Search Results (2,062)

Accurately forecasting agricultural commodity prices is a complex and persistent problem for producers, traders, and policymakers. In this study we examine how artificial intelligence can be combined with large-scale global news data to refine daily corn price forecasts. A Long Short-Term Memory (LSTM) neural network was trained on Chicago corn futures between 2021 and 2024 to capture price dynamics, while agriculture-related news features were derived from the Global Database of Events, Language, and Tone (GDELT). Rather than sentiment polarity, the analysis shows that attention-based indicators, such as article volume, rolling intensity measures, and persistence of elevated coverage, carry stronger predictive information. These features are incorporated through a Ridge regression residual correction applied to the LSTM predictions, forming a lightweight two-stage hybrid model. While absolute forecast accuracy remains comparable to the price-only baseline (RMSE ≈ 9 ¢/bu; MAE ≈ 5.8 ¢/bu; R² ≈ 0.99), the hybrid framework improves directional accuracy by approximately 2.4 percentage points, with gains concentrated during periods of moderate news intensity. Feature attribution results indicate that media attention intensity and persistence dominate sentiment-tone variables, which receive zero weight under regularization. Overall, the proposed framework offers a transparent, computationally efficient, and reproducible approach for integrating open global news data into short-term agricultural price forecasting. Full article

Conventional transformer protection schemes are limited by the difficulty in distinguishing inrush currents from internal and external faults, which restricts operational accuracy to below 70%. Existing solutions are constrained by a trade-off: sensitivity is compromised when setting values are increased, while speed is sacrificed when time delays are introduced. To address these limitations, a novel deep learning-based method for transformer fault identification is proposed. First, a feature model is constructed utilizing the time-domain sum of voltage and current fault components alongside current polarity characteristics. Subsequently, a channel attention-based Capsule Network (SE-CapsuleNet) is employed to automatically extract deep features across normal operation, inrush currents, and fault types. Simulation results demonstrate that inrush conditions are accurately differentiated from fault states. Robustness is maintained under high fault resistance (400 Ω) and 20 dB noise interference, while immunity to current transformer (CT) saturation and core residual magnetism is exhibited. The proposed protection relay simultaneously meets the requirements of rapid response, high sensitivity, and safety stability. Full article

The X-ray structural analysis of the N-terminal domain cavity from eleven transcription regulators (TFs) of the Fumarate Nitrate Reduction regulator/cAMP Regulator Protein family shows several significant trends. The conservancy of effector-binding phosphate binding cassette features in three TFs suggests a closer connection among them than the one obtained through the comparison of overall amino acid sequences. Conversely, there are also three clearly different allosteric activation mechanisms, which most likely evolved independently. Interestingly, several TFs of this family adopt the DNA-binding conformation without binding any ligand; instead, the buried region corresponding to the “allosteric” cavity is partially filled with salt bridges (which is also the case for two allosteric apo TFs). One plausible conclusion from these observations is that the non-allosteric TFs evolved from an allosteric counterpart and used salt bridges to fill and stabilize the formally polar ligand-binding cavity. O₂-sensing TFs share some residues in the relevant N-terminal domain cavity and might have had an already non-allosteric common ancestor. Full article

This work presents, to the best of our knowledge, the first experimental report of an erbium/ytterbium double-clad ring fiber laser based on nonlinear polarization rotation (NPR) operating in a self-starting Q-switched high-order harmonic mode locking noise-like pulse (QHML-NLP) regime. The NPR mechanism relies on an arrangement composed of a beam splitter cube, a half-wave retarder, and a quarter-wave retarder. Through specific adjustments of the wave retarders and pump power, the laser cavity engages the QHML-NLP regime, where mode-locked burst-like pulses containing a significant number of NLPs are modulated by a giant Q-switched envelope. The laser system emits at the 132nd-order harmonic mode locking (HML) frequency, representing the highest order achieved to date in the framework of QHML-NLP. Additional features include a broadband optical spectrum with dual-wavelength emission at 1568.4 nm and 1605.9 nm, and maximum energies of 2.37 µJ for the Q-switched envelope and 200 nJ for the mode-locked burst-like pulse. These detailed experimental results reveal remarkable aspects in the NLP dynamics, contributing to a deeper understanding of their physical mechanisms and highlighting their potential as novel laser sources for micromachining and nonlinear optics. Full article

