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To address challenges such as co-frequency interference, spectrum scarcity, and uneven traffic distribution in multi-beam LEO satellites, a resource allocation algorithm based on decision performance evaluation was proposed. The system fairness was measured by a user satisfaction index and the system throughput was optimized while considering fairness. The optimization problem was modeled as a multi-objective optimization. The continuous resource allocation process with temporal correlation was modeled as a Markov decision process, and a decision-evaluation dual-network algorithm was proposed to solve it. The decision network parameters were adjusted based on evaluation network results to optimize resource allocation and update the evaluation network parameters. Through iterative optimization, the decision network achieved accurate predictions. Simulation results show that the proposed algorithm outperforms traditional resource allocation algorithms in terms of throughput and fairness.

To examine the independent and interactive associations of being bullied and non-suicidal self-injury (NSSI) with suicidal behaviors (suicidal ideation, suicidal plan, and suicidal attempt) among junior and senior high school students in China. A cross-sectional study was conducted in 4 cities from November 2017 to January 2018 and a total of 18,900 students were enrolled in this study. Being bullied, NSSI and suicidal behaviors were measured by self-reported questionnaires. Analyses were conducted with Chi-square tests, multivariable logistic regression, and additive interaction models. Results showed that the prevalence of suicidal ideation, suicidal plan, and suicidal attempt was 26.4%, 13.2% and 5.2%, respectively. Being bullied was significantly associated with suicidal ideation (OR = 2.35, 95% CI: 2.17-2.55), suicidal plan (OR = 2.25, 95% CI: 2.03-2.48) and suicidal attempt (OR = 2.27, 95% CI: 1.96-2.62). NSSI was significantly positively correlated with suicidal ideation (OR = 3.92, 95% CI: 3.64-4.21), suicidal plan (OR = 3.72, 95% CI: 3.39-4.08) and suicidal attempt (OR = 3.29, 95% CI: 2.86-3.79). Additive interaction models indicated that NSSI was associated with an increase in the risk of suicidal behaviors among adolescents being bullied than those not being bullied. Namely, additive interactions of NSSI and being bullied on suicidal behaviors were found. The NSSI and being bullied have synergistic effects, and co-victimization, rather than single. Meanwhile, considering the perpetrator is critical to intervening suicidal behaviors in adolescence. Ideally, intervention programs should take action from multiple aspects according to different types of factors, including individual level, social environment and institution., Author(s): Shuai Zhao [sup.1] [sup.2] , Jie Hu [sup.1] , Danlin Li [sup.1] , Yuhui Wan [sup.1] , Lingli Zhao [sup.1] [sup.2] , Jun Fang [sup.1] [sup.2] [sup.3] , Shichen [...]

Mapping soil organic carbon (SOC) plays a crucial role in agricultural productivity and water management. This study discusses the potential of active and passive remote sensing for SOC estimation modeling in agricultural areas, incorporating synthetic aperture radar (SAR) data (L-band quad-polarization and C-band dual-polarization), multi-spectrum (MS) data, and brightness temperature (TB) data. The performance of five advanced machine learning regression (MLR) models for SOC modeling was assessed, focusing on spatial interpolation accuracy and cross-spatial transfer accuracy, using two field observation datasets for modeling and validation. Results indicate that the SOC estimation accuracy when using MS data alone is comparable to that of using TB data alone, and both perform slightly better than SAR data. Radar cross-polarization ratio index, microwave polarization difference index, shortwave infrared reflectance, and soil parameters (elevation and soil moisture) demonstrate high correlation with the measured SOC. Incorporating temporal features, as opposed to single-phase features, allows each regression model to reach its upper limit of SOC estimation accuracy. The spatial interpolation accuracy of each MLR algorithm is satisfactory, with the Gaussian process regression (GPR) model demonstrating optimal modeling performance. When SAR, MS, or TB data are used individually in modeling, the estimation errors (RMSE) for SOC are 0.637 g/kg, 0.492 g/kg, and 0.229 g/kg for the SMAPVEX12 sampling campaign, and 0.706 g/kg, 0.454 g/kg, and 0.474 g/kg for the SMAPVEX16-MB sampling campaign, respectively. After incorporating soil moisture and topographic factors, the above RMSEs for SOC are further reduced by 57.8%, 35.6%, and 3.5% for the SMAPVEX12, and by 18.4%, 8.8%, and 3.4% for the SMAPVEX16-MB, respectively. However, cross-spatial transfer accuracy of the regression models remains limited (RMSE = 0.866–1.043 g/kg and 0.995–1.679 g/kg for different data sources). To address this, this study reduces uncertainties in SOC cross-spatial transfer by introducing terrain factors sensitive to SOC (RMSE = 0.457–0.516 g/kg and 0.799–1.198 g/kg for different data sources). The proposed SOC estimation and transfer framework, based on active and passive remote sensing data, provides guidance for high-resolution regional-scale SOC mapping and applications. [ABSTRACT FROM AUTHOR]

