Search Results (12,324)

Coordination polymer [{Co(tta)₂(4,4′-bipy)}_n] (1) (tta = 4,4,4 trifluoro-1-(2-thienyl)-1,3-butanedionate; 4,4′-bipy = 4,4′-bipyridine) was synthesized by reacting [Co(tta)₂-(H₂O)₂] with equivalent of 4,4′-bipy, whereby the aqua ligands in [Co(tta)₂-(H₂O)₂] were replaced by 4,4′-bipy ligand. Thermal behavior, investigated via thermogravimetric analysis (TGA), revealed that 1 decomposes between 290 and 400 °C. The solid-state structure of 1 was confirmed by single-crystal X-ray diffraction, which established its polymeric nature of 1. Each monomer unit of 1 features a cobalt center in an octahedral coordination environment, with two equatorially chelating tta ligands and one axially oriented 4,4′-bipy ligand. Sulfur–sulfur interactions lead to the formation of a two-dimensional supramolecular network. In addition, compound 1 is stabilized by various intermolecular interactions, including C-H···π, C-F···F-C, and C-H···F-C contacts. Hirshfeld surface analysis and 2D-fingerprint plots were employed to further investigate the non-covalent intermolecular interactions in the solid state, providing strong evidence for their role in stabilizing the crystal structure. Full article

The gas reservoir of the Sinian Dengying Formation (Member 4) in Sichuan Basin exhibits extensive development of inter-clast dissolution pores and vugs within its carbonate reservoirs, characterized by low porosity (average 3.21%) and low permeability (average 2.19 mD). With the progressive development of the Moxi (MX)structure, the existing stimulation techniques require further optimization based on the specific geological characteristics of these reservoirs. Through large-scale true tri-axial physical simulation experiments, this study systematically evaluated the performance of three principal acid systems in reservoir stimulation: (1) Self-generating acid systems, which enhance etching through the thermal decomposition of ester precursors to provide sustained reactive capabilities. (2) Gelled acid systems, characterized by high viscosity and effectiveness in reducing breakdown pressure (18%~35% lower than conventional systems), are ideal for generating complex fracture networks. (3) Diverting acid systems, designed to improve fracture branching density by managing fluid flow heterogeneity. This study emphasizes hybrid acid combinations, particularly self-generating acid prepad coupled with gelled acid systems, to leverage their synergistic advantages. Field trials implementing these optimized systems revealed that conventional guar-based fracturing fluids demonstrated 40% higher breakdown pressures compared to acid systems, rendering hydraulic fracturing unsuitable for MX reservoirs. Comparative analysis confirmed gelled acid’s superiority over diverting acid in tensile strength reduction and fracture network complexity. Field implementations using reservoir-quality-adaptive strategies—gelled acid fracturing for main reservoir sections and integrated self-generating acid prepad + gelled acid systems for marginal zones—demonstrated the technical superiority of the hybrid system under MX reservoir conditions. This optimized protocol enhanced fracture length by 28% and stimulated reservoir volume by 36%, achieving a 36% single-well production increase. The technical framework provides an engineered solution for productivity enhancement in deep carbonate gas reservoirs within the G-M structural domain, with particular efficacy for reservoirs featuring dual low-porosity and low-permeability characteristics. Full article

Intergrown mixtures of nanostructured manganese oxide phases have been obtained using a highly complexing agent (ethylenediamine) and a weak complexer (urea) during their solvothermal synthesis. In this work, through a detailed structural analysis, it is evidenced the formation of an intergrown mixture of three distinct manganese oxide phases (β-MnO₂, α-Mn₂O₃, and Mn₃O₄). Scanning electron microscopy shows that the products have just one morphology, indicating that the different manganese oxide phases may have grown together, organizing themselves in a 3D crystal network. The reaction mechanisms are discussed in this paper. It is of great interest to produce intergrown mixtures of manganese oxide phases to take advantage of the availability of the different oxidation states of Mn in neighboring crystallites for applications like catalysis. Full article