Microscopes have long been an important tool for paleontology, but most researchers use biological microscopes that are designed for transmitted light illumination. Micropaleontology has traditionally involved investigations of individual organisms (e.g., foraminifera, radiolarian and diatoms), or fossil pollen. Optical microscopy can also be a useful method for the study of macrofossils. Polarized light illumination, long a mainstay of geological research, has largely been missing from paleontology investigations. However, adapting a standard microscope for polarized light is not a difficult task. The preparation of mineralized fossils as petrographic thin sections greatly expands the possibilities for microscopic examination of macrofossils. Scanning electron microscopy (SEM) has long been used for the study of fossils, most commonly for observing individual microfossils or anatomical features of larger organisms. X-ray fluorescence analysis (SEM/EDS), a standard method for geology research, has had minimal use by paleontologists, but it is a method that merits wider acceptance. This paper emphasizes inexpensive methods for researchers who want to expand their microscopy horizons without needing deep funding or access to specialized facilities. Full article

A central challenge in reconfigurable photonics based on quasi bound states in the continuum (quasi-BICs) is to move beyond binary switching toward multistate and polarization-aware programmability. Here we propose a dual-phase-change material (PCM) metasurface that enables four-state nonvolatile switching and polarization-divergent dispersion rewriting within a single unit cell. Two independently switchable PCM layers provide four addressable configurations (0-0, 0-1, 1-0, 1-1) at a fixed geometry, allowing the resonance landscape to be reprogrammed through complex-index rewriting without structural modification. Angle-resolved transmission maps reveal fundamentally different evolution pathways for orthogonal polarizations. For p polarization, the quasi-BIC exhibits strong state sensitivity with dispersion reshaping and multi-branch features near normal incidence; the resonance red-shifts from ~1331 nm to ~1355 nm while the quality factor decreases from ~6.7 × 10⁴ to ~4.0 × 10⁴. In contrast, for s polarization, a single weakly dispersive branch translates coherently across states, producing a much larger shift from ~1635 nm to ~1790 nm while the quality factor increases from ~9.0 × 10³ to ~1.8 × 10⁴. The opposite quality-factor trajectories, together with the polarization-contrasting tuning ranges, demonstrate that dual-PCM programming reconfigures polarization-selective radiative coupling rather than imposing a uniform resonance shift. This compact two-bit metasurface platform provides multistate, high-Q control with active dispersion engineering, enabling polarization-multiplexed reconfigurable filters, state-addressable sensors, and other programmable photonic devices. Full article

Severe convective weather is marked by abrupt onset, rapid evolution, and substantial destructive potential, posing major threats to economic activities and human safety. To address this challenge, this study proposes MRKAN, a multi-parameter prediction algorithm for dual-polarization radar that integrates Mamba, radial basis functions (RBFs), and the Kolmogorov–Arnold Network (KAN). The method predicts radar reflectivity, differential reflectivity, and the specific differential phase, enabling a refined depiction of the dynamic structure of severe convective systems. MRKAN incorporates four key innovations. First, a Cross-Scan Mamba module is designed to enhance global spatiotemporal dependencies through point-wise modeling across multiple complementary scans. Second, a Multi-Order KAN module is developed that employs multi-order $\beta$-spline functions to overcome the linear limitations of convolution kernels and to achieve high-order representations of nonlinear local features. Third, a Gaussian and Inverse Multiquadratic RBF module is constructed to extract mesoscale features using a combination of Gaussian radial basis functions and Inverse Multiquadratic radial basis functions. Finally, a Multi-Scale Feature Fusion module is designed to integrate global, local, and mesoscale information, thereby enhancing multi-scale adaptive modeling capability. Experimental results show that MRKAN significantly outperforms mainstream methods across multiple key metrics and yields a more accurate depiction of the spatiotemporal evolution of severe convective weather. Full article

The Global Navigation Satellite System-Scattering (GNSS-S) radar has a wide coverage and strong concealment, enabling large-scale and long-term monitoring of sea surface targets. However, its signal power is extremely low and susceptible to sea clutter interference. To address the challenge of detecting and tracking moving targets in complex maritime environments using low-resolution radar, this paper proposes a method for extracting moving target trajectories from GNSS-S radar under low signal-to-noise ratio (SNR) conditions. The method constructs a feature plane consisting of statistical and polarization characteristics, based on the unique distribution of different motion targets in this plane, the distinction between sea clutter and multi-motion targets is carried out using machine learning algorithms, and finally the trajectory association of the targets is achieved by the Kalman filter, and the tracking correctness can reach more than 93.89%. Compared with the tracking method based on high-resolution imaging targets, this technique does not require complex imaging operations, and only requires certain processing on the radar echo, which has the advantages of easy operation and high reliability. Full article