This letter attempts to extend the model-based polarimetric decomposition (PD) theorems with multiple polarimetric synthetic aperture radar (PolSAR) observations. To consider both the surface and dihedral scattering depolarization effects, the X-Bragg model, the extended double Fresnel scattering model, and the step-wise volume scattering model are introduced in the model-based PD. The proposed methodology is explored by exploiting Airborne Synthetic Aperture Radar (AIRSAR) C-band and L-band data from southern Oklahoma, United States of America, and L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) polarimetric data from Winnipeg, Canada, with two flight lines of 31605-03 and 31606-03 in different incidence angles. The potential and superiority of the proposed decomposition method are examined by illustrating the false color composition images, and the power ratio statistics of surface, dihedral, and volume scattering components over selected bare soil, forest, grass, and urban patches. [ABSTRACT FROM AUTHOR]

A novel tetraimide‐functionalized non‐alternant π‐conjugated system, namely, benzodifluoranthene tetraimides (BDFTI), has been designed and synthesized through highly efficient UV‐photocyclization of a vinyl‐bridged fluoranthene diimide dimer (i. e., FDI−V). The synthesis of FDI−V starts from a straightforward three‐step route to produce novel 7,8,9,10‐fluoranthene diimide (FDIs) building‐blocks, followed by nearly complete bromination and then Stille‐coupling reaction to give the desired dimer. The analysis by X‐ray crystallography confirms a near‐coplanar geometry for FDIs, while BDFTI shows a U‐shaped and distorted backbone configuration proven by theoretical optimizations. The tetraimide BDFTI exhibits several advantages over the FDI cores, including an extended absorption band and a red‐shift in photoluminescence spectra. This enhancement can be attributed to the presence of additional electron‐deficient imide units, which promotes increased intramolecular charge transfer and improved electron affinity. All the imides show a local aromatic characteristic owing to the incorporation of pentagon rings in the π‐frameworks. The fully fused BDFTI exhibits nonlinear optical properties as analyzed by the open‐aperture Z‐scan technique, demonstrating superior optical‐limiting performance compared to vinyl‐bridged FDI−V. The versatile UV‐photocyclization chemistries provide a pathway for developing complex and unique multiimide‐functionalized π‐conjugated systems, paving the way for creating high‐performance optical‐limiting materials. [ABSTRACT FROM AUTHOR]