The Yunyao Aerospace Constellation Program is the core project being developed by Yunyao Aerospace Technology Co., Ltd., Tianjin, China. It aims to provide scientific data for weather forecasting, as well as research on the ionosphere and neutral atmosphere. It is expected to launch 90 high time resolution weather satellites. Currently, the Yunyao space constellation provides nearly 16,000 BDS, GPS, GLONASS, and Galileo multi-system occultation profile products on a daily basis. This study initially calculates the precise orbits of Yunyao LEO satellites independently using each GNSS constellation, allowing the derivation of the neutral atmospheric refractive index profile. The precision of the orbit product was evaluated by comparing carrier-phase residuals (ranging from 1.48 cm to 1.68 cm) and overlapping orbits. Specifically, for GPS-based POD, the average 3D overlap accuracy was 4.93 cm, while for BDS-based POD, the average 3D overlap accuracy was 5.18 cm. Simultaneously, the global distribution, the local time distribution, and penetration depth of the constellation were statistically analyzed. BDS demonstrates superior performance with 21,093 daily occultation profiles, significantly exceeding GPS and GLONASS by 15.9% and 121%, respectively. Its detection capability is evidenced by 79.75% of profiles penetrating below a 2 km altitude, outperforming both GPS (78.79%) and GLONASS (71.75%) during the 7-day analysis period (DOY 169–175, 2023). The refractive index profile product was also compared with the ECWMF ERA5 product. At 35 km, the standard deviation of atmospheric refractivity for BDS remains below 1%, while for GPS and GLONASS it is found at around 1.5%. BDS also outperforms GPS and GLONASS in terms of the standard deviation in the atmospheric refractive index. These results indicate that Yunyao satellites can provide high-quality occultation product services, like for weather forecasting. With the successful establishment of the global BDS-3 network, the space signal accuracy has been significantly enhanced, with BDS-3 achieving a Signal-in-Space Ranging Error (SISRE) of 0.4 m, outperforming GPS (0.6 m) and GLONASS (1.7 m). This enables superior full-link occultation products for BDS. Full article

In hyperspectral image (HSI) classification, feature learning and label accuracy play a crucial role. In actual hyperspectral scenes, however, noisy labels are unavoidable and seriously impact the performance of methods. While deep learning has achieved remarkable results in HSI classification tasks, its noise-resistant performance usually comes at the cost of feature representation capabilities. High-dimensional and deep convolution can capture rich deep semantic features, but with high complexity and resource consumption. To deal with these problems, we propose a CViT Weakly Supervised Network (CWSN) for HSI classification. Specifically, a lightweight 1D-2D two-branch network is used for local generalization and enhancement of spatial–spectral features. Then, the fusion and characterization of local and global features are achieved through the CNN-Vision Transformer (CViT) cascade strategy. The experimental results on four benchmark HSI datasets show that CWSN has good anti-noise ability and ensures the robustness and versatility of the network facing both clean and noisy training sets. Compared to other methods, the CWSN has better classification accuracy. Full article

Valued for furniture, crafts, and medicine, Dalbergia odorifera T. C. Chen confronts critically depleted wild populations and slow cultivation growth, necessitating precision nutrient formulation to overcome physiological constraints. Using a ‘3414’ regression design with four levels of N, P, and K, this study identified phosphorus (P) as the most influential nutrient in regulating growth (P > N > K). Maximal growth enhancement occurred under T7 (N2P3K2), with height and basal diameter increments increasing by 239% and 128% versus controls (p < 0.05). Both traits exhibited progressive gains with rising P but unimodal responses to N and K, initially increasing then declining. T7 boosted total biomass by 50% (p < 0.05) with stem-biased partitioning (stem > root > leaf; 52%, 26%, 22%). Photosynthetic capacity increased significantly under T7 (p < 0.05), driven by P-mediated chlorophyll gains (Chla + 70%; Chlb + 75%) and an 82% higher net photosynthetic rate. Metabolic shifts revealed peak soluble sugar in T7 (+139%) and soluble protein in T9 (+226%) (p < 0.05), associated primarily with P and K availability, respectively. Correlation networks revealed significant associations among structural growth, photosynthesis, and metabolism. Principal component analysis established T7 as optimal, defining a “medium-N, high-P medium-K” precision fertilization protocol. These findings elucidate a phosphorus-centered regulatory mechanism governing growth in D. odorifera, providing a scientific foundation for efficient cultivation. Full article