The aim of the study is to assess the effect of diagenesis on the reservoir properties of Carboniferous sandstones in Western Pomerania (NW Poland). The research focuses on Mississippian (Łobżonka Shale, Gozd Arkose, and Drzewiany Sandstone formations) and Pennsylvanian (Wolin, Rega, and Dziwna formations) rocks. A comparative analysis of the sandstones in the individual formations was carried out. The sandstone samples taken from 13 deep boreholes were studied petrographically (using a polarizing microscope, cathodoluminescence, and a scanning electron microscope), and petrophysical features were measured. The Carboniferous sandstones are represented mainly by quartz arenites ranging from very fine- to medium-grained and arkosic and lithic arenites from fine- to coarse-grained. The main diagenetic processes that affected the porosity and permeability of quartz arenites were compaction and cementation. Compaction reduced the primary porosity by an average of about 60% and cementation by about 40% in the Pennsylvanian sandstones. Primary porosity of arkosic and lithic arenites was affected mainly by compaction, cementation, and dissolution. Arkosic arenites have lost an average of 80% of their primary porosity as a result of mechanical compaction. The porosity of these sandstones increased due to the dissolution of mainly feldspar grains and the formation of secondary porosity. Among the Mississippian sandstones, quartz arenites of the Łobżonka Shale Formation exhibit unfavorable reservoir properties (porosity approx. 1%, impermeable). The volcaniclastic arkosic and lithic arenites of the Gozd Arkose Formation have poor reservoir qualities (porosity usually around 5%, mostly impermeable). The quartz arenites of the Drzewiany Sandstone Formation show the best reservoir properties (porosity of about 18%, permeability up to 1000 mD). The Pennsylvanian sandstones, quartz arenites of the Wolin and Rega formations, are characterized by good reservoir qualities (porosity approx. 10%, permeability up to 200 mD), while the Dziwna Formation sandstones show worse properties (porosity approx. 10%, often impermeable). Full article

Background/Objectives: Hippocampal neuroinflammation (HNF) is a key pathological feature in neurodegenerative disorders. Milk-derived exosomes, as bioactive extracellular vesicles, have underexplored potential in regulating brain neuroinflammatory responses. This study aimed to characterize desert milk exosomes (D-Exo) and investigate their neuroprotective and anti-neuroinflammatory effects in LPS-induced HNF mice model and an LPS-stimulated BV2 microglia. Methods: Exosomes were isolated from desert and non-desert milk (ND-Exo) for proteomic analysis. After pretreating BV2 cells with exosomes and stimulating with LPS, their inflammatory responses and polarization were assessed by RT-PCR. Balb/c mice were orally gavaged with D-Exo or 0.9% NaCl for 28 days before LPS injection. Cognitive function was assessed via behavioral tests, with microglial/astrocyte activation analyzed by immunofluorescence. Results: D-Exo exhibited superior stability and a unique proteomic profile enriched with proteins linked to neuroinflammation and blood-brain barrier (BBB) integrity, notably within the AMPK signaling pathway. In vitro, D-Exo shifted LPS-stimulated microglia from the M1 to the M2 phenotype. In vivo, it alleviated HNF and cognitive decline, reduced Aβ_1-42 and Tau deposition, elevated BDNF and MAP2, and suppressed neuroinflammation and glial activation. Conclusions: D-Exo is enriched with specific proteins, attenuates neuroinflammation and cognitive decline by regulating microglial M1/M2 polarization and AMPK pathway, highlighting its preventive potential. Full article

Sea fog is a major meteorological hazard that severely disrupts maritime transportation and economic activities in the South China Sea. As China’s next-generation geostationary meteorological satellite, Fengyun-4B (FY-4B) supplies continuous observations that are well suited for sea fog monitoring, yet a satellite-specific recognition method has been lacking. A key obstacle is the radiometric inconsistency between the Advanced Geostationary Radiation Imager (AGRI) sensors on FY-4A and FY-4B, compounded by the cessation of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) observations, which prevents direct transfer of fog labels. To address these challenges and fill this research gap, we propose a machine learning framework that integrates cross-satellite radiometric recalibration and physical mechanism constraints for robust daytime sea fog detection. First, we innovatively apply a radiation recalibration transfer technique based on the radiative transfer model to normalize FY-4A/B radiances and, together with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) cloud/fog classification products and ERA5 reanalysis, construct a highly consistent joint training set of FY-4A/B for the winter-spring seasons since 2019. Secondly, to enhance the model’s physical performance, we incorporate key physical parameters related to the sea fog formation process (such as temperature inversion, near-surface humidity, and wind field characteristics) as physical constraints, and combine them with multispectral channel sensitivity and the brightness temperature (BT) standard deviation that characterizes texture smoothness, resulting in an optimized 13-dimensional feature matrix. Using this, we optimize the sea fog recognition model parameters of decision tree (DT), random forest (RF), and support vector machine (SVM) with grid search and particle swarm optimization (PSO) algorithms. The validation results show that the RF model outperforms others with the highest overall classification accuracy (0.91) and probability of detection (POD, 0.81) that surpasses prior FY-4A-based work for the South China Sea (POD 0.71–0.76). More importantly, this study demonstrates that the proposed FY-4B framework provides reliable technical support for operational, continuous sea fog monitoring over the South China Sea. Full article