Semiconducting photoresists hold great promise for scale‐up manufacturing of organic field‐effect transistors (OFETs) for integrated organic electronics. While photolithographic p‐type OFETs have achieved a considerable balance among patterning precision, electrical properties and process stability, it remains challenging for n‐type OFETs due to the inherent limited mobility and ambient instability. Herein, a n‐type semiconducting photoresist (SPr) is developed that is compatible with photolithography procedures. By utilizing the solvent‐driven force, a self‐encapsulated blend film with gradient semiconductor phase is prepared, where the underneath transistor active layer is protected by the upper cross‐linked network, avoiding solvent erosion and air doping. As such, a mobility up to 1.1 cm2 V−1 s−1 that is comparable with amorphous Si is achieved, with remained mobility by ≈90% after long‐term exposure to developer and stripper or atmospheric conditions. The sub‐micrometer patterning accuracy of SPr enables the fabrication of organic transistor arrays with a density of 9 × 105 units cm−1, which is comparable to other state‐of‐the‐art devices fabricated by the printing or photolithography, demonstrating immense potential in integrated organic electronics. [ABSTRACT FROM AUTHOR]

Pansharpening is an important technology for obtaining high-resolution multispectral (HRMS) images by fusing low-resolution multispectral (LRMS) images and high-resolution panchromatic (PAN) images. Although many pansharpening models have emerged by taking advantage of deep learning (DL) technology, there remains a pressing need to further assess pansharpening accuracy and stability when LRMS images with complex land-cover types. What's more, these models often overlook the exploitation of PAN images' inherent high-frequency information. To address these issues, we propose a pansharpening model combining multi-level and multi-scale network architectures. The multi-level network architecture is used to build spatial-spectral dependence on LRMS-PAN pairs, and strengthen the network's feature capture capability by keeping the multi-level texture details. The multi-scale architecture is subsequently used to extract the spatial structure and deep texture of the PAN images at different scales. Downsampled experiments and real experiments in four standard datasets show that the proposed model achieves a state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Using polystyrene two-dimensional photonic crystal as template, l-lysine molecularly imprinted two-dimensional photonic crystal hydrogel (l-Lys MIPCH) sensor with highly sensitive response to l-lysine was prepared by photoinitiated polymerization. The response performance of the l-Lys MIPCH sensor was investigated by observing the change of the diameter of the Debye ring (ΔD) and the structural color of the sensor. In the process of recognizing l-Lys molecules by l-Lys MIPCH sensor, the volume of hydrogel swells, the diameter of Debye ring decreases, and the particle spacing of photonic crystal increases. As the l-Lys concentration increased from 0 to 10−4 mol/L, the diameter of Debye ring decreased by 15.7 mm, the particle spacing of photonic crystals increased by 75.1 mm, and the structural color of hydrogel changed from blue to red correspondingly. When the l-Lys concentration was in the range of 10−12–10−4 mol/L, there was a linear relationship between the change in the diameter of the Debye ring (ΔD) and the logarithm value of the l-Lys concentration (lgc), and the linear relationship was as follows: ΔD = 19.04 + 0.90 lgc, R2=0.9962. In addition, the detection limit of the sensor is as low as 10−16 mol/L. The prepared sensor not only has high selectivity, but also can be used for chiral recognition of l-Lys, and the results of l-Lys in the actual sample of lysiinositol vitamin B12 oral solution were also satisfactory. [ABSTRACT FROM AUTHOR]

Marine oil spills are currently among the most challenging problems in marine environments. Synthetic Aperture Radar (SAR), with all-day and all-weather observation, has demonstrated great potential in oil disaster monitoring. A simple encoder-decoder network model was proposed by using SAR images for effective oil spill detection. The proposed model incorporates an optimized U-Net architecture that reduces computational requirements while maintaining detection performance. This is achieved through shortened encoder and decoder stages, depthwise separable convolutions, group normalization, and bilinear interpolation-based upsampling. To improve the model's generalization, the auto-learning rate and focal loss function are also included. Two public SAR datasets acquired by different sensors have been used to illustrate the efficiency of the proposed model. In addition, the polarimetric information has also been assessed for the detection of oil spill monitoring. The results show that the model can achieve high efficiency at a small model size. The sub-dataset with polarimetric features also achieves a high F1-score of 91.65% and an IoU of 84.59%. [ABSTRACT FROM AUTHOR]