The dynamic process of water depletion plays a critical role in both surface coalbed methane (CBM) development and underground gas extraction, reshaping water–rock interactions and inducing complex permeability responses. Addressing the limited understanding of the coupling mechanism between heterogeneous pore water evolution and permeability during dynamic processes, this study simulates reservoir transitions across four zones (prospective planning, production preparation, active production, and mining-affected zones) via centrifugal experiments. The results reveal a pronounced scale dependence in pore water distribution. During low-pressure stages (0–0.54 MPa), rapid drainage from fractures and seepage pores leads to a ~12% reduction in total water content. In contrast, high-pressure stages (0.54–3.83 MPa) promote water retention in adsorption pores, with their relative contribution rising to 95.8%, forming a dual-structure of macropore drainage and micropore retention. Multifractal analysis indicates a dual-mode evolution of movable pore space. Under low centrifugal pressure, D₋₁₀ and Δα decrease by approximately 34% and 36%, respectively, reflecting improved connectivity within large-pore networks. At high centrifugal pressure, an ~8% increase in D₀−D₂ suggests that pore-scale heterogeneity in adsorption pores inhibits further seepage. A quantitative coupling model establishes a quadratic relationship between fractal parameters and permeability, illustrating that permeability enhancement results from the combined effects of pore volume expansion and structural homogenization. As water saturation decreases from 1.0 to 0.64, permeability increases by more than 3.5 times. These findings offer theoretical insights into optimizing seepage pathways and improving gas recovery efficiency in dynamically evolving reservoirs. Full article

To combat the decline in substation image clarity resulting from adverse weather phenomena like haze, which often leads to poor illumination and altered color perception, a compact image dehazing model called the Substation Image Enhancement Network with Decomposition Convolution and Adaptive Fusion (SDCNet) is introduced. In contrast to traditional dehazing methods that expand the convolutional kernel to widen the receptive field and improve feature acquisition, commonly at the cost of increased parameters and computational load, SDCNet employs a decomposition-based convolutional enhancement module. This component efficiently extracts spatial features while keeping computation lightweight. Moreover, an adaptive fusion mechanism is incorporated to better align and merge features from both encoder and decoder stages, aiding in the retention of essential image information. To further enhance model learning, a contrastive regularization strategy is applied, leveraging both hazy and clear substation images during training. Empirical evaluations show that SDCNet substantially enhances visual brightness and restores accurate structural and color details. On the MIIS dataset of substation haze images, it delivers gains of 4.053 dB in PSNR and 0.006 in SSIM compared to current state-of-the-art approaches. Additional assessment on the SSDF dataset further confirms its reliability in detecting substation defects under unfavorable weather conditions. Full article

Camera tracking plays a pivotal role in augmented reality geographic information systems (AR-GIS) and location-based services (LBS), serving as a crucial component for accurate spatial awareness and navigation. Current learning-based camera tracking techniques, while achieving superior accuracy in pose estimation, often overlook changes in scale. This oversight results in less stable localization performance and challenges in coping with dynamic environments. To address these tasks, we propose a pyramid convolution-based scene coordinate regression network (PSN). Our approach leverages a pyramidal convolutional structure, integrating kernels of varying sizes and depths, alongside grouped convolutions that alleviate computational demands while capturing multi-scale features from the input imagery. Subsequently, the network incorporates a novel randomization strategy, effectively diminishing correlated gradients and markedly bolstering the training process’s efficiency. The culmination lies in a regression layer that maps the 2D pixel coordinates to their corresponding 3D scene coordinates with precision. The experimental outcomes show that our proposed method achieves centimeter-level accuracy in small-scale scenes and decimeter-level accuracy in large-scale scenes after only a few minutes of training. It offers a favorable balance between localization accuracy and efficiency, and effectively supports augmented reality visualization in dynamic environments. Full article