Valley-dependent topological physics offers a promising avenue for designing nanoscale devices based on gapless single-layer graphene. To demonstrate this potential, we investigate an electrical bias-controlled topological discontinuity in valley polarization within a two-segment armchair nanoribbon of gapless single-layer graphene. This discontinuity is created at the interface by applying opposite in-plane, transverse electrical biases to the two segments. An efficient tight-binding theoretical formulation is developed to calculate electron states in the structure. In a reference configuration, we obtain energy eigenvalues and probability distributions that feature interface-confined electron eigenstates induced by the topological discontinuity. Moreover, to elucidate the implications of interface confinement for electron transport, a modified configuration is introduced to transform the eigenstates into transport-active, quasi-localized ones. We show that such states result in Fano “anti-resonances” in transmission spectra. The resilience of these quasi-localized states and their associated Fano fingerprints is examined with respect to fluctuations. Finally, a proof-of-concept band-stop electron energy filter is presented, highlighting the potential of this confinement mechanism and, more broadly, valley-dependent topological physics in designing nanoscale devices in gapless single-layer graphene. Full article

Sub-meter-resolution Polarimetric Synthetic Aperture Radar (PolSAR) imagery enables precise building footprint extraction but introduces complex scattering correlated with fine spatial structures. This change renders both traditional methods, which rely on simplified scattering models, and existing deep learning approaches, which sacrifice spatial detail through multi-looking, inadequate for high-precision extraction tasks. To address this, we propose an Orthogonal Dual-Resolution Network (ODRNet) for end-to-end, precise segmentation directly from single-look complex (SLC) data. Unlike complex-valued neural networks that suffer from high computational cost and optimization difficulties, our approach decomposes complex-valued data into its orthogonal real and imaginary components, which are then concurrently fed into a Dual-Resolution Branch (DRB) with Bilateral Information Fusion (BIF) to effectively balance the trade-off between semantic and spatial details. Crucially, we introduce an auxiliary Polarization Orientation Angle (POA) regression task to enforce physical consistency between the orthogonal branches. To tackle the challenge of diverse building scales, we designed a Multi-scale Aggregation Pyramid Pooling Module (MAPPM) to enhance contextual awareness and a Pixel-attention Fusion (PAF) module to adaptively fuse dual-branch features. Furthermore, we have constructed a VHR PolSAR building footprint segmentation dataset to support related research. Experimental results demonstrate that ODRNet achieves 64.3% IoU and 78.27% F1-score on our dataset, and 73.61% IoU with 84.8% F1-score on a large-scale SLC scene, confirming the method’s significant potential and effectiveness in high-precision building extraction directly from SLC. Full article

For 5G tunnel communications, antennas often face critical challenges arising from severe path loss and multipath fading in confined environments, as well as polarization mismatch under dynamic propagation conditions. This paper proposes a 3.5-GHz circularly polarized (CP) endfire antenna array with bidirectional right-hand CP radiation, featuring high gain, low profile, and compact configuration. The array is implemented on a single-layer F4B substrate and integrates eight pairs of electric and magnetic dipoles to synthesize orthogonal linear field components required for CP radiation. By applying the extended method of maximum power transmission efficiency, constraints on the amplitude and phase are introduced to maximize the CP gain in the endfire direction. A 16-element linear array prototype is fabricated and tested for validation. Measurement results show that the proposed array achieves a bidirectional right-hand CP endfire gain exceeding 12.2 dBic, an impedance bandwidth from 3.1 to 3.78 GHz, and a 3 dB axial ratio bandwidth of 19.5%, demonstrating its suitability for 5G tunnel communication applications. Full article

A first-principles investigation was conducted to characterize the novel Cr₂ZnC MAX phase and its exfoliated MXene nanosheet, Cr₂C. The study critically examines the effect of electron correlations on the bulk phase, revealing that the PBE+U framework, unlike standard PBE, yields a dramatically enhanced magnetic moment of 12.80 μ_B (vs. 1.88 μ_B), confirming the necessity of this approach for Cr-based carbides. The phase stability is confirmed through rigorous analysis of its thermodynamic, dynamic, and mechanical properties. For the derived 2D Cr₂C, results confirm a robust half-metallic state with a total magnetic moment of 8.00 μ_B, characterized by a metallic spin-majority channel and a semiconducting spin-minority channel with a 2.41 eV direct gap, leading to near-ideal spin polarization. These combined features establish Cr₂C as a highly promising candidate for next-generation spintronic applications and 2D magnetic devices requiring room-temperature stability. Full article