split-agent fusion protocol. In the latter protocol, fusion reduces ATE from 0.045 m to 0.031 m, translational RPE from 0.030 m to 0.017 m, and rotational RPE from $1.{56}^{°}$ to $0.{93}^{°}$. The serialized anchor packet is 0.66 MB, approximately $101.2\times$ smaller than the complete Gaussian submap. On Jetson AGX Orin, the full system processes EuRoC V101 and V103 at 1.49 FPS and 1.42 FPS, respectively, while ATE increases by only 0.001 m relative to the RTX 3090 Ti workstation results. These gains represent recovery from split-agent degradation and compact representation exchange rather than an improvement over full-sequence single-agent processing. The present study therefore evaluates a representation-level fusion interface and does not reproduce the full conditions of a field-deployed decentralized multi-UAV system. Full article

Active surface shape control is a key engineering technology enabling high-frequency operation and high-performance observations in modern large-aperture radio telescopes. By determining the achievable controllable accuracy of the primary reflector, its performance further constrains the aperture efficiency and long-term stability of telescope sensitivity. As millimeter- and submillimeter-wave astronomy advances toward higher operating frequencies and larger survey scales, key astrophysical questions increasingly demand the simultaneous achievement of high angular resolution, high surface-brightness sensitivity, and high imaging efficiency over wide fields of view. Limited by field-of-view coverage, sensitivity, or spatial-scale uniformity, traditional single-dish or interferometric array systems struggle to simultaneously satisfy these observational requirements. Consequently, large-aperture, wide-field millimeter/submillimeter single-dish telescopes are regarded as an important technological pathway for achieving multi-scale, high-fidelity observational capability. Their performance critically depends on effective control of primary reflector accuracy and system stability under multiple disturbance sources, such as gravity and thermal effects. From a system-level perspective, this paper provides an overview of the overall architecture of active surface control technologies for large-aperture millimeter- and submillimeter-wave single-dish radio telescopes. Focusing on three core components—surface measurement, actuator execution, and surface control strategies—it systematically reviews the underlying technical principles, representative engineering practices, technological evolution, and recent research progress. The characteristics of different technical approaches are summarized and analyzed, providing a reference for the design and further study of active surface control systems for large-aperture radio telescopes. Full article

Conventional cooled infrared optical systems employ a cold stop, which disrupts optical-path symmetry and constrains exit-pupil matching. Consequently, reducing the refractive lens count increases the residual broadband-aberration burden, motivating the introduction of an ultrathin phase-compensation element near the exit pupil. Conventional solutions therefore tend to use complex optical configurations with large volume and high weight, making it challenging to meet the demands of modern lightweight and compact detection systems. Metalenses offer a new approach for aberration correction through the flexible phase manipulation enabled by their unit cells. However, severe chromatic dispersion of metalenses under broadband conditions remains a major obstacle to their practical application. To address this issue, a hybrid refractive–metalens design method for cooled infrared optical systems is proposed. Based on the distinctive phase distribution characteristics of metalenses, an achromatic theoretical formulation applicable to broadband infrared wavelengths is derived. Guided by this theory, a cooled mid-wave infrared refractive–metalens hybrid optical system is designed, featuring a full field of view of 126°, an F-number of 2, and an operating wavelength band of 3.3–5 μm. In comparison with a conventional eight-element refractive system of identical specifications, the proposed hybrid system reduces the total optical-element count from eight to four and achieves reductions of 21% in total track length and 79% in system weight, while maintaining a full-field polychromatic MTF above 0.4 at 33 lp/mm. In addition, the narcissus effect is effectively mitigated under the modeled conditions. This approach enables high-performance aberration correction using metalenses while offering a new design paradigm for simplified infrared optical systems. Full article

Primordial black holes (PBHs) in the asteroid-mass range, with typical masses ${10}^{17}\mathrm{g}\lesssim M\lesssim {10}^{23}\mathrm{g}$, have drawn significant recent attention as viable dark matter candidates. The peak frequencies of photons emitted via Hawking radiation from asteroid-mass PBHs range from infrared to $\gamma$-ray bands. We calculate expected local transit rates for extended PBH mass distributions that could comprise all dark matter. We evaluate prospects for detecting Hawking-radiated photons from local PBH transits through the inner Solar System and from PBH explosions in the far outer edges of the Solar System. We consider several existing and proposed ground-based and space-based instruments sensitive to photons from the radio band to ultrahigh-energy $\gamma$-rays. We find that the proposed instruments, such as the All-sky Medium Energy Gamma-ray Observatory eXplorer (AMEGO-X) satellite, can reliably detect PBH transits within $\mathcal{O}\left(0.1\mathrm{AU}\right)$ of the Earth, while the High Altitude Water Cherenkov (HAWC) observatory and Large High Altitude Air Shower Observatory (LHAASO) are both sensitive to PBH explosions out to $\mathcal{O}\left(0.1\mathrm{pc}\right)$ and $\mathcal{O}\left(0.5\mathrm{pc}\right)$, respectively. We conclude by specifically considering potential companion electromagnetic signatures in the case of a PBH explosion about ${10}^3\mathrm{AU}$ from Earth, which has been suggested as a potential source for the ∼220 PeV ultrahigh-energy KM3-230213A neutrino event observed by the KM3NeT collaboration in 2023. Whereas we find that the recent KM3NeT event would not have yielded detectable electromagnetic signals—due to its location on the sky, proposed distance from Earth, and the offline status of the HAWC observatory at that time—we demonstrate that future PBH explosions at comparable distances could yield electromagnetic signals measurable from Earth, depending on the alignment of the PBH burst with detector fields of view. Full article