The electric energy metering box serves as a crucial node in power grid operations, offering essential protection for key components in the distribution network, such as smart meters, data acquisition terminals, and circuit breakers, thereby ensuring their safe and reliable operation. However, the non-metallic housings of these boxes are susceptible to aging under environmental stress, which can result in diminished flame-retardant properties and an increased risk of fire. Currently, there is a lack of rapid and accurate methods for assessing the fire resistance of non-metallic metering box enclosures. In this study, laser-induced breakdown spectroscopy (LIBS), which enables fast, multi-element, and non-contact analysis, was utilized to develop an effective assessment approach. Thermal aging experiments were conducted to systematically investigate the degradation patterns and mechanisms underlying the reduced flame-retardant performance of sheet molding compound (SMC), a representative non-metallic material used in metering box enclosures. The results showed that the intensity ratio of aluminum ionic spectral lines is highly correlated with the flame-retardant grade, serving as an effective performance indicator. On this basis, a one-dimensional convolutional neural network (1D-CNN) model was developed utilizing LIBS data, which achieved over 92% prediction accuracy for different flame-retardant grades on the test set and demonstrated high recognition accuracy for previously unseen samples. This method offers significant potential for rapid, on-site evaluation of flame-retardant grades of non-metallic electric energy metering boxes, thereby supporting the safe and reliable operation of power systems. Full article

The localization and pose estimation of clinicians in the operating room is a critical component for building intelligent perception systems, playing a vital role in enhancing surgical standardization and safety. Multi-view, multi-person 3D pose estimation is a highly challenging task—especially in the operating room, where the presence of sterile clothing, occlusion from surgical instruments, and limited data availability due to privacy concerns exacerbate the difficulty. While voxel-based 3D pose estimation methods have shown promising results in general scenarios, their performance is significantly challenged in surgical environments with limited camera views and severe occlusions. To address these issues, this paper proposes a fine-grained voxel feature reconstruction method enhanced with depth information, effectively mitigating projection errors caused by reduced viewpoints. Additionally, an attention mechanism is integrated into the encoder–decoder architecture to improve the network’s capacity for global information modeling and enhance the accuracy of keypoint regression. Experiments conducted in real-world operating room scenarios, using the Multi-View Operating Room (MVOR) dataset, demonstrate that the proposed method maintains high accuracy even under limited camera views and outperforms existing state-of-the-art multi-view 3D pose estimation approaches. This work provides a novel and efficient solution for human pose estimation (HPE) in complex medical environments. Full article

Most existing deep learning-based super-resolution (SR) methods for remote sensing images rely on predefined degradation assumptions (e.g., bicubic downsampling). However, when real-world degradations deviate from these assumptions, their performance deteriorates significantly. Moreover, explicit degradation estimation approaches based on iterative schemes inevitably lead to accumulated estimation errors and time-consuming processes. In this paper, instead of explicitly estimating degradation types, we first innovatively introduce an MSCN_G coefficient to capture global prior information corresponding to different distortions. Subsequently, distortion-enhanced representations are implicitly estimated through contrastive learning and embedded into a super-resolution network equipped with multiple distortion decoders (D-Decoder). Furthermore, we propose a distortion-related channel segmentation (DCS) strategy that reduces the network’s parameters and computation (FLOPs). We refer to this Global Prior-guided Distortion-enhanced Representation Learning Network as GDRNet. Experiments on both synthetic and real-world remote sensing images demonstrate that our GDRNet outperforms state-of-the-art blind SR methods for remote sensing images in terms of overall performance. Under the experimental condition of anisotropic Gaussian blurring without added noise, with a kernel width of 1.2 and an upscaling factor of 4, the super-resolution reconstruction of remote sensing images on the NWPU-RESISC45 dataset achieves a PSNR of 28.98 dB and SSIM of 0.7656. Full article

Global agricultural intensification has exacerbated soil compaction and nitrogen (N) inefficiency, thereby threatening sustainable crop production. Sub-soiling, a tillage technique that fractures subsurface layers while preserving surface structure, offers potential solutions by modifying soil physical properties and enhancing microbial-mediated N cycling. This study investigated the effects of subsoiling depth (0, 20, and 40 cm) on soil microbial communities and N transformations in a semi-arid maize system in China. The results demonstrated that subsoiling to a depth of 40 cm (D2) significantly enhanced the retention of nitrate-N and ammonium-N, which correlated with improved soil porosity and microbial activity. High-throughput 16S rDNA sequencing revealed subsoiling depth-driven reorganization of microbial communities, with D2 increasing the abundance of Proteobacteria (+11%) and ammonia-oxidizing archaea (Nitrososphaeraceae, +19.9%) while suppressing denitrifiers (nosZ gene: −41.4%). Co-occurrence networks indicated greater complexity in microbial interactions under subsoiling, driven by altered aeration and carbon redistribution. Functional gene analysis highlighted a shift from denitrification to nitrification-mineralization coupling, with D2 boosting maize yield by 9.8%. These findings elucidate how subsoiling depth modulates microbiome assembly to enhance N retention, providing a mechanistic basis for optimizing tillage practices in semi-arid agroecosystems. Full article

This paper presents a Ka-band cylindrical conformal transceiver active phased array (CCTAPA) with a full-azimuth scanning gain fluctuation of 0.8 dB and low power consumption. The array comprises 20 panels of 4 × 4 antenna elements, RF beam-control circuits, a Wilkinson power divider network, and frequency converters. The proposed three-subarray architecture enables ±9° beam scanning with minimal gain degradation. By dynamically switching subarrays and transceiver channels across azimuthal directions, the array achieves full 360° coverage with low gain fluctuation and power consumption. Fabrication and testing demonstrate a gain fluctuation of 0.8 dB, equivalent isotropically radiated power (EIRP) between 50.6 and 51.3 dBm, and a gain-to-noise-temperature ratio (G/T) ranging from −8 dB/K to −8.5 dB/K at 28.5 GHz. The RF power consumption remains below 8.73 W during full-azimuth scanning. This design is particularly suitable for airborne platforms requiring full-azimuth coverage with stringent power budgets. Full article

Emotion recognition in speech is essential for enhancing human–computer interaction (HCI) systems. Despite progress in Bangla speech emotion recognition, challenges remain, including low accuracy, speaker dependency, and poor generalization across emotional expressions. Previous approaches often rely on traditional machine learning or basic deep learning models, struggling with robustness and accuracy in noisy or varied data. In this study, we propose a novel multi-stream deep learning feature fusion approach for Bangla speech emotion recognition, addressing the limitations of existing methods. Our approach begins with various data augmentation techniques applied to the training dataset, enhancing the model’s robustness and generalization. We then extract a comprehensive set of handcrafted features, including Zero-Crossing Rate (ZCR), chromagram, spectral centroid, spectral roll-off, spectral contrast, spectral flatness, Mel-Frequency Cepstral Coefficients (MFCCs), Root Mean Square (RMS) energy, and Mel-spectrogram. Although these features are used as 1D numerical vectors, some of them are computed from time–frequency representations (e.g., chromagram, Mel-spectrogram) that can themselves be depicted as images, which is conceptually close to imaging-based analysis. These features capture key characteristics of the speech signal, providing valuable insights into the emotional content. Sequentially, we utilize a multi-stream deep learning architecture to automatically learn complex, hierarchical representations of the speech signal. This architecture consists of three distinct streams: the first stream uses 1D convolutional neural networks (1D CNNs), the second integrates 1D CNN with Long Short-Term Memory (LSTM), and the third combines 1D CNNs with bidirectional LSTM (Bi-LSTM). These models capture intricate emotional nuances that handcrafted features alone may not fully represent. For each of these models, we generate predicted scores and then employ ensemble learning with a soft voting technique to produce the final prediction. This fusion of handcrafted features, deep learning-derived features, and ensemble voting enhances the accuracy and robustness of emotion identification across multiple datasets. Our method demonstrates the effectiveness of combining various learning models to improve emotion recognition in Bangla speech, providing a more comprehensive solution compared with existing methods. We utilize three primary datasets—SUBESCO, BanglaSER, and a merged version of both—as well as two external datasets, RAVDESS and EMODB, to assess the performance of our models. Our method achieves impressive results with accuracies of 92.90%, 85.20%, 90.63%, 67.71%, and 69.25% for the SUBESCO, BanglaSER, merged SUBESCO and BanglaSER, RAVDESS, and EMODB datasets, respectively. These results demonstrate the effectiveness of combining handcrafted features with deep learning-based features through ensemble learning for robust emotion recognition in Bangla speech. Full article